def interact_model(model_name='117M',
restore_from=None,
seed=None,
nsamples=1,
step=1,
length=64,
prompt="\n",
clear=None,
maxlen=-1,
temperature=1,
top_k=0,
top_p=0,
penalize=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
:step=1 : Number of tokens to generate at a time
:length=64 : Window size; use 1024 for maximum size per sample
:prompt="\\n" : Prompt to start with. The default of "" prompts with an <|endoftext|> token.
:clear=None : If this string is encountered, clear the context window.
:maxlen=-1 : if this many tokens are generated without
encountering --clear, then print it and clear the context window.
: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.
:penalize=0.0 : Float value controlling "used" penalty. Implements repetition
reduction (similar to CTRL) if set to a value > 0. A decent setting might be 0.85
with temperature 0.3 and top_k 40.
"""
batch_size = 1
assert nsamples % batch_size == 0
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 > hparams.n_ctx:
raise ValueError("Length can't be largeer than n_ctx: %s" %
hparams.n_ctx)
if step > length:
raise ValueError("Can't get samples longer than length: %s" % length)
with tflex.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=step,
context=context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p,
penalize=penalize)
saver = tflex.Saver(reshape=True)
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
while True:
tflex.check_commands()
if tflex.should_quit():
break
try:
with open(prompt) as f:
tflex.raw_text = f.read()
if tflex.raw_text.endswith('\n'):
tflex.raw_text = tflex.raw_text[:-1]
if tflex.raw_text.endswith('\r'):
tflex.raw_text = tflex.raw_text[:-1]
except:
tflex.raw_text = prompt
tflex.raw_text = tflex.raw_text.replace('\\n', '\n')
tflex.raw_text = tflex.raw_text.replace('\\t', '\t')
#print(repr(tflex.raw_text))
tflex.context_tokens = enc.encode(
tflex.raw_text) if len(tflex.raw_text) > 0 else [50256]
while len(tflex.context_tokens) > length - step - 1:
tflex.context_tokens = tflex.context_tokens[1:]
tflex.prompt_tokens = tflex.context_tokens[:]
tflex.first = True
tflex.backlog = []
tflex.backlog_count = 0
tflex.context_text = ""
tflex.context_count = 0
while True:
for tokens in generate_result(
context_tokens=tflex.context_tokens,
enc=enc,
output=output,
context=context,
nsamples=1,
batch_size=batch_size,
sess=sess):
tflex.tokens = tokens
if tflex.first:
#clear_output(wait=True)
sys.stdout.write(enc.decode(tflex.context_tokens))
sys.stdout.flush()
tflex.first = False
tflex.backlog.extend(tflex.tokens)
tflex.backlog_count += 1
if is_ascii(enc.decode([tflex.backlog[-1]
])) or tflex.backlog_count > 16:
text = enc.decode(tflex.backlog)
result = text
if clear is not None:
result, *rest = text.split(clear)
sys.stdout.write(result)
sys.stdout.flush()
tflex.context_text += text
tflex.context_count += len(tflex.backlog)
def reset_context():
tflex.context_text = ""
tflex.context_count = 0
tflex.context_tokens = []
tflex.first = True
tflex.tokens = tflex.prompt_tokens[:]
tflex.reset_context = reset_context
if maxlen > 0 and tflex.context_count > maxlen or clear is not None and clear in tflex.context_text:
tflex.reset_context()
tflex.backlog = []
tflex.backlog_count = 0
tflex.check_commands()
tflex.context_tokens.extend(tflex.tokens)
while len(tflex.context_tokens) > length - step - 1:
tflex.context_tokens = tflex.context_tokens[1:]
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
sess = tf.Session(graph=tf.Graph()).__enter__()
context = tf.placeholder(tf.int32, [4, None])
np.random.seed(None)
tf.set_random_seed(None)
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 = tflex.Saver()
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
class Article(BaseModel):
text: str
@app.post("/text_rewrite")
def text_rewrite(
article: Article
):
text = article.text
result = []
for sent in nlp(text).sents:
sent_text = sent.text.strip()
def main():
args = parser.parse_args()
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
hparams.res_dropout = args.dropout
hparams.attn_dropout = args.dropout
epsilon = -1e10
if args.dtype == 'float32':
hparams.dtype = tf.float32
elif args.dtype == 'float16':
hparams.dtype = tf.float16
epsilon = -65500
elif args.dtype == 'bfloat16':
hparams.dtype = tf.bfloat16
epsilon = -65500
else:
print('Unknown dtype', args.dtype)
if args.float16:
hparams.dtype = tf.bfloat16
epsilon = -65500
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.n_ctx >= 0:
hparams.n_ctx=args.n_ctx
if args.n_embd >= 0:
hparams.n_embd=args.n_embd
if args.n_head >= 0:
hparams.n_head=args.n_head
if args.n_layer >= 0:
hparams.n_layer=args.n_layer
if args.sample_length < 0:
args.sample_length = hparams.n_ctx - 1
if args.sample_length > hparams.n_ctx:
raise ValueError(
"Can't get samples longer than window size: %s" % hparams.n_ctx)
if args.sample_ctx < 0:
args.sample_ctx = 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()
if args.allow_growth:
config.gpu_options.allow_growth = True
if args.disable_layout_optimizer:
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tflex.Session(config=config, init_tpu=args.init_tpu) 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,
epsilon=epsilon)
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
parameter_count = sum([np.prod(v.shape.as_list()) for v in train_vars])
print("This model is using %d parameters (%.2fM)" % (parameter_count, parameter_count/(1024.0*1024.0)))
with tf.variable_scope(tf.get_variable_scope().name, reuse=tf.AUTO_REUSE):
global_step = tflex.get_variable('global_step') or tf.get_variable('global_step', shape=(), dtype=tf.int32, trainable=False)
current_step = args.learning_rate_initial_step
global_step.load(current_step, session=sess)
if args.learning_rate_cos:
lr = tflex_sgdr.sgdr_decay_with_warmup(args.learning_rate, global_step,
warmup_steps=args.learning_rate_warmup, initial_period_steps=args.learning_rate_period, learning_rate_min=args.learning_rate_min)
else:
lr = tflex.get_variable('learn_rate') or tf.get_variable('learn_rate', shape=(), dtype=tf.float32, trainable=False)
lr.load(args.learning_rate, session=sess)
def update_lr(rate=None, step=None):
if not args.learning_rate_cos:
if step is None:
step = global_step.eval(session=sess)
if rate is None:
rate = args.learning_rate
if callable(rate):
rate = rate(step)
lr.load(rate, session=sess)
return lr.eval(session=sess)
@tflex.register_command
def set_learning_rate():
print("Current learn rate: %0.8f" % update_lr())
print("New learn rate?")
rate = input('')
if not rate:
print("Empty input; not changing anything.")
else:
try:
rate = float(rate)
except:
print("Invalid input; must be a float")
print("Setting learn rate to %0.8f" % rate)
args.learning_rate = rate
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=lr)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate=lr)
elif args.optimizer == 'ada':
import tensor2tensor.utils.optimize
from tensor2tensor.utils import hparam
import tensor2tensor.models.research
from tensor2tensor.utils import registry
ada_hparams = registry.hparams('afx_mimic_adam')
ada_hparams.optimizer_adafactor_beta1 = 0.0
ada_hparams.optimizer_adafactor_factored = True
opt = tensor2tensor.utils.optimize.adafactor(learning_rate=lr, hparams=ada_hparams)
else:
exit('Bad optimizer:', args.optimizer)
#if tpu_addr:
# # https://pulsejet.github.io/blog/posts/tpu-without-estimator/
# from tensorflow.contrib.tpu.python.tpu import tpu_function
# tpu_function.get_tpu_context().set_number_of_shards(8)
# opt = tf.contrib.tpu.CrossShardOptimizer(opt)
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', lr)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
if args.save_graph:
summary_log.add_graph(tf.get_default_graph())
saver = tflex.Saver(
var_list=all_vars,
max_to_keep=args.max_to_keep,
keep_checkpoint_every_n_hours=100000,
reshape=args.truncate_weights)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tflex.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tflex.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tflex.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tflex.latest_checkpoint(args.restore_from)
print('Loading snapshot %s...' % ckpt)
t0 = time.time()
if not args.fresh_model:
saver.restore(sess, ckpt)
t1 = time.time()
print('Loaded in %f seconds' % (t1 - t0))
def make_sampler(dataset, enc, seed, combine):
if os.path.isdir(dataset) or dataset.endswith('.npz'):
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks, seed=seed)
print('dataset has', data_sampler.total_size, 'tokens', len(chunks), 'chunks')
else:
data_sampler = TextSampler(dataset, enc, seed=seed)
return data_sampler
print('Loading dataset...')
seed = None if args.seed < 0 else args.seed
data_sampler = make_sampler(dataset=args.dataset, enc=enc, seed=seed, combine=args.combine)
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_dataset = args.val_dataset if args.val_dataset else args.dataset
val_data_sampler = make_sampler(dataset=val_dataset, enc=enc, seed=1, combine=args.combine)
val_batches = [[val_data_sampler.sample(hparams.n_ctx) for _ in range(args.val_batch_size)]
for _ in range(args.val_batch_count)]
print('Training...')
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
@tflex.register_command
def get_tarfile_name(checkpoint_folder):
"""Converts a folder path into a filename for a .tar archive"""
tarfile_name = checkpoint_folder.replace(os.path.sep, '_') + '.tar'
return tarfile_name
def copy_checkpoint_to_gdrive(run_name='run1', copy_folder=False):
"""Copies the checkpoint folder to a mounted Google Drive."""
#is_mounted()
checkpoint_folder = os.path.join('checkpoint', run_name)
if copy_folder:
shutil.copytree(checkpoint_folder, "/content/drive/My Drive/" + checkpoint_folder)
else:
file_path = get_tarfile_name(checkpoint_folder)
# Reference: https://stackoverflow.com/a/17081026
with tarfile.open(file_path, 'w') as tar:
tar.add(checkpoint_folder)
shutil.copyfile(file_path, "/content/drive/My Drive/" + file_path)
@tflex.register_command
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
t0 = time.time()
saver.save(
sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
t1 = time.time()
print('Saved in %f seconds' % (t1 - t0))
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
#copy_checkpoint_to_gdrive()
@tflex.register_command
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)
print(text)
all_text.append(text)
index += 1
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))
@tflex.register_command
def validation():
if args.val_every <= 0:
return
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(
'{stamp} [{counter} | {time:2.4f}] validation loss = {loss:2.4f}'
.format(
stamp=timestamp(),
counter=counter,
time=time.time() - start_time,
loss=v_val_loss))
start_time = time.time()
def elapsed():
return time.time() - start_time
def say(msg):
print('{stamp} [{counter} | {time:2.4f}] {msg}'.format(counter=counter, time=elapsed(), msg=msg, stamp=timestamp()))
def sample_batch():
#return [data_sampler.sample(args.sample_ctx) for _ in range(args.batch_size)]
#say('Sampling batch...')
r = []
times = []
for _ in range(args.batch_size):
start = time.time()
sample = data_sampler.sample(args.sample_ctx)
end = time.time()
elapsed = (end - start)
r += [sample]
times += [elapsed]
total = sum(times)
avg = total / len(times)
#say('Sampled %d batches in %.4f seconds (avg per batch: %.4f)' % (args.batch_size, total, avg))
return r
prev_time = time.time()
avg_loss = (0.0, 0.0)
if args.debug_before_training:
import pdb
pdb.set_trace()
last_saved_time = elapsed()
while True:
try:
now = elapsed()
if args.save_time > 0 and (((now - last_saved_time) / 60.0) >= args.save_time):
save()
last_saved_time = now
elif args.save_every > 0 and (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_rate = update_lr()
if args.accumulate_gradients > 1:
#say('Running opt_reset...')
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
batch = sample_batch()
say('Running opt_compute...')
sess.run(opt_compute, feed_dict={context: batch})
say('Running opt_apply...')
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
batch = sample_batch()
say('Running opt_apply...')
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summaries),
feed_dict={context: batch})
if args.float16:
v_loss = tf.to_float(v_loss).eval()
summary_log.add_summary(v_summary, counter)
summary_log.flush()
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
now = time.time()
print('{stamp} [{counter} | {time:2.4f} | {delta:2.2f}s | {ops:2.6f}tokens/s] loss={loss:2.4f} avg={avg:2.4f} rate={rate:0.7f} step={step}'
.format(
stamp=timestamp(),
counter=counter,
time=now - start_time,
delta=now - prev_time,
ops=args.sample_ctx * args.batch_size / (now - prev_time),
rate=v_rate,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1],
step=current_step,
))
counter += 1
current_step += 1
global_step.load(current_step, session=sess)
tflex.check_commands_with_args(
session=sess,
stamp=timestamp(),
counter=counter,
time=now - start_time,
delta=now - prev_time,
ops=args.batch_size / (now - prev_time),
rate=v_rate,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1],
avg_loss=avg_loss,
step=current_step,
train_vars=train_vars,
all_vars=all_vars,
args=args,
data_sampler=data_sampler,
ckpt=ckpt,
saver=saver,
)
if tflex.should_quit():
break
prev_time = now
if args.debug_print_all_vars:
print('all variables:')
print('name/shape/parameter_count')
param_count = 0
for x in tf.all_variables():
shape = x.shape.as_list()
count = np.prod(shape)
print(x.name, shape, count)
param_count += count
print('Total parameters:', param_count)
args.debug_print_all_vars = False
if args.debug_print_trainable_vars:
print('trainable variables:')
print('name/shape/parameter_count')
param_count = 0
for x in tf.trainable_variables():
shape = x.shape.as_list()
count = np.prod(shape)
print(x.name, shape, count)
param_count += count
print('Total parameters:', param_count)
args.debug_print_trainable_vars = False
except KeyboardInterrupt:
print('interrupted')
if args.save_on_ctrlc:
save()
if args.debug_on_ctrlc:
import pdb
pdb.set_trace()
else:
break
def interact_model(model_name='117M',
restore_from=None,
seed=None,
nsamples=1,
batch_size=1,
length=None,
temperature=1,
top_k=0,
top_p=0.0,
penalize=0,
prompt=None):
"""
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.
: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.
:penalize=0.0 : Float value controlling "used" penalty. Implements repetition
reduction (similar to CTRL) if set to a value > 0. A decent setting might be 0.85
with temperature 0.3 and top_k 40.
"""
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('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 tflex.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,
penalize=penalize)
saver = tflex.Saver()
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
while True:
if prompt is not None:
if os.path.isfile(prompt):
with open(prompt) as f:
raw_text = f.read()
else:
raw_text = prompt
else:
raw_text = input("Model prompt >>> ")
if not raw_text:
raw_text = "\n"
if len(raw_text) > 1 and raw_text.endswith('\n'):
raw_text = raw_text[:-1]
print('Prompt:', repr(raw_text))
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)
sys.stdout.write(raw_text)
print(text)
sys.stdout.flush()
print("=" * 80)
def text_rewrite(
input_filepath,
output_filepath,
model_name='1558M',
restore_from=None,
seed=None,
batch_size=4,
length=70,
temperature=1,
top_k=0,
top_p=1,
init_tpu=False
):
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
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))
sess_initer = tflex if init_tpu else tf
with sess_initer.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 = tflex.Saver()
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
with open(input_filepath, encoding='utf-8') as f:
text = f.read()
result = []
for sent in nlp(text).sents:
sent_text = sent.text.strip()
if not sent_text:
continue
model_input = "ORIGINAL_SENT: {} >>>>>".format(sent_text)
context_tokens = enc.encode(model_input)
print(model_input)
print('*' * 80)
out = sess.run(output, feed_dict={
context: [context_tokens for _ in range(batch_size)]
})[:, len(context_tokens):]
texts = []
for i in range(batch_size):
text = enc.decode(out[i]) + "\n"
print("SAMPLE {}".format(i))
print(text)
print('-' * 80)
texts.append(text)
rephrased = get_best_candidate(sent_text, texts)
rephrased = add_new_line(sent.text, rephrased)
result.append(rephrased)
print("REPHRASED: {}".format(rephrased))
print('=' * 80)
with open(output_filepath, encoding='utf-8', mode='w') as f:
f.write(" ".join(result))
def interact_model(model_name='117M',
restore_from=None,
seed=None,
nsamples=1,
step=1,
length=64,
prompt="\n",
clear=None,
maxlen=-1,
temperature=1,
top_k=0,
top_p=0,
penalize=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
:step=1 : Number of tokens to generate at a time
:length=64 : Window size; use 1024 for maximum size per sample
:prompt="\\n" : Prompt to start with. The default of "" prompts with an <|endoftext|> token.
:clear=None : If this string is encountered, clear the context window.
:maxlen=-1 : if this many tokens are generated without
encountering --clear, then print it and clear the context window.
: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.
:penalize=0.0 : Float value controlling "used" penalty. Implements repetition
reduction (similar to CTRL) if set to a value > 0. A decent setting might be 0.85
with temperature 0.3 and top_k 40.
"""
batch_size = 1
assert nsamples % batch_size == 0
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 > hparams.n_ctx:
raise ValueError("Length can't be largeer than n_ctx: %s" %
hparams.n_ctx)
if step > length:
raise ValueError("Can't get samples longer than length: %s" % length)
with tflex.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=step,
context=context,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
top_p=top_p,
penalize=penalize)
saver = tflex.Saver(reshape=True)
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
saver2 = tf.train.Saver()
counter = int(ckpt.split('-')[-1].split('.')[0])
saver2.save(sess, os.path.join('saved', 'model'), global_step=counter)
def sample_model(model_name='117M',
restore_from=None,
seed=None,
nsamples=0,
batch_size=1,
length=None,
temperature=1,
top_k=0,
top_p=0.0,
penalize=0):
"""
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.
: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.
:penalize=0.0 : Float value controlling "used" penalty. Implements repetition
reduction (similar to CTRL) if set to a value > 0. A decent setting might be 0.85
with temperature 0.3 and top_k 40.
"""
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 tflex.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,
top_p=top_p,
penalize=penalize)[:, 1:]
saver = tflex.Saver()
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
generated = 0
while nsamples == 0 or generated < nsamples:
out = sess.run(output)
for i in range(batch_size):
generated += 1
text = enc.decode(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
def interact_model(model_name='117M',
asker=None,
responder=None,
restore_from=None,
seed=None,
length=None,
temperature=1,
top_k=0,
top_p=0.0,
penalize=0,
prompt=None):
"""
Interactively chat with the model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to reproduce
results
: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.
:penalize=0.0 : Float value controlling "used" penalty. Implements repetition
reduction (similar to CTRL) if set to a value > 0. A decent setting might be 0.85
with temperature 0.3 and top_k 40.
"""
if asker is None:
raise Exception(
"Add a name present in the training dataset that you will be chatting as"
)
if responder is None:
raise Exception(
"Add a name present in the training dataset that gpt will be chatting as"
)
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 tflex.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [1, None])
np.random.seed(seed)
tf.set_random_seed(seed)
output = sample.sample_sequence(hparams=hparams,
length=length,
context=context,
batch_size=1,
temperature=temperature,
top_k=top_k,
top_p=top_p,
penalize=penalize)
saver = tflex.Saver()
if restore_from is None:
restore_from = os.path.join('models', model_name)
ckpt = tflex.latest_checkpoint(restore_from)
saver.restore(sess, ckpt)
input_ = ''
time = 1924862493344
while True:
time = increase_time(time)
input_ = input_ + f'({time}) {asker}: ' + input(f"{asker}: ")
time = increase_time(time)
input_ = input_ + f'\n ({time}) {responder}: '
if len(input_) > 1 and input_.endswith('\n'):
input_ = input_[:-1]
context_tokens = enc.encode(input_)
out = sess.run(output,
feed_dict={context:
[context_tokens]})[:,
len(context_tokens):]
enc.decode(out[0])
text = enc.decode(out[0]).split(f') {asker}', 1)[0]
print(f'\n ({time}) {responder}: ' + text.rsplit('(', 1)[0])
input_ = input_ + text
sys.stdout.flush()