def predict(sess,
text,
run_name='run1',
checkpoint_dir='checkpoint',
model_name=None,
model_dir='models',
sample_dir='samples',
seed=None,
temperature=1,
top_k=2):
if model_name:
checkpoint_path = os.path.join(model_dir, model_name)
else:
checkpoint_path = os.path.join(checkpoint_dir, run_name)
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))
context = tf.compat.v1.placeholder(tf.int32, [1, None])
context_tokens = enc.encode(text)
np.random.seed(seed)
tf.compat.v1.set_random_seed(seed)
start_time = time.time()
proba_t = sample.sample_sequence(hparams=hparams,
context=context,
temperature=temperature)
# print("total time 1: {}".format(time.time() - start_time))
start_time = time.time()
proba = sess.run(proba_t, feed_dict={context: [context_tokens]})
# print("total time 2: {}".format(time.time() - start_time))
top_k_idxs = np.flip(np.argsort(proba))[:top_k]
top_k_tokens = enc.decode(top_k_idxs).split()
top_k_proba = proba[top_k_idxs]
return top_k_tokens, top_k_proba
def generate(sess,
run_name='run1',
checkpoint_dir='checkpoint',
model_name=None,
model_dir='models',
sample_dir='samples',
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
top_p=0.0,
include_prefix=True):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ''
if prefix == '':
prefix = None
if model_name:
checkpoint_path = os.path.join(model_dir, model_name)
else:
checkpoint_path = os.path.join(checkpoint_dir, run_name)
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 prefix:
context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
context_tokens = enc.encode(prefix)
np.random.seed(seed)
tf.compat.v1.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams,
length=min(length, 1023 - (len(context_tokens) if prefix else 0)),
start_token=enc.encoder['<|endoftext|>'] if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)[:, 1:]
if destination_path:
f = open(destination_path, 'w')
generated = 0
gen_texts = []
while generated < nsamples:
if not prefix:
out = sess.run(output)
else:
out = sess.run(output,
feed_dict={context: batch_size * [context_tokens]})
for i in range(batch_size):
generated += 1
gen_text = enc.decode(out[i])
if prefix:
gen_text = enc.decode(context_tokens[:1]) + gen_text
if truncate:
truncate_esc = re.escape(truncate)
if prefix and not include_prefix:
prefix_esc = re.escape(prefix)
pattern = '(?:{})(.*?)(?:{})'.format(
prefix_esc, truncate_esc)
else:
pattern = '(.*?)(?:{})'.format(truncate_esc)
trunc_text = re.search(pattern, gen_text, re.S)
if trunc_text:
gen_text = trunc_text.group(1)
gen_text = gen_text.lstrip('\n')
if destination_path:
f.write("{}\n{}".format(gen_text, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen_text, sample_delim), end='')
gen_texts.append(gen_text)
if destination_path:
f.close()
if return_as_list:
return gen_texts
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, "[ERROR] 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(
"[ERROR] 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(
"[ERROR] 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,
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(
"[INFO] 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)
summaries = [summary_loss]
summary_op = tf.summary.merge(summaries)
summary_writer = tf.compat.v1.summary.FileWriter(checkpoint_path,
sess.graph)
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('[INFO] Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('[INFO] Loading dataset...')
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks)
print('[INFO] dataset has', data_sampler.total_size, 'tokens')
print('[INFO] 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('[INFO] 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(header=True):
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])
if header:
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))
return text
def sample_batch(sample_size=1024):
return [data_sampler.sample(sample_size) 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()
best_loss = 1000.00
if steps:
steps = int(steps)
try:
while True:
t = time.time() - start_time
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_op))
else:
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summary_op),
feed_dict={context: sample_batch()})
monitoring.push_metric('loss', v_loss, counter, t)
summary_writer.add_summary(v_summary, counter)
if steps > 0 and counter == (counter_base + steps):
save()
return
if (counter - 1) % save_every == 0 and counter > 1:
save()
sample_text = generate_samples(False)
summary_sample = tf.compat.v1.summary.text(
'generated_sample', tf.convert_to_tensor(sample_text))
text = sess.run(summary_sample)
summary_writer.add_summary(text, counter)
monitoring.push_text('generated_text', sample_text, counter,
v_loss, t)
if v_loss < best_loss and v_loss > 1.00:
best_loss = v_loss
loss_text = generate_samples(False)
loss_sample_text = "===== LOSS : " + str(
best_loss) + "=====" + loss_text
summary_loss_sample = tf.compat.v1.summary.text(
'best_loss_sample', tf.convert_to_tensor(loss_sample_text))
loss_text_ = sess.run(summary_loss_sample)
summary_writer.add_summary(loss_text_, counter)
monitoring.push_text('best_loss_text', loss_text, counter,
best_loss, t)
monitoring.push_metric('best_loss', best_loss, counter, t)
if counter % print_every == 0:
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
monitoring.push_metric('avg_loss', avg_loss[0] / avg_loss[1],
counter, t)
print(
'[{counter} | {time:2.2f}] loss={loss:2.2f} avg={avg:2.2f}'
.format(counter=counter,
time=t,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1]))
counter += 1
except KeyboardInterrupt:
print('[STOP] interrupted')
save()
def finetune(sess,
dataset,
steps=-1,
model_name='117M',
combine=50000,
batch_size=1,
learning_rate=0.0001,
accumulate_gradients=5,
restore_from='latest',
run_name='run1',
sample_every=100,
sample_length=1023,
sample_num=1,
save_every=1000,
print_every=1,
max_checkpoints=1,
use_memory_saving_gradients=False,
only_train_transformer_layers=False,
model_load=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.
"""
CHECKPOINT_DIR = 'checkpoint'
SAMPLE_DIR = 'samples'
checkpoint_path = os.path.join(CHECKPOINT_DIR, run_name)
def maketree(path):
try:
os.makedirs(path)
except:
pass
maketree(checkpoint_path)
if not model_load:
for file in ['hparams.json', 'encoder.json', 'vocab.bpe']:
shutil.copyfile(os.path.join('models', model_name, file),
os.path.join(checkpoint_path, file))
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 != '117M':
use_memory_saving_gradients = True
only_train_transformer_layers = True
accumulate_gradients = 1
context = tf.placeholder(tf.int32, [batch_size, None])
output = model.model(hparams=hparams, X=context)
loss = tf.reduce_mean(
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,
temperature=1.0,
top_k=40)
all_vars = [v for v in tf.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 accumulate_gradients > 1:
if use_memory_saving_gradients:
exit(
"Memory saving gradients are not implemented for gradient accumulation yet."
)
opt = AccumulatingOptimizer(
opt=tf.train.AdamOptimizer(learning_rate=learning_rate),
var_list=train_vars)
opt_reset = opt.reset()
opt_compute = opt.compute_gradients(loss)
opt_apply = opt.apply_gradients()
summary_loss = tf.summary.scalar('loss', opt_apply)
else:
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
if use_memory_saving_gradients:
opt_grads = memory_saving_gradients.gradients(loss, train_vars)
else:
opt_grads = tf.gradients(loss, train_vars)
opt_grads = list(zip(opt_grads, train_vars))
opt_apply = opt.apply_gradients(opt_grads)
summary_loss = tf.summary.scalar('loss', loss)
summary_log = tf.summary.FileWriter(checkpoint_path)
saver = tf.train.Saver(var_list=all_vars, max_to_keep=max_checkpoints)
sess.run(tf.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('models', model_name))
elif restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
else:
ckpt = tf.train.latest_checkpoint(restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
if model_load:
return
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))
def sample_batch():
return [data_sampler.sample(1024) for _ in range(batch_size)]
avg_loss = (0.0, 0.0)
start_time = time.time()
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()
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:
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 generate(
sess,
run_name="run1",
checkpoint_dir="checkpoint",
model_name=None,
model_dir="models",
sample_dir="samples",
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim="=" * 20 + "\n",
prefix=None,
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
top_p=0.0,
include_prefix=True,
enc=None,
hparams=None,
):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ""
if prefix == "":
prefix = None
checkpoint_path = get_checkpoint_path(run_name, checkpoint_dir, model_name,
checkpoint_dir)
if enc is None:
enc = get_encoder(checkpoint_path)
if hparams is None:
hparams = get_hparams(checkpoint_path)
if prefix:
context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
context_tokens = enc.encode(prefix)
np.random.seed(seed)
tf.compat.v1.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams,
length=min(length, 1023 - (len(context_tokens) if prefix else 0)),
start_token=enc.encoder["<|endoftext|>"] if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p,
)[:, 1:]
if destination_path:
f = open(destination_path, "w")
generated = 0
gen_texts = []
while generated < nsamples:
if not prefix:
out = sess.run(output)
else:
out = sess.run(output,
feed_dict={context: batch_size * [context_tokens]})
for i in range(batch_size):
generated += 1
gen_text = enc.decode(out[i])
if prefix:
gen_text = enc.decode(context_tokens[:1]) + gen_text
if truncate:
truncate_esc = re.escape(truncate)
if prefix and not include_prefix:
prefix_esc = re.escape(prefix)
pattern = "(?:{})(.*?)(?:{})".format(
prefix_esc, truncate_esc)
else:
pattern = "(.*?)(?:{})".format(truncate_esc)
trunc_text = re.search(pattern, gen_text, re.S)
if trunc_text:
gen_text = trunc_text.group(1)
gen_text = gen_text.lstrip("\n")
if destination_path:
f.write("{}\n{}".format(gen_text, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen_text, sample_delim), end="")
gen_texts.append(gen_text)
if destination_path:
f.close()
if return_as_list:
return gen_texts
def generate(sess,
run_name='run1',
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
top_p=0.0,
include_prefix=True,
split_context=0.5):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ''
if prefix == '':
prefix = None
if not length:
assert truncate is not None, "If generating a non-fixed length \
sample, must have a truncation term."
CHECKPOINT_DIR = 'checkpoint'
SAMPLE_DIR = 'samples'
checkpoint_path = os.path.join(CHECKPOINT_DIR, run_name)
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))
context = tf.placeholder(tf.int32, [batch_size, None])
if prefix:
context_tokens = [enc.encode(prefix)] * batch_size
else:
context_tokens = [[enc.encoder['<|endoftext|>']] for _ in range(batch_size)]
np.random.seed(seed)
tf.set_random_seed(seed)
if destination_path:
f = open(destination_path, 'w')
generated = 0
gen_texts = []
while generated < nsamples:
gen_text = [''] * batch_size
truncated = [False] * batch_size
total_tokens = 0
while False in truncated:
num_tokens = 1023 - (len(context_tokens[0]))
output = sample.sample_sequence(
hparams=hparams,
length=min(length if length else 1023, num_tokens),
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)[:, 1:]
out = sess.run(output, feed_dict={
context: context_tokens
})
total_tokens += num_tokens
for i in range(batch_size):
text = enc.decode(out[i])
if prefix:
text = enc.decode(context_tokens[i][:1]) + text
if truncate or all(gen_text):
context_tokens[i] = out[i][int(len(out[i])*(1-split_context)):]
if gen_text[i] != '':
split = re.split('[.!?]', gen_text[i])
text = text.partition(list(filter(None, split))[-1])[-1]
if truncate:
truncate_esc = re.escape(truncate)
if prefix and not include_prefix:
prefix_esc = re.escape(prefix)
pattern = '(?:{})(.*?)(?:{})'.format(prefix_esc,
truncate_esc)
else:
pattern = '(.*?)(?:{})'.format(truncate_esc)
trunc_text = re.search(pattern, text, re.S)
if trunc_text:
text = trunc_text.group(1)
if not truncated[i]:
gen_text[i] += text.lstrip('\n')
if trunc_text or (length is not None and total_tokens >= length-1):
truncated[i] = True
for gen in gen_text:
if destination_path:
f.write("{}\n{}".format(gen, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen, sample_delim), end='')
gen_texts.append(gen)
generated += batch_size
if destination_path:
f.close()
if return_as_list:
return gen_texts
def generate(sess,
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
model_name='117M',
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
run_name='run1'):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ''
if prefix:
context = tf.placeholder(tf.int32, [batch_size, None])
CHECKPOINT_DIR = 'checkpoint'
SAMPLE_DIR = 'samples'
checkpoint_path = os.path.join(CHECKPOINT_DIR, run_name)
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))
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams,
length=length,
start_token=enc.encoder['<|endoftext|>'] if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k)[:, 1:]
if destination_path:
f = open(destination_path, 'w')
if prefix:
context_tokens = enc.encode(prefix)
generated = 0
gen_texts = []
while generated < nsamples:
if not prefix:
out = sess.run(output)
else:
out = sess.run(output,
feed_dict={context: batch_size * [context_tokens]})
for i in range(batch_size):
generated += 1
gen_text = enc.decode(out[i])
if prefix:
gen_text = prefix[0] + gen_text
if truncate:
trunc_text = re.search(r'(.*?)(?:{})'.format(truncate),
gen_text, re.S)
if trunc_text:
gen_text = trunc_text.group(1)
if destination_path:
f.write("{}\n{}".format(gen_text, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen_text, sample_delim))
gen_texts.append(gen_text)
if destination_path:
f.close()
if return_as_list:
return gen_texts
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 FileExistsError:
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]))
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)
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))
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()
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:
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 generate(sess,
run_name='run1',
checkpoint_dir='checkpoint',
model_name=None,
model_dir='models',
sample_dir='samples',
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
top_p=0.0,
include_prefix=True,
split_context=0.5):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ''
if prefix == '':
prefix = None
if not length:
assert truncate is not None, "If generating a non-fixed length \
sample, must have a truncation term."
assert 0 < split_context < 1
if model_name:
checkpoint_path = os.path.join(model_dir, model_name)
else:
checkpoint_path = os.path.join(checkpoint_dir, run_name)
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))
context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
if prefix:
prefix_enc = enc.encode(prefix)
np.random.seed(seed)
tf.compat.v1.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams,
length=min(length, 1023 - (len(prefix_enc) if prefix else 0)),
start_token=enc.encoder['<|endoftext|>'] if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)[:, 1:]
split_length = int(1023 * split_context)
split_output_length = min(length, 1023 - split_length)
split_output = sample.sample_sequence(
hparams=hparams,
length=split_output_length,
start_token=enc.encoder['<|endoftext|>'] if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)[:, 1:]
if destination_path:
f = open(destination_path, 'w')
generated = 0
gen_texts = []
while generated < nsamples:
gen_text = [np.array([])] * batch_size
truncated = [False] * batch_size
if prefix:
context_tokens = [prefix_enc] * batch_size
else:
context_tokens = [[enc.encoder['<|endoftext|>']]] * batch_size
total_tokens = len(context_tokens[0])
generated_once = False
while False in truncated:
num_tokens = 1023 - (len(context_tokens[0]))
if generated_once:
new_split_output_length = min(length - total_tokens,
1023 - split_length)
if new_split_output_length != split_output_length:
split_output = sample.sample_sequence(
hparams=hparams,
length=new_split_output_length,
start_token=enc.encoder['<|endoftext|>']
if not prefix else None,
context=context if prefix else None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)[:, 1:]
out = sess.run(split_output,
feed_dict={context: context_tokens})
else:
out = sess.run(output, feed_dict={context: context_tokens})
total_tokens += num_tokens
for i in range(batch_size):
text = out[i]
trunc_text = ""
if prefix:
text = np.append(context_tokens[i][:1], text)
if truncate or all(gen_text):
context_tokens[i] = out[i][(1023 - split_length - 1):]
if generated_once:
text = out[i][split_length:]
if truncate:
to_trunc = enc.decode(text)
truncate_esc = re.escape(truncate)
if prefix and not include_prefix:
prefix_esc = re.escape(prefix)
pattern = '(?:{})(.*?)(?:{})'.format(
prefix_esc, truncate_esc)
else:
pattern = '(.*?)(?:{})'.format(truncate_esc)
trunc_text = re.search(pattern, to_trunc, re.S)
if trunc_text:
text = enc.encode(trunc_text.group(1))
# better to re-encode here then decode every generation cycle, I think
if not truncated[i]:
gen_text[i] = np.concatenate((gen_text[i], text),
axis=None)
if trunc_text or (length is not None
and total_tokens >= length - 1):
truncated[i] = True
gen = enc.decode(gen_text[i]).lstrip('\n')
if destination_path:
f.write("{}\n{}".format(gen, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen, sample_delim), end='')
gen_texts.append(gen)
generated_once = True
generated += batch_size
if destination_path:
f.close()
if return_as_list:
return gen_texts
def generate(sess,
run_name='run1',
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
seed=None,
nsamples=1,
batch_size=1,
length=1023,
temperature=0.7,
top_k=0,
top_p=0.0,
include_prefix=True,
split_context=0.5,
batch_prefix=None):
"""Generates text from a model loaded into memory.
Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if nsamples == 1:
sample_delim = ''
assert not (prefix and batch_prefix)
assert not batch_prefix or len(batch_prefix) == batch_size
if prefix == '':
prefix = None
if batch_prefix == []:
batch_prefix = None
if not length:
assert truncate is not None, "If generating a non-fixed length \
sample, must have a truncation term."
CHECKPOINT_DIR = 'checkpoint'
SAMPLE_DIR = 'samples'
checkpoint_path = os.path.join(CHECKPOINT_DIR, run_name)
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))
context = tf.placeholder(tf.int32, [batch_size, None])
if prefix:
context_tokens = [enc.encode(prefix)] * batch_size
elif batch_prefix:
# context_tokens = [enc.encode(batch_prefix[0])] * batch_size
context_tokens = [enc.encode(pre) for pre in batch_prefix]
# print([enc.decode(c) for c in context_tokens])
# assert all(len(context_tokens[0]) == len(c) for c in context_tokens)
ml = max([len(p) for p in context_tokens])
for p in context_tokens:
while len(p) < ml:
p.insert(0, 0)
# padding front :)
else:
context_tokens = [[enc.encoder['<|endoftext|>']] for _ in range(batch_size)]
np.random.seed(seed)
tf.set_random_seed(seed)
if destination_path:
f = open(destination_path, 'w')
generated = 0
gen_texts = []
while generated < nsamples:
# gen_text = [''] * batch_size
gen_text = [[]] * batch_size
truncated = [False] * batch_size
total_tokens = 0
while False in truncated:
num_tokens = 1023 - (len(context_tokens[0]))
output = sample.sample_sequence(
hparams=hparams,
length=min(length if length else 1023, num_tokens),
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)[:, 1:]
out = sess.run(output, feed_dict={
context: context_tokens
})
total_tokens += num_tokens
for i in range(batch_size):
if truncated[i]:
continue
text = out[i]
trunc_text = "" #added to patch to fix unassigned variable
if prefix or batch_prefix:
text = np.append(context_tokens[i][:1], text)
if truncate or all(gen_text):
context_tokens[i] = out[i][int(len(out[i])*(1-split_context)):]
if gen_text[i]:
text = out[i][int(len(out[i])*(split_context)):]
# OLD, leaving for now for xref
# split = re.split('[.!?]', gen_text[i])
# text = text.partition(list(filter(None, split))[-1])[-1]
# ok so the idea is, split up gen_text, which is cumulative
# then you get the latest in gen_text, and find where it is in the new text
# then you split the new text on that, and get everything after it.
# but it seems like it sometimes chooses an empty string...
# yo just leave it as tokens until the end tho and use count
if truncate:
to_trunc = enc.decode(text)
truncate_esc = re.escape(truncate)
if prefix and not include_prefix:
prefix_esc = re.escape(prefix)
pattern = '(?:{})(.*?)(?:{})'.format(prefix_esc,
truncate_esc)
else:
pattern = '(.*?)(?:{})'.format(truncate_esc)
trunc_text = re.search(pattern, to_trunc, re.S)
if trunc_text:
text = enc.encode(trunc_text.group(1)) #inefficient, but let's just get this working for now
if not truncated[i]:
gen_text[i] += [text] #.lstrip('\n')
if trunc_text or (length is not None and total_tokens >= length-1):
truncated[i] = True
for gen in gen_text:
gen = [enc.decode(g).lstrip('\n') for g in gen]
gen = ''.join(gen)
if destination_path:
f.write("{}\n{}".format(gen, sample_delim))
if not return_as_list and not destination_path:
print("{}\n{}".format(gen, sample_delim), end='')
gen_texts.append(gen)
generated += batch_size
if destination_path:
f.close()
if return_as_list:
return gen_texts
context_none = tf.compat.v1.placeholder(tf.int32, [None, None])
gpus = []
gpus = get_available_gpus()
output = model.model(hparams=hparams, X=context_none, gpus=gpus)
loss = tf.reduce_mean(
input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=context_none[:, 1:], logits=output['logits'][:, :-1]))
context = tf.compat.v1.placeholder(tf.int32, [1, None])
tf_sample = sample.sample_sequence(
hparams=hparams,
length=sample_length,
context=context,
batch_size=1,
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)
opt = tf.compat.v1.train.AdamOptimizer(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)
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.
"""
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)
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,
save_every=1000,
print_every=1,
max_checkpoints=1,
use_memory_saving_gradients=False,
only_train_transformer_layers=False,
optimizer='adam',
overwrite=False,
mixed_precision=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.
"""
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])
output = model.model(hparams=hparams, X=context)
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,
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)
elif optimizer == 'sm3':
opt = SM3Optimizer(learning_rate=learning_rate, momentum=0.9)
elif optimizer == 'adafactor':
opt = AdafactorOptimizer(learning_rate=learning_rate)
def mp_check_tf_version():
# check TensorFlow >= 1.14
tf_version_list = tf.__version__.split(".")
if int(tf_version_list[0]) < 2:
return int(tf_version_list[1]) >= 14
def mp_check_tensor_core_gpu_present():
from tensorflow.python.client import device_lib
# check Compute Capability >= 7.0
local_device_protos = device_lib.list_local_devices()
for line in local_device_protos:
if "compute capability" in str(line):
compute_capability = float(
line.physical_device_desc.split("compute capability: ")
[-1])
if compute_capability >= 7.0:
return True
if mixed_precision and mp_check_tf_version(
) and mp_check_tensor_core_gpu_present():
if isinstance(opt, tf.keras.optimizers.Optimizer) or isinstance(
opt, tf.compat.v1.train.Optimizer):
opt = tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite(
opt)
else:
mixed_precision = False
else:
mixed_precision = False
print('Mixed precision enabled: %s' % mixed_precision)
# Fix warning: https://github.com/tensorflow/tensorflow/blob/233d3d/tensorflow/python/training/experimental/mixed_precision.py#L355-L357
if sess is None:
sess = start_tf_sess()
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))
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()
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:
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()
