def __init__(self):
if ChatBotModel =="gpt2":
model_name='355M'
seed=None
length=None
temperature=0.7
top_k=40
top_p=0.2
models_dir= os.path.join(root_file_path,'gpt2/models')
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
self.enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3,allow_growth = True)
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self.context = tf.placeholder(tf.int32, [1, None])
np.random.seed(seed)
tf.set_random_seed(seed)
self.output = sample.sample_sequence(
hparams=hparams, length=length,
context=self.context,
batch_size=1,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
saver.restore(self.sess, ckpt)
def interact_model(
model_name='117M',
seed=None,
nsamples=1,
batch_size=None,
length=None,
temperature=1,
top_k=0,
):
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
np.random.seed(seed)
tf.set_random_seed(seed)
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))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
output = sample.sample_sequence(hparams=hparams,
length=length,
context=context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
while True:
raw_text = input("Model prompt >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("Model prompt >>> ")
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(output,
feed_dict={
context:
[context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " +
"=" * 40)
print(text)
print("=" * 80)
def main():
args = parser.parse_args()
enc = get_encoder(args.model_name)
print('Reading files')
chunks = load_dataset(enc,
args.in_text,
args.combine,
encoding=args.encoding)
print('Writing', args.out_npz)
np.savez_compressed(args.out_npz, *chunks)
def preinit_model(self):
np.random.seed(self.seed)
tf.set_random_seed(self.seed)
self.enc = encoder.get_encoder(self.model_name)
self.hparams = model.default_hparams()
with open(os.path.join('models', self.model_name, 'hparams.json')) as f:
self.hparams.override_from_dict(json.load(f))
if self.length is None:
self.length = self.hparams.n_ctx // 2
elif self.length > self.hparams.n_ctx:
logging.error("Can't get samples longer than window size: %s" % self.hparams.n_ctx)
def init_model(args):
args.input_stack = []
if args.gpu_index:
physdevs = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_virtual_device_configuration(
physdevs[args.gpu_index],
[tf.config.LogicalDeviceConfiguration(args.gpu_max_mem)])
tf.compat.v1.disable_eager_execution()
if not args.model_dir:
print('model path must be provided!')
exit()
if not args.custom_model:
args.model_path = args.model_dir + "model.ckpt"
else:
args.model_path = args.custom_model
args.json_hparams = args.model_dir + "hparams.json"
args.json_encoder = args.model_dir + "encoder.json"
args.vocab_bpe = args.model_dir + "vocab.bpe"
args.enc = encoder.get_encoder(args.json_encoder, args.vocab_bpe)
if args.model_path.split('.')[-1] == 'h5':
args.model = keras.models.load_model(args.model_path,
custom_objects={
'EmbeddingSim': EmbeddingSim,
'EmbeddingRet': EmbeddingRet,
'PositionEmbedding':
PositionEmbedding,
'LayerNormalization':
LayerNormalization,
'ScaledDotProductAttention':
ScaledDotProductAttention,
'MultiHeadAttention':
MultiHeadAttention,
'FeedForward': FeedForward,
'gelu': gelu,
'loss': net.loss
})
elif args.model_path.split('.')[-1] == 'ckpt':
args.model_ckpt = args.model_path
args.model = net.create_model(args)
args.model = net.load_weights(args.model, args)
args.model.compile(optimizer=keras.optimizers.Adam(), loss=net.loss)
else:
print('Unsupported custom model format!')
exit()
args.model.trainable = False
def __init__(self,
model_name='117M',
seed=None,
batch_size=6,
length=1,
temperature=1,
top_k=0,
top_p=0.0,
ckpt='checkpoint/12-8'):
"""
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:batch_size=1 : Number of batches (only affects speed/memory).
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:top_p=0.0 : Float value controlling diversity. Implements nucleus sampling,
overriding top_k if set to a value > 0. A good setting is 0.9.
"""
self.seed = seed
self.batch_size = batch_size
self.enc = encoder.get_encoder(model_name)
self.hparams = model.default_hparams()
self.temperature = temperature
self.top_k = top_k
self.top_p = top_p
self.model_name = model_name
self.length = length
self.endoftext = self.enc.encode('<|endoftext|>')
if ckpt:
self.ckpt = ckpt
else:
self.ckpt = os.path.join('models', self.model_name)
with open(os.path.join('models', model_name, 'hparams.json')) as f:
self.hparams.override_from_dict(json.load(f))
if length is None:
length = self.hparams.n_ctx // 2
elif length > self.hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
def main():
if not args.eager:
tf.compat.v1.disable_eager_execution()
if not args.json_encoder:
print('json_encoder must be provided.')
print('quit program.')
exit()
if not args.vocab_bpe:
print('vocab.bpe must be provided.')
print('quit program.')
exit()
if not os.path.exists('output'):
os.makedirs('output')
enc = encoder.get_encoder(args.json_encoder, args.vocab_bpe)
ds = importlib.import_module("src.load_" +
args.data_loader).create_dataset(
enc, args.length, args.dataset_path,
args.batch_size, args.steps_per_epoch,
args.num_epoch)
# for value in ds.take(10):
# x = enc.decode(value[0][0].numpy())
# print(x)
# print(len(value[0][0]))
# input("Press Enter to continue...")
# exit()
model = net.create_model(args)
# restore weight
model = net.load_weights(model, args)
model.compile(optimizer=keras.optimizers.Adam(), loss=net.loss)
# fine tune
model.fit(ds,
epochs=args.num_epoch,
steps_per_epoch=args.steps_per_epoch,
callbacks=[LearningRateScheduler(net.create_schedule(args))])
model.save(os.path.join('output', args.output_name),
include_optimizer=False)
def generate(hints, model_name='345M', seed=None,
nsamples=10, batch_size=1, length=None,
temperature=1, top_k=0, top_p=1, models_dir='models'):
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
batch_size = batch_size or 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
results = defaultdict(set)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
saver.restore(sess, ckpt)
for hint in hints:
print("[%s]begin to generate for: %s" % (datetime.utcnow(), hint))
context_tokens = enc.encode(hint)
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for out_data in out:
text = enc.decode(out_data)
text = postprocess(hint, text.strip())
results[hint].add(text)
print("[%s]finished generating for: %s" % (datetime.utcnow(), hint))
return results
def get_encoder(gpt2_path):
try:
sys.path.append(gpt2_path)
import src.encoder as encoder
# The encoder assumes that the models are available in it's cwd.
cwd = os.getcwd()
os.chdir(gpt2_path)
enc = encoder.get_encoder("124M", "models")
os.chdir(cwd)
return enc
except ModuleNotFoundError as err:
raise ModuleNotFoundError(
"Could not locate GPT-2 Repository. Please clone from "
"https://github.com/openai/gpt-2 and provide the path "
"using the --gpt2-repo-path flag.") from err
def sample_model(
model_name='117M',
seed=None,
nsamples=0,
batch_size=1,
length=None,
temperature=1,
top_k=0,
):
np.random.seed(seed)
tf.set_random_seed(seed)
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))
if length is None:
length = hparams.n_ctx
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
output = sample.sample_sequence(
hparams=hparams,
length=length,
start_token=enc.encoder['<|endoftext|>'],
batch_size=batch_size,
temperature=temperature,
top_k=top_k)[:, 1:]
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
generated = 0
while nsamples == 0 or generated < nsamples:
out = sess.run(output)
for i in range(batch_size):
generated += batch_size
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
def sample_model(
#model_name="117M",
model_name="345M",
seed=None,
nsamples=5,
batch_size=1,
length=200,
temperature=1,
top_k=40,
):
"""
Run the sample_model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to
reproduce results
:nsamples=0 : Number of samples to return, if 0, continues to
generate samples indefinately.
:batch_size=1 : Number of batches (only affects speed/memory).
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
"""
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(hparams)
if length is None:
length = hparams.n_ctx
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams,
length=length,
start_token=enc.encoder['<|endoftext|>'],
batch_size=batch_size,
temperature=temperature,
top_k=top_k)[:, 1:]
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
generated = 0
while nsamples == 0 or generated < nsamples:
out = sess.run(output)
#print(out)
for i in range(batch_size):
generated += batch_size
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
from src import encoder
from src.load_dataset import load_dataset
enc = encoder.get_encoder('117M')
chunks = load_dataset(enc, 'run.txt', 30, encoding='utf-8')
print(chunks)
def main():
if not args.model_dir:
print('model_path must be provided')
exit()
if args.custom_model:
args.model = args.custom_model
else:
args.model = args.model_dir + "model.ckpt"
args.json_hparams = args.model_dir + "hparams.json"
args.json_encoder = args.model_dir + "encoder.json"
args.vocab_bpe = args.model_dir + "vocab.bpe"
if not os.path.exists('output'):
os.makedirs('output')
enc = encoder.get_encoder(args.json_encoder, args.vocab_bpe)
ds = importlib.import_module("src.load_" +
args.data_loader).create_dataset(
enc, args.length, args.dataset_path,
args.batch_size, args.steps_per_epoch,
args.num_epoch)
# Setup TensorFlow to use a distribution strategy to perform compute across multiple devices.
strategy = tf.distribute.experimental.CentralStorageStrategy()
with strategy.scope():
if args.model.split('.')[-1] == 'h5':
model = keras.models.load_model(args.model,
custom_objects={
'EmbeddingSim': EmbeddingSim,
'EmbeddingRet': EmbeddingRet,
'PositionEmbedding':
PositionEmbedding,
'LayerNormalization':
LayerNormalization,
'ScaledDotProductAttention':
ScaledDotProductAttention,
'MultiHeadAttention':
MultiHeadAttention,
'FeedForward': FeedForward,
'gelu': gelu,
'loss': net.loss
})
elif args.model.split('.')[-1] == 'ckpt':
args.model_ckpt = args.model
model = net.create_model(args)
model = net.load_weights(model, args)
else:
print('Unrecognized model format')
exit()
model.compile(optimizer=keras.optimizers.Adam(), loss=net.loss)
# fine tune
model.fit(ds,
epochs=args.num_epoch,
steps_per_epoch=args.steps_per_epoch,
callbacks=[LearningRateScheduler(net.create_schedule(args))])
model.save(os.path.join('output', args.output_name),
include_optimizer=False)
model.evaluate(ds)
def conditional_model(
model_name='1558M',
seed=None,
nsamples=1,
batch_size=1,
length=None,
temperature=1,
top_k=40,
top_p=0,
models_dir='models',
sentences=None,
):
"""
Run the model on multilple sentences and return a dict.
:model_name : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=40 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:top_p=0.0 : Float value controlling diversity. Implements nucleus sampling,
overriding top_k if set to a value > 0. A good setting is 0.9.
:sentences : List of strings or string. Model returns an answer or a continuation
to that string. If list of strings the model return a dictionary of sentences and their
respective model replies.
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
if sentences == None:
raise ValueError('Sentences cannot be None')
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))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('../models', model_name))
saver.restore(sess, ckpt)
listy = []
n = 0
if isinstance(sentences, list):
for i in sentences:
context_tokens = enc.encode(i)
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
text = i + enc.decode(out[0])
listy.append(text)
n += 1
print(n)
return dict(zip(sentences,listy))
else:
context_tokens = enc.encode(sentences)
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
text = sentences + enc.decode(out[0])
return {sentences: text}
def interact_model(
raw_poem=None,
model_name='100_fal',
seed=150,
nsamples=1,
batch_size=1,
length=140,
temperature=1,
top_k=0,
top_p=0.0,
):
"""
Interactively run the model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=40 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
"""
input_list = [raw_poem]
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join(basedir, 'models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(basedir, 'models', model_name))
saver.restore(sess, ckpt)
out_text = []
if raw_poem in input_list:
context_tokens = enc.encode(raw_poem)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
out_text.append(text)
text = get_poem_chunks(out_text)
return textFilter(text)
def __init__(
self,
model_name='345M',
seed=None,
nsamples=1,
batch_size=1,
length=None,
temperature=1,
top_k=0,
raw_text="",
):
"""
Interactively run the model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
"""
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
self.nsamples = nsamples
self.batch_size = batch_size
self.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))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
self.sess = tf.Session(graph=tf.Graph())
self.sess.__enter__()
self.context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
self.output = sample.sample_sequence(hparams=hparams,
length=length,
context=self.context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k)
saver = tf.train.Saver()
self.ckpt = tf.train.latest_checkpoint(
os.path.join('models', model_name))
saver.restore(self.sess, self.ckpt)
def nointeract_model(model_name, seed, nsamples, batch_size, length,
temperature, top_k, top_p, models_dir, inputGPT2, nrepeat,
filePath, nconcepts, nphases, ntest):
"""
Non Interactively run the model
:model_name=124M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:models_dir : path to parent folder containing model subfolders
(i.e. contains the <model_name> folder)
"""
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(hparams=hparams,
length=length,
context=context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
saver.restore(sess, ckpt)
f = open(filePath, 'w')
for c in range(1, nconcepts + 1):
for p in range(1, nphases + 1):
for t in range(1, ntest + 1):
pos = (c - 1) * nphases * ntest + (t - 1)
for r in range(1, nrepeat + 1):
raw_text = inputGPT2[pos]
context_tokens = enc.encode(raw_text)
for _ in range(nsamples // batch_size):
out = sess.run(output,
feed_dict={
context: [
context_tokens
for _ in range(batch_size)
]
})[:, len(context_tokens):]
for i in range(batch_size):
text = enc.decode(out[i])
f.write("Concept " + str(c) + " Phase " +
str(p) + " Test " + str(t) +
" Repetition " + str(r) +
" // Input= " + raw_text +
" // Output= " + text)
def get_encoder(self):
return encoder.get_encoder(self.model_name)
def main():
if not args.model_dir:
print('model path must be provided.')
print('quit program.')
exit()
if args.custom_model:
args.model_path = args.custom_model
else:
args.model_path = args.model_dir + "model.ckpt"
args.json_hparams = args.model_dir + "hparams.json"
args.json_encoder = args.model_dir + "encoder.json"
args.vocab_bpe = args.model_dir + "vocab.bpe"
args.starter = args.starter.replace("\\n", "\n")
args.starter = args.starter.replace("\\'", "'")
print()
enc = encoder.get_encoder(args.json_encoder, args.vocab_bpe)
# load model
if args.model_path.split('.')[-1] == 'h5':
model = keras.models.load_model(args.model_path,
custom_objects={
'EmbeddingSim': EmbeddingSim,
'EmbeddingRet': EmbeddingRet,
'PositionEmbedding':
PositionEmbedding,
'LayerNormalization':
LayerNormalization,
'ScaledDotProductAttention':
ScaledDotProductAttention,
'MultiHeadAttention':
MultiHeadAttention,
'FeedForward': FeedForward,
'gelu': gelu,
'loss': net.loss
})
elif args.model_path.split('.')[-1] == 'ckpt':
args.model_ckpt = args.model_path
model = net.create_model(args)
model = net.load_weights(model, args)
model.compile(optimizer=keras.optimizers.Adam(), loss=net.loss)
else:
print('Unrecognized model format: ' + args.model_path.split('.')[-1])
exit()
model.trainable = False
# prepare input data
input_data = [enc.encode(args.starter)] * args.batch_size
start_length = [len(data) for data in input_data]
flag_stop = [False] * args.batch_size
stop = False
# run inference
for shift in range(args.output_length):
output_data = model.predict(np.array(input_data), batch_size=2)
for index in range(args.batch_size):
if not flag_stop[index]:
probs = [
(prob, i)
for i, prob in enumerate(output_data[index,
start_length[index] +
shift - 1])
]
probs.sort(reverse=True)
if args.nucleus:
next_token = utils.find_top_p(probs, args.top_p,
args.temperature)
else:
next_token = utils.find_top_p(probs, args.top_k,
args.temperature)
input_data[index].append(next_token)
if next_token == 50256:
flag_stop[index] = True
else:
input_data[index].append(50256)
output = enc.decode(input_data[index])
if '\n' in output:
stop = True
if stop:
break
print(output)
def interact_model(
model_name='124M',
seed=None,
nsamples=1,
batch_size=1,
length=None,
temperature=1,
top_k=0,
top_p=1,
models_dir='models',
):
"""
Interactively run the model
:model_name=124M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
:nsamples=1 : Number of samples to return total
:batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples.
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:temperature=1 : Float value controlling randomness in boltzmann
distribution. Lower temperature results in less random completions. As the
temperature approaches zero, the model will become deterministic and
repetitive. Higher temperature results in more random completions.
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:models_dir : path to parent folder containing model subfolders
(i.e. contains the <model_name> folder)
"""
models_dir = os.path.expanduser(os.path.expandvars(models_dir))
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
enc = encoder.get_encoder(model_name, models_dir)
hparams = model.default_hparams()
with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx // 2
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [batch_size, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(
hparams=hparams, length=length,
context=context,
batch_size=batch_size,
temperature=temperature, top_k=top_k, top_p=top_p
)
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join(models_dir, model_name))
saver.restore(sess, ckpt)
while True:
raw_text = input("Model prompt >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("Model prompt >>> ")
context_tokens = enc.encode(raw_text)
generated = 0
for _ in range(nsamples // batch_size):
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
print("=" * 80)
def main():
# args = parser.parse_args()
args = Opts()
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))
hparams.override_from_dict(json.loads(f.read()))
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)
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 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='117M',
seed=None,
batch_size=1,
sample_length=1023,
sample_num=50,
sample_every=100,
run_name='dnd_biographies08',
restore_from='latest',
mode="test",
max_iterations=50000,
loss_threshold=0.8,
save_every=1000):
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))
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)
config = tf.ConfigProto()
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=1.0,
top_k=40)
train_vars = [v for v in tf.trainable_variables() if 'model' in v.name]
opt = tf.train.AdamOptimizer(1e-4).minimize(loss, var_list=train_vars)
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('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)
print('Loading dataset...')
chunks = load_dataset(enc, dataset)
data_sampler = Sampler(chunks)
print('dataset has', data_sampler.total_size, 'tokens')
print('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 or sample_num == 0:
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)
# print(''.join(all_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:
if mode == "train":
while True and counter <= max_iterations:
if counter % save_every == 0:
save()
if counter % sample_every == 0:
generate_samples()
batch = [
data_sampler.sample(1024) for _ in range(batch_size)
]
_, lv = sess.run((opt, loss), feed_dict={context: batch})
avg_loss = (avg_loss[0] * 0.99 + lv,
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=lv,
avg=avg_loss[0] / avg_loss[1]))
counter += 1
if counter > 100:
if (avg_loss[0] / avg_loss[1]) < loss_threshold:
counter = max_iterations + 1
else:
generate_samples()
except KeyboardInterrupt:
print('interrupted')
save()
def generate(self,
sess,
return_as_list=False,
truncate=None,
destination_path=None,
sample_delim='=' * 20 + '\n',
prefix=None,
seed=None,
batch_size=1,
nsamples=1,
length=1023,
temperature=0.7,
top_k=0,
run_name='run1',
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:
context = tf.placeholder(tf.int32, [batch_size, None])
CHECKPOINT_DIR = 'checkpoint'
checkpoint_path = os.path.join(model_path, 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 = model.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 = enc.decode([context_tokens[0]]) + 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)
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
