261 lines
8.3 KiB
Plaintext
261 lines
8.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"在上一节中，我们简单介绍了nanoGPT的基本方式，但是我们也能看出这个GPT过于简陋，其生成效果急需进一步的提高\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 首先是编码，嵌入方式的改进\n",
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"- 之前我们采用的是简单的一一对应的方式\n",
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" - 对于只有26个大写和26个小写的英文字符来说，这样似乎还算合理，因为只是把50多个或者60多个字符按照顺序去编码为对应的数字而已"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- 例如，OpenAI 在之前的GPT-2，GPT-3，GPT-4系列中使用的是其\n",
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"发布的 tiktoken 库，而 Google 也有其自己的分词/编码工具 SentencePiece，他们只是\n",
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"不同的方式，但做的都是将“完整的句子转化成整数向量编码”这样的一件事情。例\n",
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"如，我们现在可以利用 tiktoken 库来十分方便地调用 GPT-2 中所训练的 tokenizer，从\n",
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"而实现编解码过程"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[37046, 21043, 10890, 247, 162, 224, 253, 35894]\n",
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"我是孙悟空\n"
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]
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}
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],
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"source": [
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"# Way2\n",
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"import tiktoken\n",
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"enc = tiktoken.get_encoding(\"cl100k_base\")\n",
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"# enc = tiktoken.get_encoding(\"gpt2\")\n",
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"print(enc.encode(\"我是孙悟空\"))\n",
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"print(enc.decode(enc.encode(\"我是孙悟空\")))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"之前经过encoder之后的数量不会改变"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"489540"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 还是跟之前一样，我们先进行文本的读取\n",
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"with open('../data/Xiyou.txt', 'r', encoding='utf-8') as f:\n",
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" text = f.read()\n",
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"\n",
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"n = int(0.5*len(text)) # 前90%都是训练集，后10%都是测试集\n",
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"text = text[:n]\n",
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"\n",
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" # 对文本进行编码\n",
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"len(enc.encode(text)) # 获取编码之后的长度"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"接下来我们检查一下这样是否work、以及这样是否可以提升性能"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import torch.nn as nn\n",
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"\n",
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"\n",
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"# 开始划分训练集和测试集\n",
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"code = enc.encode(text)\n",
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"data = torch.tensor(code, dtype=torch.long) # Way 1 \n",
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"\n",
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"vocab_size = len(code)\n",
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"n = int(0.9*len(data)) # 前90%都是训练集，后10%都是测试集\n",
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"train_data = data[:n]\n",
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"val_data = data[n:]\n",
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"\n",
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"# 进行参数的设置\n",
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"device = 'cpu' # 模型运行的设备\n",
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"block_size = 16 # 每个单元的最大长度\n",
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"batch_size = 32 # 同时运行的批次大小\n",
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"learning_rate = 0.3\n",
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"max_iters = 1000\n",
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"eval_interval = 300 # 对当前模型的运行结果进行评估的epoch数量\n",
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"eval_iters = 200\n",
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"\n",
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"# 每次从数据集中获取x和y\n",
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"def get_batch(split):\n",
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" # generate a small batch of data of inputs x and targets y\n",
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" data = train_data if split == 'train' else val_data\n",
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" ix = torch.randint(len(data) - block_size, (batch_size,))\n",
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" x = torch.stack([data[i:i+block_size] for i in ix])\n",
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" y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n",
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" return x, y\n",
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"\n",
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"class BLM(nn.Module):\n",
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" def __init__(self,vocab_size):\n",
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" super().__init__()\n",
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" self.token_embedding_table = nn.Embedding(vocab_size,vocab_size)\n",
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" \n",
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" def forward(self,idx,targets = None):\n",
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" # 这里的self，就是我们之前的x，target就是之前的y\n",
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" logits = self.token_embedding_table(idx) # (B,T) -> (B,T,C) # 这里我们通过Embedding操作直接得到预测分数\n",
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" # 这里的预测分数过程与二分类或者多分类的分数是大致相同的\n",
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"\n",
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" \n",
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" if targets is None:\n",
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" loss = None\n",
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" else: \n",
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" B, T, C = logits.shape\n",
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" logits = logits.view(B*T, C)\n",
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" targets = targets.view(B*T) # 这里我们调整一下形状，以符合torch的交叉熵损失函数对于输入的变量的要求\n",
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" loss = F.cross_entropy(logits, targets)\n",
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"\n",
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" return logits, loss\n",
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"\n",
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" def generate(self, idx, max_new_tokens):\n",
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" '''\n",
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" idx 是现在的输入的(B, T) array of indices in the current context\n",
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" max_new_tokens 是产生的最大的tokens数量\n",
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" '''\n",
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"\n",
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" for _ in range(max_new_tokens):\n",
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" # 得到预测的结果\n",
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" logits, loss = self(idx)\n",
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" \n",
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" # 只关注最后一个的预测\n",
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" logits = logits[:, -1, :] # becomes (B, C)\n",
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" # 对概率值应用softmax\n",
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" probs = F.softmax(logits, dim=-1) # (B, C)\n",
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" # 对input的每一行做n_samples次取值，输出的张量是每一次取值时input张量对应行的下标，也即找到概率值输出最大的下标，也对应着最大的编码\n",
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" idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n",
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" # 将新产生的编码加入到之前的编码中，形成新的编码\n",
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" idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n",
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" return idx "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。\n",
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"\u001b[1;31m请查看单元格中的代码，以确定故障的可能原因。\n",
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"\u001b[1;31m单击<a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>了解详细信息。\n",
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"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
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]
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}
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],
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"source": [
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"# 创建模型\n",
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"model = BLM(vocab_size)\n",
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"m = model.to(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 简单的写下\n",
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"optimizer = torch.optim.AdamW(m.parameters(), lr=learning_rate)\n",
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"for steps in range(1000): # 随着迭代次数的增长，获得的效果会不断变好\n",
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"\n",
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" xb, yb = get_batch('train')\n",
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"\n",
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" logits, loss = m(xb, yb) # 用于评估损失\n",
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" optimizer.zero_grad(set_to_none=True)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" print(epoch)\n",
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" print(loss.item())\n",
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"\n",
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"print(loss.item())\n",
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"\n",
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"optimizer.step()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(enc.decode(m.generate(idx = torch.zeros((1, 1), dtype=torch.long), max_new_tokens=500)[0].tolist()))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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