def main():
ck500 = "/tmp/model.ckpt-500"
qck0 = "/tmp/model.ckpt-0"
var_d1 = load_checkpoint_vars(ck500)
var_d2 = load_checkpoint_vars(qck0)
var_name1 = "bert/encoder/layer_9/output/dense_29/bias/adam_v"
var_name2 = "SCOPE2/bert/encoder/layer_9/output/dense_66/bias/adam_v"
print(var_d1[var_name1])
print(var_d2[var_name2])
def main():
start = os.path.join(output_path, "model", "runs", "nli_ex_21",
"model-73630")
modified = os.path.join(output_path, "model", "runs", "nli_pairing_1",
"model-11171")
var_d1 = load_checkpoint_vars(start)
var_d2 = load_checkpoint_vars(modified)
for key in var_d1:
if key in var_d2:
v1 = var_d1[key]
v2 = var_d2[key]
print(key, np.sum(v1 - v2))
def work(model_path, save_path):
model = load_checkpoint_vars(model_path)
var_list = []
source_prefix = dual_model_prefix2
with tf.compat.v1.Session() as sess:
for old_name in model:
if old_name.startswith(source_prefix):
drop_n = len(source_prefix) + 1
new_name = old_name[drop_n:]
if "/dense" in new_name:
new_name = parse_shift_name(new_name, "dense", -37)
if "/layer_normalization" in new_name:
new_name = parse_shift_name(new_name,
"layer_normalization", -25)
var_value = model[old_name]
new_var = tf.Variable(var_value, name=new_name)
var_list.append(new_var)
print("Old: " + old_name)
print("New: " + new_name)
elif old_name.startswith("cls_dense_1/"):
var_value = model[old_name]
new_name = old_name.replace("cls_dense_1/", "cls_dense/")
new_var = tf.Variable(var_value, name=new_name)
var_list.append(new_var)
print("Old: " + old_name)
print("New: " + new_name)
else:
pass
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())
saver.save(sess, save_path)
def show_record():
path = "C:\work\Code\Chair\output\\uncased_L-12_H-768_A-12\\bert_model.ckpt"
vars = load_checkpoint_vars(path)
for name, val in vars.items():
print(name, val.shape)
def compare_before_after():
tokenizer = get_tokenizer()
ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize("heavy metal"))
dir_name = pjoin(pjoin(output_path, FileName("model")),
FileName("alt_emb_heavy_metal_D"))
before = pjoin(dir_name, FileName("model.ckpt-0"))
after = pjoin(dir_name, FileName("model.ckpt-10000"))
v1_d = load_checkpoint_vars(before)
v2_d = load_checkpoint_vars(after)
for key in v1_d:
if key in v2_d:
s = np.sum(v1_d[key] - v2_d[key])
if np.abs(s) > 0.01:
print(key, s)
ori_emb = v2_d['bert/embeddings/word_embeddings']
alt_emb_before = v1_d['bert/embeddings/word_embeddings_alt']
alt_emb_after = v2_d['bert/embeddings/word_embeddings_alt']
def show_diff_from_ori(token_id):
diff = np.sum(np.abs(ori_emb[token_id] - alt_emb_after[token_id]))
print(token_id, diff)
def show_diff_from_step0(token_id):
diff = np.sum(
np.abs(alt_emb_before[token_id] - alt_emb_after[token_id]))
print(token_id, diff)
print("Diff against original embedding")
print("Target words")
for token_id in ids:
show_diff_from_ori(token_id)
print("Random words")
for token_id in [321, 598, 5854]:
show_diff_from_ori(token_id)
print("Diff against step0 random init embedding")
print("Target words")
for token_id in range(0, 30000):
diff = np.sum(
np.abs(alt_emb_before[token_id] - alt_emb_after[token_id]))
if diff > 0.001:
print(token_id, diff)
def main():
d = load_checkpoint_vars(sys.argv[1])
var_list = [
"dense_75/kernel", "dense_75/bias", "dense_77/kernel", "dense_77/bias",
"k1", "k2", "bias"
]
for var in var_list:
print(var, d[var])
def show_record():
path = "C:\work\Code\Chair\output\model\BERT_Base_trained_on_MSMARCO\model.ckpt-100000"
path = "C:\work\Code\Chair\output\model\msmarco_2\msmarco_2"
path = "C:\work\Code\Chair\output\model\msmarco_2\msmarco_2"
vars = load_checkpoint_vars(path)
for name, val in vars.items():
print(name, val.shape)
def combine_pdcd_pc_bert(pc_bert_checkpoint, pdcd_checkpoint, save_path):
pc_bert = load_checkpoint_vars(pc_bert_checkpoint)
pdcd = load_checkpoint_vars(pdcd_checkpoint)
var_list = []
name_set = set()
with tf.compat.v1.Session() as sess:
for old_name in pc_bert:
new_name = "{}/".format(triple_model_prefix1) + old_name
new_var = tf.Variable(pc_bert[old_name], name=new_name)
var_list.append(new_var)
assert new_name not in name_set
print(old_name, new_name)
name_set.add(new_name)
for old_name in pdcd:
if dual_model_prefix1 in old_name:
new_name = old_name.replace(dual_model_prefix1,
triple_model_prefix2)
elif dual_model_prefix2 in old_name:
new_name = old_name.replace(dual_model_prefix2,
triple_model_prefix3)
else:
new_name = old_name
if "/dense" in new_name:
parse_shift_name(new_name, "dense", 37)
elif "/layer_normalization" in new_name:
parse_shift_name(new_name, "layer_normalization", 25)
new_var = tf.Variable(pdcd[old_name], name=new_name)
print(old_name, new_name)
if new_name in name_set:
print(new_name)
assert new_name not in name_set
name_set.add(new_name)
var_list.append(new_var)
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables())
saver.save(sess, save_path)
def combine(nli_checkpoint, alt_emb_checkpoint, save_path):
print("Combining...")
# Write variables names of nli_checkpoint on alt_emb_checkpoint
nli_d = load_checkpoint_vars(nli_checkpoint)
alt_emb_d = load_checkpoint_vars(alt_emb_checkpoint)
var_list = []
with tf.Session() as sess:
for key in alt_emb_d:
if key in nli_d:
new_var = tf.Variable(nli_d[key], name=key)
else:
new_var = tf.Variable(alt_emb_d[key], name=key)
var_list.append(new_var)
for key in nli_d:
if key not in alt_emb_d:
new_var = tf.Variable(nli_d[key], name=key)
var_list.append(new_var)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
saver.save(sess, save_path)
def rewrite(checkpoint, save_path):
# Write variables names of nli_checkpoint on alt_emb_checkpoint
checkpoint_vars = load_checkpoint_vars(checkpoint)
var_list = []
layer_norm_idx = 0
layer_prefix_d = {}
dense_prefix_d = {}
dense_idx = 0
with tf.Session() as sess:
keys = list(checkpoint_vars.keys())
keys.sort(key=natural_keys)
for key in keys:
print(key)
if "LayerNorm" in key:
prefix = key[:key.find("LayerNorm")]
if prefix in layer_prefix_d:
layer_norm = layer_prefix_d[prefix]
else:
layer_norm = "layer_normalization"
if layer_norm_idx > 0:
layer_norm += "_{}".format(layer_norm_idx)
layer_prefix_d[prefix] = layer_norm
layer_norm_idx += 1
new_name = re.sub("LayerNorm", layer_norm, key)
elif "dense" in key:
prefix = key[:key.find("dense")]
if prefix in dense_prefix_d:
dense = dense_prefix_d[prefix]
else:
dense = "dense"
if dense_idx > 0:
dense += "_{}".format(dense_idx)
dense_idx += 1
dense_prefix_d[prefix] = dense
new_name = re.sub("dense", dense, key)
else:
new_name = key
new_var = tf.Variable(checkpoint_vars[key], name=new_name)
var_list.append(new_var)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
saver.save(sess, save_path)
def work(model_path, save_path):
model = load_checkpoint_vars(model_path)
var_list = []
with tf.Session() as sess:
for key in model:
new_name = key
new_name = re.sub("layer_normalization[_]?\d*", "LayerNorm", new_name)
new_name = re.sub("dense[_]?\d*", "dense", new_name)
new_name = re.sub("cls_dense/kernel", "output_weights", new_name)
new_name = re.sub("cls_dense/bias", "output_bias", new_name)
var_value = model[key]
if new_name == "output_weights":
print(var_value.shape)
var_value = np.transpose(var_value, [1, 0])
new_var = tf.Variable(var_value, name=new_name)
var_list.append(new_var)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
saver.save(sess, save_path)
def get_embedding_table(model_path):
vars = load_checkpoint_vars(model_path)
return vars['bert/embeddings/word_embeddings']
def show_checkpoint(lm_checkpoint, var_name):
d = load_checkpoint_vars(lm_checkpoint)
print(var_name)
print(d[var_name])
