def finetune(sess,
dataset,
steps=-1,
model_name='124M',
model_dir='models',
combine=50000,
batch_size=1,
learning_rate=0.0001,
accumulate_gradients=5,
restore_from='latest',
run_name='run1',
checkpoint_dir='checkpoint',
sample_every=100,
sample_length=1023,
sample_num=1,
multi_gpu=False,
save_every=1000,
print_every=1,
max_checkpoints=1,
use_memory_saving_gradients=False,
only_train_transformer_layers=False,
optimizer='adam',
overwrite=False,
val_dataset=None,
val_batch_size=2,
val_batch_count=40,
val_every=0):
"""Finetunes the model on the given dataset.
Adapted from https://github.com/nshepperd/gpt-2/blob/finetuning/train.py.
See that file for parameter definitions.
"""
# assert model_name not in ['774M', '1558M'] or multi_gpu, "Currently, a modern single GPU cannot finetune the 774M GPT-2 model or larger."
SAMPLE_DIR = 'samples'
checkpoint_path = os.path.join(checkpoint_dir, run_name)
def maketree(path):
try:
os.makedirs(path)
except:
pass
maketree(checkpoint_path)
files = [f for f in os.listdir(checkpoint_path)]
for file in ['hparams.json', 'encoder.json', 'vocab.bpe']:
try:
shutil.copyfile(os.path.join(model_dir, model_name, file),
os.path.join(checkpoint_path, file))
except FileNotFoundError as fnf_error:
print(
"You need to download the GPT-2 model first via download_gpt2()"
)
raise (fnf_error)
enc = encoder.get_encoder(checkpoint_path)
hparams = model.default_hparams()
with open(os.path.join(checkpoint_path, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
if model_name not in ['117M', '124M']:
use_memory_saving_gradients = True
only_train_transformer_layers = True
accumulate_gradients = 1
context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
gpus = []
if multi_gpu:
gpus = get_available_gpus()
output = model.model(hparams=hparams, X=context, gpus=gpus)
loss = tf.reduce_mean(
input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=context[:, 1:], logits=output['logits'][:, :-1]))
# validation code
if val_every > 0:
val_context = tf.placeholder(tf.int32, [val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context,
reuse=True) # added reuse=True
val_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=val_context[:,
1:], logits=val_output['logits'][:, :-1]))
val_loss_summary = tf.summary.scalar('val_loss', val_loss)
tf_sample = sample.sample_sequence(hparams=hparams,
length=sample_length,
context=context,
batch_size=batch_size,
temperature=1.0,
top_k=40)
all_vars = [
v for v in tf.compat.v1.trainable_variables() if 'model' in v.name
]
train_vars = [v for v in all_vars if '/h' in v.name
] if only_train_transformer_layers else all_vars
if optimizer == 'adam':
opt = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
elif optimizer == 'sgd':
opt = tf.compat.v1.train.GradientDescentOptimizer(
learning_rate=learning_rate)
if accumulate_gradients > 1:
if use_memory_saving_gradients:
exit(
"Memory saving gradients are not implemented for gradient accumulation yet."
)
opt = AccumulatingOptimizer(opt=opt, var_list=train_vars)
opt_reset = opt.reset()
opt_compute = opt.compute_gradients(loss)
opt_apply = opt.apply_gradients()
summary_loss = tf.compat.v1.summary.scalar('loss', opt_apply)
else:
if use_memory_saving_gradients:
opt_grads = memory_saving_gradients.gradients(loss, train_vars)
else:
opt_grads = tf.gradients(ys=loss, xs=train_vars)
opt_grads = list(zip(opt_grads, train_vars))
opt_apply = opt.apply_gradients(opt_grads)
summary_loss = tf.compat.v1.summary.scalar('loss', loss)
summary_log = tf.compat.v1.summary.FileWriter(checkpoint_path)
saver = tf.compat.v1.train.Saver(var_list=all_vars,
max_to_keep=max_checkpoints)
sess.run(tf.compat.v1.global_variables_initializer())
if restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(checkpoint_path)
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join(model_dir, model_name))
elif restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(os.path.join(model_dir, model_name))
else:
ckpt = tf.train.latest_checkpoint(restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('Loading dataset...')
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks)
# validation code
if val_every > 0:
if val_dataset:
val_chunks = load_dataset(enc, val_dataset, combine)
else:
val_chunks = chunks
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
# validation code
if val_every > 0:
# Sample from validation set once with fixed seed to make
# it deterministic during training as well as across runs.
val_data_sampler = Sampler(val_chunks, seed=1)
val_batches = [[
val_data_sampler.sample(1024) for _ in range(val_batch_size)
] for _ in range(val_batch_count)]
counter = 1
counter_path = os.path.join(checkpoint_path, 'counter')
if os.path.exists(counter_path) and restore_from == 'latest':
# Load the step number if we're resuming a run
# Add 1 so we don't immediately try to save again
with open(counter_path, 'r') as fp:
counter = int(fp.read()) + 1
counter_base = counter
def save():
maketree(checkpoint_path)
print('Saving',
os.path.join(checkpoint_path, 'model-{}').format(counter - 1))
saver.save(sess,
os.path.join(checkpoint_path, 'model'),
global_step=counter - 1)
with open(counter_path, 'w') as fp:
fp.write(str(counter - 1) + '\n')
def generate_samples():
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < sample_num:
out = sess.run(tf_sample,
feed_dict={context: batch_size * [context_tokens]})
for i in range(min(sample_num - index, batch_size)):
text = enc.decode(out[i])
text = '======== SAMPLE {} ========\n{}\n'.format(
index + 1, text)
all_text.append(text)
index += 1
print(text)
maketree(os.path.join(SAMPLE_DIR, run_name))
with open(
os.path.join(SAMPLE_DIR, run_name,
'samples-{}').format(counter), 'w') as fp:
fp.write('\n'.join(all_text))
# validation code
def validation():
print('Calculating validation loss...')
losses = []
for batch in tqdm(val_batches):
losses.append(sess.run(val_loss, feed_dict={val_context: batch}))
v_val_loss = np.mean(losses)
v_summary = sess.run(val_loss_summary,
feed_dict={val_loss: v_val_loss})
summary_log.add_summary(v_summary, counter)
summary_log.flush()
print('[{counter} | {time:2.2f}] validation loss = {loss:2.2f}'.format(
counter=counter, time=time.time() - start_time, loss=v_val_loss))
def sample_batch():
return [data_sampler.sample(1024) for _ in range(batch_size)]
if overwrite and restore_from == 'latest':
for file in files:
if file.startswith('model') or file.startswith('events'):
os.remove(os.path.join(checkpoint_path, file))
save()
avg_loss = (0.0, 0.0)
start_time = time.time()
if steps:
steps = int(steps)
try:
while True:
if steps > 0 and counter == (counter_base + steps):
save()
return
if (counter - 1) % save_every == 0 and counter > 1:
save()
if (counter - 1) % sample_every == 0 and counter > 1:
generate_samples()
# validation code
if val_every > 0 and (counter % val_every == 0 or counter == 1):
validation()
if accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(accumulate_gradients):
sess.run(opt_compute, feed_dict={context: sample_batch()})
(v_loss, v_summary) = sess.run((opt_apply, summary_loss))
else:
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summary_loss),
feed_dict={context: sample_batch()})
summary_log.add_summary(v_summary, counter)
if (counter % print_every == 0) or counter == 1:
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
print(
'[{counter} | {time:2.2f}] loss={loss:2.2f} avg={avg:2.2f}'
.format(counter=counter,
time=time.time() - start_time,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1]))
counter += 1
except KeyboardInterrupt:
print('interrupted')
save()
def finetune(
sess,
dataset,
steps=-1,
model_name="124M",
model_dir="models",
combine=50000,
batch_size=1,
learning_rate=0.0001,
accumulate_gradients=5,
restore_from="latest",
run_name="run1",
checkpoint_dir="checkpoint",
sample_every=100,
sample_length=1023,
sample_num=1,
multi_gpu=False,
save_every=1000,
print_every=1,
max_checkpoints=1,
use_memory_saving_gradients=False,
only_train_transformer_layers=False,
optimizer="adam",
overwrite=False,
):
"""Finetunes the model on the given dataset.
Adapted from https://github.com/nshepperd/gpt-2/blob/finetuning/train.py.
See that file for parameter definitions.
"""
# assert model_name not in ['774M', '1558M'] or multi_gpu, "Currently, a modern single GPU cannot finetune the 774M GPT-2 model or larger."
SAMPLE_DIR = "samples"
checkpoint_path = os.path.join(checkpoint_dir, run_name)
def maketree(path):
try:
os.makedirs(path)
except:
pass
maketree(checkpoint_path)
files = [f for f in os.listdir(checkpoint_path)]
for file in ["hparams.json", "encoder.json", "vocab.bpe"]:
try:
src_file = os.path.join(model_dir, model_name, file)
dst_file = os.path.join(checkpoint_path, file)
shutil.copyfile(src_file, dst_file)
except FileNotFoundError as fnf_error:
print(
"You need to download the GPT-2 model first via download_gpt2()"
)
raise (fnf_error)
enc = encoder.get_encoder(checkpoint_path)
hparams = model.default_hparams()
with open(os.path.join(checkpoint_path, "hparams.json")) as f:
hparams.override_from_dict(json.load(f))
if sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
if model_name not in ["117M", "124M"]:
use_memory_saving_gradients = True
only_train_transformer_layers = True
accumulate_gradients = 1
context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
gpus = []
if multi_gpu:
gpus = get_available_gpus()
output = model.model(hparams=hparams, X=context, gpus=gpus)
loss = tf.reduce_mean(
input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=context[:, 1:], logits=output["logits"][:, :-1]))
# tf_sample = sample.sample_sequence(
# hparams=hparams,
# length=sample_length,
# context=context,
# batch_size=batch_size,
# te mperature=1.0,
# top_k=40,
# )
all_vars = [
v for v in tf.compat.v1.trainable_variables() if "model" in v.name
]
train_vars = [v for v in all_vars if "/h" in v.name
] if only_train_transformer_layers else all_vars
if optimizer == "adam":
opt = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
elif optimizer == "sgd":
opt = tf.compat.v1.train.GradientDescentOptimizer(
learning_rate=learning_rate)
if accumulate_gradients > 1:
if use_memory_saving_gradients:
exit(
"Memory saving gradients are not implemented for gradient accumulation yet."
)
opt = AccumulatingOptimizer(opt=opt, var_list=train_vars)
opt_reset = opt.reset()
opt_compute = opt.compute_gradients(loss)
opt_apply = opt.apply_gradients()
summary_loss = tf.compat.v1.summary.scalar("loss", opt_apply)
else:
if use_memory_saving_gradients:
opt_grads = memory_saving_gradients.gradients(loss, train_vars)
else:
opt_grads = tf.gradients(ys=loss, xs=train_vars)
opt_grads = list(zip(opt_grads, train_vars))
opt_apply = opt.apply_gradients(opt_grads)
summary_loss = tf.compat.v1.summary.scalar("loss", loss)
summary_log = tf.compat.v1.summary.FileWriter(checkpoint_path)
saver = tf.compat.v1.train.Saver(var_list=all_vars,
max_to_keep=max_checkpoints)
sess.run(tf.compat.v1.global_variables_initializer())
if restore_from == "latest":
ckpt = tf.train.latest_checkpoint(checkpoint_path)
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join(model_dir, model_name))
elif restore_from == "fresh":
ckpt = tf.train.latest_checkpoint(os.path.join(model_dir, model_name))
else:
ckpt = tf.train.latest_checkpoint(restore_from)
print("Loading checkpoint", ckpt)
saver.restore(sess, ckpt)
print("Loading dataset...")
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks)
print("dataset has", data_sampler.total_size, "tokens")
print("Training...")
counter = 1
counter_path = os.path.join(checkpoint_path, "counter")
if os.path.exists(counter_path) and restore_from == "latest":
# Load the step number if we're resuming a run
# Add 1 so we don't immediately try to save again
with open(counter_path, "r") as fp:
counter = int(fp.read()) + 1
counter_base = counter
def save():
maketree(checkpoint_path)
print("Saving",
os.path.join(checkpoint_path, "model-{}").format(counter - 1))
saver.save(sess,
os.path.join(checkpoint_path, "model"),
global_step=counter - 1)
with open(counter_path, "w") as fp:
fp.write(str(counter - 1) + "\n")
# def generate_samples():
# context_tokens = data_sampler.sample(1)
# all_text = []
# index = 0
# while index < sample_num:
# out = sess.run(
# tf_sample,
# feed_dict={context: batch_size * [context_tokens]})
# for i in range(min(sample_num - index, batch_size)):
# text = enc.decode(out[i])
# text = '======== SAMPLE {} ========\n{}\n'.format(
# index + 1, text)
# all_text.append(text)
# index += 1
# print(text)
# maketree(os.path.join(SAMPLE_DIR, run_name))
# with open(
# os.path.join(SAMPLE_DIR, run_name,
# 'samples-{}').format(counter), 'w') as fp:
# fp.write('\n'.join(all_text))
if overwrite and restore_from == "latest":
for file in files:
if file.startswith("model") or file.startswith("events"):
os.remove(os.path.join(checkpoint_path, file))
save()
avg_loss = (0.0, 0.0)
start_time = time.time()
if steps:
steps = int(steps)
try:
while True:
if steps > 0 and counter == (counter_base + steps):
save()
return
if (counter - 1) % save_every == 0 and counter > 1:
save()
# if (counter - 1) % sample_every == 0 and counter > 1:
# generate_samples()
batch = [data_sampler.sample(1024) for _ in range(batch_size)]
if accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(accumulate_gradients):
sess.run(opt_compute, feed_dict={context: batch})
(v_loss, v_summary) = sess.run((opt_apply, summary_loss))
else:
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summary_loss),
feed_dict={context: batch})
summary_log.add_summary(v_summary, counter)
if counter % print_every == 0:
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
print(
"[{counter} | {time:2.2f}] loss={loss:2.2f} avg={avg:2.2f}"
.format(
counter=counter,
time=time.time() - start_time,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1],
))
counter += 1
except KeyboardInterrupt:
print("interrupted")
save()