version4.py

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

#hyperparameters
batch_size = 64 # how many independent sequences will we process in parallel?
block_size = 256 # what is the maximum context length for predictions?
max_iters = 5000
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

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 of size(batch,time,channels)
        #output of size(batch,time,head_size)
        B,T,C = x.shape
        k = self.key(x)
        q = self.query(x)
        #compute attention scores
        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # to reduce variance
        wei = wei.masked_fill(self.tril[:T,:T]==0,float('-inf'))
        wei = F.softmax(wei,dim=-1)
        wei = self.dropout(wei)
        # perform weighted aggregation of 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(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),
            #originally nn.ReLU
            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 dimension,n_head: 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):
        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__()
        # each token directly reads off the logits for the next token in the 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)
        self.lm_head = nn.Linear(n_embd,vocab_size)

        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

        #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 #(B,T,C)
        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) of integers
        for _ in range(max_new_tokens):
            #crop idx to the last block_size tokens
            idx_cond = idx[:,-block_size:]
            logits,loss = self(idx_cond)
            #focus only on the last timestep
            logits = logits[:,-1,:]
            #apply softmax to get probabilities
            probs = F.softmax(logits,dim=-1)
            #sample from the distribution
            idx_next = torch.multinomial(probs,num_samples=1)
            #append sampled index to 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')   

optimizer = torch.optim.AdamW(model.parameters(),lr = learning_rate)

for iter in range(max_iters):

    #every once in a while evaluate tje 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(model.generate(context, max_new_tokens = 200)[0].tolist()))
open('lmao_Shakespeare.txt','w').write(decode(model.generate(context,max_new_tokens = 10000)[0].tolist()))