def get_squad_data_loader(tokenizer, file, shuffle, args):
examples = read_squad_examples(file, is_training=True, debug=args.debug)
features = convert_examples_to_features_answer_id(
examples,
tokenizer=tokenizer,
max_seq_length=args.max_c_len,
max_query_length=args.max_q_len,
max_ans_length=args.max_q_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in features], dtype=torch.long)
all_q_ids = torch.tensor([f.q_ids for f in features], dtype=torch.long)
all_tag_ids = torch.tensor([f.tag_ids for f in features], dtype=torch.long)
all_a_ids = (all_tag_ids != 0).long()
all_start_positions = torch.tensor(
[f.noq_start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.noq_end_position for f in features],
dtype=torch.long)
all_data = TensorDataset(all_c_ids, all_q_ids, all_a_ids,
all_start_positions, all_end_positions)
data_loader = DataLoader(all_data, args.batch_size, shuffle=shuffle)
return data_loader, examples, features
def get_data_loader(self, file):
train_examples = read_squad_examples(file,
is_training=True,
debug=config.debug)
train_features = convert_examples_to_features(
train_examples,
tokenizer=self.tokenizer,
max_seq_length=config.max_seq_len,
max_query_length=config.max_query_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in train_features],
dtype=torch.long)
all_c_lens = torch.sum(torch.sign(all_c_ids), 1)
all_q_ids = torch.tensor([f.q_ids for f in train_features],
dtype=torch.long)
all_tag_ids = torch.tensor([f.tag_ids for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_c_ids, all_c_lens, all_tag_ids,
all_q_ids)
train_loader = DataLoader(train_data, shuffle=False, batch_size=1)
self.all_c_tokens = [f.context_tokens for f in train_features]
self.all_answer_text = [f.answer_text for f in train_features]
self.golden_q_ids = all_q_ids
return train_loader
def get_data_loader(self, file):
train_examples = read_squad_examples(file, is_training=True, debug=config.debug)
train_features = convert_examples_to_features(train_examples,
tokenizer=self.tokenizer,
max_seq_length=config.max_seq_len,
max_query_length=config.max_query_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in train_features], dtype=torch.long)
all_c_lens = torch.sum(torch.sign(all_c_ids), 1)
all_tag_ids = torch.tensor([f.tag_ids for f in train_features], dtype=torch.long)
all_q_ids = torch.tensor([f.q_ids for f in train_features], dtype=torch.long)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
all_noq_start_positions = torch.tensor([f.noq_start_position for f in train_features], dtype=torch.long)
all_noq_end_positions = torch.tensor([f.noq_end_position for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_c_ids, all_c_lens, all_tag_ids,
all_q_ids, all_input_ids, all_input_mask,
all_segment_ids, all_start_positions, all_end_positions,
all_noq_start_positions, all_noq_end_positions)
sampler = RandomSampler(train_data)
batch_size = int(config.batch_size / config.gradient_accumulation_steps)
train_loader = DataLoader(train_data, sampler=sampler, batch_size=batch_size)
return train_loader
def get_harv_data_loader(tokenizer, file, shuffle, ratio, args):
examples = read_squad_examples(file, is_training=True, debug=args.debug)
random.shuffle(examples)
num_ex = int(len(examples) * ratio)
examples = examples[:num_ex]
features = convert_examples_to_harv_features(examples,
tokenizer=tokenizer,
max_seq_length=args.max_c_len,
max_query_length=args.max_q_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in features], dtype=torch.long)
dataset = TensorDataset(all_c_ids)
dataloader = DataLoader(dataset, shuffle=shuffle, batch_size=args.batch_size)
return features, dataloader
def get_harv_data_loader(tokenizer, file, shuffle, args):
examples = read_squad_examples(file, is_training=True, debug=args.debug)
num_examples = len(examples)
random.shuffle(examples)
examples = examples[:5000]
features = convert_examples_to_features_answer_id(examples,
tokenizer=tokenizer,
max_seq_length=args.max_c_len,
max_query_length=args.max_q_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in features], dtype=torch.long)
all_data = TensorDataset(all_c_ids)
data_loader = DataLoader(all_data, args.batch_size, shuffle=shuffle)
return data_loader
def get_data_loader(self, file):
train_examples = read_squad_examples(file, is_training=True, debug=config.debug)
train_features = convert_examples_to_features(train_examples,
tokenizer=self.tokenizer,
max_seq_length=config.max_seq_len,
max_query_length=config.max_query_len,
doc_stride=128,
is_training=True)
all_c_ids = torch.tensor([f.c_ids for f in train_features], dtype=torch.long)
all_c_lens = torch.sum(torch.sign(all_c_ids), 1).long()
all_noq_start_positions = torch.tensor([f.noq_start_position for f in train_features], dtype=torch.long)
all_noq_end_positions = torch.tensor([f.noq_end_position for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_c_ids, all_c_lens, all_noq_start_positions, all_noq_end_positions)
train_loader = DataLoader(train_data, shuffle=True, batch_size=config.batch_size)
return train_loader
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
tf.get_logger().propagate = False
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
validate_flags_or_throw(albert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
print("Output:", 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
print("GS: ", global_step, step)
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)
print("EVAL RESULTS")
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")
print("Sleeping")
time.sleep(10)
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(_):
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
# if FLAGS.do_train:
# 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)
# 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"])
cls_logits = float(result["cls_logits"].flat[0])
all_results.append(
squad_utils.RawResultV2(unique_id=unique_id,
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)
from squad_utils import make_qid_to_has_ans
import numpy as np
qid_to_has_ans = make_qid_to_has_ans(
prediction_json) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
print("has_ans", len(has_ans_qids))
print("no_ans", len(no_ans_qids))
def compute_metrics_with_threshold(threshold):
nonlocal result_dict
result_dict = {}
tp = 0
tn = 0
fp = 0
fn = 0
for example_index, example in enumerate(eval_examples):
m = np.min(cls_dict[example_index])
predict_is_impossible = 1 / (1 + np.exp(-m)) > threshold
# predict_is_impossible = m > threshold
result_dict[example.qas_id] = m
if example.is_impossible:
if predict_is_impossible:
tp += 1
else:
fn += 1
else:
if predict_is_impossible:
fp += 1
else:
tn += 1
precision = tp / (tp + fp)
recall = tp / (fn + tp)
f1 = 2 * tp / (2 * tp + fp + fn)
tf.logging.info(f"precision: {precision}"
f"recall: {recall}"
f"f1: {f1}")
return precision, recall, f1
# precision, recall, f1 = compute_metrics_with_threshold(0.4)
precision, recall, f1 = compute_metrics_with_threshold(0.5)
# precision, recall, f1 = compute_metrics_with_threshold(0.6)
with tf.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(result_dict, indent=4) + "\n")
return {
"precision": precision,
"recall": recall,
"f1": f1,
"total": len(eval_examples)
}, 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")
result, global_step = get_result(FLAGS.init_checkpoint)
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'exact_match': exact_match, 'f1': f1}
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
test_file = "./squad/new_test-v1.1.json"
eval_examples = read_squad_examples(test_file, is_training=False, debug=False)
eval_features = convert_examples_to_features(eval_examples,
tokenizer=tokenizer,
max_seq_length=config.max_seq_len,
max_query_length=config.max_query_len,
doc_stride=128,
is_training=False)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0))
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=8)
def main(args):
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
args.tokenizer = tokenizer
device = torch.cuda.current_device()
checkpoint = torch.load(args.checkpoint, map_location="cpu")
vae = DiscreteVAE(checkpoint["args"])
vae.load_state_dict(checkpoint["state_dict"])
vae.eval()
vae = vae.to(device)
if args.squad:
examples = read_squad_examples(args.data_file,
is_training=True,
debug=args.debug)
features = convert_examples_to_harv_features(
examples,
tokenizer=tokenizer,
max_seq_length=args.max_c_len,
max_query_length=args.max_q_len,
doc_stride=128,
is_training=True)
else:
examples = read_examples(args.data_file,
is_training=True,
debug=args.debug)
features = convert_examples_to_harv_features(
examples,
tokenizer=tokenizer,
max_seq_length=args.max_c_len,
max_query_length=args.max_q_len,
doc_stride=128,
is_training=True)
features = features[:int(len(features) * args.ratio)]
all_c_ids = torch.tensor([f.c_ids for f in features], dtype=torch.long)
data = TensorDataset(all_c_ids)
data_loader = DataLoader(data, shuffle=False, batch_size=args.batch_size)
new_features = []
for batch in tqdm(data_loader, total=len(data_loader)):
c_ids = batch[0]
_, c_len = return_mask_lengths(c_ids)
max_c_len = torch.max(c_len)
c_ids = c_ids[:, :max_c_len].to(device)
# sample latent variable K times
for _ in range(args.k):
with torch.no_grad():
_, _, zq, _, za = vae.prior_encoder(c_ids)
batch_q_ids, batch_start, batch_end = vae.generate(
zq, za, c_ids)
all_input_ids, all_seg_ids, \
all_input_mask, all_start, all_end = post_process(batch_q_ids, batch_start, batch_end, c_ids)
for i in range(c_ids.size(0)):
new_features.append(
InputFeatures(unique_id=None,
example_index=None,
doc_span_index=None,
tokens=None,
token_to_orig_map=None,
token_is_max_context=None,
input_ids=all_input_ids[i].cpu().tolist(),
input_mask=all_input_mask[i].cpu().tolist(),
c_ids=None,
context_tokens=None,
q_ids=None,
q_tokens=None,
answer_text=None,
tag_ids=None,
segment_ids=all_seg_ids[i].cpu().tolist(),
noq_start_position=None,
noq_end_position=None,
start_position=all_start[i].cpu().tolist(),
end_position=all_end[i].cpu().tolist(),
is_impossible=None))
dir_name = os.path.dirname(args.output_file)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
with open(args.output_file, "wb") as f:
pickle.dump(new_features, f)
examples = processor.get_dev_examples('glue_data')
print('RTE Dev: ' + str(len(examples)))
processor = classifier_utils.Sst2Processor(use_spm=True, do_lower_case=True)
examples = processor.get_train_examples('glue_data')
print('SST-2 Train: ' + str(len(examples)))
examples = processor.get_dev_examples('glue_data')
print('SST-2 Dev: ' + str(len(examples)))
processor = classifier_utils.StsbProcessor(use_spm=True, do_lower_case=True)
examples = processor.get_train_examples('glue_data')
print('STS-B Train: ' + str(len(examples)))
examples = processor.get_dev_examples('glue_data')
print('STS-B Dev: ' + str(len(examples)))
examples = squad_utils.read_squad_examples(
input_file='squad_data/train-v1.1.json', is_training=True)
print('SQuAD Train: ' + str(len(examples)))
examples = squad_utils.read_squad_examples(
input_file='squad_data/dev-v1.1.json', is_training=False)
print('SQuAD Dev: ' + str(len(examples)))
examples = squad_utils.read_squad_examples(
input_file='squad_data/train-v2.0.json', is_training=True)
print('SQuADV2 Train: ' + str(len(examples)))
examples = squad_utils.read_squad_examples(
input_file='squad_data/dev-v2.0.json', is_training=False)
print('SQuADV2 Dev: ' + str(len(examples)))
examples = race_utils.RaceProcessor(
