def main():
args = parser.parse_args()
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
if args.model_name == '355M':
args.memory_saving_gradients = True
if args.optimizer == 'adam':
args.only_train_transformer_layers = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [args.batch_size, None])
context_in = randomize(context, hparams, args.noise)
output = model.model(hparams=hparams, X=context_in)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=context[:, 1:], logits=output['logits'][:, :-1]))
if args.val_every > 0:
val_context = tf.placeholder(tf.int32, [args.val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context)
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=args.sample_length,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p)
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 args.only_train_transformer_layers else all_vars
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(
learning_rate=args.learning_rate)
else:
exit('Bad optimizer:', args.optimizer)
if args.accumulate_gradients > 1:
if args.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.summary.scalar('loss', opt_apply)
else:
if args.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_lr = tf.summary.scalar('learning_rate', args.learning_rate)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(var_list=all_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('Loading dataset...')
chunks = load_dataset(enc,
args.dataset,
args.combine,
encoding=args.encoding)
data_sampler = Sampler(chunks)
if args.val_every > 0:
if args.val_dataset:
val_chunks = load_dataset(enc,
args.val_dataset,
args.combine,
encoding=args.encoding)
else:
val_chunks = chunks
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
if args.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(args.val_batch_size)
] for _ in range(args.val_batch_count)]
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
def generate_samples():
print('Generating samples...')
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(
tf_sample,
feed_dict={context: args.batch_size * [context_tokens]})
for i in range(min(args.sample_num - index, args.batch_size)):
text = enc.decode(out[i])
text = '======== SAMPLE {} ========\n{}\n'.format(
index + 1, text)
all_text.append(text)
index += 1
print(text.encode('utf8'))
maketree(os.path.join(SAMPLE_DIR, args.run_name))
with open(os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter),
'w',
encoding=args.encoding) as fp:
fp.write('\n'.join(all_text))
def validation():
print('Calculating validation loss...')
losses = []
for batch in tqdm.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(args.batch_size)]
avg_loss = (0.0, 0.0)
start_time = time.time()
try:
while True:
if counter % args.save_every == 0:
save()
if counter % args.sample_every == 0:
generate_samples()
if args.val_every > 0 and (counter % args.val_every == 0
or counter == 1):
validation()
if args.accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
sess.run(opt_compute,
feed_dict={context: sample_batch()})
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summaries),
feed_dict={context: sample_batch()})
summary_log.add_summary(v_summary, counter)
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 train_main(dataset,
model_name='1250M',
seed=None,
msg=True,
batch_size=16,
learning_rate=0.00002,
sample_length=512,
sample_num=1,
sample_every=100,
run_name='run1',
restore_from='latest',
save_every=1000,
combine=50000):
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
print('n_ctx: ', hparams.n_ctx, 'n_head: ', hparams.n_head, 'n_embd: ',
hparams.n_embd, 'n_layer: ', hparams.n_layer)
if sample_length is None:
sample_length = hparams.n_ctx
elif sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
# TF config
config = tf.ConfigProto()
#device_map = { 0:2, 0:3, 1:2, 1:3 }
#config.gpu_options.visible_device_list = str(device_map[hvd.rank()])
config.gpu_options.visible_device_list = str(hvd.local_rank())
config.gpu_options.allow_growth = True
global_step = tf.Variable(0, trainable=False)
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
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=0.9,
top_k=40)
#global_step = tf.Variable(0, trainable=False)
counter = 1
train_vars = [v for v in tf.trainable_variables() if 'model' in v.name]
#opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
# l4rz 11/10/2019
decayed_lr = tf.train.exponential_decay(learning_rate,
global_step,
200,
0.999,
staircase=True)
opt = tf.train.AdamOptimizer(decayed_lr)
#opt = tf.train.GradientDescentOptimizer(decayed_lr)
opt = hvd.DistributedOptimizer(opt)
# this is original horovod
#train_op = opt.minimize(loss, var_list=train_vars)
# this is ours
if (msg):
print('Using memory saving gradients')
opt_grads = memory_saving_gradients.gradients(loss, train_vars)
opt_grads = list(zip(opt_grads, train_vars))
train_op = opt.apply_gradients(opt_grads, global_step=global_step)
else:
print('Not using memory saving gradients')
#train_op = opt.minimize(loss, var_list=train_vars)
# l4rz 11/10
train_op = opt.minimize(loss,
var_list=train_vars,
global_step=global_step)
# [1,2]<stderr>:TypeError: apply_gradients() missing 1 required positional argument: 'grads_and_vars'
#summary_loss = tf.summary.scalar('loss', train_op)
#_, lv = sess.run((train_op, loss), feed_dict={context: batch})
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
print('Running hvd.broadcast_global_variables')
bcast = hvd.broadcast_global_variables(0)
print('Done')
saver = tf.train.Saver(var_list=train_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
print('Running global_variables_initializer')
sess.run(tf.global_variables_initializer())
print('Done')
if restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, run_name))
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))
# comment out when running for 1st time
else:
ckpt = tf.train.latest_checkpoint(restore_from)
print(str(hvd.local_rank()), 'Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
# uncomment when running for first time INIT THE MODEL
#print('tf.global_variables_initializer()')
#sess.run(tf.global_variables_initializer())
bcast.run()
print(str(hvd.local_rank()), 'Loading dataset...')
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks)
print(str(hvd.local_rank()), 'dataset has', data_sampler.total_size,
'tokens')
print(str(hvd.local_rank()), 'Training...')
counter = 1
if os.path.exists(os.path.join(CHECKPOINT_DIR, run_name, 'counter')):
# Load the step number if we're resuming a run
# Add 1 so we don't immediately try to save again
with open(os.path.join(CHECKPOINT_DIR, run_name, 'counter'),
'r') as fp:
counter = int(fp.read()) + 1
def save():
maketree(os.path.join(CHECKPOINT_DIR, run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, run_name,
'model-{}').format(counter))
saver.save(sess,
os.path.join(CHECKPOINT_DIR, run_name, 'model'),
global_step=counter)
with open(os.path.join(CHECKPOINT_DIR, run_name, 'counter'),
'w') as fp:
fp.write(str(counter) + '\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))
avg_loss = (0.0, 0.0)
start_time = time.time()
try:
while True:
batch = [data_sampler.sample(1024) for _ in range(batch_size)]
_, lv = sess.run((train_op, loss), feed_dict={context: batch})
avg_loss = (avg_loss[0] * 0.99 + lv, avg_loss[1] * 0.99 + 1.0)
if hvd.rank() == 0:
if counter % save_every == 0:
save()
if counter % sample_every == 0:
generate_samples()
print(
'[{counter} | {time:2.2f}] loss={loss:2.4f} avg={avg:2.4f} lr={lr:.2e}'
.format(counter=counter,
time=time.time() - start_time,
loss=lv,
avg=avg_loss[0] / avg_loss[1],
lr=decayed_lr.eval()))
counter += 1
except KeyboardInterrupt:
print('interrupted')
if hvd.rank() == 0:
save()
def main():
args = parser.parse_args()
try:
logdir = os.path.join(CHECKPOINT_DIR, args.run_name)
with open('logdir.txt', 'w') as z:
z.write(logdir)
except:
pass
enc = get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join(model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
if args.model_name == '345M':
args.memory_saving_gradients = True
args.only_train_transformer_layers = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [args.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]))
if args.val_every > 0:
val_context = tf.placeholder(tf.int32, [args.val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context)
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=args.sample_length,
context=context,
batch_size=args.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 args.only_train_transformer_layers else all_vars
if args.accumulate_gradients > 1:
if args.memory_saving_gradients:
exit(
"Memory saving gradients are not implemented for gradient accumulation yet."
)
opt = AccumulatingOptimizer(
opt=tf.train.AdamOptimizer(learning_rate=args.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=args.learning_rate)
if args.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(
os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(var_list=all_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(os.path.join(model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(os.path.join(model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
#print('Loading dataset...')
#chunks = load_dataset(enc, args.dataset, args.combine)
#data_sampler = Sampler(chunks)
print('Loading train dataset...')
from_name, ques_name, to_name = name_parts(args.dataset)
trn_chunks_from = load_dataset(
enc, from_name, args.combine) #if args.dataset else chunks
#trn_chunks_ques = load_dataset(enc, ques_name, args.combine) if args.dataset else chunks
trn_chunks_to = load_dataset(
enc, to_name, args.combine) #if args.dataset else chunks
skip_delimeter = True
char = '\t'
trn_data_sampler_from = SamplerVal(trn_chunks_from,
enc,
char=char,
skip_delimeter=skip_delimeter)
#trn_data_sampler_ques = SamplerVal(trn_chunks_ques, enc, char=char, skip_delimeter=skip_delimeter)
trn_data_sampler_to = SamplerVal(trn_chunks_to,
enc,
char=char,
skip_delimeter=skip_delimeter)
len_v = 0
data_sampler = []
for i in range(trn_data_sampler_from.total_size):
v = (
#enc.encode('\nQ: ') +
trn_data_sampler_from.get(i) +
#enc.encode('. \nA: ') +
trn_data_sampler_to.get(i) # +
#enc.encode('. ')
)
v = v[:HIDDEN_SIZE - 1]
len_v += len(v)
#data_sampler.extend(v) ##
data_sampler.append(v)
pass
if len_v < HIDDEN_SIZE:
mult = HIDDEN_SIZE // len_v + 1
for i in range(mult):
x = data_sampler[:]
data_sampler.extend(x)
data_sampler = Sampler([np.array(data_sampler)])
#if not args.train_special and len_v >= HIDDEN_SIZE:
# data_sampler = Sampler([np.array(data_sampler)])
if args.val_every > 0 and False:
val_chunks = load_dataset(
enc, args.val_dataset,
args.combine) if args.val_dataset else chunks
if not isinstance(data_sampler, list):
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
if args.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(args.val_batch_size)
] for _ in range(args.val_batch_count)]
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
#print(model_name, 'mn')
GPT2_DIR_X = model_name
cd = CHECKPOINT_DIR + "/" + args.run_name
if not os.path.isfile(cd + '/' + 'encoder.json'):
os.system("cp " + GPT2_DIR_X + '/' + 'encoder.json ' + cd +
'/.')
os.system('cp ' + GPT2_DIR_X + "/" + 'vocab.bpe ' + cd + '/.')
def generate_samples():
print('Generating samples...')
#context_tokens = data_sampler.sample(1)
#context_tokens = data_sampler[0]
context_tokens = trn_data_sampler_from.get(
random.randint(0, trn_data_sampler_from.total_size))
#print(enc.decode(context_tokens), len(context_tokens))
#print(args.batch_size * [context_tokens])
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(
tf_sample,
feed_dict={context: args.batch_size * [context_tokens]})
for i in range(min(args.sample_num - index, args.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, args.run_name))
with open(
os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter), 'w') as fp:
fp.write('\n'.join(all_text))
def validation():
print('Calculating validation loss...')
losses = []
for batch in tqdm.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():
#z = [data_sampler.sample(1024) for _ in range(args.batch_size)]
#print(len(data_sampler))
#print(len(data_sampler[0]))
z = [data_sampler[random.randint(0, args.batch_size)]]
#print(enc.decode(z[0]))
#print(z[1],'\n1' ,z[2],'\n2' ,z[3] ,len(data_sampler[0]))
#exit()
return z
avg_loss = (0.0, 0.0)
start_time = time.time()
try:
while counter != args.stop_after:
if counter % args.save_every == 0:
save()
if counter % args.sample_every == 0:
generate_samples()
pass
if args.val_every > 0 and (counter % args.val_every == 0
or counter == 1):
validation()
if args.accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(args.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)
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('\ninterrupted')
finally:
save()
def train_main(dataset,
model_name='117M',
seed=None,
batch_size=2,
sample_length=1023,
sample_num=1,
sample_every=4500,
run_name='run1',
restore_from='latest',
save_every=2000,
combine=50000):
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(
os.path.join('chatbot_model', 'trained_models', model_name,
'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if sample_length is None:
sample_length = hparams.n_ctx // 2
elif sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
# TF config
config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
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=0.8,
top_k=40)
train_vars = [v for v in tf.trainable_variables() if 'model' in v.name]
opt = tf.train.AdamOptimizer()
opt = hvd.DistributedOptimizer(opt)
train_op = opt.minimize(loss, var_list=train_vars)
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
bcast = hvd.broadcast_global_variables(0)
saver = tf.train.Saver(var_list=train_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join('chatbot_model', 'trained_models',
model_name))
elif restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(
os.path.join('chatbot_model', 'trained_models', model_name))
else:
ckpt = tf.train.latest_checkpoint(restore_from)
print(str(hvd.local_rank()), 'Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
bcast.run()
print(str(hvd.local_rank()), 'Loading dataset...')
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks)
print(str(hvd.local_rank()), 'dataset has', data_sampler.total_size,
'tokens')
print(str(hvd.local_rank()), 'Training...')
counter = 1
if os.path.exists(os.path.join(CHECKPOINT_DIR, run_name, 'counter')):
# Load the step number if we're resuming a run
# Add 1 so we don't immediately try to save again
with open(os.path.join(CHECKPOINT_DIR, run_name, 'counter'),
'r') as fp:
counter = int(fp.read()) + 1
def save():
maketree(os.path.join(CHECKPOINT_DIR, run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, run_name,
'model-{}').format(counter))
saver.save(sess,
os.path.join(CHECKPOINT_DIR, run_name, 'model'),
global_step=counter)
with open(os.path.join(CHECKPOINT_DIR, run_name, 'counter'),
'w') as fp:
fp.write(str(counter) + '\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))
avg_loss = (0.0, 0.0)
start_time = time.time()
try:
while True:
batch = [data_sampler.sample(1024) for _ in range(batch_size)]
_, lv = sess.run((train_op, loss), feed_dict={context: batch})
avg_loss = (avg_loss[0] * 0.99 + lv, avg_loss[1] * 0.99 + 1.0)
if hvd.rank() == 0:
if counter % save_every == 0:
save()
if counter % sample_every == 0:
generate_samples()
print(
'[{counter} | {time:2.2f}] loss={loss:2.2f} avg={avg:2.2f}'
.format(counter=counter,
time=time.time() - start_time,
loss=lv,
avg=avg_loss[0] / avg_loss[1]))
counter += 1
except KeyboardInterrupt:
print('interrupted')
if hvd.rank() == 0:
save()
def main():
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
hparams.batch_size=args.batch_size
hparams.seq_len=args.seq_len
##data_path
args.train_data_path=args.data_dir+args.dataset+'/train.txt'
args.eval_data_path=args.data_dir+args.dataset+'/dev.txt'
args.test_data_path=args.data_dir+args.dataset+'/test.txt'
args.eval_data_path=args.test_data_path ###Test mode only!
args.gpt_save_path=args.gpt_save_dir+args.dataset+'/'
args.dis_save_path=args.dis_save_dir+args.dataset+'/'
args.gpt_sample_dir2=args.gpt_sample_dir+args.dataset+'/'
args.dis_sample_dir2=args.dis_sample_dir+args.dataset+'/'
args.log_path=args.log_dir+args.dataset+'/'
maketree(args.gpt_save_dir)
maketree(args.dis_save_dir)
maketree(args.gpt_save_path)
maketree(args.dis_save_path)
maketree(args.gpt_sample_dir)
maketree(args.dis_sample_dir)
maketree(args.gpt_sample_dir2)
maketree(args.dis_sample_dir2)
maketree(args.log_dir)
maketree(args.log_path)
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError(
"Can't get samples longer than window size: %s" % hparams.n_ctx)
if args.model_name == '345M':
args.memory_saving_gradients = True
if args.optimizer == 'adam':
args.only_train_transformer_layers = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tf.Session(config=config) as sess:
scope_discri='distri'
def get_dis_logit_and_prob_single_step(context, scope):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
context=tf.reshape(context, [-1, args.seq_len])
emb=tf.get_variable(name='emb', initializer=tf.random.normal([hparams.n_vocab, 32], 0, 0.02))
context_emb=tf.nn.embedding_lookup(emb, context)
logit=dis(context_emb, scope=scope_discri)
prob=tf.sigmoid(logit+1e-7)
return logit, prob
def get_dis_logit_and_prob(context, context_len, scope):
##Pay attention to context_len here. temporary changes!!!!!!!!!!!!!!!!!!!
context_mask=(1-tf.sequence_mask(context_len-1, args.seq_len-1, dtype=tf.float32))*1e3
context_mask2=tf.sequence_mask(context_len-1, args.seq_len-1, dtype=tf.float32)
ones=tf.ones(shape=[tf.shape(context_len)[0], args.seq_len], dtype=tf.int32)*enc.encoder['<|endoftext|>']
input_tensor_list=[]
for i in range(1, args.seq_len):
input_tensor_list.append(tf.concat([context[:, :i+1], ones[:,i+1:]], axis=1))
input_tensor=tf.concat(input_tensor_list, axis=0)
log_prob, _=get_dis_logit_and_prob_single_step(input_tensor, scope=scope)
log_prob=tf.transpose(tf.reshape(log_prob, [args.seq_len-1, -1]))
log_prob+=tf.cast(context_mask, tf.float32)
log_prob_min=tf.reduce_min(log_prob, axis=1)
prob_min=tf.exp(log_prob_min)
return log_prob_min, prob_min, log_prob
##Build discriminator
def build_dis_layer(scope):
context_pos_discri = tf.placeholder(tf.int32, [None, args.seq_len])
context_pos_discri_len = tf.placeholder(tf.int32, [None])
context_neg_discri = tf.placeholder(tf.int32, [None, args.seq_len])
context_neg_discri_len = tf.placeholder(tf.int32, [None])
label_pos_discri=tf.ones([tf.shape(context_pos_discri_len)[0]], dtype=tf.float32)
label_neg_discri=tf.zeros([tf.shape(context_neg_discri_len)[0]], dtype=tf.float32)
logit_pos_discri, prob_pos_discri, mask=get_dis_logit_and_prob(context_pos_discri, context_pos_discri_len, scope=scope)
logit_neg_discri, _, _=get_dis_logit_and_prob(context_neg_discri, context_neg_discri_len, scope=scope)
loss_pre_pos_discri=tf.nn.sigmoid_cross_entropy_with_logits(labels=label_pos_discri, logits=logit_pos_discri)
loss_pos_discri=tf.reduce_mean(loss_pre_pos_discri)
loss_pre_neg_discri=tf.nn.sigmoid_cross_entropy_with_logits(labels=label_neg_discri, logits=logit_neg_discri)
loss_neg_discri=tf.reduce_mean(loss_pre_neg_discri)
loss_discri=(loss_pos_discri*args.pos_loss_weight+loss_neg_discri)/(1+args.pos_loss_weight)
train_var_list_discri=[x for x in tf.global_variables() if scope in x.name]
train_op_discri=tf.train.AdamOptimizer().minimize(loss_discri, var_list=train_var_list_discri)
var_list_discri=[x for x in tf.global_variables() if scope in x.name]
initializer_discri=tf.variables_initializer(var_list_discri)
saver_discri=tf.train.Saver(var_list=var_list_discri, max_to_keep=1)
print('discri: {} build succeed!'.format(scope))
return context_pos_discri,context_pos_discri_len, context_neg_discri,context_neg_discri_len, loss_pos_discri, loss_neg_discri, loss_discri, train_op_discri, initializer_discri, saver_discri, prob_pos_discri, mask, logit_pos_discri
class dis_class:
def __init__(self, layer_num=1, scope=scope_discri):
self.model=[]
self.dis=np.zeros([layer_num], dtype=np.float32)
print(layer_num)
for i in range(layer_num):
layer={'scope': scope+str(i)}
layer['context_pos_discri'],layer['context_pos_discri_len'], layer['context_neg_discri'],layer['context_neg_discri_len'], layer['loss_pos_discri'], layer['loss_neg_discri'], layer['loss_discri'], layer['train_op_discri'], layer['initializer_discri'], layer['saver_discri'], layer['prob_pos_discri'], layer['mask'], layer['logit_pos_discri'] = build_dis_layer(scope+str(i))
self.model.append(layer)
def prob(self, context, context_len, layer=-1):
if layer==-1:
layer=len(self.model)
prob_final=tf.ones(tf.shape(context)[0], dtype=tf.float32)
for i in range(layer):
item=self.model[i]
scope=item['scope']
_, prob, _=get_dis_logit_and_prob(context, context_len, scope=scope)
prob_final*=prob
return prob_final
def log_prob_step(self, context, layer=-1):
if layer==-1:
layer=len(self.model)
prob_final=tf.ones(tf.shape(context)[0], dtype=tf.float32)
log_prob_list=[]
for i in range(layer):
item=self.model[i]
scope=item['scope']
log_prob, prob=get_dis_logit_and_prob_single_step(context, scope=scope)
log_prob_list.append(tf.expand_dims(log_prob, 1))
log_prob_final=tf.concat(log_prob_list, axis=1)
return log_prob_final
Dis=dis_class(layer_num=args.layer_num)
context = tf.placeholder(tf.int32, [None, None])
context_len=tf.placeholder(tf.int32, [None])
context_mask=tf.sequence_mask(context_len-1, args.seq_len-1, dtype=tf.float32)
context_in=context
output = model.model(hparams=hparams, X=context_in)
loss_tensor = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=context[:, 1:], logits=output['logits'][:, :-1])*context_mask
loss=tf.reduce_sum(loss_tensor, axis=1)/(tf.reduce_sum(context_mask, axis=1)+1e-7)
loss_sen=tf.reduce_sum(loss)
loss=tf.reduce_mean(loss)
if args.val_every > 0:
def transform_np(x, lift=args.exponential_param):
x=x-0.5
x=x+np.abs(x)
return lift*x**2
def transform(x, lift=args.exponential_param):
x=x-0.5
x=x+tf.abs(x)
return lift*x**2
val_context = tf.placeholder(tf.int32, [args.val_batch_size, args.seq_len])
val_context_len=tf.placeholder(tf.int32, [args.batch_size])
NLL_bias=tf.placeholder(tf.float32, [])
val_context_mask=tf.sequence_mask(val_context_len-1, args.seq_len-1, dtype=tf.float32)
val_output = model.model(hparams=hparams, X=val_context)
val_loss_tensor =tf.nn.sparse_softmax_cross_entropy_with_logits(labels=val_context[:, 1:], logits=val_output['logits'][:, :-1])*val_context_mask
val_context_prob_cut=Dis.prob(val_context, val_context_len)
val_NLL_cut=tf.log(val_context_prob_cut+1e-7)
val_loss=tf.reduce_sum(val_loss_tensor, axis=1)/(tf.reduce_sum(val_context_mask, axis=1)+1e-7)
val_loss_cut=(tf.reduce_sum(val_loss_tensor, axis=1)+NLL_bias)/(tf.reduce_sum(val_context_mask, axis=1)+1e-7)-val_NLL_cut/tf.cast(val_context_len, tf.float32)
val_loss_sum=tf.reduce_sum(val_loss_tensor, axis=1)
val_loss_cut_sum=(tf.reduce_sum(val_loss_tensor, axis=1)+NLL_bias)-val_NLL_cut
val_loss_mean=tf.reduce_mean(val_loss)
val_loss_cut_mean=tf.reduce_mean(val_loss_cut)
val_loss_summary = tf.summary.scalar('val_loss', val_loss_mean)
tf_sample = sample.sample_sequence(
hparams=hparams,
length=args.seq_len,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p,
start_token=enc.encoder['<|endoftext|>'])
start_token=enc.encoder['<|endoftext|>']
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 args.only_train_transformer_layers else all_vars
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate=args.learning_rate)
else:
exit('Bad optimizer:', args.optimizer)
if args.accumulate_gradients > 1:
if args.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.summary.scalar('loss', opt_apply)
else:
if args.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_lr = tf.summary.scalar('learning_rate', args.learning_rate)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(var_list=all_vars, max_to_keep=1)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('Loading dataset...')
data_list, data_len = load_dataset(enc, args.train_data_path, args.seq_len)
data_sampler = Sampler(data_list, data_len )
if args.val_every > 0:
val_data_list, val_data_len = load_dataset(enc, args.eval_data_path, args.seq_len)
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
if args.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_data_list, val_data_len, seed=1)
val_batches = [val_data_sampler.sample(args.batch_size) for _ in range(args.val_batch_count)]
counter = 0
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(
sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
def train_step_discri(layer_id=0, mask_train_epoch=0):
pos_samples, pos_samples_len=data_sampler.sample(args.batch_size)
neg_samples=generate_negative_sample(layer_id=layer_id)
neg_samples_len=get_array_len(neg_samples)
_, loss=sess.run([Dis.model[layer_id]['train_op_discri'], Dis.model[layer_id]['loss_discri']], feed_dict={Dis.model[layer_id]['context_pos_discri']: pos_samples,Dis.model[layer_id]['context_pos_discri_len']: pos_samples_len, Dis.model[layer_id]['context_neg_discri']: neg_samples, Dis.model[layer_id]['context_neg_discri_len']: neg_samples_len})
return loss
def generate_negative_samples(layer_id, generate_num=args.batch_size):
result_list=[]
generate_num_now=0
samples_mem=[]
while generate_num_now<generate_num:
t=time.time()
sample_id=generate_negative_sample(layer_id=layer_id)
samples=[]
t1=time.time()
selected_id_list=np.arange(len(sample_id))
t2=time.time()
result_list.append(sample_id[selected_id_list])
generate_num_now+=len(selected_id_list)
return np.concatenate(result_list, axis=0)[:generate_num]
def get_array_len(sample_array):
lens=[]
for item in sample_array:
for i in range(1, len(item)):
if item[i]==enc.encoder['<|endoftext|>']:
break
lens.append(i)
return np.array(lens).astype(np.int32)
def generate_discri_sample3(layer_id=-1, sample_size=10000, save_path='/mnt/cephfs_new_wj/mlnlp/miaoning/Experiment/gpt-2-sep/samples/discri/sample2.txt'):
samples=[]
while len(samples)<sample_size:
sample_id=generate_negative_sample(layer_id)
for i in range(len(sample_id)):
sample_tem=enc.decode(sample_id[i]).split('<|endoftext|>')[1].split('\n')[0]
samples.append(sample_tem)
print(len(samples))
with open(save_path, 'w') as g:
g.write('\n'.join(samples))
def eval_discri_NLL(layer_id=0):
losses_pos=[]
losses_neg=[]
for batch in tqdm.tqdm(val_batches):
pos_samples, pos_samples_len=batch
neg_samples=generate_negative_sample(layer_id=layer_id)
neg_samples_len=get_array_len(neg_samples)
loss_pos, mask=sess.run([Dis.model[layer_id]['loss_pos_discri'], Dis.model[layer_id]['mask']], feed_dict={Dis.model[layer_id]['context_pos_discri']: pos_samples, Dis.model[layer_id]['context_pos_discri_len']: pos_samples_len})
#print(mask)
loss_neg=sess.run(Dis.model[layer_id]['loss_neg_discri'], feed_dict={Dis.model[layer_id]['context_neg_discri']: neg_samples, Dis.model[layer_id]['context_neg_discri_len']: neg_samples_len})
losses_pos.append(loss_pos)
losses_neg.append(loss_neg)
return np.mean(losses_pos), np.mean(losses_neg)
def get_discri_quantile(layer_id=0, quantile=0.85):
logits_list=[]
for batch in tqdm.tqdm(val_batches):
pos_samples, pos_samples_len=batch
logits, mask=sess.run([Dis.model[layer_id]['logit_pos_discri'], Dis.model[layer_id]['mask']], feed_dict={Dis.model[layer_id]['context_pos_discri']: pos_samples, Dis.model[layer_id]['context_pos_discri_len']: pos_samples_len})
print(np.min(mask, axis=1)[:10])
print(logits[:10])
with open('mask.pkl', 'wb') as g:
pkl.dump(mask, g)
logits_list.extend(list(logits))
break
with open('logits.pkl', 'wb') as g:
pkl.dump(sorted(logits_list), g)
#print(sorted(logits_list))
print('finish')
return sorted(logits_list)[int(len(logits_list)*(1-quantile))]
def train_discri(train_step, eval_every, train_layer_list=list(range(len(Dis.model)))):
#sess.run(initializer_discri)
print('Start Discri training')
train_losses=[]
for layer_id in train_layer_list:
flag=0
for epoch in range(train_step):
if epoch % eval_every==0:
train_losses=np.mean(train_losses)
train_losses=[]
eval_NLL_pos, eval_NLL_neg=eval_discri_NLL(layer_id)
eval_loss=(eval_NLL_pos*args.pos_loss_weight+eval_NLL_neg)/(args.pos_loss_weight+1)
print('layer_id:{} discri eval loss:{}'.format(layer_id, eval_loss))
print('layer_id:{} discri NLL pos: {}, discri NLL neg: {}'.format(layer_id, eval_NLL_pos, eval_NLL_neg))
print(epoch)
if epoch==0:
eval_loss_old=eval_loss
else:
print(eval_loss, eval_loss_old)
if eval_loss<eval_loss_old:
eval_loss_old=eval_loss
save_path=args.dis_save_path+str(layer_id)+'/'
if not os.path.isdir(save_path):
os.mkdir(save_path)
Dis.model[layer_id]['saver_discri'].save(sess, save_path+'a')
print('model discri saved!')
flag=0
else:
if epoch>=200:
flag+=1
if flag>=4:
break
train_loss=train_step_discri(layer_id)
print('layer_id:{} discri train loss:{}'.format(layer_id, train_loss))
train_losses.append(train_loss)
return eval_loss_old
tf_sample_0 = sample_link.sample_sequence(
hparams=hparams,
length=args.seq_len,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p,
start_token=enc.encoder['<|endoftext|>'])
tf_sample_dict={}
def generate_negative_sample(layer_id=0):
##output the filtered result of layer layer_id-1
if layer_id==0:
tf_sample=tf_sample_0
sample = data_sampler.sample(args.batch_size)[0][:,0:1]
out = sess.run(
tf_sample,
feed_dict={context: sample})[:,:args.seq_len]
for i in range(len(out)):
flag=0
for j in range(len(out[i])):
if flag==2:
out[i][j]=start_token
continue
if out[i][j]==start_token:
flag+=1
return out
else:
if layer_id==-1:
layer_id=len(Dis.model)
if layer_id in tf_sample_dict:
tf_sample=tf_sample_dict[layer_id]
else:
tf_sample = sample_link.sample_sequence_ISMC_threshold(
Dis=Dis,
layer=layer_id,
hparams=hparams,
length=args.seq_len,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p,
start_token=enc.encoder['<|endoftext|>'])
tf_sample_dict[layer_id]=tf_sample
sample = data_sampler.sample(args.batch_size)[0][:,0:1]
out = sess.run(
tf_sample,
feed_dict={context: sample})[:,:args.seq_len]
for i in range(len(out)):
flag=0
for j in range(len(out[i])):
if flag==2:
out[i][j]=start_token
continue
if out[i][j]==start_token:
flag+=1
return out
def validation():
print('Calculating validation loss...')
start_time=time.time()
losses = []
rates=[]
for batch in tqdm.tqdm(val_batches):
losses.append(sess.run(val_loss_mean, feed_dict={val_context: batch[0], val_context_len: batch[1]}))
v_val_loss = np.mean(losses)
v_summary = sess.run(val_loss_summary, feed_dict={val_loss_mean: 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))
return v_val_loss
def validation_cut(NLL_bias_0=0):
print('Calculating validation loss...')
losses = []
rates=[]
for batch in tqdm.tqdm(val_batches):
losses.append(sess.run(val_loss_cut_mean, feed_dict={val_context: batch[0], val_context_len: batch[1], NLL_bias:NLL_bias_0}))
v_val_loss = np.mean(losses)
print(
'[{counter} | {time:2.2f}] validation cut loss = {loss:2.2f}'
.format(
counter=counter,
time=time.time() - start_time,
loss=v_val_loss))
return v_val_loss
def sample_batch():
return [data_sampler.sample(1024) for _ in range(args.batch_size)]
def train_gpt():
val_loss_old=10000.0
avg_loss = (0.0, 0.0)
start_time = time.time()
counter=0
while True:
#pretraining
if counter % args.save_every == 0:
pass
#save()
if counter % args.sample_every == 0:
pass
#generate_samples()
if args.val_every > 0 and (counter % args.val_every == 0 or counter == 1):
val_loss_1=validation()
print(str(counter //args.val_every))
if val_loss_1>=val_loss_old:
print('pre-training ends!')
break
else:
val_loss_old=val_loss_1
saver.save(sess, args.gpt_save_path+'a')
print('save succeed!')
if args.accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
batch, batch_len=data_sampler.sample(args.batch_size)
sess.run(
opt_compute, feed_dict={context: batch, context_len:batch_len})
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
batch, batch_len=data_sampler.sample(args.batch_size)
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summaries),
feed_dict={context: batch, context_len:batch_len})
summary_log.add_summary(v_summary, counter)
avg_loss = (avg_loss[0] * 0.9 + v_loss,
avg_loss[1] * 0.9 + 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
class log_writer:
def __init__(self, path):
self.path=path
with open(path, 'w') as g:
g.write('')
def __call__(self, string, verbose=False):
with open(self.path, 'a') as g:
g.write(string+'\n')
if verbose:
print(string)
try:
if args.finetune:
#Finetune GPT-2
train_gpt()
if True:
#Restore Finetuned model
save_path=tf.train.latest_checkpoint(args.gpt_save_path)
saver.restore(sess, save_path)
print('Load gpt2 succeeded!')
if args.evaluate_finetune:
#Evaluate finetuning baseline
print(validation())
if args.evaluate_finetune:
#Calculate reverse-ppl for finetuning baseline
sample_path=args.gpt_sample_dir2+'sample.txt'
generate_discri_sample3(layer_id=0, sample_size=3000, save_path=sample_path)
rev_ppl=train.file_f(train_data_path=sample_path, val_data_path=args.eval_data_path)
Log_writer=log_writer(args.log_path+'finetune')
Log_writer('finetuning_rev_ppl: {}'.format(rev_ppl), verbose=True)
##Begin tailoring
if True:
Log_writer=log_writer(args.log_path+'discri')
for layer in range(args.layer_num):
print(layer)
if args.train_tailor:
#Train ratio estimator
train_discri(500, 10, [layer])
if True:
#Restore ratio estimator
for layer_id in range(layer+1):
save_path=args.dis_save_path+str(layer_id)+'/'
print(save_path)
save_path=tf.train.latest_checkpoint(save_path)
print(save_path)
Dis.model[layer_id]['saver_discri'].restore(sess, save_path)
if False:
#Save quantile for analysis
with open(args.dis_sample_dir2+'quantile.pkl', 'rb') as f:
pkl.load(f)
print('Load dis model succeeded!')
if True:
if layer==0:
quantile=0.85
else:
quantile=0.9
Dis.dis[layer]=get_discri_quantile(layer, quantile)
with open(args.dis_sample_dir2+'quantile.pkl', 'wb') as g:
pkl.dump(Dis.dis, g)
print(Dis.dis)
if args.evaluate_tailor:
#Generate sample for ERS and calculate reverse-ppl
sample_path=args.dis_sample_dir2+'_sample_layer_'+str(layer)
generate_discri_sample3(layer_id=layer+1, sample_size=3000, save_path=sample_path)
rev_ppl=train.file_f(train_data_path=sample_path, val_data_path=args.eval_data_path)
Log_writer('layer: {}, dis_rev_ppl: {}'.format(layer, rev_ppl), verbose=True)
except KeyboardInterrupt:
print('interrupted')
def main():
args = parser.parse_args()
folder_id = get_id(args.gdir)
#xmpp = SendMsgBot(jid, password, to, "Starting GPT-2")
#xmpp.register_plugin('xep_0030') # Service Discovery
#xmpp.register_plugin('xep_0199') # XMPP Ping
#xmpp.connect()
#threading = Thread(target=xmpp.process, daemon=True).start()
download_checkpoint(folder_id)
#send_m('checkpoint downloaded')
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError(
"Can't get samples longer than window size: %s" % hparams.n_ctx)
if args.model_name == '345M':
args.memory_saving_gradients = True
# if args.optimizer == 'adam':
# args.only_train_transformer_layers = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [args.batch_size, None])
context_in = randomize(context, hparams, args.noise)
output = model.model(hparams=hparams, X=context_in)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=context[:, 1:], logits=output['logits'][:, :-1]))
if args.val_every > 0:
val_context = tf.placeholder(tf.int32, [args.val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context)
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=args.sample_length,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p)
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 args.only_train_transformer_layers else all_vars
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate=args.learning_rate)
else:
exit('Bad optimizer:', args.optimizer)
if args.accumulate_gradients > 1:
if args.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.summary.scalar('loss', opt_apply)
else:
if args.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_lr = tf.summary.scalar('learning_rate', args.learning_rate)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(var_list=all_vars, max_to_keep=5, keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(os.path.join('models', args.model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
#send_m('Loading ' + str(ckpt))
saver.restore(sess, ckpt)
print('Loading dataset...')
#send_m('Loading dataset...')
#chunks = load_dataset(enc, args.dataset, args.combine)
ds_path = f'{CHECKPOINT_DIR}//run1//{args.dataset}'
chunks = load_dataset(enc, ds_path, args.combine)
data_sampler = Sampler(chunks)
print(f'{ds_path} has', data_sampler.total_size, 'tokens')
if args.val_every > 0:
val_chunks = load_dataset(enc, args.val_dataset, args.combine) if args.val_dataset else chunks
if args.enc:
print(colored(f'Trying writing Data.npz encoded from this dataset to {args.enc}', 'red'))
np.savez_compressed(args.enc, *chunks)
upload_npz(args.enc, folder_id)
#send_m(f'{args.dataset} has ' + str(data_sampler.total_size) + ' tokens' + ' Start training...')
print('Training...')
if args.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(args.val_batch_size)]
for _ in range(args.val_batch_count)]
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(
sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
save_gdisk(counter, folder_id)
def generate_samples():
print('Generating samples...')
#send_m('Generating samples...')
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(
tf_sample,
feed_dict={context: args.batch_size * [context_tokens]})
for i in range(min(args.sample_num - index, args.batch_size)):
text = enc.decode(out[i])
text = '======== SAMPLE {} ========\n{}\n'.format(
index + 1, text)
all_text.append(text)
index += 1
print(text)
#send_m(text)
maketree(os.path.join(SAMPLE_DIR, args.run_name))
with open(
os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter), 'w') as fp:
fp.write('\n'.join(all_text))
def validation():
print('Calculating validation loss...')
losses = []
for batch in tqdm.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(args.batch_size)]
avg_loss = (0.0, 0.0)
start_time = time.time()
last_time = time.time()
cur_counter, min_loss = 1, 2.0
print(colored(f'Model >>> {args.gdir}\nLearning rate is {args.learning_rate}', 'blue'))
print(colored(f'model optimizer >>> {args.optimizer}\nRestricted to train only transformer layer={args.only_train_transformer_layers}', 'blue'))
#send_m(f'Model >>> {args.model_name}\nLearning rate is {args.learning_rate}')
try:
while True:
if counter % args.save_every == 0:
save()
if check_quota():
return() # exit train
if counter % args.sample_every == 0:
generate_samples()
if args.val_every > 0 and (counter % args.val_every == 0 or counter == 1):
validation()
if args.accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
sess.run(opt_compute, feed_dict={context: sample_batch()})
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
(_, v_loss, v_summary) = sess.run((opt_apply, loss, summaries), feed_dict={context: sample_batch()})
summary_log.add_summary(v_summary, counter)
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
a_loss = avg_loss[0] / avg_loss[1]
time_all = int((time.time() - start_time) / 60)
time_iter = time.time() - last_time
stats = f'[{counter} | {cur_counter} | {time_all}m | {time_iter:2.2f}s] loss={v_loss:2.2f} avg={a_loss:2.2f}'
if not(cur_counter % 50):
print(colored(stats, 'red' if a_loss > min_loss else 'yellow'))
if a_loss < min_loss:
min_loss = a_loss
#send_m(stats)
last_time = time.time()
counter += 1
cur_counter += 1
except Exception as e:
#send_m('Stoped ' + str(e.__class__))
print('Stoped', e.__class__)
def main():
args = parser.parse_args()
enc = encoder.get_encoder(args.model_name, models_dir=args.models_dir)
hparams = model.default_hparams()
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session() as sess:
# Fully static shape required to make memory accounting in
# twremat accurate.
train_context = tf.placeholder(tf.int32, [args.batch_size, 1024])
train_context_in = randomize(train_context, hparams, args.noise)
train_output = model.model(hparams=hparams, X=train_context_in)
train_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=train_context[:, 1:],
logits=train_output['logits'][:, :-1]))
if args.val_every > 0:
val_context = tf.placeholder(tf.int32, [args.val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context)
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)
sample_context = tf.placeholder(tf.int32, [args.batch_size, None])
tf_sample = sample.sample_sequence(hparams=hparams,
length=args.sample_length,
context=sample_context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p)
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 args.only_train_transformer_layers else all_vars
if args.optimizer == 'adam':
print('Using Adam optimizer', file=sys.stderr)
opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'sgd':
print('Using SGD optimizer', file=sys.stderr)
opt = tf.train.GradientDescentOptimizer(
learning_rate=args.learning_rate)
else:
exit('Bad optimizer:', args.optimizer)
if args.memory_saving_gradients:
if tf.VERSION >= '2':
exit(
'Memory saving gradients are not supported in tensorflow 2.x'
)
import memory_saving_gradients
opt_grads = memory_saving_gradients.gradients(
train_loss, train_vars)
elif args.twremat:
import tfremat
opt_grads = tf.gradients(train_loss, train_vars)
(train_loss, opt_grads) = tfremat.tf_remat(
(train_loss, opt_grads), memlimit=args.twremat_memlimit)
else:
opt_grads = tf.gradients(train_loss, train_vars)
opt_grads = list(zip(opt_grads, train_vars))
opt_apply = opt.apply_gradients(opt_grads)
summary_loss = tf.summary.scalar('loss', train_loss)
# if args.twremat:
# import tfremat
# # Applying tfremat to opt_apply has more accurate
# # accounting but is a bit iffier since side effecting ops
# # have more restrictions for correctness. If in doubt
# # revert back to version using opt_grads above.
# (opt_apply, train_loss, summary_loss) = (
# tfremat.tf_remat((opt_apply, train_loss, summary_loss), memlimit=args.twremat_memlimit))
summary_lr = tf.summary.scalar('learning_rate', args.learning_rate)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(var_list=all_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('Loading dataset...')
chunks = load_dataset(enc,
args.dataset,
args.combine,
encoding=args.encoding)
data_sampler = Sampler(chunks)
if args.val_every > 0:
if args.val_dataset:
val_chunks = load_dataset(enc,
args.val_dataset,
args.combine,
encoding=args.encoding)
else:
val_chunks = chunks
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
if args.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(args.val_batch_size)
] for _ in range(args.val_batch_count)]
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
def generate_samples():
print('Generating samples...')
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(tf_sample,
feed_dict={
sample_context:
args.batch_size * [context_tokens]
})
for i in range(min(args.sample_num - index, args.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, args.run_name))
with open(os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter),
'w',
encoding=args.encoding) as fp:
fp.write('\n'.join(all_text))
def validation():
print('Calculating validation loss...')
losses = []
for batch in tqdm.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(args.batch_size)]
avg_loss = (0.0, 0.0)
start_time = time.time()
# print('Evaluating grads..')
# tf2.profiler.experimental.start('logdir')
# sess.run((opt_apply, train_loss, summaries), feed_dict={train_context: sample_batch()})
# tf2.profiler.experimental.stop()
# print('Succeeded')
# exit()
try:
while True:
if counter % args.save_every == 0:
save()
if counter % args.sample_every == 0:
generate_samples()
if args.val_every > 0 and (counter % args.val_every == 0
or counter == 1):
validation()
(_, v_loss, v_summary) = sess.run(
(opt_apply, train_loss, summaries),
feed_dict={train_context: sample_batch()})
summary_log.add_summary(v_summary, counter)
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,
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))
return 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()
#Trying out a change to finetune that saves only when validation loss decreases
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 == 1:
v_val_loss = validation()
save()
elif val_every > 0 and counter == counter_base:
v_val_loss = validation()
elif val_every > 0 and (counter % val_every == 0):
new_v_val_loss = validation()
if new_v_val_loss < v_val_loss:
v_val_loss = new_v_val_loss
save()
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 main():
args = parser.parse_args()
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.sample_length > hparams.n_ctx:
raise ValueError(
"Can't get samples longer than window size: %s" % hparams.n_ctx)
if args.model_name == '774M':
args.memory_saving_gradients = True
if args.optimizer == 'adam':
args.only_train_transformer_layers = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tf.Session(config=config) as sess:
context = tf.placeholder(tf.int32, [args.batch_size, None])
context_in = randomize(context, hparams, args.noise)
output = model.model(hparams=hparams, X=context_in)
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=args.sample_length,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p)
all_vars = [v for v in tf.trainable_variables() if 'model' in v.name]
#this line is to hopefully reduce memory usage (found on Twitter: https://twitter.com/BasedBlue/status/1169601983046672385?s=20)
edgeindex = -1 * args.layers_to_train
train_vars = all_vars[edgeindex:]
print("Training", args.layers_to_train, "raw layers out of", len(all_vars))
train_vars = [v for v in train_vars if '/h' in v.name] if args.only_train_transformer_layers else train_vars
print("Training", len(train_vars), "net layers out of", len(all_vars))
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate=args.learning_rate)
elif args.optimizer == 'adafactor':
opt = AdafactorOptimizer(learning_rate=args.learning_rate)
else:
exit('Bad optimizer:', args.optimizer)
if args.accumulate_gradients > 1:
if args.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.summary.scalar('loss', opt_apply)
else:
if args.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_lr = tf.summary.scalar('learning_rate', args.learning_rate)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
saver = tf.train.Saver(
var_list=all_vars,
max_to_keep=5,
keep_checkpoint_every_n_hours=2)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tf.train.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tf.train.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tf.train.latest_checkpoint(args.restore_from)
print('Loading checkpoint', ckpt)
saver.restore(sess, ckpt)
print('Loading dataset...')
chunks = load_dataset(enc, args.dataset, args.combine, encoding=args.encoding)
data_sampler = Sampler(chunks)
if args.val_every > 0:
if args.val_dataset:
val_chunks = load_dataset(enc, args.val_dataset, args.combine, encoding=args.encoding)
else:
val_chunks = chunks
print('dataset has', data_sampler.total_size, 'tokens')
print('Training...')
if args.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(args.val_batch_size)]
for _ in range(args.val_batch_count)]
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# 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
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
saver.save(
sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
def generate_samples():
print('Generating samples...')
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(
tf_sample,
feed_dict={context: args.batch_size * [context_tokens]})
for i in range(min(args.sample_num - index, args.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, args.run_name))
with open(
os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter), 'w', encoding=args.encoding) as fp:
fp.write('\n'.join(all_text))
def sample_batch():
ret = [data_sampler.sample(1024) for _ in range(args.batch_size)]
# print (enc.decode(ret[0]))
return ret
avg_loss = (0.0, 0.0)
bval_loss = (0.0, 0.0)
start_time = time.time()
best_val_loss = 99
missed_val_checkpoints = 0
try:
while counter < args.stop_after:
if counter % args.sample_every == 0:
generate_samples()
if args.accumulate_gradients > 1:
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
sess.run(
opt_compute, feed_dict={context: sample_batch()})
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summaries),
feed_dict={context: sample_batch()})
summary_log.add_summary(v_summary, counter)
avg_loss = (avg_loss[0] * 0.98 + v_loss,
avg_loss[1] * 0.98 + 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]))
if args.val_every > 0 and counter % args.val_every == 0:
valbatch = [val_data_sampler.sample(1024) for _ in range(args.batch_size)]
valacc = sess.run(loss, feed_dict={context: valbatch})
bval_loss = (bval_loss[0] * 0.9 + valacc, bval_loss[1] * 0.9 + 1.0)
av_val_loss = bval_loss[0] / bval_loss[1]
av_train_loss = avg_loss[0] / avg_loss[1]
print(
'[{counter} | {time:2.2f}] VAL_loss={loss:2.4f} VAL_avg={avg:2.4f} best={best:2.4f}'
.format(
counter=counter,
time=time.time() - start_time,
loss=valacc,
avg=av_val_loss,
best=best_val_loss))
if counter >= args.save_every and counter % args.save_every == 0: # check for validation checkpoints every save_every iterations.
if av_val_loss < best_val_loss and av_val_loss > av_train_loss: # got a good one from validation, save a checkpoint (every save_every) -- but don't save before val loss goes above train loss
save()
best_val_loss = av_val_loss
missed_val_checkpoints = 0
else: # missed a validation checkpoint. tolerate like 10 of these.
if av_val_loss > av_train_loss: # don't count a missed checkpoint while val loss is under training loss
missed_val_checkpoints += 1
if missed_val_checkpoints > 19: # missed too many save opportunities, stop training
counter = args.stop_after + 1
print('stopping training due to val loss not improving.')
counter += 1
except KeyboardInterrupt:
print('interrupted')