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自然语言处理NLP星空智能对话机器人系列：深入理解Transformer自然语言处理 Text completion with GPT-2 step 6-8
Steps 6-7: Intermediate instructions
本文将介绍步骤6、7和7a，它们是进入第8步的中间步骤，我们将定义并激活模型。在模型与控制台交互时将UTF编码的文本打印到控制台，定义UTF-8编码格式：
!export PYTHONIOENCODING=UTF-8
接下来准备与GPT-2模型进行交互
import os
os.chdir("/Chapter06/gpt-2-master/src")
interactive_conditional_samples.py首先导入所需的必要模块
import json
import os
import numpy as np
import tensorflow as tf
Steps 7b-8: Importing and defining the model
使用interactive_conditional_samples.py与模型交互。
导入三个模块
import model, sample, encoder
其中：
- model.py 定义了模型的结构：超参数、多注意力tf.matmul操作、激活函数和所有其他的属性。
- sample.py 处理交互作用，并控制样本生成，确保标记更有意义。sample.py可以进行Top-k采样及为语言建模激活Top-p采样。
- encoder.py 使用定义的模型编码器对样本序列进行编码。encoder.json及 vocab.bpe.包含一个BPE编码器和一个文本解码器
def interact_model(
model_name,
seed,
nsamples,
batch_size,
length,
temperature,
top_k,
models_dir
):
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
)
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)
import numpy as np
import tensorflow as tf
from tensorflow.contrib.training import HParams
def default_hparams():
return HParams(
n_vocab=0,
n_ctx=1024,
n_embd=768,
n_head=12,
n_layer=12,
)
def shape_list(x):
"""Deal with dynamic shape in tensorflow cleanly."""
static = x.shape.as_list()
dynamic = tf.shape(x)
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def softmax(x, axis=-1):
x = x - tf.reduce_max(x, axis=axis, keepdims=True)
ex = tf.exp(x)
return ex / tf.reduce_sum(ex, axis=axis, keepdims=True)
def gelu(x):
return 0.5*x*(1+tf.tanh(np.sqrt(2/np.pi)*(x+0.044715*tf.pow(x, 3))))
def norm(x, scope, *, axis=-1, epsilon=1e-5):
"""Normalize to mean = 0, std = 1, then do a diagonal affine transform."""
with tf.variable_scope(scope):
n_state = x.shape[-1].value
g = tf.get_variable('g', [n_state], initializer=tf.constant_initializer(1))
b = tf.get_variable('b', [n_state], initializer=tf.constant_initializer(0))
u = tf.reduce_mean(x, axis=axis, keepdims=True)
s = tf.reduce_mean(tf.square(x-u), axis=axis, keepdims=True)
x = (x - u) * tf.rsqrt(s + epsilon)
x = x*g + b
return x
def split_states(x, n):
"""Reshape the last dimension of x into [n, x.shape[-1]/n]."""
*start, m = shape_list(x)
return tf.reshape(x, start + [n, m
def merge_states(x):
"""Smash the last two dimensions of x into a single dimension."""
*start, a, b = shape_list(x)
return tf.reshape(x, start + [a*b])
def conv1d(x, scope, nf, *, w_init_stdev=0.02):
with tf.variable_scope(scope):
*start, nx = shape_list(x)
w = tf.get_variable('w', [1, nx, nf], initializer=tf.random_normal_initializer(stddev=w_init_stdev))
b = tf.get_variable('b', [nf], initializer=tf.constant_initializer(0))
c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, start+[nf])
return c
def attention_mask(nd, ns, *, dtype):
"""1's in the lower triangle, counting from the lower right corner.
Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs.
"""
i = tf.range(nd)[:,None]
j = tf.range(ns)
m = i >= j - ns + nd
return tf.cast(m, dtype)
def attn(x, scope, n_state, *, past, hparams):
assert x.shape.ndims == 3 # Should be [batch, sequence, features]
assert n_state % hparams.n_head == 0
if past is not None:
assert past.shape.ndims == 5 # Should be [batch, 2, heads, sequence, features], where 2 is [k, v]
def split_heads(x):
# From [batch, sequence, features] to [batch, heads, sequence, features]
return tf.transpose(split_states(x, hparams.n_head), [0, 2, 1, 3])
def merge_heads(x):
# Reverse of split_heads
return merge_states(tf.transpose(x, [0, 2, 1, 3]))
def mask_attn_weights(w):
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
_, _, nd, ns = shape_list(w)
b = attention_mask(nd, ns, dtype=w.dtype)
b = tf.reshape(b, [1, 1, nd, ns])
w = w*b - tf.cast(1e10, w.dtype)*(1-b)
return w
def multihead_attn(q, k, v):
# q, k, v have shape [batch, heads, sequence, features]
w = tf.matmul(q, k, transpose_b=True)
w = w * tf.rsqrt(tf.cast(v.shape[-1].value, w.dtype))
w = mask_attn_weights(w)
w = softmax(w)
a = tf.matmul(w, v)
return a
with tf.variable_scope(scope):
c = conv1d(x, 'c_attn', n_state*3)
q, k, v = map(split_heads, tf.split(c, 3, axis=2))
present = tf.stack([k, v], axis=1)
if past is not None:
pk, pv = tf.unstack(past, axis=1)
k = tf.concat([pk, k], axis=-2)
v = tf.concat([pv, v], axis=-2)
a = multihead_attn(q, k, v)
a = merge_heads(a)
a = conv1d(a, 'c_proj', n_state)
return a, present
def mlp(x, scope, n_state, *, hparams):
with tf.variable_scope(scope):
nx = x.shape[-1].value
h = gelu(conv1d(x, 'c_fc', n_state))
h2 = conv1d(h, 'c_proj', nx)
return h2
def block(x, scope, *, past, hparams):
with tf.variable_scope(scope):
nx = x.shape[-1].value
a, present = attn(norm(x, 'ln_1'), 'attn', nx, past=past, hparams=hparams)
x = x + a
m = mlp(norm(x, 'ln_2'), 'mlp', nx*4, hparams=hparams)
x = x + m
return x, present
def past_shape(*, hparams, batch_size=None, sequence=None):
return [batch_size, hparams.n_layer, 2, hparams.n_head, sequence, hparams.n_embd
def expand_tile(value, size):
"""Add a new axis of given size."""
value = tf.convert_to_tensor(value, name='value')
ndims = value.shape.ndims
return tf.tile(tf.expand_dims(value, axis=0), [size] + [1]*ndims)
def positions_for(tokens, past_length):
batch_size = tf.shape(tokens)[0]
nsteps = tf.shape(tokens)[1]
return expand_tile(past_length + tf.range(nsteps), batch_size)
def model(hparams, X, past=None, scope='model', reuse=False):
with tf.variable_scope(scope, reuse=reuse):
results = {}
batch, sequence = shape_list(X)
wpe = tf.get_variable('wpe', [hparams.n_ctx, hparams.n_embd],
initializer=tf.random_normal_initializer(stddev=0.01))
wte = tf.get_variable('wte', [hparams.n_vocab, hparams.n_embd],
initializer=tf.random_normal_initializer(stddev=0.02))
past_length = 0 if past is None else tf.shape(past)[-2]
h = tf.gather(wte, X) + tf.gather(wpe, positions_for(X, past_length))
# Transformer
presents = []
pasts = tf.unstack(past, axis=1) if past is not None else [None] * hparams.n_layer
assert len(pasts) == hparams.n_layer
for layer, past in enumerate(pasts):
h, present = block(h, 'h%d' % layer, past=past, hparams=hparams)
presents.append(present)
results['present'] = tf.stack(presents, axis=1)
h = norm(h, 'ln_f')
# Language model loss. Do tokens <n predict token n?
h_flat = tf.reshape(h, [batch*sequence, hparams.n_embd])
logits = tf.matmul(h_flat, wte, transpose_b=True)
logits = tf.reshape(logits, [batch, sequence, hparams.n_vocab])
results['logits'] = logits
return results
import tensorflow as tf
import model
def top_k_logits(logits, k):
if k == 0:
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):
"""Nucleus sampling"""
batch, _ = logits.shape.as_list()
sorted_logits = tf.sort(logits, direction='DESCENDING', axis=-1)
cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1)
indices = tf.stack([
tf.range(0, batch),
tf.maximum(tf.reduce_sum(tf.cast(cumulative_probs <= p, tf.int32), axis=-1) - 1, 0),
], axis=-1)
min_values = tf.gather_nd(sorted_logits, indices)
return tf.where(
logits < min_values,
tf.ones_like(logits) * -1e10,
logits,
)
def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=1):
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'):
def body(past, prev, output):
next_outputs = step(hparams, prev, past=past)
logits = next_outputs['logits'][:, -1, :] / tf.to_float(temperature)
logits = top_k_logits(logits, k=top_k)
logits = top_p_logits(logits, p=top_p)
samples = tf.multinomial(logits, num_samples=1, output_dtype=tf.int32)
return [
next_outputs['presents'] if past is None else tf.concat([past, next_outputs['presents']], axis=-2),
samples,
tf.concat([output, samples], axis=1)
]
past, prev, output = body(None, context, context)
def cond(*args):
return True
_, _, tokens = tf.while_loop(
cond=cond, body=body,
maximum_iterations=length - 1,
loop_vars=[
past,
prev,
output
],
shape_invariants=[
tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)),
tf.TensorShape([batch_size, None]),
tf.TensorShape([batch_size, None]),
],
back_prop=False,
)
return tokens
"""Byte pair encoding utilities"""
import os
import json
import regex as re
from functools import lru_cache
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("?"), ord("?")+1))+list(range(ord("?"), ord("?")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class Encoder:
def __init__(self, encoder, bpe_merges, errors='replace'):
self.encoder = encoder
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def encode(self, text):
bpe_tokens = []
for token in re.findall(self.pat, text):
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def decode(self, tokens):
text = ''.join([self.decoder[token] for token in tokens])
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def get_encoder(model_name, models_dir):
with open(os.path.join(models_dir, model_name, 'encoder.json'), 'r') as f:
encoder = json.load(f)
with open(os.path.join(models_dir, model_name, 'vocab.bpe'), 'r', encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
Copyright 2020, Denis Rothman MIT License. Denis Rothman created the Colab notebook using the OpenAI repository, adding title steps for educational purposes only.
It is important to note that we are running a low-level GPT-2 model
and not a one-line call to obtain a result. We are also
avoiding pre-packaged versions. We are getting our hands dirty to
understand the architecture of a GPT-2 from scratch. You might get
some deprecation messages. However, the effort is worthwhile.
星空智能对话机器人系列博客
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