self_attention.py

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

#hyperparameters
batch_size = 32 #how many sequences to run in parallel
block_size = 8 #the maximum context length of predictions
max_iters = 3000
eval_interval = 300
learning_rate = 1e-2
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters =100
n_embed = 32
print(device)
torch.manual_seed(1337)

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

#here are the unique characters in the text
chars = sorted(list(set(text)))
vocab_size = len(chars)
#making an encoder Decoder
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]
decode = lambda d: ''.join([itos[i] for i in d])

#encoding whole dataset
data = torch.tensor(encode(text),dtype = torch.long)

#making a train test split
n = int(0.9*len(data))
train_data = data[:n]
val_data = data[n:]
train_data[:block_size+1]

#data loading
def get_batch(split):
    #generate a small batch of inputs 
    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+1+block_size] 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().item()
    model.train()
    return out

# making a bigram language model
class BigramLanguageModel(nn.Module):

    def __init__(self):
        super().__init__()
        #each token directly reads the logits from the next token in the lookup table
        #table size is vocab_Size x vocab_Size
        self.token_embedding_table= nn.Embedding(vocab_size,n_embed)
        self.position_embedding_table = nn.Embedding(block_size,n_embed)   
        self.lm_head = nn.Linear(n_embed,vocab_size)

    def forward(self,idx,targets = None):
        B, T = idx.shape
        #idx and targets are both (B,T) of tensor integers
        #batch x time x channel
        token_embed = self.token_embedding_table(idx) #(B,T,C)
        pos_embed = self.position_embedding_table(torch.arange(T,device = device)) #(T,C)
        x = token_embed + pos_embed #(B,T,C)
        logits = self.lm_head(x) #(B,T,Vocab_size)
        if targets is None:
            loss = None
        #pytorch wants b x c x t
        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):
            logits,loss = self(idx)
            #focus only on the last timestep
            logits = logits[:,-1,:] #becomes (B,C)
            #apply softmaxx 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 running sequence
            idx = torch.cat((idx,idx_next),dim=1) #(B,T+1)
        return idx

model = BigramLanguageModel()
m = model.to(device)

#optimizer object
optimizer = torch.optim.AdamW(m.parameters(),lr = 1e-3)


for iter in range(max_iters):
    #sample batch of data
    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()))