def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor,
"mnlimdevastest": MnliMDevAsTestProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_eval:
raise ValueError("At least '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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
# special handling for mnlimdevastest
if task_name == 'mnlimdevastest':
task_name = 'mnlim'
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
batch_size = FLAGS.eval_batch_size
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
num_labels = len(label_list)
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='segment_ids')
label_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='label_ids')
tf.compat.v1.logging.info(
"Running single-head masking out and direct evaluation!")
# we want to mask out the individual head and then evaluate. So there are 12 layers * 12 heads results.
n_layers = 12
n_heads = 12
folder = FLAGS.output_dir
save_file = 'single_head_mask.pickle'
output = np.zeros((n_layers, n_heads))
# two placeholders for the head coordinates, layer, head
head_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='head_mask')
layer_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='layer_mask')
model = modeling.BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
head_mask=head_mask_ph,
layer_mask=layer_mask_ph)
output_layer = model.get_pooled_output()
output_weights = tf.get_variable(
"output_weights", [num_labels, embed_dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
with tf.compat.v1.variable_scope("loss"):
# for stsb
if num_labels == 1:
logits = tf.squeeze(logits, [-1])
per_example_loss = tf.square(logits - label_ids_ph)
loss = tf.reduce_mean(per_example_loss)
else:
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids_ph,
depth=num_labels,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
loss = tf.reduce_mean(per_example_loss)
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# metric and summary
# metric is tf.metric object, (val, op)
metric = metric_fn(per_example_loss, label_ids_ph, logits, num_labels,
task_name)
metric_name = list(metric.keys())
metric_val = [m[0] for m in metric.values()]
metric_op = [m[1] for m in metric.values()]
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
saver_init = tf.train.Saver(tvars)
# Isolate the variables stored behind the scenes by the metric operation
var_metric = []
for key in metric.keys():
var_metric.extend(
tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=key))
# Define initializer to initialize/reset running variables
metric_vars_initializer = tf.variables_initializer(var_list=var_metric)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer)
# loop over layers, then loop over heads
for l in range(n_layers):
for h in range(n_heads):
cur_l, cur_h = l, h
head_mask = [h]
layer_mask = [l]
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
sess.run(metric_vars_initializer)
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask
})
eval_metric_val = sess.run(metric_val)
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
sess.run(metric_vars_initializer)
for _ in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask
})
eval_metric_val = sess.run(metric_val)
for name, val in zip(metric_name, eval_metric_val):
if name == 'accuracy':
output[cur_l][cur_h] = val
print(
"Mask out the head in (Layer {}, Head {}) | {}: {}"
.format(cur_l, cur_h, name, val))
joblib.dump(output, folder + save_file)
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_eval:
raise ValueError("At least '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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
train_examples = processor.get_train_examples(FLAGS.data_dir)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_train_examples = len(train_examples)
num_actual_eval_examples = len(eval_examples)
print("num_actual_train_examples", num_actual_train_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
batch_size = FLAGS.train_batch_size
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
num_labels = len(label_list)
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, seq_length], name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, seq_length], name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, seq_length], name='segment_ids')
label_ids_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, ], name='label_ids')
tf.compat.v1.logging.info("Running cumulatively mask out heads and direct evaluation!")
# we want to mask out the individual head and then evaluate. So there are 12 layers * 12 heads results.
n_layers = 12
n_heads = 12
folder = FLAGS.output_dir
save_file = 'cumulative_heads_mask.pickle'
output = np.zeros((n_layers, n_heads))
importance_coordinates = []
if FLAGS.importance_setting == 'l2_norm':
#######################
# 12 * 12, layer * head, importance increases from 1 to 144.
# [[127, 63, 67, 72, 91, 93, 124, 100, 96, 134, 15, 133],
# [143, 60, 107, 128, 57, 106, 118, 83, 144, 135, 99, 116],
# [30, 111, 115, 132, 112, 94, 122, 66, 123, 40, 75, 89],
# [108, 140, 141, 119, 121, 62, 137, 131, 125, 70, 85, 105],
# [126, 120, 113, 136, 92, 79, 110, 74, 103, 84, 86, 53],
# [69, 87, 117, 80, 142, 114, 129, 104, 97, 18, 52, 77],
# [81, 88, 48, 90, 56, 50, 58, 101, 130, 64, 35, 46],
# [20, 41, 38, 32, 71, 59, 82, 43, 78, 55, 47, 37],
# [24, 95, 27, 44, 65, 12, 28, 102, 98, 23, 14, 19],
# [17, 16, 2, 1, 9, 13, 68, 4, 139, 7, 21, 109],
# [76, 26, 8, 138, 10, 29, 31, 54, 6, 36, 3, 49],
# [61, 51, 42, 45, 34, 5, 73, 33, 25, 22, 39, 11]]
#######################
# sorted head coordinate array by importance (L2 weight magnitude), (layer, head)
# least 1 --> most 144
importance_coordinates = [[9, 3], [9, 2], [10, 10], [9, 7], [11, 5], [10, 8], [9, 9], [10, 2], [9, 4],
[10, 4], [11, 11], [8, 5], [9, 5], [8, 10], [0, 10], [9, 1], [9, 0], [5, 9],
[8, 11], [7, 0], [9, 10], [11, 9], [8, 9], [8, 0], [11, 8], [10, 1], [8, 2],
[8, 6], [10, 5], [2, 0], [10, 6], [7, 3], [11, 7], [11, 4], [6, 10], [10, 9],
[7, 11], [7, 2], [11, 10], [2, 9], [7, 1], [11, 2], [7, 7], [8, 3], [11, 3],
[6, 11], [7, 10], [6, 2], [10, 11], [6, 5], [11, 1], [5, 10], [4, 11], [10, 7],
[7, 9], [6, 4], [1, 4], [6, 6], [7, 5], [1, 1], [11, 0], [3, 5], [0, 1], [6, 9],
[8, 4], [2, 7], [0, 2], [9, 6], [5, 0], [3, 9], [7, 4], [0, 3], [11, 6], [4, 7],
[2, 10], [10, 0], [5, 11], [7, 8], [4, 5], [5, 3], [6, 0], [7, 6], [1, 7], [4, 9],
[3, 10], [4, 10], [5, 1], [6, 1], [2, 11], [6, 3], [0, 4], [4, 4], [0, 5], [2, 5],
[8, 1], [0, 8], [5, 8], [8, 8], [1, 10], [0, 7], [6, 7], [8, 7], [4, 8], [5, 7],
[3, 11], [1, 5], [1, 2], [3, 0], [9, 11], [4, 6], [2, 1], [2, 4], [4, 2], [5, 5],
[2, 2], [1, 11], [5, 2], [1, 6], [3, 3], [4, 1], [3, 4], [2, 6], [2, 8], [0, 6],
[3, 8], [4, 0], [0, 0], [1, 3], [5, 6], [6, 8], [3, 7], [2, 3], [0, 11], [0, 9],
[1, 9], [4, 3], [3, 6], [10, 3], [9, 8], [3, 1], [3, 2], [5, 4], [1, 0], [1, 8]]
elif FLAGS.importance_setting == 'per_head_score':
#######################
# 12 * 12, layer * head, importance increases from 1 to 144.
# [[127, 63, 67, 72, 91, 93, 124, 100, 96, 134, 15, 133],
# [143, 60, 107, 128, 57, 106, 118, 83, 144, 135, 99, 116],
# [30, 111, 115, 132, 112, 94, 122, 66, 123, 40, 75, 89],
# [108, 140, 141, 119, 121, 62, 137, 131, 125, 70, 85, 105],
# [126, 120, 113, 136, 92, 79, 110, 74, 103, 84, 86, 53],
# [69, 87, 117, 80, 142, 114, 129, 104, 97, 18, 52, 77],
# [81, 88, 48, 90, 56, 50, 58, 101, 130, 64, 35, 46],
# [20, 41, 38, 32, 71, 59, 82, 43, 78, 55, 47, 37],
# [24, 95, 27, 44, 65, 12, 28, 102, 98, 23, 14, 19],
# [17, 16, 2, 1, 9, 13, 68, 4, 139, 7, 21, 109],
# [76, 26, 8, 138, 10, 29, 31, 54, 6, 36, 3, 49],
# [61, 51, 42, 45, 34, 5, 73, 33, 25, 22, 39, 11]]
#######################
# sorted head coordinate array by importance (L2 weight magnitude), (layer, head)
# least 1 --> most 144
importance_coordinates = [[9, 3], [9, 2], [10, 10], [9, 7], [11, 5], [10, 8], [9, 9], [10, 2], [9, 4],
[10, 4], [11, 11], [8, 5], [9, 5], [8, 10], [0, 10], [9, 1], [9, 0], [5, 9],
[8, 11], [7, 0], [9, 10], [11, 9], [8, 9], [8, 0], [11, 8], [10, 1], [8, 2],
[8, 6], [10, 5], [2, 0], [10, 6], [7, 3], [11, 7], [11, 4], [6, 10], [10, 9],
[7, 11], [7, 2], [11, 10], [2, 9], [7, 1], [11, 2], [7, 7], [8, 3], [11, 3],
[6, 11], [7, 10], [6, 2], [10, 11], [6, 5], [11, 1], [5, 10], [4, 11], [10, 7],
[7, 9], [6, 4], [1, 4], [6, 6], [7, 5], [1, 1], [11, 0], [3, 5], [0, 1], [6, 9],
[8, 4], [2, 7], [0, 2], [9, 6], [5, 0], [3, 9], [7, 4], [0, 3], [11, 6], [4, 7],
[2, 10], [10, 0], [5, 11], [7, 8], [4, 5], [5, 3], [6, 0], [7, 6], [1, 7], [4, 9],
[3, 10], [4, 10], [5, 1], [6, 1], [2, 11], [6, 3], [0, 4], [4, 4], [0, 5], [2, 5],
[8, 1], [0, 8], [5, 8], [8, 8], [1, 10], [0, 7], [6, 7], [8, 7], [4, 8], [5, 7],
[3, 11], [1, 5], [1, 2], [3, 0], [9, 11], [4, 6], [2, 1], [2, 4], [4, 2], [5, 5],
[2, 2], [1, 11], [5, 2], [1, 6], [3, 3], [4, 1], [3, 4], [2, 6], [2, 8], [0, 6],
[3, 8], [4, 0], [0, 0], [1, 3], [5, 6], [6, 8], [3, 7], [2, 3], [0, 11], [0, 9],
[1, 9], [4, 3], [3, 6], [10, 3], [9, 8], [3, 1], [3, 2], [5, 4], [1, 0], [1, 8]]
# mask out heads from the most important one
if FLAGS.from_most:
importance_coordinates.reverse()
# two placeholders for the head coordinates, layer, head
head_mask_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, ], name='head_mask')
layer_mask_ph = tf.compat.v1.placeholder(tf.int32, shape=[None, ], name='layer_mask')
model = modeling.BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
head_mask=head_mask_ph,
layer_mask=layer_mask_ph)
output_layer = model.get_pooled_output()
output_weights = tf.get_variable(
"output_weights", [num_labels, embed_dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
with tf.compat.v1.variable_scope("loss"):
# for stsb
if num_labels == 1:
logits = tf.squeeze(logits, [-1])
per_example_loss = tf.square(logits - label_ids_ph)
loss = tf.reduce_mean(per_example_loss)
else:
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids_ph, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# metric and summary
# metric is tf.metric object, (val, op)
metric = metric_fn(per_example_loss, label_ids_ph, logits, num_labels, task_name)
metric_name = list(metric.keys())
metric_val = [m[0] for m in metric.values()]
metric_op = [m[1] for m in metric.values()]
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
saver_init = tf.train.Saver(tvars)
# Isolate the variables stored behind the scenes by the metric operation
var_metric = []
for key in metric.keys():
var_metric.extend(tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=key))
# Define initializer to initialize/reset running variables
metric_vars_initializer = tf.variables_initializer(var_list=var_metric)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer)
# loop over layers, then loop over heads
idx = 0
for l in range(n_layers):
for h in range(n_heads):
cur_l, cur_h = importance_coordinates[idx]
coor_mask = importance_coordinates[0:idx + 1]
head_mask = [head for _, head in coor_mask]
layer_mask = [layer for layer, _ in coor_mask]
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
sess.run(metric_vars_initializer)
sess.run(metric_op, feed_dict={input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask})
eval_metric_val = sess.run(metric_val)
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
sess.run(metric_vars_initializer)
for _ in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
sess.run(metric_op, feed_dict={input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask})
eval_metric_val = sess.run(metric_val)
for name, val in zip(metric_name, eval_metric_val):
if name == 'accuracy':
output[cur_l][cur_h] = val
print("Mask out the {}th head in (Layer {}, Head {}) | {}: {}"
.format(idx + 1, cur_l, cur_h, name, val))
idx += 1
joblib.dump(output, folder + save_file)
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor,
"mnlimdevastest": MnliMDevAsTestProcessor
}
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_pred:
raise ValueError(
"At least one of 'do_train', 'do_eval' or 'do_pred' 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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
# special handling for mnlimdevastest
if task_name == 'mnlimdevastest':
task_name = 'mnlim'
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
train_examples = processor.get_train_examples(FLAGS.data_dir)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_train_examples = len(train_examples)
num_actual_eval_examples = len(eval_examples)
print("num_actual_train_examples", num_actual_train_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
if FLAGS.do_pred:
test_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_test_examples = len(test_examples)
print("num_actual_test_examples", num_actual_test_examples)
batch_size = FLAGS.train_batch_size
epochs = FLAGS.num_train_epochs
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
num_labels = len(label_list)
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='segment_ids')
label_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='label_ids')
tf.compat.v1.logging.info("Running swapping layers!")
num_train_steps = num_actual_train_examples // batch_size * epochs
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Swapped layers
layer_folder_name = FLAGS.layers
if FLAGS.layers is None:
raise ValueError("In swapping experiments, layers must not be None. ")
layers = list(map(int, FLAGS.layers.split(',')))
layer_order = list(range(12)) if embed_dim == 768 else list(range(6))
layer1, layer2 = layers[0], layers[1]
if len(layers) == 2:
layers.sort()
if layer1 != layer2:
layer_order[layer1], layer_order[layer2] = layer_order[
layer2], layer_order[layer1]
else:
raise ValueError("Two layers should be different! ")
else:
layer_order = layer_order[:layers[0]] + layers[::-1] + layer_order[
layers[-1] + 1:]
print("Current layer order: ", layer_order)
# this placeholder is to control the flag for the dropout
keep_prob_ph = tf.compat.v1.placeholder(tf.float32, name="keep_prob")
is_training_ph = tf.compat.v1.placeholder(tf.bool, name='is_training')
model = modeling.BertModel(
config=bert_config,
is_training=is_training_ph,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
layer_order=layer_order,
use_estimator=False)
output_layer = model.get_pooled_output()
output_layer = tf.nn.dropout(output_layer, keep_prob=keep_prob_ph)
output_weights = tf.get_variable(
"output_weights", [num_labels, embed_dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
with tf.compat.v1.variable_scope("loss"):
# for stsb
if num_labels == 1:
logits = tf.squeeze(logits, [-1])
per_example_loss = tf.square(logits - label_ids_ph)
loss = tf.reduce_mean(per_example_loss)
else:
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids_ph,
depth=num_labels,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
loss = tf.reduce_mean(per_example_loss)
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# metric and summary
# metric is tf.metric object, (val, op)
metric = metric_fn(per_example_loss, label_ids_ph, logits, num_labels,
task_name)
metric_name = list(metric.keys())
metric_val = [m[0] for m in metric.values()]
metric_op = [m[1] for m in metric.values()]
metric_phs = [
tf.compat.v1.placeholder(tf.float32, name="{}_ph".format(key))
for key in metric.keys()
]
summaries = [
tf.compat.v1.summary.scalar(key, metric_phs[i])
for i, key in enumerate(metric.keys())
]
train_summary_total = tf.summary.merge(summaries)
eval_summary_total = tf.summary.merge(summaries)
log_dir = FLAGS.output_dir + 'layer_{}/'.format(layer_folder_name)
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
var_init = [
v for v in tvars
if 'output_weights' not in v.name and 'output_bias' not in v.name
]
var_output = [
v for v in tvars
if 'output_weights' in v.name or "output_bias" in v.name
]
if not FLAGS.load_from_finetuned:
# Init from Model0
saver_init = tf.train.Saver(var_init)
else:
# Init from Model1
saver_init = tf.train.Saver(var_init + var_output)
var_train = var_init + var_output
print("Training parameters")
for v in var_train:
print(v)
train_op = optimization.create_optimizer(loss=loss,
init_lr=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=False,
tvars=var_train)
saver_all = tf.train.Saver(var_list=var_train, max_to_keep=1)
# Isolate the variables stored behind the scenes by the metric operation
var_metric = []
for key in metric.keys():
var_metric.extend(
tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=key))
# Define initializer to initialize/reset running variables
metric_vars_initializer = tf.variables_initializer(var_list=var_metric)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
writer = tf.compat.v1.summary.FileWriter(log_dir + 'log/train/',
sess.graph)
writer_eval = tf.compat.v1.summary.FileWriter(log_dir + 'log/eval/')
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer)
start_metric = {"eval_{}".format(key): 0 for key in metric_name}
if FLAGS.do_train:
tf.logging.info("***** Run training *****")
step = 1
for n in range(epochs):
np.random.shuffle(train_examples)
num_batch = num_actual_train_examples // batch_size if num_actual_train_examples % batch_size == 0 \
else num_actual_train_examples // batch_size + 1
id = 0
for b in range(num_batch):
input_ids, input_mask, \
segment_ids, label_ids, is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=train_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id)
id += batch_size
sess.run(metric_vars_initializer)
sess.run([train_op] + metric_op,
feed_dict={
input_ids_ph: input_ids,
input_mask_ph: input_mask,
segment_ids_ph: segment_ids,
label_ids_ph: label_ids,
is_training_ph: True,
keep_prob_ph: 0.9
})
train_metric_val = sess.run(metric_val)
train_summary_str = sess.run(
train_summary_total,
feed_dict={
ph: value
for ph, value in zip(metric_phs, train_metric_val)
})
writer.add_summary(train_summary_str, step)
if step % 100 == 0 or step % num_batch == 0 or step == 1:
# evaluate on dev set
if num_actual_eval_examples <= 1000:
sess.run(metric_vars_initializer)
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
eval_metric_val = sess.run(metric_val)
eval_summary_str = sess.run(
eval_summary_total,
feed_dict={
ph: value
for ph, value in zip(
metric_phs, eval_metric_val)
})
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
sess.run(metric_vars_initializer)
for _ in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
eval_metric_val = sess.run(metric_val)
eval_summary_str = sess.run(
eval_summary_total,
feed_dict={
ph: value
for ph, value in zip(
metric_phs, eval_metric_val)
})
writer_eval.add_summary(eval_summary_str, step)
if step == 1:
for key, val in zip(metric_name, eval_metric_val):
start_metric["eval_{}".format(key)] = val
if step % 100 == 0 or step % num_batch == 0 or step == 1:
train_metric_list = []
for i in range(len(train_metric_val)):
if metric_name[i] == 'loss':
train_metric_list.append(
"{}: %2.4f".format(metric_name[i]) %
train_metric_val[i])
else:
train_metric_list.append(
"{}: %.4f".format(metric_name[i]) %
train_metric_val[i])
train_str = 'Train ' + '|'.join(train_metric_list)
eval_metric_list = []
for i in range(len(eval_metric_val)):
if metric_name[i] == 'loss':
eval_metric_list.append(
"{}: %2.4f".format(metric_name[i]) %
eval_metric_val[i])
else:
eval_metric_list.append(
"{}: %.4f".format(metric_name[i]) %
eval_metric_val[i])
eval_str = 'Eval ' + '|'.join(eval_metric_list)
print(
"Swap layer order {} | Epoch: %4d/%4d | Batch: %4d/%4d | {} | {}"
.format(layer_folder_name, train_str, eval_str) %
(n, epochs, b, num_batch))
if step % num_batch == 0:
saver_all.save(sess,
log_dir +
'swap_{}'.format(layer_folder_name),
global_step=step)
step += 1
writer.close()
writer_eval.close()
end_metric = {"eval_{}".format(key): 0 for key in metric_name}
if FLAGS.do_eval:
tf.logging.info("***** Run evaluation *****")
if num_actual_eval_examples <= 1000:
sess.run(metric_vars_initializer)
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
eval_metric_val = sess.run(metric_val)
preds = sess.run(predictions,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
eval_label_ids_lst = eval_label_ids
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
preds = np.zeros(num_actual_eval_examples)
eval_label_ids_lst = np.zeros(num_actual_eval_examples)
sess.run(metric_vars_initializer)
for i in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
pred = sess.run(predictions,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
preds[i * batch_size:min(id_eval, num_actual_eval_examples
)] = pred[:]
eval_label_ids_lst[i * batch_size:min(
id_eval, num_actual_eval_examples)] = eval_label_ids[:]
eval_metric_val = sess.run(metric_val)
for key, val in zip(metric_name, eval_metric_val):
end_metric["eval_{}".format(key)] = val
output_predict_file = os.path.join(log_dir, 'dev_predictions.tsv')
writer_output = tf.io.gfile.GFile(output_predict_file, "w")
preds = preds.astype(int)
eval_label_ids_lst = eval_label_ids_lst.astype(int)
num_written_lines = 0
if task_name != 'stsb':
writer_output.write(
"ID \t Label ID \t Label \t Ground Truth ID \t Ground Truth \n"
)
else:
writer_output.write("ID \t Label \n")
for (i, pred) in enumerate(preds):
if task_name != 'stsb':
writer_output.write("{} \t {} \t {} \t {} \t {} \n".format(
i, pred, label_list[pred], eval_label_ids_lst[i],
label_list[eval_label_ids_lst[i]]))
else:
writer_output.write("{} \t {} \n".format(
num_written_lines, pred))
writer_output.close()
tf.logging.info("***** Finish writing *****")
print("Start metric", start_metric)
print("End metric", end_metric)
test_metric = {"test_{}".format(key): 0 for key in metric_name}
if FLAGS.do_pred:
# if number of test examples < 1000, just load it directly, or load by batch.
# prediction
tf.logging.info("***** Predict results *****")
if num_actual_test_examples <= 1000:
test_input_ids, test_input_mask, test_segment_ids, \
test_label_ids, test_is_real_example = generate_ph_input(batch_size=num_actual_test_examples,
seq_length=seq_length,
examples=test_examples,
label_list=label_list,
tokenizer=tokenizer)
sess.run(metric_vars_initializer)
sess.run(metric_op,
feed_dict={
input_ids_ph: test_input_ids,
input_mask_ph: test_input_mask,
segment_ids_ph: test_segment_ids,
label_ids_ph: test_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
test_metric_val = sess.run(metric_val)
preds = sess.run(predictions,
feed_dict={
input_ids_ph: test_input_ids,
input_mask_ph: test_input_mask,
segment_ids_ph: test_segment_ids,
label_ids_ph: test_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
test_label_ids_lst = test_label_ids
else:
num_batch_test = num_actual_test_examples // batch_size \
if num_actual_test_examples % batch_size == 0 \
else num_actual_test_examples // batch_size + 1
id_test = 0
preds = np.zeros(num_actual_test_examples)
test_label_ids_lst = np.zeros(num_actual_test_examples)
sess.run(metric_vars_initializer)
for i in range(num_batch_test):
test_input_ids, test_input_mask, test_segment_ids, \
test_label_ids, test_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=test_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_test)
id_test += batch_size
sess.run(metric_op,
feed_dict={
input_ids_ph: test_input_ids,
input_mask_ph: test_input_mask,
segment_ids_ph: test_segment_ids,
label_ids_ph: test_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
pred = sess.run(predictions,
feed_dict={
input_ids_ph: test_input_ids,
input_mask_ph: test_input_mask,
segment_ids_ph: test_segment_ids,
label_ids_ph: test_label_ids,
is_training_ph: False,
keep_prob_ph: 1
})
preds[i * batch_size:min(id_test, num_actual_test_examples
)] = pred[:]
test_label_ids_lst[i * batch_size:min(
id_test, num_actual_test_examples)] = test_label_ids[:]
test_metric_val = sess.run(metric_val)
for key, val in zip(metric_name, test_metric_val):
test_metric["test_{}".format(key)] = val
output_predict_file = os.path.join(log_dir, 'test_predictions.tsv')
submit_predict_file = os.path.join(
log_dir, "{}.tsv".format(standard_file_name[task_name]))
writer_output = tf.io.gfile.GFile(output_predict_file, "w")
writer_submit = tf.io.gfile.GFile(submit_predict_file, 'w')
preds = preds.astype(int)
test_label_ids_lst = test_label_ids_lst.astype(int)
num_written_lines = 0
if task_name != 'stsb':
writer_output.write(
"ID \t Label ID \t Label \t Ground Truth ID \t Ground Truth \n"
)
else:
writer_output.write("ID \t Label \n")
writer_submit.write("ID \t Label \n")
for (i, pred) in enumerate(preds):
if task_name != 'stsb':
writer_output.write("{} \t {} \t {} \t {} \t {} \n".format(
i, pred, label_list[pred], test_label_ids_lst[i],
label_list[test_label_ids_lst[i]]))
writer_submit.write("{} \t {} \n".format(
i, label_list[pred]))
else:
writer_output.write("{} \t {} \n".format(
num_written_lines, pred))
writer_submit.write("{} \t {} \n".format(i, pred))
writer_output.close()
writer_submit.close()
tf.logging.info("***** Finish writing *****")
with tf.io.gfile.GFile(FLAGS.output_dir + 'results.txt',
'a') as writer:
eval_start, eval_end, test_end = [], [], []
for metric in metric_name:
if metric != 'loss':
eval_start.append("{}: %.4f".format(metric) %
start_metric["eval_{}".format(metric)])
eval_end.append("{}: %.4f".format(metric) %
end_metric["eval_{}".format(metric)])
test_end.append("{}: %.4f".format(metric) %
test_metric["test_{}".format(metric)])
writer.write(
"Swap layer order {}: Dev start: {} | Dev end: {} | Test end: {}\n"
.format(layer_folder_name, ','.join(eval_start),
','.join(eval_end), ','.join(test_end)))
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor,
"mnlimdevastest": MnliMDevAsTestProcessor,
"semeval": SemEvalProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
n_layers = FLAGS.n_layers
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='segment_ids')
tf.compat.v1.logging.info(
"Running tracking hidden token embeddings through the model!")
# for faster calculation
# np.random.seed(0)
# random_choice_idx = np.random.choice(num_actual_eval_examples, 1000, replace=False)
# eval_examples = [eval_examples[idx] for idx in random_choice_idx]
# num_actual_eval_examples = 1000
if FLAGS.feed_ones:
num_actual_eval_examples = 1
print("here we only take {} samples.".format(num_actual_eval_examples))
tf.compat.v1.logging.info(
"For faster calculation, we reduce the example size! ")
save_file = 'track_embeddings.pickle'
# output = {"word_embeddings": np.zeros((num_actual_eval_examples, seq_length, embed_dim)),
# "final_embeddings": np.zeros((num_actual_eval_examples, seq_length, embed_dim)),
# "layer_input": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_self_attention": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_self_attention_ff": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_attention_before_ln": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_attention": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_ffgelu": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim * 4)),
# "layer_mlp": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_ffn_before_ln": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "layer_output": np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
# "sentence": []}
output = {
"final_embeddings":
np.zeros((num_actual_eval_examples, seq_length, embed_dim)),
"layer_self_attention_ff":
np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
"layer_attention":
np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
"layer_mlp":
np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
"layer_output":
np.zeros((n_layers, num_actual_eval_examples, seq_length, embed_dim)),
"sentence": []
}
layers_cancel_skip_connection = []
if FLAGS.layers_cancel_skip_connection is not None:
layers_cancel_skip_connection = list(
map(int, FLAGS.layers_cancel_skip_connection.split(',')))
layers_cancel_skip_connection.sort()
print("Layers need to cancel skip-connection: ",
layers_cancel_skip_connection)
layers_use_relu = []
if FLAGS.layers_use_relu is not None:
layers_use_relu = list(map(int, FLAGS.layers_use_relu.split(',')))
layers_use_relu.sort()
print("Layers need to use ReLU: ", layers_use_relu)
model = modeling.BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
cancel_skip_connection=layers_cancel_skip_connection,
layer_use_relu=layers_use_relu,
feed_same=FLAGS.feed_same)
# extract all the intermediate outputs
# word_embeddings = model.get_in_embeds()[0]
final_embeddings = model.get_embedding_output()
# layer_self_attention = model.get_all_head_output()
layer_self_attention_ff = model.get_all_attention_before_dropout()
# layer_attention_before_ln = model.get_all_attention_before_layernorm()
layer_attention = model.get_all_layer_tokens_beforeMLP()
# layer_ffgelu = model.get_all_layer_tokens_after_FFGeLU()
layer_mlp = model.get_all_layer_tokens_afterMLP()
# layer_ffn_before_ln = model.get_all_ffn_before_layernorm()
layer_output = model.get_all_encoder_layers()
# load weights
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
saver_init = tf.train.Saver(tvars)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
if FLAGS.feed_ones:
input_ids = np.zeros([1, seq_length])
input_mask = np.ones([1, seq_length])
segment_ids = np.zeros([1, seq_length])
else:
input_ids, input_mask, segment_ids, \
label_ids, is_real_example, input_tokens = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
return_tokens=True)
# word_embeddings_val, final_embeddings_val, \
# layer_self_attention_val, layer_self_attention_ff_val, \
# layer_attention_before_ln_val, layer_attention_val, \
# layer_ffgelu_val, layer_mlp_val, layer_ffn_before_ln_val, \
# layer_output_val = sess.run([word_embeddings, final_embeddings,
# layer_self_attention, layer_self_attention_ff,
# layer_attention_before_ln, layer_attention,
# layer_ffgelu, layer_mlp, layer_ffn_before_ln, layer_output],
# feed_dict={input_ids_ph: input_ids,
# input_mask_ph: input_mask,
# segment_ids_ph: segment_ids})
final_embeddings_val, \
layer_self_attention_ff_val, layer_attention_val, layer_mlp_val, \
layer_output_val = sess.run([final_embeddings,
layer_self_attention_ff,
layer_attention,
layer_mlp, layer_output],
feed_dict={input_ids_ph: input_ids,
input_mask_ph: input_mask,
segment_ids_ph: segment_ids})
# assign values to output dict
# output["word_embeddings"] = np.reshape(word_embeddings_val,
# [num_actual_eval_examples, seq_length, embed_dim])
output["final_embeddings"] = np.reshape(
final_embeddings_val,
[num_actual_eval_examples, seq_length, embed_dim])
for layer in tqdm(range(n_layers)):
# if FLAGS.feed_same:
# output["layer_input"][layer] = np.reshape(final_embeddings_val,
# [num_actual_eval_examples, seq_length, embed_dim])
# else:
# if layer == 0:
# output["layer_input"][layer] = np.reshape(final_embeddings_val,
# [num_actual_eval_examples, seq_length, embed_dim])
# else:
# output["layer_input"][layer] = np.reshape(layer_output_val[layer - 1],
# [num_actual_eval_examples, seq_length, embed_dim])
# output["layer_self_attention"][layer] = np.reshape(layer_self_attention_val[layer],
# [num_actual_eval_examples, seq_length, embed_dim])
output["layer_self_attention_ff"][layer] = np.reshape(
layer_self_attention_ff_val[layer],
[num_actual_eval_examples, seq_length, embed_dim])
# output["layer_attention_before_ln"][layer] = np.reshape(layer_attention_before_ln_val[layer],
# [num_actual_eval_examples, seq_length, embed_dim])
output["layer_attention"][layer] = np.reshape(
layer_attention_val[layer],
[num_actual_eval_examples, seq_length, embed_dim])
# output["layer_ffgelu"][layer] = np.reshape(layer_ffgelu_val[layer],
# [num_actual_eval_examples, seq_length, embed_dim * 4])
output["layer_mlp"][layer] = np.reshape(
layer_mlp_val[layer],
[num_actual_eval_examples, seq_length, embed_dim])
# output["layer_ffn_before_ln"][layer] = np.reshape(layer_ffn_before_ln_val[layer],
# [num_actual_eval_examples, seq_length, embed_dim])
output["layer_output"][layer] = np.reshape(
layer_output_val[layer],
[num_actual_eval_examples, seq_length, embed_dim])
if FLAGS.feed_ones:
output["sentence"] = [[''] * seq_length]
else:
output["sentence"] = input_tokens
joblib.dump(output, os.path.join(FLAGS.output_dir, save_file))
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor,
"mnlimdevastest": MnliMDevAsTestProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
# special handling for mnlimdevastest
if task_name == 'mnlimdevastest':
task_name = 'mnlim'
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
train_examples = processor.get_train_examples(FLAGS.data_dir)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_train_examples = len(train_examples)
num_actual_eval_examples = len(eval_examples)
print("num_actual_train_examples", num_actual_train_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
batch_size = FLAGS.train_batch_size
epochs = FLAGS.num_train_epochs
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='segment_ids')
# train an independent linear model to approximate the projection of MLP
tf.compat.v1.logging.info("Training Approximator!")
# get the layer(s) which need replacement
if FLAGS.layers is None:
raise ValueError(
"In training non-linearity experiments, layers must not be None. ")
layer_folder_name = FLAGS.layers
layers = list(map(int, FLAGS.layers.split(',')))
layers.sort()
if len(layers) != 1:
raise ValueError("Here it allows only one single layer. ")
approximated_layer = layers[0]
print("Current approximated layer: ", approximated_layer)
approximator_setting = FLAGS.approximator_setting
layers_cancel_skip_connection = []
if FLAGS.layers_cancel_skip_connection is not None:
layers_cancel_skip_connection = list(
map(int, FLAGS.layers_cancel_skip_connection.split(',')))
layers_cancel_skip_connection.sort()
print("Layers need to cancel skip-connection: ",
layers_cancel_skip_connection)
layers_use_relu = []
if FLAGS.layers_use_relu is not None:
layers_use_relu = list(map(int, FLAGS.layers_use_relu.split(',')))
layers_use_relu.sort()
print("Layers need to use ReLU: ", layers_use_relu)
model = modeling.BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
approximator_setting=approximator_setting,
cancel_skip_connection=layers_cancel_skip_connection,
layer_use_relu=layers_use_relu)
# define the input and output according to the approximated part
if FLAGS.approximate_part == 'mlp':
# only FFGeLU and FF in FFN, without dropout and layernorm
x = model.get_all_layer_tokens_beforeMLP()[approximated_layer]
y = model.get_all_layer_tokens_afterMLP()[approximated_layer]
elif FLAGS.approximate_part == 'ffgelu':
# only FFGeLU in FFN
x = model.get_all_layer_tokens_beforeMLP()[approximated_layer]
y = model.get_all_layer_tokens_after_FFGeLU()[approximated_layer]
elif FLAGS.approximate_part == 'self_attention':
# only self-attention part, without linear layer, dropout and layernorm
x = model.get_all_encoder_layers()[
approximated_layer -
1] if approximated_layer > 0 else model.get_embedding_output()
y = model.get_all_head_output()[approximated_layer]
elif FLAGS.approximate_part == 'self_attention_ff':
# only self-attention + linear part part, without dropout and layernorm
x = model.get_all_encoder_layers()[
approximated_layer -
1] if approximated_layer > 0 else model.get_embedding_output()
y = model.get_all_attention_before_dropout()[approximated_layer]
elif FLAGS.approximate_part == 'ff_after_self_attention':
# only the linear layer after self-attention
x = model.get_all_head_output()[approximated_layer]
y = model.get_all_attention_before_dropout()[approximated_layer]
elif FLAGS.approximate_part == 'attention_before_ln':
# only self-attention + linear part part, before layernorm
x = model.get_all_encoder_layers()[
approximated_layer -
1] if approximated_layer > 0 else model.get_embedding_output()
y = model.get_all_attention_before_layernorm()[approximated_layer]
elif FLAGS.approximate_part == 'attention':
# whole attention block including dropout and layernorm
x = model.get_all_encoder_layers()[
approximated_layer -
1] if approximated_layer > 0 else model.get_embedding_output()
y = model.get_all_layer_tokens_beforeMLP()[approximated_layer]
elif FLAGS.approximate_part == 'ffn':
# whole FFN block including dropout and layernorm
x = model.get_all_layer_tokens_beforeMLP()[approximated_layer]
y = model.get_all_encoder_layers()[approximated_layer]
elif FLAGS.approximate_part == 'ffn_before_ln':
# only FFGeLU and FF in FFN, before layernorm
x = model.get_all_layer_tokens_beforeMLP()[approximated_layer]
y = model.get_all_ffn_before_layernorm()[approximated_layer]
elif FLAGS.approximate_part == 'encoder':
# whole encoder including dropout and layernorm
x = model.get_all_encoder_layers()[
approximated_layer -
1] if approximated_layer > 0 else model.get_embedding_output()
y = model.get_all_encoder_layers()[approximated_layer]
else:
raise ValueError("Need to specify correct value. ")
if FLAGS.approximator_setting in [
'HS_MLP', 'HS*4+HS_MLP', 'HS_Self_Attention',
'HS_Self_Attention_FF', 'HS_FFN', 'HS_Attention', 'HS_Encoder',
'HS_Attention_Before_LN', 'HS_FFN_Before_LN',
'HS_FF_After_Self_Attention'
]:
approximator_dim = embed_dim
else: # HS*4_FFGeLU
approximator_dim = embed_dim * 4
x = tf.reshape(x, [-1, seq_length, embed_dim])
y = tf.reshape(y, [-1, seq_length, approximator_dim])
# non-linearity
if not FLAGS.use_nonlinear_approximator:
y_pred = approximator.linear_approximator(
input=x,
approximated_layer=approximated_layer,
hidden_size=approximator_dim)
else:
y_pred = approximator.nonlinear_approximator(
input=x,
approximated_layer=approximated_layer,
hidden_size=approximator_dim,
num_layer=1,
use_dropout=FLAGS.use_dropout,
dropout_p=0.2)
# with tf.compat.v1.variable_scope("bert/encoder/layer_{}/non-linearity".format(approximated_layer)):
# y_pred = tf.layers.dense(
# x,
# approximator_dim,
# kernel_initializer=tf.truncated_normal_initializer(stddev=0.02))
# NOTE, here is alias of cosine_proximity, [0, 1]. Not the TF2.0 version, which is [-1. 0].
similarity_token = tf.keras.losses.cosine_similarity(y, y_pred, axis=-1)
l2_distance_token = tf.norm(y - y_pred, ord=2, axis=-1)
# loss: similarity or mean squared error or l2 norm
# when using cosine similarity, the target becomes minimizing to 0.
per_example_loss = 1. - tf.reduce_mean(similarity_token, axis=-1) \
if FLAGS.loss == 'cosine' \
else (tf.reduce_mean(tf.keras.losses.mse(y, y_pred), axis=-1)
if FLAGS.loss == 'mse'
else (tf.reduce_mean(l2_distance_token, axis=-1)
if FLAGS.loss == 'l2'
else 0.0))
loss = tf.reduce_mean(per_example_loss, axis=-1)
similarity = tf.reduce_mean(tf.reduce_mean(similarity_token, axis=-1),
axis=-1)
l2_distance = tf.reduce_mean(tf.reduce_mean(l2_distance_token, axis=-1),
axis=-1)
# for summary
loss_ph = tf.compat.v1.placeholder(tf.float32, name='loss')
similarity_ph = tf.compat.v1.placeholder(tf.float32, name='similarity')
l2_distance_ph = tf.compat.v1.placeholder(tf.float32, name='l2_distance')
loss_sum = tf.summary.scalar("loss", loss_ph)
similarity_sum = tf.summary.scalar("similarity", similarity_ph)
l2_distance_sum = tf.summary.scalar("l2_distance", l2_distance_ph)
train_summary_total = tf.summary.merge(
[loss_sum, similarity_sum, l2_distance_sum])
eval_summary_total = tf.summary.merge(
[loss_sum, similarity_sum, l2_distance_sum])
log_dir = FLAGS.output_dir + 'layer_{}/'.format(layer_folder_name)
# define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
# load weights
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
var_init = [var for var in tvars if "non-linearity" not in var.name]
saver_init = tf.train.Saver(var_init)
# take variables related to approximators
var_approximator = [var for var in tvars if "non-linearity" in var.name]
print("Training variables")
for var in var_approximator:
print(var)
# define optimizer op and saver
optimizer_op = optimizer.minimize(loss=loss, var_list=var_approximator)
saver_approximator = tf.train.Saver(var_list=var_approximator,
max_to_keep=1)
# add this GPU settings
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
writer = tf.compat.v1.summary.FileWriter(log_dir + 'log/train/',
sess.graph)
writer_eval = tf.compat.v1.summary.FileWriter(log_dir + 'log/eval/')
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example, \
eval_input_tokens = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
return_tokens=True)
step = 1
for n in range(epochs):
np.random.shuffle(train_examples)
num_batch = num_actual_train_examples // batch_size if num_actual_train_examples % batch_size == 0 \
else num_actual_train_examples // batch_size + 1
id = 0
for b in range(num_batch):
input_ids, input_mask, \
segment_ids, label_ids, is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=train_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id)
id += batch_size
train_loss, train_similarity, train_l2_distance, _ = sess.run(
[loss, similarity, l2_distance, optimizer_op],
feed_dict={
input_ids_ph: input_ids,
input_mask_ph: input_mask,
segment_ids_ph: segment_ids
})
train_summary_str = sess.run(train_summary_total,
feed_dict={
loss_ph: train_loss,
similarity_ph:
train_similarity,
l2_distance_ph:
train_l2_distance
})
writer.add_summary(train_summary_str, step)
# evaluate on dev set
if step % 100 == 0 or step % num_batch == 0 or step == 1:
if num_actual_eval_examples <= 1000:
eval_loss, eval_similarity, eval_l2_distance = sess.run(
[loss, similarity, l2_distance],
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids
})
eval_summary_str = sess.run(eval_summary_total,
feed_dict={
loss_ph:
eval_loss,
similarity_ph:
eval_similarity,
l2_distance_ph:
eval_l2_distance
})
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
acc_loss, acc_similarity, acc_l2_distance = 0, 0, 0
for _ in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
eval_loss, eval_similarity, eval_l2_distance = sess.run(
[loss, similarity, l2_distance],
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids
})
acc_loss += eval_loss
acc_similarity += eval_similarity
acc_l2_distance += eval_l2_distance
eval_loss = acc_loss / num_batch_eval
eval_similarity = acc_similarity / num_batch_eval
eval_l2_distance = acc_l2_distance / num_batch_eval
eval_summary_str = sess.run(eval_summary_total,
feed_dict={
loss_ph:
eval_loss,
similarity_ph:
eval_similarity,
l2_distance_ph:
eval_l2_distance
})
writer_eval.add_summary(eval_summary_str, step)
if step % 100 == 0 or step % num_batch == 0 or step == 1:
print(
"Approximating layer: %2d | Epoch: %4d/%4d | Batch: %4d/%4d | "
"Train loss: %2.4f | Train similarity/L2 distance: %.4f/%2.4f | "
"Eval loss: %2.4f | Eval similarity/L2 distance: %.4f/%2.4f"
% (approximated_layer, n, epochs, b, num_batch,
train_loss, train_similarity, train_l2_distance,
eval_loss, eval_similarity, eval_l2_distance))
if step % num_batch == 0:
saver_approximator.save(
sess,
log_dir + 'approximator_{}'.format(layer_folder_name),
global_step=step)
step += 1
writer.close()
writer_eval.close()
# eval
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example, \
eval_input_tokens = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
return_tokens=True)
eval_token_length = [len(elem) for elem in eval_input_tokens]
ground_truths, predictions, cosine_similarity_all = sess.run(
[y, y_pred, similarity],
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids
})
cosine_similarity_actual_token = 0
cosine_similarity_padding = 0
cosine_similarity_all = 0
num_sample_no_padding = 0
for i in range(num_actual_eval_examples):
if eval_token_length[i] >= seq_length:
num_sample_no_padding += 1
continue
cosine_similarity_actual_token += np.mean([
1 - cosine(ground_truths[i][j], predictions[i][j])
for j in range(eval_token_length[i])
])
cosine_similarity_padding += np.mean([
1 - cosine(ground_truths[i][j], predictions[i][j])
for j in range(eval_token_length[i], seq_length)
])
cosine_similarity_all += np.mean([
1 - cosine(ground_truths[i][j], predictions[i][j])
for j in range(seq_length)
])
cosine_similarity_actual_token /= (num_actual_eval_examples -
num_sample_no_padding)
cosine_similarity_padding /= (num_actual_eval_examples -
num_sample_no_padding)
cosine_similarity_all /= (num_actual_eval_examples -
num_sample_no_padding)
print("Skip {} samples without paddings".format(num_sample_no_padding))
with tf.io.gfile.GFile(FLAGS.output_dir + 'results.txt',
'a') as writer:
writer.write(
"Approximating layer %2d: Actual: %.4f/%.4f | Pad: %.4f/%.4f | All: %.4f/%.4f\n"
% (approximated_layer, cosine_similarity_actual_token,
1 - cosine_similarity_actual_token,
cosine_similarity_padding, 1 - cosine_similarity_padding,
cosine_similarity_all, 1 - cosine_similarity_all))
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnlim": MnliMProcessor,
"mnlimm": MnliMMProcessor,
"mrpc": MrpcProcessor,
"qnli": QnliProcessor,
"qqp": QqpProcessor,
"rte": RteProcessor,
"sst2": Sst2Processor,
"stsb": StsbProcessor,
"wnli": WnliProcessor,
"ax": AxProcessor,
"mnlimdevastest": MnliMDevAsTestProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train:
raise ValueError("At least 'do_train' 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.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
print("Current task", task_name)
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
# special handling for mnlimdevastest
if task_name == 'mnlimdevastest':
task_name = 'mnlim'
label_list = processor.get_labels()
print("Label list of current task", label_list)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
train_examples = processor.get_train_examples(FLAGS.data_dir)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_train_examples = len(train_examples)
num_actual_eval_examples = len(eval_examples)
print("num_actual_train_examples", num_actual_train_examples)
print("num_actual_eval_examples", num_actual_eval_examples)
batch_size = FLAGS.train_batch_size
epochs = FLAGS.num_train_epochs
embed_dim = FLAGS.hidden_size # hidden size, 768 for BERT-base, 512 for BERT-small
seq_length = FLAGS.max_seq_length
num_labels = len(label_list)
# Define some placeholders for the input
input_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_ids')
input_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='input_mask')
segment_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[None, seq_length],
name='segment_ids')
label_ids_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='label_ids')
tf.compat.v1.logging.info(
"Running leave the most important head per layer then fine-tune!")
num_train_steps = num_actual_train_examples // batch_size * epochs
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
cur_layer = FLAGS.cur_layer
most_important_head = FLAGS.most_important_head
print("Current most important head:", cur_layer, most_important_head)
# this placeholder is to control the flag for the dropout
keep_prob_ph = tf.compat.v1.placeholder(tf.float32, name="keep_prob")
is_training_ph = tf.compat.v1.placeholder(tf.bool, name='is_training')
# two placeholders for the head coordinates, layer, head
head_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='head_mask')
layer_mask_ph = tf.compat.v1.placeholder(tf.int32,
shape=[
None,
],
name='layer_mask')
model = modeling.BertModel(
config=bert_config,
is_training=is_training_ph,
input_ids=input_ids_ph, # input_ids,
input_mask=input_mask_ph, # input_mask,
token_type_ids=segment_ids_ph, # segment_ids,
use_one_hot_embeddings=False,
use_estimator=False,
head_mask=head_mask_ph,
layer_mask=layer_mask_ph)
output_layer = model.get_pooled_output()
output_layer = tf.nn.dropout(output_layer, keep_prob=keep_prob_ph)
output_weights = tf.get_variable(
"output_weights", [num_labels, embed_dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
with tf.compat.v1.variable_scope("loss"):
# for stsb
if num_labels == 1:
logits = tf.squeeze(logits, [-1])
per_example_loss = tf.square(logits - label_ids_ph)
loss = tf.reduce_mean(per_example_loss)
else:
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids_ph,
depth=num_labels,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
loss = tf.reduce_mean(per_example_loss)
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# metric and summary
# metric is tf.metric object, (val, op)
metric = metric_fn(per_example_loss, label_ids_ph, logits, num_labels,
task_name)
metric_name = list(metric.keys())
metric_val = [m[0] for m in metric.values()]
metric_op = [m[1] for m in metric.values()]
log_dir = FLAGS.output_dir + 'layer_{}_head_{}/'.format(
cur_layer, most_important_head)
metric_phs = [
tf.compat.v1.placeholder(tf.float32, name="{}_ph".format(key))
for key in metric.keys()
]
summaries = [
tf.compat.v1.summary.scalar(key, metric_phs[i])
for i, key in enumerate(metric.keys())
]
train_summary_total = tf.summary.merge(summaries)
eval_summary_total = tf.summary.merge(summaries)
init_checkpoint = FLAGS.init_checkpoint
tvars = tf.compat.v1.trainable_variables()
var_init = [
v for v in tvars
if 'output_weights' not in v.name and 'output_bias' not in v.name
]
var_output = [
v for v in tvars
if 'output_weights' in v.name or "output_bias" in v.name
]
if not FLAGS.load_from_finetuned:
# Init from Model0
saver_init = tf.train.Saver(var_init)
else:
# Init from Model1
saver_init = tf.train.Saver(var_init + var_output)
var_train = var_init + var_output
print("Training parameters")
for v in var_train:
print(v)
train_op = optimization.create_optimizer(loss=loss,
init_lr=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=False,
tvars=var_train)
saver_all = tf.train.Saver(var_list=var_init + var_output, max_to_keep=1)
# Isolate the variables stored behind the scenes by the metric operation
var_metric = []
for key in metric.keys():
var_metric.extend(
tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=key))
# Define initializer to initialize/reset running variables
metric_vars_initializer = tf.variables_initializer(var_list=var_metric)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver_init.restore(sess, init_checkpoint)
writer = tf.compat.v1.summary.FileWriter(log_dir + 'log/train/',
sess.graph)
writer_eval = tf.compat.v1.summary.FileWriter(log_dir + 'log/eval/')
# heads need to be masked out in cur_layer
head_mask = [head for head in range(12) if head != most_important_head]
layer_mask = [cur_layer for _ in range(11)]
# if number of eval examples < 1000, just load it directly, or load by batch.
if num_actual_eval_examples <= 1000:
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=num_actual_eval_examples,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer)
start_metric = {"eval_{}".format(key): 0 for key in metric_name}
end_metric = {"eval_{}".format(key): 0 for key in metric_name}
if FLAGS.do_train:
tf.logging.info("***** Run training *****")
step = 1
for n in range(epochs):
np.random.shuffle(train_examples)
num_batch = num_actual_train_examples // batch_size if num_actual_train_examples % batch_size == 0 \
else num_actual_train_examples // batch_size + 1
id = 0
for b in range(num_batch):
input_ids, input_mask, \
segment_ids, label_ids, is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=train_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id)
id += batch_size
sess.run(metric_vars_initializer)
sess.run([train_op] + metric_op,
feed_dict={
input_ids_ph: input_ids,
input_mask_ph: input_mask,
segment_ids_ph: segment_ids,
label_ids_ph: label_ids,
is_training_ph: True,
keep_prob_ph: 0.9,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask
})
train_metric_val = sess.run(metric_val)
train_summary_str = sess.run(
train_summary_total,
feed_dict={
ph: value
for ph, value in zip(metric_phs, train_metric_val)
})
writer.add_summary(train_summary_str, step)
if step % 100 == 0 or step % num_batch == 0 or step == 1:
# evaluate on dev set
if num_actual_eval_examples <= 1000:
sess.run(metric_vars_initializer)
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask
})
eval_metric_val = sess.run(metric_val)
eval_summary_str = sess.run(
eval_summary_total,
feed_dict={
ph: value
for ph, value in zip(
metric_phs, eval_metric_val)
})
else:
num_batch_eval = num_actual_eval_examples // batch_size \
if num_actual_eval_examples % batch_size == 0 \
else num_actual_eval_examples // batch_size + 1
id_eval = 0
sess.run(metric_vars_initializer)
for _ in range(num_batch_eval):
eval_input_ids, eval_input_mask, eval_segment_ids, \
eval_label_ids, eval_is_real_example = generate_ph_input(batch_size=batch_size,
seq_length=seq_length,
examples=eval_examples,
label_list=label_list,
tokenizer=tokenizer,
train_idx_offset=id_eval)
id_eval += batch_size
sess.run(metric_op,
feed_dict={
input_ids_ph: eval_input_ids,
input_mask_ph: eval_input_mask,
segment_ids_ph: eval_segment_ids,
label_ids_ph: eval_label_ids,
is_training_ph: False,
keep_prob_ph: 1,
head_mask_ph: head_mask,
layer_mask_ph: layer_mask
})
eval_metric_val = sess.run(metric_val)
eval_summary_str = sess.run(
eval_summary_total,
feed_dict={
ph: value
for ph, value in zip(
metric_phs, eval_metric_val)
})
writer_eval.add_summary(eval_summary_str, step)
if step == 1:
for key, val in zip(metric_name, eval_metric_val):
start_metric["eval_{}".format(key)] = val
if step == epochs * num_batch:
for key, val in zip(metric_name, eval_metric_val):
end_metric["eval_{}".format(key)] = val
if step % 100 == 0 or step % num_batch == 0 or step == 1:
train_metric_list = []
for i in range(len(train_metric_val)):
if metric_name[i] == 'loss':
train_metric_list.append(
"{}: %2.4f".format(metric_name[i]) %
train_metric_val[i])
else:
train_metric_list.append(
"{}: %.4f".format(metric_name[i]) %
train_metric_val[i])
train_str = 'Train ' + '|'.join(train_metric_list)
eval_metric_list = []
for i in range(len(eval_metric_val)):
if metric_name[i] == 'loss':
eval_metric_list.append(
"{}: %2.4f".format(metric_name[i]) %
eval_metric_val[i])
else:
eval_metric_list.append(
"{}: %.4f".format(metric_name[i]) %
eval_metric_val[i])
eval_str = 'Eval ' + '|'.join(eval_metric_list)
print(
"Layer {}, leave only one head {} | Epoch: %4d/%4d | Batch: %4d/%4d | {} | {}"
.format(cur_layer, most_important_head, train_str,
eval_str) % (n, epochs, b, num_batch))
if step % num_batch == 0:
saver_all.save(sess,
log_dir + 'layer{}_head_{}'.format(
cur_layer, most_important_head),
global_step=step)
step += 1
writer.close()
writer_eval.close()
print("Start metric", start_metric)
print("End metric", end_metric)
with tf.io.gfile.GFile(FLAGS.output_dir + 'results.txt',
'a') as writer:
eval_start, eval_end = [], []
for metric in metric_name:
if metric != 'loss':
eval_start.append("{}: %.4f".format(metric) %
start_metric["eval_{}".format(metric)])
eval_end.append("{}: %.4f".format(metric) %
end_metric["eval_{}".format(metric)])
writer.write(
"Layer {}, leave only one head {}: Start: {} | End: {}\n".
format(cur_layer, most_important_head, ','.join(eval_start),
','.join(eval_end)))
