我正在尝试使用gpt-2模型的条件文本生成来对其进行调整，以使其成为一个好的聊天机器人。我正在使用nsheppard's code在我的自定义数据集中对其进行重新训练。
我在从Facebook数据中提取的对话的自定义数据集上训练了模型。我将样本长度更改为20，因为它们是交互式条件生成过程中的对话。
数据集看起来像这样：
How are you
Hi Great and you
Am also good
So you re a graphic designer
Yeah
How can you contribute to making the game In d graphics aspect
Can you show me some of your work if u don t mind
Am planning to learn making it a motion type
U can go through my photos
K
Can you make animations for it
Flash animations to be specific
No please only stable ones
Ok
但是，在训练之后，当我尝试与之聊天时，它代替了我的句子，而不是答复他们。
User >>> bye
======================================== SAMPLE 1 ========================================
and
hi
are there any positions in khrzh being appointed right now
我了解到，interactive_conditional_samples.py是根据提示完成句子的，但我认为更改数据集会起作用，并确保它不起作用。
train.py
#!/usr/bin/env python3
# Usage:
# PYTHONPATH=src ./train --dataset <file|directory|glob>
import argparse
import json
import os
import numpy as np
import tensorflow as tf
import time
import tqdm
from tensorflow.core.protobuf import rewriter_config_pb2
import model, sample, encoder
from load_dataset import load_dataset, Sampler
from accumulate import AccumulatingOptimizer
import memory_saving_gradients
CHECKPOINT_DIR = 'checkpoint'
SAMPLE_DIR = 'samples'
parser = argparse.ArgumentParser(
description='Fine-tune GPT-2 on your custom dataset.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--dataset', metavar='PATH', type=str, required=True, help='Input file, directory, or glob pattern (utf-8 text, or preencoded .npz files).')
parser.add_argument('--model_name', metavar='MODEL', type=str, default='117M', help='Pretrained model name')
parser.add_argument('--combine', metavar='CHARS', type=int, default=50000, help='Concatenate input files with <|endoftext|> separator into chunks of this minimum size')
parser.add_argument('--batch_size', metavar='SIZE', type=int, default=1, help='Batch size')
parser.add_argument('--learning_rate', metavar='LR', type=float, default=0.00002, help='Learning rate for Adam')
parser.add_argument('--accumulate_gradients', metavar='N', type=int, default=1, help='Accumulate gradients across N minibatches.')
parser.add_argument('--memory_saving_gradients', default=False, action='store_true', help='Use gradient checkpointing to reduce vram usage.')
parser.add_argument('--only_train_transformer_layers', default=False, action='store_true', help='Restrict training to the transformer blocks.')
parser.add_argument('--optimizer', type=str, default='adam', help='Optimizer. <adam|sgd>.')
parser.add_argument('--noise', type=float, default=0.0, help='Add noise to input training data to regularize against typos.')
parser.add_argument('--top_k', type=int, default=40, help='K for top-k sampling.')
parser.add_argument('--top_p', type=float, default=0.0, help='P for top-p sampling. Overrides top_k if set > 0.')
parser.add_argument('--restore_from', type=str, default='latest', help='Either "latest", "fresh", or a path to a checkpoint file')
parser.add_argument('--run_name', type=str, default='run1', help='Run id. Name of subdirectory in checkpoint/ and samples/')
parser.add_argument('--sample_every', metavar='N', type=int, default=100, help='Generate samples every N steps')
parser.add_argument('--sample_length', metavar='TOKENS', type=int, default=1023, help='Sample this many tokens')
parser.add_argument('--sample_num', metavar='N', type=int, default=1, help='Generate this many samples')
parser.add_argument('--save_every', metavar='N', type=int, default=1000, help='Write a checkpoint every N steps')
parser.add_argument('--val_dataset', metavar='PATH', type=str, default=None, help='Dataset for validation loss, defaults to --dataset.')
parser.add_argument('--val_batch_size', metavar='SIZE', type=int, default=2, help='Batch size for validation.')
parser.add_argument('--val_batch_count', metavar='N', type=int, default=40, help='Number of batches for validation.')
parser.add_argument('--val_every', metavar='STEPS', type=int, default=0, help='Calculate validation loss every STEPS steps.')
def maketree(path):
try:
os.makedirs(path)
except:
pass
def randomize(context, hparams, p):
if p > 0:
mask = tf.random.uniform(shape=tf.shape(context)) < p
noise = tf.random.uniform(shape=tf.shape(context), minval=0, maxval=hparams.n_vocab, dtype=tf.int32)
return tf.where(mask, noise, context)
else:
return context
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 == '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)
data_sampler = Sampler(chunks)
if args.val_every > 0:
val_chunks = load_dataset(enc, args.val_dataset, args.combine) if args.val_dataset else 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') 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()
if __name__ == '__main__':
main()
sample.py
import tensorflow as tf
import model
def top_k_logits(logits, k):
if k == 0:
# no truncation
return logits
def _top_k():
values, _ = tf.nn.top_k(logits, k=k)
min_values = values[:, -1, tf.newaxis]
return tf.where(
logits < min_values,
tf.ones_like(logits, dtype=logits.dtype) * -1e10,
logits,
)
return tf.cond(
tf.equal(k, 0),
lambda: logits,
lambda: _top_k(),
)
def top_p_logits(logits, p):
with tf.variable_scope('top_p_logits'):
logits_sort = tf.sort(logits, direction='DESCENDING')
probs_sort = tf.nn.softmax(logits_sort)
probs_sums = tf.cumsum(probs_sort, axis=1, exclusive=True)
logits_masked = tf.where(probs_sums < p, logits_sort, tf.ones_like(logits_sort)*1000) # [batchsize, vocab]
min_logits = tf.reduce_min(logits_masked, axis=1, keepdims=True) # [batchsize, 1]
return tf.where(
logits < min_logits,
tf.ones_like(logits, dtype=logits.dtype) * -1e10,
logits,
)
def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=0.0):
if start_token is None:
assert context is not None, 'Specify exactly one of start_token and context!'
else:
assert context is None, 'Specify exactly one of start_token and context!'
context = tf.fill([batch_size, 1], start_token)
def step(hparams, tokens, past=None):
lm_output = model.model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE)
logits = lm_output['logits'][:, :, :hparams.n_vocab]
presents = lm_output['present']
presents.set_shape(model.past_shape(hparams=hparams, batch_size=batch_size))
return {
'logits': logits,
'presents': presents,
}
with tf.name_scope('sample_sequence'):
# Don't feed the last context token -- leave that to the loop below
# TODO: Would be slightly faster if we called step on the entire context,
# rather than leaving the last token transformer calculation to the while loop.
context_output = step(hparams, context[:, :-1])
def body(past, prev, output):
next_outputs = step(hparams, prev[:, tf.newaxis], past=past)
logits = next_outputs['logits'][:, -1, :] / tf.to_float(temperature)
if top_p > 0.0:
logits = top_p_logits(logits, p=top_p)
else:
logits = top_k_logits(logits, k=top_k)
samples = tf.multinomial(logits, num_samples=1, output_dtype=tf.int32)
return [
tf.concat([past, next_outputs['presents']], axis=-2),
tf.squeeze(samples, axis=[1]),
tf.concat([output, samples], axis=1),
]
def cond(*args):
return True
_, _, tokens = tf.while_loop(
cond=cond, body=body,
maximum_iterations=length,
loop_vars=[
context_output['presents'],
context[:, -1],
context,
],
shape_invariants=[
tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)),
tf.TensorShape([batch_size]),
tf.TensorShape([batch_size, None]),
],
back_prop=False,
)
return tokens
interactive_conditional_samples.py
#!/usr/bin/env python3
import fire
import json
import os
import numpy as np
import tensorflow as tf
import model, sample, encoder
def interact_model(
model_name='chatbot',
seed=None,
nsamples=1,
batch_size=1,
length=20,
temperature=1,
top_k=0,
top_p=0.0
):
"""
Interactively run the model
:model_name=chatbot : 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.
"""
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 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)
while True:
raw_text = input("User >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("User >>> ")
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)
if __name__ == '__main__':
fire.Fire(interact_model)
如何调整代码以使其像聊天机器人一样工作？我猜测这与sample.py中的上下文部分有关，尽管我不确定这将如何工作。
答案 0 :(得分：1)
问题是，所有模型看到的都是正在查看您提供的一系列文本，并试图预测下一个最可能的/令牌是准确的。它不是编码器-解码器体系结构。您需要的是将这种架构微调为聊天机器人架构。我发现的唯一实现是在这里。但这是在pytorch中完成的，所以恐怕这不是您想要的。
答案 1 :(得分：1)
我知道这是一个老问题了，但是我已经成功调整了GPT-2上的许多问答风格数据集，并提出了一条建议，该建议将对以后发现此问题的人有用。
GPT-2读取非结构化文本数据，但是非常擅长推断和服从该数据中的结构。基本上，您的问题是您没有使用GPT-2可以理解的标识符来终止输入行，所以它使句子继续。
解决此问题的简单方法是为数据集添加注释。实际上，任何带有停止/启动标记的东西都可以使用，但是您还应该注释说话者的身份。我会做这样的事情：
A: How are you <EOL>
B: Hi Great and you <EOL>
A: Am also good <EOL>
B: So you re a graphic designer <EOL>
B: Another line from B <EOL>
此方法的另一个好处是GPT-2将学习多行输入/输出以及两个对话者的不同身份。