volgpt_model.py

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

# Define the function train_and_generate
def train_and_generate(text_file_path, max_iters=5000, learning_rate=1e-3, device='cuda', max_new_tokens=5000):

    # hyperparameters
    batch_size = 16 # how many independent sequences will we process in parallel?
    block_size = 32 # what is the maximum context length for predictions?
    max_iters = 5000
    eval_interval = 100
    learning_rate = 1e-3
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    eval_iters = 200
    n_embd = 64
    n_head = 4
    n_layer = 4
    dropout = 0.0
    # ------------

    torch.manual_seed(3407)

    # get data
    with open(text_file_path, 'r', encoding='utf-8') as f: # text_file_path passed as an argument
        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

    data = torch.tensor(encode(text), dtype=torch.long)
    n_train = int(0.8 * len(data))  # first 80% will be train
    n_val = int(0.1 * len(data))  # next 10% will be validation
    train_data = data[:n_train] # final 10% will be test
    val_data = data[n_train:n_train + n_val]
    test_data = data[n_train + n_val:]

    def get_batch(split):
        # generate a small batch of data of inputs x and targets y
        if split == 'train':
            data = train_data
        elif split == 'val':
            data = val_data
        else:
            data = test_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', 'test']:
            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)   # (B,T,C)
            q = self.query(x) # (B,T,C)
            # compute attention scores ("affinities")
            wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
            wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
            wei = F.softmax(wei, dim=-1) # (B, T, T)
            wei = self.dropout(wei)
            # perform the weighted aggregation of the values
            v = self.value(x) # (B,T,C)
            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):
            out = torch.cat([h(x) for h in self.heads], dim=-1)
            out = self.dropout(self.proj(out))
            return out

    class FeedFoward(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 dimension, 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 = FeedFoward(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

    # Bigram model
    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) tensor 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 # (B,T,C)
            x = self.blocks(x) # (B,T,C)
            x = self.ln_f(x) # (B,T,C)
            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 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

    model = BigramLanguageModel()
    m = model.to(device)
    # print the number of parameters in the model
    print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')

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

    # generate from the model
    context = torch.zeros((1, 1), dtype=torch.long, device=device)

    # training loop
    for iter in range(max_iters):

        # periodically evaluate loss on train, val, and test 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}")

        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}, test loss {losses['test']:.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()

        # Ensure function returns tuple containing  test_data tensor and the generated text when training loop finishes, along with itos mapping
        if iter == max_iters - 1:
            return test_data, decode(m.generate(context, max_new_tokens)[0].tolist()), itos

    # return test_data, decode(m.generate(context, max_new_tokens)[0].tolist())
    # Ensure function returns tuple containing  test_data tensor and the generated text when training loop finishes, along with itos mapping
    return test_data, decode(m.generate(context, max_new_tokens)[0].tolist()), itos