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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| n_embd = 384 | |
| block_size = 256 | |
| dropout = 0.1 | |
| n_head = 8 | |
| n_layer = 6 | |
| # read text file | |
| with open('input.txt', 'r', encoding='utf-8') as f: | |
| text = f.read() | |
| # collect all the unique characters that occur in this text | |
| chars = sorted(list(set(text))) | |
| vocab_size = len(chars) | |
| cuda = torch.cuda.is_available() | |
| device = 'cuda' if cuda else 'cpu' | |
| 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) | |
| self.register_buffer('trill', torch.tril(torch.ones(block_size, block_size))) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| B,T,C = x.shape | |
| k = self.key(x) | |
| q = self.query(x) | |
| # compute attention scores ("affinities") | |
| wei = q @ k.transpose(-2, -1) * C**-0.5 | |
| wei = wei.masked_fill(self.trill[:T, :T] == 0, float('-inf')) | |
| wei = F.softmax(wei, dim=-1) | |
| wei = self.dropout(wei) | |
| # perform the weighted aggregation of the values | |
| v = self.value(x) | |
| out = wei @ v | |
| return out | |
| 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(n_embd, 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 | |
| class FeedForward(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) | |
| class Block(nn.Module): | |
| """ Transformer block: communication followed by computation""" | |
| def __init__(self, n_embd, n_head) -> None: | |
| super().__init__() | |
| head_size = n_embd // n_head | |
| self.sa = MultiHeadAttention(n_head, head_size) | |
| self.ffwd = FeedForward(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) | |
| class BigramLanguageModel(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.sa_head = MultiHeadAttention(4, n_embd//4) | |
| # self.ffwd = FeedForward(n_embd) | |
| # self.blocks = nn.Sequential( | |
| # Block(n_embd, n_head=4), | |
| # Block(n_embd, n_head=4), | |
| # Block(n_embd, n_head=4), | |
| # nn.LayerNorm(n_embd) | |
| # ) | |
| self.blocks = nn.Sequential(*[Block(n_embd, n_head) for _ in range (n_layer)]) | |
| self.ln_f = nn.LayerNorm(n_embd) | |
| self.lm_head = nn.Linear(n_embd, vocab_size) | |
| 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)) | |
| x = tok_emb + pos_emb | |
| # x = self.sa_head(x) | |
| # x = self.ffwd(x) | |
| x = self.blocks(x) | |
| x = self.ln_f(x) | |
| logits = self.lm_head(x) | |
| 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): | |
| 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 | |