import torch import torch.nn as nn from torch.nn import functional as F #hyperparameters batch_size = 64 block_size = 256 max_iters = 5000 eval_interval = 1 learning_rate = 3e-4 device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu") #metal m1 mac # usually you use: # 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() chars = sorted(list(set(text))) vocab_size = len(chars) 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] # defines function taking in string, outputs list of ints decode = lambda l: ''.join([itos[i] for i in l]) # input: list of integers, outputs string # splitting training and validation data = torch.tensor(encode(text), dtype=torch.long) n = int(0.9*len(data)) train_data = data[:n] val_data = data[n:] # data loading def get_batch(split): 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 # the below context marker is important so torch does not # load the gradients into memory @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 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('tril',torch.tril(torch.ones(block_size, block_size))) self.dropout = nn.Dropout(dropout) def forward(self, x): # input: (batch, time-step, channels) # output: (batch, time-step, head size) B, T, C = x.shape k = self.key(x) q = self.query(x) # affinities wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) wei = F.softmax(wei, dim=-1) wei = self.dropout(wei) # weighted aggregation: 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(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 class FeedForward(nn.Module): """ a simple linear layer followed by 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 and computation """ def __init__(self, n_embd, n_head): # n_embd = embedding dimension, # n_head = number of heads we want 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 GPTLanguageModel(nn.Module): def __init__(self): super().__init__() 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) self.lm_head = nn.Linear(n_embd, vocab_size) # better init, andrej followup self.apply(self._init_weights) def _init_weights(self, module): 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) 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 tok_emb = self.token_embedding_table(idx) pos_emb = self.position_embedding_table(torch.arange(T, device=device)) x = tok_emb + pos_emb 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): for _ in range(max_new_tokens): idx_cond = idx[:, -block_size:] logits, loss = self(idx_cond) logits = logits[:,-1,:] probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) return idx model = GPTLanguageModel() m = model.to(device) # number of parameters in the model: print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters') optimiser = torch.optim.AdamW(model.parameters(), lr=learning_rate) for iter in range(max_iters): # periodically evaluate 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 xb, yb = get_batch('train') # evaluate loss logits, loss = model(xb, yb) optimiser.zero_grad(set_to_none=True) loss.backward() optimiser.step() context = torch.zeros((1,1),dtype=torch.long, device=device) print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))