large_bigram.py

import torch
import torch.nn as nn
from torch.nn import functional as F

""" MUST BE RUN ON GPU, IF NOT, DECREASE n_layer, n_embd, and other variables """

batch_size = 64 # how many independent sequences will we process in parallel?
block_size = 256 # what is the maxmimum context length for predictions
max_iters = 5000
eval_interval = 300
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 # 20% of all intermediate calculations are dropped to 0

torch.manual_seed(1)


with open('english_input_developed_by_AI.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# temp
print("Number of characters in text file: ", len(text))

chars = sorted(list(set(text)))
vocab_size = len(chars)

# temp
print(''.join(chars))
print(vocab_size)

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, outut a list of integers
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string

data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9*len(data)) # first 90% will train, rest validate
train_data = data[:n]
val_data = data[n:]

train_data[:block_size + 1] #temp?

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

@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):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        
        # compute attention scores
        wei = q @ k.transpose(-2, -1) * C**-0.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
        wei = F.softmax(wei, dim = -1)
        wei = self.dropout(wei)

        # perform the weighted aggregation of the values
        v = self.value(x)
        out = wei @ v # (B, T, T) @ (B, T, C) --> (B, T, C)
        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):
        # projection back into the residual pathway
        out = torch.cat([h(x) for h in self.heads], dim =- 1)
        out = 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):
        # n_embd: embedding dimnesion, 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 = FeedForward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
         # residual connections
        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.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)

    def forward(self, idx, targets = None):
        B, T = idx.shape

        #idx and targets are both (B, T) tensors 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
        x = self.blocks(x) 
        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 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 = BigramLanguageModel()
m = model.to(device)

# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr = learning_rate)

for iter in range(max_iters):
    if iter % eval_interval == 0:
        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()))

total_params = sum(p.numel() for p in model.parameters())
print(f'Total number of parameters: {total_params}')