From d60cf48c966030e6abe764b61d2a80d3bdd64156 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=98=8E=E7=A1=95?= <91956930+cfcys@users.noreply.github.com> Date: Wed, 8 May 2024 23:29:55 +0800 Subject: [PATCH 1/6] 5.08 Updata --- Lecture/l3.ipynb | 3 +- Lecture/l5.ipynb | 183 +++++++++++++++++++++++++++++++++--- Lecture/l6.py | 237 +++++++++++++++++++++++++++++++++++++++++++++++ README.md | 13 ++- README_EN.md | 2 +- 5 files changed, 421 insertions(+), 17 deletions(-) create mode 100644 Lecture/l6.py diff --git a/Lecture/l3.ipynb b/Lecture/l3.ipynb index 96aa322..74fff31 100644 --- a/Lecture/l3.ipynb +++ b/Lecture/l3.ipynb @@ -355,7 +355,8 @@ "# version 4: 自注意力的实现\n", "torch.manual_seed(1337)\n", "B,T,C = 4,8,32 # batch, time, channels\n", - "x = torch.randn(B,T,C)" + "x = torch.randn(B,T,C)\n", + "\n" ] }, { diff --git a/Lecture/l5.ipynb b/Lecture/l5.ipynb index ee966f1..a55c3ef 100644 --- a/Lecture/l5.ipynb +++ b/Lecture/l5.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -45,20 +45,13 @@ "metadata": {}, "outputs": [], "source": [ - "# version 4: 自注意力的实现\n", + "# Lecture3中我们提到的最后一种: 自注意力的实现方式\n", "torch.manual_seed(1337)\n", "B,T,C = 4,8,32 # batch, time, channels\n", "x = torch.randn(B,T,C)\n", "\n", - "\n", - "# 一个简单的自注意力头的实现\n", - "head_size = 16 # 指定头的大小\n", - "key = nn.Linear(C,head_size,bias = False)\n", - "query = nn.\n", - "\n", - "\n", "trils = torch.tril(torch.ones(T,T))\n", - "weight = torch.zeros((T,T)) # 构造一个全为0的向量\n", + "weight = torch.zeros(T,T)\n", "weight = weight.masked_fill(trils == 0,float('-inf')) # 使所有tril为0的位置都变为无穷大\n", "# 然后，我们选择在每行的维度上去使用sotfmax，\n", "weight = F.softmax(weight,dim=-1)\n", @@ -67,6 +60,174 @@ "\n", "out.shape" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Query & Key\n", + "torch.manual_seed(1337)\n", + "B,T,C = 4,8,32 # batch, time, channels\n", + "x = torch.randn(B,T,C)\n", + "\n", + "\n", + "# 一个简单的自注意力头的实现\n", + "head_size = 16 # 指定头的大小\n", + "\n", + "# 实例化线性层\n", + "key = nn.Linear(C,head_size,bias = False)\n", + "query = nn.Linear(C,head_size = False)\n", + "\n", + "# \n", + "k = key(x) # (B,T,C) ---> (B,T,16)\n", + "q = query(x) # (B,T,C) ---> (B,T,16)\n", + "\n", + "weight = q @ k.transpose() # 将query与key进行点乘 (B,T,16) @ (B,16,T) ---> (B,T,T),得到我们想要的权重\n", + "\n", + "trils = torch.tril(torch.ones(T,T))\n", + "weight = weight.masked_fill(trils == 0,float('-inf')) # 使所有tril为0的位置都变为无穷大\n", + "# 然后，我们选择在每行的维度上去使用sotfmax，\n", + "weight = F.softmax(weight,dim=-1)\n", + "\n", + "out = weight @ x\n", + "\n", + "out.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Query & Key\n", + "torch.manual_seed(1337)\n", + "B,T,C = 4,8,32 # batch, time, channels\n", + "x = torch.randn(B,T,C)\n", + "\n", + "\n", + "# 一个简单的自注意力头的实现\n", + "head_size = 16 # 指定头的大小\n", + "\n", + "# 实例化线性层\n", + "key = nn.Linear(C,head_size,bias = False)\n", + "query = nn.Linear(C,head_size = False)\n", + "\n", + "# \n", + "k = key(x) # (B,T,C) ---> (B,T,16)\n", + "q = query(x) # (B,T,C) ---> (B,T,16)\n", + "\n", + "weight = q @ k.transpose() # 将query与key进行点乘 (B,T,16) @ (B,16,T) ---> (B,T,T),得到我们想要的权重\n", + "\n", + "trils = torch.tril(torch.ones(T,T))\n", + "weight = weight.masked_fill(trils == 0,float('-inf')) # 使所有tril为0的位置都变为无穷大\n", + "# 然后，我们选择在每行的维度上去使用sotfmax，\n", + "weight = F.softmax(weight,dim=-1)\n", + "\n", + "out = weight @ x\n", + "\n", + "out.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 8, 16])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query & Key & Value\n", + "torch.manual_seed(1337)\n", + "B,T,C = 4,8,32 # batch, time, channels\n", + "x = torch.randn(B,T,C)\n", + "\n", + "\n", + "# 一个简单的自注意力头的实现\n", + "head_size = 16 # 每个自注意力头的大小\n", + "\n", + "# 实例化线性层\n", + "key = nn.Linear(C,head_size,bias = False)\n", + "query = nn.Linear(C,head_size,bias = False)\n", + "value = nn.Linear(C,head_size,bias = False)\n", + "\n", + "# \n", + "k = key(x) # (B,T,C) ---> (B,T,16)\n", + "q = query(x) # (B,T,C) ---> (B,T,16)\n", + "\n", + "weight = q @ k.transpose(-2,-1) # 将query与key进行点乘 (B,T,16) @ (B,16,T) ---> (B,T,T),得到我们想要的权重\n", + "\n", + "# 根据原版的公式，我们还要做除以headsize的开方\n", + "weight = weight * head_size ** 0.5\n", + "\n", + "trils = torch.tril(torch.ones(T,T))\n", + "weight = weight.masked_fill(trils == 0,float('-inf')) # 使所有tril为0的位置都变为无穷大\n", + "# 然后，我们选择在每行的维度上去使用sotfmax，\n", + "weight = F.softmax(weight,dim=-1)\n", + "\n", + "\n", + "# 我们让x也经过一个线性层进行分头 ，对于这里的value 我们可以理解为将x进行剥皮，去发现其本质是什么东西，从而更好的来利用q和k\n", + "x = value(x)\n", + "out = weight @ x\n", + "\n", + "\n", + "out.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.1574, 0.8426, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.2088, 0.1646, 0.6266, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.5792, 0.1187, 0.1889, 0.1131, 0.0000, 0.0000, 0.0000, 0.0000],\n", + " [0.0294, 0.1052, 0.0469, 0.0276, 0.7909, 0.0000, 0.0000, 0.0000],\n", + " [0.0176, 0.2689, 0.0215, 0.0089, 0.6812, 0.0019, 0.0000, 0.0000],\n", + " [0.1691, 0.4066, 0.0438, 0.0416, 0.1048, 0.2012, 0.0329, 0.0000],\n", + " [0.0210, 0.0843, 0.0555, 0.2297, 0.0573, 0.0709, 0.2423, 0.2391]],\n", + " grad_fn=)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 此时，我们可以看到之前每个编码的权重变得不再一样了\n", + "weight[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. 注意力本身是一种通信机制，可以将其视为在一个有向的图中，每个节点都会有指向其他节点的边，同时边的权重还是不同的。\n", + "2. 注意力其实并没有空间的概念，可以将数字的先后想象成一个高维度的向量，向量此时如果进行顺序的变换其实是不会影响结果的，" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -85,7 +246,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/Lecture/l6.py b/Lecture/l6.py new file mode 100644 index 0000000..ee657f7 --- /dev/null +++ b/Lecture/l6.py @@ -0,0 +1,237 @@ +# 这是GPT的完整代码 + +import torch +import torch.nn as nn +from torch.nn import functional as F + +# hyperparameters +batch_size = 64 # 并行处理的长度 +block_size = 256 # 预测的长度大小 +max_iters = 5000 # 最大的迭代次数 +eval_interval = 500 +learning_rate = 3e-4 +device = 'cuda' if torch.cuda.is_available() else 'cpu' +eval_iters = 200 +n_embd = 384 +n_head = 6 +n_layer = 6 +dropout = 0.2 +# ------------ + +torch.manual_seed(1337) + +with open('./input.txt', 'r', encoding='utf-8') as f: + text = f.read() + +# here are all the unique characters that occur in this text +chars = sorted(list(set(text))) +vocab_size = len(chars) +# create a mapping from characters to integers +stoi = { ch:i for i,ch in enumerate(chars) } +itos = { i:ch for i,ch in enumerate(chars) } +encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers +decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string + +# Train and test splits +data = torch.tensor(encode(text), dtype=torch.long) +n = int(0.9*len(data)) # first 90% will be train, rest val +train_data = data[:n] +val_data = data[n:] + +# data loading +def get_batch(split): + # generate a small batch of data of inputs x and targets y + data = train_data if split == 'train' else val_data + ix = torch.randint(len(data) - block_size, (batch_size,)) + x = torch.stack([data[i:i+block_size] for i in ix]) + y = torch.stack([data[i+1:i+block_size+1] for i in ix]) + x, y = x.to(device), y.to(device) + return x, y + +@torch.no_grad() +def estimate_loss(): + out = {} + model.eval() + for split in ['train', 'val']: + losses = torch.zeros(eval_iters) + for k in range(eval_iters): + X, Y = get_batch(split) + logits, loss = model(X, Y) + losses[k] = loss.item() + out[split] = losses.mean() + model.train() + return out + +# 1. 模型Head定义 +class Head(nn.Module): + """ one head of self-attention """ + + def __init__(self, head_size): + super().__init__() + self.key = nn.Linear(n_embd, head_size, bias=False) + self.query = nn.Linear(n_embd, head_size, bias=False) + self.value = nn.Linear(n_embd, head_size, bias=False) + # 这不是参数，而是缓存区，必须使用寄存器缓冲区将其分配给模块， + # 模型训练时不会更新（即调用 optimizer.step() 后该组参数不会变化，只可人为地改变它们的值） + # 但是保存模型时，该组参数又作为模型参数不可或缺的一部分被保存。 + self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size))) + + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + # input of size (batch, time-step, channels) + # output of size (batch, time-step, head size) + B,T,C = x.shape + k = self.key(x) # (B,T,hs) + q = self.query(x) # (B,T,hs) + # compute attention scores ("affinities") + wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T) + wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T) + wei = F.softmax(wei, dim=-1) # (B, T, T) + wei = self.dropout(wei) + # perform the weighted aggregation of the values + v = self.value(x) # (B,T,hs) + out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs) + return out + +# 2. 模型MultiHeadAttention定义 +class MultiHeadAttention(nn.Module): + """ multiple heads of self-attention in parallel """ + + def __init__(self, num_heads, head_size): + super().__init__() + self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) + self.proj = nn.Linear(head_size * num_heads, n_embd) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + out = torch.cat([h(x) for h in self.heads], dim=-1) + out = self.dropout(self.proj(out)) + return out + +# 3. 模型前向传播过程 +class FeedFoward(nn.Module): + """ a simple linear layer followed by a non-linearity """ + + def __init__(self, n_embd): + super().__init__() + self.net = nn.Sequential( + nn.Linear(n_embd, 4 * n_embd), + nn.ReLU(), + nn.Linear(4 * n_embd, n_embd), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + +# 4. TransformerBlock定义 +class Block(nn.Module): + """ Transformer block: communication followed by computation """ + + def __init__(self, n_embd, n_head): + # n_embd: embedding dimension, n_head: the number of heads we'd like + super().__init__() + head_size = n_embd // n_head + self.sa = MultiHeadAttention(n_head, head_size) + self.ffwd = FeedFoward(n_embd) + self.ln1 = nn.LayerNorm(n_embd) + self.ln2 = nn.LayerNorm(n_embd) + + def forward(self, x): + x = x + self.sa(self.ln1(x)) + x = x + self.ffwd(self.ln2(x)) + return x + +# 5. 模型GPT语言模型的定义 +class GPTLanguageModel(nn.Module): + + def __init__(self): + super().__init__() + # each token directly reads off the logits for the next token from a lookup table + self.token_embedding_table = nn.Embedding(vocab_size, n_embd) + self.position_embedding_table = nn.Embedding(block_size, n_embd) + self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) + self.ln_f = nn.LayerNorm(n_embd) # final layer norm + self.lm_head = nn.Linear(n_embd, vocab_size) + + # better init, not covered in the original GPT video, but important, will cover in followup video + self.apply(self._init_weights) + + # 初始化神经网络模块的权重 + def _init_weights(self, module): + # 如果 module 是 nn.Linear 类型（即全连接层），那么它的权重将被初始化为均值为 0，标准差为 0.02 的正态分布。如果全连接层有偏置项 (bias)，那么偏置项将被初始化为 0。 + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + # 如果 module 是 nn.Embedding 类型（即嵌入层），那么它的权重也将被初始化为均值为 0，标准差为 0.02 的正态分布。 + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx, targets=None): + B, T = idx.shape + + # idx and targets are both (B,T) tensor of integers + tok_emb = self.token_embedding_table(idx) # (B,T,C) + pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C) + x = tok_emb + pos_emb # (B,T,C) + x = self.blocks(x) # (B,T,C) + x = self.ln_f(x) # (B,T,C) + logits = self.lm_head(x) # (B,T,vocab_size) + + if targets is None: + loss = None + else: + B, T, C = logits.shape + logits = logits.view(B*T, C) + targets = targets.view(B*T) + loss = F.cross_entropy(logits, targets) + + return logits, loss + + def generate(self, idx, max_new_tokens): + # idx is (B, T) array of indices in the current context + for _ in range(max_new_tokens): + # crop idx to the last block_size tokens + idx_cond = idx[:, -block_size:] + # get the predictions + logits, loss = self(idx_cond) + # focus only on the last time step + logits = logits[:, -1, :] # becomes (B, C) + # apply softmax to get probabilities + probs = F.softmax(logits, dim=-1) # (B, C) + # sample from the distribution + idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) + # append sampled index to the running sequence + idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) + return idx + +model = GPTLanguageModel() +m = model.to(device) +# print the number of parameters in the model +print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters') + +# create a PyTorch optimizer +optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + +for iter in range(max_iters): + + # every once in a while evaluate the loss on train and val sets + if iter % eval_interval == 0 or iter == max_iters - 1: + losses = estimate_loss() + print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") + + # sample a batch of data + xb, yb = get_batch('train') + + # evaluate the loss + logits, loss = model(xb, yb) + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + +# generate from the model +context = torch.zeros((1, 1), dtype=torch.long, device=device) +print(decode(m.generate(context, max_new_tokens=500)[0].tolist())) +#open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist())) diff --git a/README.md b/README.md index e9a76d3..5d07ff1 100644 --- a/README.md +++ b/README.md @@ -12,10 +12,11 @@ **该仓库在持续更新中.**

- 📺 BiliBili + 📺 BiliBili 🌐:youtube

+

@@ -74,11 +75,15 @@ > Lecture1 : [教程初衷](Lecture/l1.ipynb) > -> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[视频在制作中 ] +> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[https://www.bilibili.com/video/BV1XJ4m1P7uj/ ] > -> Lecture3 : [数学推导与模型优化](Lecture/l3.ipynb) ，[视频在制作中 ] +> Lecture3 : [均值自注意力的几种方式数学推导](Lecture/l3.ipynb) ，[视频在制作中 ] > -> Lecture4 : [对话能力实现](Lecture/l4.ipynb) ，[视频在制作中 ] +> Lecture4 : [搭建自注意力的准备工作](Lecture/l4.ipynb) ，[视频在制作中 ] +> +> Lecture5 : [Q,K,V的引入以及多头自注意力的实现](Lecture/l5.ipynb) ，[视频在制作中 ] +> +> Lecture6 : [对话能力实现](Lecture/l4.ipynb) ，[视频在制作中 ] > > diff --git a/README_EN.md b/README_EN.md index 4d19601..43311ec 100644 --- a/README_EN.md +++ b/README_EN.md @@ -1 +1 @@ -No meaning~ +Not now~ From 55cdea778f85df71087418d75fe58c5245b3feef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=98=8E=E7=A1=95?= <91956930+cfcys@users.noreply.github.com> Date: Wed, 8 May 2024 23:49:03 +0800 Subject: [PATCH 2/6] 11 --- Lecture/l4.ipynb | 5 ----- README.md | 4 ++-- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/Lecture/l4.ipynb b/Lecture/l4.ipynb index 7331ba8..7f3569e 100644 --- a/Lecture/l4.ipynb +++ b/Lecture/l4.ipynb @@ -8,11 +8,6 @@ " - 在这里，做一些一些准备工作，从而实现后续搭建自注意力模块" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", "execution_count": 1, diff --git a/README.md b/README.md index 5d07ff1..bd55e9b 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ **该仓库在持续更新中.**

- 📺 BiliBili + 📺 BiliBili 🌐:youtube

@@ -75,7 +75,7 @@ > Lecture1 : [教程初衷](Lecture/l1.ipynb) > -> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[https://www.bilibili.com/video/BV1XJ4m1P7uj/ ] +> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[https://www.bilibili.com/video/BV1XJ4m1P7uj/?vd_source=32f9de072b771f1cd307ca15ecf84087] > > Lecture3 : [均值自注意力的几种方式数学推导](Lecture/l3.ipynb) ，[视频在制作中 ] > From b83b49e48926f7d66cbe691d2e940a39db8faa42 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=98=8E=E7=A1=95?= <91956930+cfcys@users.noreply.github.com> Date: Thu, 9 May 2024 08:42:49 +0800 Subject: [PATCH 3/6] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bd55e9b..4e7d863 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@

📺 BiliBili - 🌐:youtube + 🌐:youtube

@@ -75,7 +75,7 @@ > Lecture1 : [教程初衷](Lecture/l1.ipynb) > -> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[https://www.bilibili.com/video/BV1XJ4m1P7uj/?vd_source=32f9de072b771f1cd307ca15ecf84087] +> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[BiliBili](https://www.bilibili.com/video/BV1XJ4m1P7uj/?vd_source=32f9de072b771f1cd307ca15ecf84087) [Youtube][https://www.youtube.com/watch?v=sBqyBSb7K6U] > > Lecture3 : [均值自注意力的几种方式数学推导](Lecture/l3.ipynb) ，[视频在制作中 ] > From 0b634853cb477a7c74f4640b648b5726384fa19b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=98=8E=E7=A1=95?= <91956930+cfcys@users.noreply.github.com> Date: Fri, 10 May 2024 09:04:45 +0800 Subject: [PATCH 4/6] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4e7d863..b631bba 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,7 @@ > Lecture1 : [教程初衷](Lecture/l1.ipynb) > -> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[BiliBili](https://www.bilibili.com/video/BV1XJ4m1P7uj/?vd_source=32f9de072b771f1cd307ca15ecf84087) [Youtube][https://www.youtube.com/watch?v=sBqyBSb7K6U] +> Lecture2 : [基础GPT框架构造与初步效果](Lecture/l2.ipynb) ，[BiliBili](https://www.bilibili.com/video/BV1XJ4m1P7uj/?vd_source=32f9de072b771f1cd307ca15ecf84087) [Youtube][https://www.youtube.com/watch?v=sBqyBSb7K6U] > > Lecture3 : [均值自注意力的几种方式数学推导](Lecture/l3.ipynb) ，[视频在制作中 ] > From 8535a3282d757f77a2d657f68d4ec346313771bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=98=8E=E7=A1=95?= <91956930+cfcys@users.noreply.github.com> Date: Sat, 11 May 2024 15:08:40 +0800 Subject: [PATCH 5/6] Create LICENSE --- LICENSE | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..bc90ce2 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 明硕 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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