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_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)
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)
model = BertForQuestionAnswering.from_pretrained("./save/dual/train_507200353/bert_1_2.958")
model = model.to(config.device)
device = "cuda:2"
model.eval()
def main(args,
shuffle_data=True,
model=None,
qamodel=None,
tokenizer=None,
zsre=False,
v2=True,
must_choose_answer=False,
condition_on_answer_exists=False,
condition_on_single_token=False,
condition_on_multi_token=False,
condition_on_answer_does_not_exist=False):
if len(args.models_names) > 1:
raise ValueError(
'Please specify a single language model (e.g., --lm "bert").')
msg = ""
[model_type_name] = args.models_names
print(model)
#if model is None:
# #model = build_model_by_name(model_type_name, args)
if model_type_name == "fairseq":
model_name = "fairseq_{}".format(args.fairseq_model_name)
elif model_type_name == "bert":
model_name = "BERT_{}".format(args.bert_model_name)
elif model_type_name == "elmo":
model_name = "ELMo_{}".format(args.elmo_model_name)
else:
model_name = model_type_name.title()
# initialize logging
if args.full_logdir:
log_directory = args.full_logdir
else:
log_directory = create_logdir_with_timestamp(args.logdir, model_name)
logger = init_logging(log_directory)
msg += "model name: {}\n".format(model_name)
# deal with vocab subset
vocab_subset = None
index_list = None
msg += "args: {}\n".format(args)
logger.info("\n" + msg + "\n")
# dump arguments on file for log
with open("{}/args.json".format(log_directory), "w") as outfile:
json.dump(vars(args), outfile)
# stats
samples_with_negative_judgement = 0
samples_with_positive_judgement = 0
# Mean reciprocal rank
MRR = 0.0
MRR_negative = 0.0
MRR_positive = 0.0
# Precision at (default 10)
Precision = 0.0
Precision1 = 0.0
Precision_negative = 0.0
Precision_positivie = 0.0
# EM
EM = 0.0
# F1
F1 = 0.0
is_error = 0
no_overlap = 0
larger_by_1 = 0
larger_by_2 = 0
larger_by_3 = 0
larger_by_4 = 0
larger_by_5_or_more = 0
data = load_file(args.dataset_filename)
print(len(data))
if args.lowercase:
# lowercase all samples
logger.info("lowercasing all samples...")
all_samples = lowercase_samples(data)
else:
# keep samples as they are
all_samples = data
all_samples, ret_msg = filter_samples(
model,
data,
vocab_subset,
args.max_sentence_length,
args.template,
condition_on_answer_exists=condition_on_answer_exists,
condition_on_single_token=condition_on_single_token,
condition_on_multi_token=condition_on_multi_token,
condition_on_answer_does_not_exist=condition_on_answer_does_not_exist,
is_zsre=zsre)
# OUT_FILENAME = "{}.jsonl".format(args.dataset_filename)
# with open(OUT_FILENAME, 'w') as outfile:
# for entry in all_samples:
# json.dump(entry, outfile)
# outfile.write('\n')
logger.info("\n" + ret_msg + "\n")
print(len(all_samples))
if len(all_samples) == 0: # or len(all_samples) >= 50:
return None, None, None, None, None, None, None, None, None, None, None, None
# if template is active (1) use a single example for (sub,obj) and (2) ...
if args.template and args.template != "":
facts = []
sub_objs = []
for sample in all_samples:
sub = sample["sub_label"]
obj = sample["obj_label"]
target = sample['reconstructed_word']
question = args.question
if 'reconstructed_word' not in sample:
raise Exception('Reconstructed word not in sample... fix this')
else:
if 'masked_sentences' in sample:
# Some of the masked sentences don't have a mask in them, need to find first with mask
context = None
for sent in sample['masked_sentences']:
if not zsre:
if '[MASK]' in sent:
context = sent.replace(
'[MASK]', sample['reconstructed_word'])
break
else:
context = sent
if context is None:
print('No valid context found, skipping sample')
continue
else:
context = None
for evidence in sample['evidences']:
if not zsre:
if '[MASK]' in evidence['masked_sentence']:
context = evidence['masked_sentence'].replace(
'[MASK]', sample['reconstructed_word'])
break
else:
context = evidence['masked_sentence']
if context is None:
print('No valid context found, skipping sample')
continue
#context = context.replace('(', '')
#context = context.replace(')', '')
if (sub, target, context) not in sub_objs:
sub_objs.append((sub, target, context))
if 'reconstructed_word' in sample:
facts.append((sub, obj, context, question,
sample['reconstructed_word']))
else:
facts.append((sub, obj, context, question, obj))
#break
local_msg = "distinct template facts: {}".format(len(facts))
logger.info("\n" + local_msg + "\n")
print(local_msg)
all_samples = []
for fact in facts:
(sub, obj, context, question, rw) = fact
sample = {}
sample["sub_label"] = sub
sample["obj_label"] = obj
sample["reconstructed_word"] = rw
# sobstitute all sentences with a standard template
sample['context'] = context
sample["masked_sentences"] = parse_template(
args.template.strip(), sample["sub_label"].strip(), base.MASK)
question = question.replace('[X]', sub)
sample['question'] = question
#query = sample['masked_sentences'][0].replace(base.MASK, '')
#sample['query'] = query
#print(f'query={query}')
#docs = retrieve_docs(query, ranker, conn, 30)
#sample['context'] = docs[0]
#print(f'docs={docs}')
all_samples.append(sample)
#else:
# for sample in all_samples:
# query = sample['masked_sentences'][0].replace(base.MASK, '')
# sample['query'] = query
# #print(f'query={query}')
# docs = retrieve_docs(query, ranker, conn, 1)
# sample['context'] = docs[0]
# create uuid if not present
i = 0
for sample in all_samples:
if "uuid" not in sample:
sample["uuid"] = i
i += 1
# shuffle data
if shuffle_data:
shuffle(all_samples)
samples_batches, sentences_batches, ret_msg = batchify(
all_samples, args.batch_size)
logger.info("\n" + ret_msg + "\n")
# ThreadPool
num_threads = args.threads
if num_threads <= 0:
# use all available threads
num_threads = multiprocessing.cpu_count()
pool = ThreadPool(num_threads)
list_of_results = []
viz = False
num_viz = 10
final_viz = []
viz_thres = 11
qamodel.eval().cuda()
# Defaults from huggingface
do_lower_case = True
max_answer_length = 30
verbose_logging = False
null_score_diff_threshold = 0.0
n_best = 20
max_query_length = 64
# Training specifics:
doc_stride = 128
max_seq_length = 384
for i in tqdm(range(len(samples_batches))):
samples_b = samples_batches[i]
sentences_b = sentences_batches[i]
mymodel_probs_list = []
predictions_list = []
examples = read_input_examples(samples_b)
features = convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
cls_token_segment_id=0,
pad_token_segment_id=0,
cls_token_at_end=False,
sequence_a_is_doc=False)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features],
dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features],
dtype=torch.float)
all_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_example_index, all_cls_index, all_p_mask)
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=len(samples_b))
all_results = []
for batch in tqdm(eval_dataloader, desc="Evaluating"):
#stime = time.time()
batch = tuple(t.cuda() for t in batch)
with torch.no_grad():
inputs = {'input_ids': batch[0], 'attention_mask': batch[1]}
inputs['token_type_ids'] = batch[
2] # XLM don't use segment_ids
example_indices = batch[3]
outputs = qamodel(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
result = RawResult(unique_id=unique_id,
start_logits=to_list(outputs[0][i]),
end_logits=to_list(outputs[1][i]))
all_results.append(result)
#total_time = time.time() - stime
#print(total_time)
#import ipdb
#ipdb.set_trace()
predictions = get_predictions(examples,
features,
all_results,
n_best,
max_answer_length,
do_lower_case,
verbose_logging,
v2,
null_score_diff_threshold,
must_choose_answer=must_choose_answer)
predictions = [predictions[p] for p in predictions]
predictions_list.extend(predictions)
torch.cuda.empty_cache()
original_log_probs_list, token_ids_list, masked_indices_list = model.get_batch_generation(
sentences_b, logger=logger)
mymodel_probs_list = original_log_probs_list
#obj_len = 0
#for obj in gc.get_objects():
# try:
# if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
# print(type(obj), obj.size())
# obj_len += 1
# except:
# pass
#print(obj_len)
if vocab_subset is not None:
# filter log_probs
filtered_log_probs_list = model.filter_logprobs(
original_log_probs_list, filter_logprob_indices)
else:
filtered_log_probs_list = original_log_probs_list
label_index_list = []
for sample in samples_b:
obj_label_id = model.get_id(sample["obj_label"])
# MAKE SURE THAT obj_label IS IN VOCABULARIES
if obj_label_id is None:
raise ValueError(
"object label {} not in model vocabulary".format(
sample["obj_label"]))
#elif model.vocab[obj_label_id[0]] != sample["obj_label"]:
# raise ValueError(
# "object label {} not in model vocabulary".format(
# sample["obj_label"]
# )
# )
elif vocab_subset is not None and sample[
"obj_label"] not in vocab_subset:
raise ValueError("object label {} not in vocab subset".format(
sample["obj_label"]))
label_index_list.append(obj_label_id)
arguments = [{
"mymodel_probs":
mymodel_probs,
"original_log_probs":
original_log_probs,
"filtered_log_probs":
filtered_log_probs,
"target":
sample["reconstructed_word"],
"prediction":
pred,
"token_ids":
token_ids,
"vocab":
model.vocab,
"label_index":
label_index[0] if len(label_index) > 0 else 0,
"masked_indices":
masked_indices,
"interactive":
args.interactive,
"index_list":
index_list,
"sample":
sample,
} for mymodel_probs, original_log_probs, filtered_log_probs, token_ids,
masked_indices, label_index, sample, pred in zip(
mymodel_probs_list,
original_log_probs_list,
filtered_log_probs_list,
token_ids_list,
masked_indices_list,
label_index_list,
samples_b,
predictions_list,
)]
# single thread for debug
# for isx,a in enumerate(arguments):
# print(samples_b[isx])
# run_thread(a)
# multithread
res = pool.map(run_thread, arguments)
for idx, result in enumerate(res):
result_masked_topk, sample_MRR, sample_P, sample_perplexity, msg, sample_em, sample_f1, sample_is_error, sample_no_overlap, sample_larger_by_1, sample_larger_by_2, sample_larger_by_3, sample_larger_by_4, sample_larger_by_5_or_more = result
logger.info("\n" + msg + "\n")
sample = samples_b[idx]
element = {}
element["sample"] = sample
element["uuid"] = sample["uuid"]
element["token_ids"] = token_ids_list[idx]
element["masked_indices"] = masked_indices_list[idx]
element["label_index"] = label_index_list[idx]
element["masked_topk"] = result_masked_topk
element["sample_MRR"] = sample_MRR
element["sample_Precision"] = sample_P
element["sample_perplexity"] = sample_perplexity
element["sample_Precision1"] = result_masked_topk["P_AT_1"]
element['sample_em'] = sample_em
element['sample_f1'] = sample_f1
# print()
# print("idx: {}".format(idx))
# print("masked_entity: {}".format(result_masked_topk['masked_entity']))
# for yi in range(10):
# print("\t{} {}".format(yi,result_masked_topk['topk'][yi]))
# print("masked_indices_list: {}".format(masked_indices_list[idx]))
# print("sample_MRR: {}".format(sample_MRR))
# print("sample_P: {}".format(sample_P))
# print("sample: {}".format(sample))
# print()
MRR += sample_MRR
Precision += sample_P
Precision1 += element["sample_Precision1"]
is_error += sample_is_error
no_overlap += sample_no_overlap
larger_by_1 += sample_larger_by_1
larger_by_2 += sample_larger_by_2
larger_by_3 += sample_larger_by_3
larger_by_4 += sample_larger_by_4
larger_by_5_or_more += sample_larger_by_5_or_more
EM += sample_em
F1 += sample_f1
# the judgment of the annotators recording whether they are
# evidence in the sentence that indicates a relation between two entities.
num_yes = 0
num_no = 0
if "judgments" in sample:
# only for Google-RE
for x in sample["judgments"]:
if x["judgment"] == "yes":
num_yes += 1
else:
num_no += 1
if num_no >= num_yes:
samples_with_negative_judgement += 1
element["judgement"] = "negative"
MRR_negative += sample_MRR
Precision_negative += sample_P
else:
samples_with_positive_judgement += 1
element["judgement"] = "positive"
MRR_positive += sample_MRR
Precision_positivie += sample_P
list_of_results.append(element)
if viz:
with open('viz.pkl', 'wb') as wf:
pickle.dump(final_viz, wf)
pool.close()
pool.join()
# stats
# Mean reciprocal rank
MRR /= len(list_of_results)
# Precision
Precision /= len(list_of_results)
Precision1 /= len(list_of_results)
EM /= len(list_of_results)
F1 /= len(list_of_results)
msg = "all_samples: {}\n".format(len(all_samples))
msg += "list_of_results: {}\n".format(len(list_of_results))
msg += "global MRR: {}\n".format(MRR)
msg += "global Precision at 10: {}\n".format(Precision)
msg += "global Precision at 1: {}\n".format(Precision1)
msg += "global EM {}\n".format(EM)
msg += "global F1: {}\n".format(F1)
if samples_with_negative_judgement > 0 and samples_with_positive_judgement > 0:
# Google-RE specific
MRR_negative /= samples_with_negative_judgement
MRR_positive /= samples_with_positive_judgement
Precision_negative /= samples_with_negative_judgement
Precision_positivie /= samples_with_positive_judgement
msg += "samples_with_negative_judgement: {}\n".format(
samples_with_negative_judgement)
msg += "samples_with_positive_judgement: {}\n".format(
samples_with_positive_judgement)
msg += "MRR_negative: {}\n".format(MRR_negative)
msg += "MRR_positive: {}\n".format(MRR_positive)
msg += "Precision_negative: {}\n".format(Precision_negative)
msg += "Precision_positivie: {}\n".format(Precision_positivie)
logger.info("\n" + msg + "\n")
print("\n" + msg + "\n")
# dump pickle with the result of the experiment
all_results = dict(list_of_results=list_of_results,
global_MRR=MRR,
global_P_at_10=Precision)
with open("{}/result.pkl".format(log_directory), "wb") as f:
pickle.dump(all_results, f)
return Precision1, Precision, MRR, EM, F1, is_error, no_overlap, larger_by_1, larger_by_2, larger_by_3, larger_by_4, larger_by_5_or_more
