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使用 KerasHub 的 GPT2 文本生成

作者： Chen Qian
创建日期 2023/04/17
最后修改日期 2024/04/12
描述： 使用 KerasHub GPT2 模型和 samplers 进行文本生成。

ⓘ 此示例使用 Keras 3

在 Colab 中查看 • GitHub 源代码

在本教程中，您将学习如何使用 KerasHub 加载预训练的大型语言模型 (LLM) - GPT-2 模型（最初由 OpenAI 发明），将其微调为特定的文本风格，并根据用户的输入（也称为提示）生成文本。您还将学习 GPT2 如何快速适应非英语语言，例如中文。

开始之前

Colab 提供不同类型的运行时。请确保转到 运行时 -> 更改运行时类型 并选择 GPU 硬件加速器运行时（应具有 >12G 主机 RAM 和 ~15G GPU RAM），因为您将微调 GPT-2 模型。在 CPU 运行时上运行本教程将需要数小时。

安装 KerasHub、选择后端并导入依赖项

此示例使用 Keras 3 在任何 "tensorflow"、"jax" 或 "torch" 中工作。对 Keras 3 的支持已内置于 KerasHub 中，只需更改 "KERAS_BACKEND" 环境变量即可选择您选择的后端。我们在下面选择 JAX 后端。

!pip install git+https://github.com/keras-team/keras-hub.git -q

import os

os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"

import keras_hub
import keras
import tensorflow as tf
import time

keras.mixed_precision.set_global_policy("mixed_float16")

生成式大型语言模型 (LLM) 简介

大型语言模型 (LLM) 是一种机器学习模型，它在大型文本数据语料库上进行训练，以生成各种自然语言处理 (NLP) 任务的输出，例如文本生成、问题解答和机器翻译。

生成式 LLM 通常基于深度学习神经网络，例如 Google 研究人员在 2017 年发明的 Transformer 架构，并在大量文本数据上进行训练，通常涉及数十亿个单词。这些模型，例如 Google LaMDA 和 PaLM，使用来自各种数据源的大型数据集进行训练，这使得它们能够为许多任务生成输出。生成式 LLM 的核心是预测句子中的下一个单词，通常称为因果 LM 预训练。通过这种方式，LLM 可以根据用户提示生成连贯的文本。有关语言模型的更多教学讨论，您可以参考 斯坦福 CS324 LLM 课程。

KerasHub 简介

大型语言模型的构建复杂且从头开始训练成本高昂。幸运的是，现在可以使用预训练的 LLM。 KerasHub 提供了大量的预训练检查点，允许您在无需自行训练的情况下尝试 SOTA 模型。

KerasHub 是一个自然语言处理库，可在用户的整个开发周期中为用户提供支持。 KerasHub 提供预训练模型和模块化构建块，因此开发人员可以轻松地重用预训练模型或堆叠自己的 LLM。

简而言之，对于生成式 LLM，KerasHub 提供

加载预训练的 GPT-2 模型并生成一些文本

KerasHub 提供了许多预训练模型，例如 Google Bert 和 GPT-2。您可以在 KerasHub 存储库中查看可用模型的列表。

加载 GPT-2 模型非常容易，如下所示

# To speed up training and generation, we use preprocessor of length 128
# instead of full length 1024.
preprocessor = keras_hub.models.GPT2CausalLMPreprocessor.from_preset(
    "gpt2_base_en",
    sequence_length=128,
)
gpt2_lm = keras_hub.models.GPT2CausalLM.from_preset(
    "gpt2_base_en", preprocessor=preprocessor
)

加载模型后，您可以立即使用它来生成一些文本。运行以下单元格以试一试。它就像调用单个函数 generate() 一样简单

start = time.time()

output = gpt2_lm.generate("My trip to Yosemite was", max_length=200)
print("\nGPT-2 output:")
print(output)

end = time.time()
print(f"TOTAL TIME ELAPSED: {end - start:.2f}s")

GPT-2 output:
My trip to Yosemite was pretty awesome. The first time I went I didn't know how to go and it was pretty hard to get around. It was a bit like going on an adventure with a friend. The only things I could do were hike and climb the mountain. It's really cool to know you're not alone in this world. It's a lot of fun. I'm a little worried that I might not get to the top of the mountain in time to see the sunrise and sunset of the day. I think the weather is going to get a little warmer in the coming years.

This post is a little more in-depth on how to go on the trail. It covers how to hike on the Sierra Nevada, how to hike with the Sierra Nevada, how to hike in the Sierra Nevada, how to get to the top of the mountain, and how to get to the top with your own gear.

The Sierra Nevada is a very popular trail in Yosemite
TOTAL TIME ELAPSED: 25.36s

再试一个

start = time.time()

output = gpt2_lm.generate("That Italian restaurant is", max_length=200)
print("\nGPT-2 output:")
print(output)

end = time.time()
print(f"TOTAL TIME ELAPSED: {end - start:.2f}s")

GPT-2 output:
That Italian restaurant is known for its delicious food, and the best part is that it has a full bar, with seating for a whole host of guests. And that's only because it's located at the heart of the neighborhood.

The menu at the Italian restaurant is pretty straightforward:

The menu consists of three main dishes:

Italian sausage

Bolognese

Sausage

Bolognese with cheese

Sauce with cream

Italian sausage with cheese

Bolognese with cheese

And the main menu consists of a few other things.

There are two tables: the one that serves a menu of sausage and bolognese with cheese (the one that serves the menu of sausage and bolognese with cheese) and the one that serves the menu of sausage and bolognese with cheese. The two tables are also open 24 hours a day, 7 days a week.

TOTAL TIME ELAPSED: 1.55s

请注意第二次调用速度快得多。这是因为计算图在第一次运行时已XLA 编译，并在后台的第二次运行时重用。

生成的文本质量看起来还可以，但是我们可以通过微调来改进它。

关于 KerasHub 的 GPT-2 模型的更多信息

接下来，我们将实际微调模型以更新其参数，但在执行此操作之前，让我们看一下可用于 GPT2 的全套工具。

可以在此处找到 GPT2 的代码。从概念上讲，GPT2CausalLM 可以分层分解为 KerasHub 中的几个模块，所有这些模块都具有加载预训练模型的 from_preset() 函数

在 Reddit 数据集上进行微调

现在您已经了解了 KerasHub 中的 GPT-2 模型，您可以更进一步地微调模型，使其生成具有特定风格、长或短、严格或随意的文本。在本教程中，我们将使用 reddit 数据集作为示例。

import tensorflow_datasets as tfds

reddit_ds = tfds.load("reddit_tifu", split="train", as_supervised=True)

让我们看一下来自 reddit TensorFlow 数据集的示例数据。 有两个特征

  • document：帖子的文本。
  • title：标题。
for document, title in reddit_ds:
    print(document.numpy())
    print(title.numpy())
    break

b"me and a friend decided to go to the beach last sunday. we loaded up and headed out. we were about half way there when i decided that i was not leaving till i had seafood. \n\nnow i'm not talking about red lobster. no friends i'm talking about a low country boil. i found the restaurant and got directions. i don't know if any of you have heard about the crab shack on tybee island but let me tell you it's worth it. \n\nwe arrived and was seated quickly. we decided to get a seafood sampler for two and split it. the waitress bought it out on separate platters for us. the amount of food was staggering. two types of crab, shrimp, mussels, crawfish, andouille sausage, red potatoes, and corn on the cob. i managed to finish it and some of my friends crawfish and mussels. it was a day to be a fat ass. we finished paid for our food and headed to the beach. \n\nfunny thing about seafood. it runs through me faster than a kenyan \n\nwe arrived and walked around a bit. it was about 45min since we arrived at the beach when i felt a rumble from the depths of my stomach. i ignored it i didn't want my stomach to ruin our fun. i pushed down the feeling and continued. about 15min later the feeling was back and stronger than before. again i ignored it and continued. 5min later it felt like a nuclear reactor had just exploded in my stomach. i started running. i yelled to my friend to hurry the fuck up. \n\nrunning in sand is extremely hard if you did not know this. we got in his car and i yelled at him to floor it. my stomach was screaming and if he didn't hurry i was gonna have this baby in his car and it wasn't gonna be pretty. after a few red lights and me screaming like a woman in labor we made it to the store. \n\ni practically tore his car door open and ran inside. i ran to the bathroom opened the door and barely got my pants down before the dam burst and a flood of shit poured from my ass. \n\ni finished up when i felt something wet on my ass. i rubbed it thinking it was back splash. no, mass was covered in the after math of me abusing the toilet. i grabbed all the paper towels i could and gave my self a whores bath right there. \n\ni sprayed the bathroom down with the air freshener and left. an elderly lady walked in quickly and closed the door. i was just about to walk away when i heard gag. instead of walking i ran. i got to the car and told him to get the hell out of there."
b'liking seafood'

在我们的例子中，我们在语言模型中执行下一个单词预测，因此我们只需要“document”特征。

train_ds = (
    reddit_ds.map(lambda document, _: document)
    .batch(32)
    .cache()
    .prefetch(tf.data.AUTOTUNE)
)

现在，您可以使用熟悉的 fit() 函数微调模型。请注意，由于 GPT2CausalLM 是 keras_hub.models.Task 实例，因此 preprocessor 将在 fit 方法中自动调用。

如果我们要将其训练到完全训练好的状态，此步骤会占用大量的 GPU 内存并花费很长时间。在这里，我们仅使用数据集的一部分用于演示目的。

train_ds = train_ds.take(500)
num_epochs = 1

# Linearly decaying learning rate.
learning_rate = keras.optimizers.schedules.PolynomialDecay(
    5e-5,
    decay_steps=train_ds.cardinality() * num_epochs,
    end_learning_rate=0.0,
)
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
gpt2_lm.compile(
    optimizer=keras.optimizers.Adam(learning_rate),
    loss=loss,
    weighted_metrics=["accuracy"],
)

gpt2_lm.fit(train_ds, epochs=num_epochs)

 500/500 ━━━━━━━━━━━━━━━━━━━━ 75s 120ms/step - accuracy: 0.3189 - loss: 3.3653

<keras.src.callbacks.history.History at 0x7f2af3fda410>

微调完成后，您可以再次使用相同的 generate() 函数生成文本。这一次，文本将更接近 Reddit 的写作风格，并且生成的长度将接近我们在训练集中预设的长度。

start = time.time()

output = gpt2_lm.generate("I like basketball", max_length=200)
print("\nGPT-2 output:")
print(output)

end = time.time()
print(f"TOTAL TIME ELAPSED: {end - start:.2f}s")

GPT-2 output:
I like basketball. it has the greatest shot of all time and the best shot of all time. i have to play a little bit more and get some practice time.

today i got the opportunity to play in a tournament in a city that is very close to my school so i was excited to see how it would go. i had just been playing with a few other guys, so i thought i would go and play a couple games with them. 

after a few games i was pretty confident and confident in myself. i had just gotten the opportunity and had to get some practice time. 

so i go to the
TOTAL TIME ELAPSED: 21.13s

进入采样方法

在 KerasHub 中，我们提供了几种采样方法，例如对比搜索、Top-K 和 Beam 采样。默认情况下，我们的 GPT2CausalLM 使用 Top-k 搜索，但是您可以选择自己的采样方法。

与优化器和激活函数非常相似，有两种方法可以指定您的自定义采样器

  • 使用字符串标识符，例如 “greedy”，您正在通过这种方式使用默认配置。
  • 传递一个 keras_hub.samplers.Sampler 实例，您可以通过这种方式使用自定义配置。
# Use a string identifier.
gpt2_lm.compile(sampler="top_k")
output = gpt2_lm.generate("I like basketball", max_length=200)
print("\nGPT-2 output:")
print(output)

# Use a `Sampler` instance. `GreedySampler` tends to repeat itself,
greedy_sampler = keras_hub.samplers.GreedySampler()
gpt2_lm.compile(sampler=greedy_sampler)

output = gpt2_lm.generate("I like basketball", max_length=200)
print("\nGPT-2 output:")
print(output)

GPT-2 output:
I like basketball, and this is a pretty good one. 

first off, my wife is pretty good, she is a very good basketball player and she is really, really good at playing basketball. 

she has an amazing game called basketball, it is a pretty fun game. 

i play it on the couch.  i'm sitting there, watching the game on the couch.  my wife is playing with her phone.  she's playing on the phone with a bunch of people. 

my wife is sitting there and watching basketball.  she's sitting there watching

GPT-2 output:
I like basketball, but i don't like to play it. 

so i was playing basketball at my local high school, and i was playing with my friends. 

i was playing with my friends, and i was playing with my brother, who was playing basketball with his brother. 

so i was playing with my brother, and he was playing with his brother's brother. 

so i was playing with my brother, and he was playing with his brother's brother. 

so i was playing with my brother, and he was playing with his brother's brother. 

so i was playing with my brother, and he was playing with his brother's brother. 

so i was playing with my brother, and he was playing with his brother's brother. 

so i was playing with my brother, and he was playing with his brother

有关 KerasHub Sampler 类的更多详细信息，您可以查看此处的代码。

在中文诗歌数据集上进行微调

我们还可以在非英语数据集上微调 GPT2。对于懂中文的读者，这部分说明了如何在中文诗歌数据集上微调 GPT2，以教我们的模型成为诗人！

因为GPT2使用了字节对编码器，并且原始的预训练数据集包含一些汉字，我们可以使用原始词汇表在中文数据集上进行微调。

!# Load chinese poetry dataset.
!git clone https://github.com/chinese-poetry/chinese-poetry.git

Cloning into 'chinese-poetry'...

从json文件中加载文本。为了演示目的，我们只使用《全唐诗》。

import os
import json

poem_collection = []
for file in os.listdir("chinese-poetry/全唐诗"):
    if ".json" not in file or "poet" not in file:
        continue
    full_filename = "%s/%s" % ("chinese-poetry/全唐诗", file)
    with open(full_filename, "r") as f:
        content = json.load(f)
        poem_collection.extend(content)

paragraphs = ["".join(data["paragraphs"]) for data in poem_collection]

让我们看看示例数据。

print(paragraphs[0])

毋謂支山險，此山能幾何。崎嶔十年夢，知歷幾蹉跎。

与Reddit示例类似，我们将其转换为TF数据集，并且仅使用部分数据进行训练。

train_ds = (
    tf.data.Dataset.from_tensor_slices(paragraphs)
    .batch(16)
    .cache()
    .prefetch(tf.data.AUTOTUNE)
)

# Running through the whole dataset takes long, only take `500` and run 1
# epochs for demo purposes.
train_ds = train_ds.take(500)
num_epochs = 1

learning_rate = keras.optimizers.schedules.PolynomialDecay(
    5e-4,
    decay_steps=train_ds.cardinality() * num_epochs,
    end_learning_rate=0.0,
)
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
gpt2_lm.compile(
    optimizer=keras.optimizers.Adam(learning_rate),
    loss=loss,
    weighted_metrics=["accuracy"],
)

gpt2_lm.fit(train_ds, epochs=num_epochs)

 500/500 ━━━━━━━━━━━━━━━━━━━━ 49s 71ms/step - accuracy: 0.2357 - loss: 2.8196

<keras.src.callbacks.history.History at 0x7f2b2c192bc0>

让我们检查一下结果！

output = gpt2_lm.generate("昨夜雨疏风骤", max_length=200)
print(output)

昨夜雨疏风骤，爲臨江山院短靜。石淡山陵長爲羣，臨石山非處臨羣。美陪河埃聲爲羣，漏漏漏邊陵塘

还不错 😀

使用 KerasHub 的 GPT2 文本生成
◆ 开始之前
◆ 安装 KerasHub，选择后端并导入依赖项
◆ 生成式大型语言模型 (LLMs) 简介
◆ KerasHub 简介
◆ 加载预训练的 GPT-2 模型并生成一些文本
◆ 关于 KerasHub 中 GPT-2 模型的更多信息
◆ 在 Reddit 数据集上进行微调
◆ 深入了解采样方法
◆ 在中国诗歌数据集上进行微调