app.py

import tiktoken
import torch
import time
import math
import re
from torch.utils.data import Dataset, DataLoader

import gradio as gr
import torch.nn as nn

class GPTModel(nn.Module):

    def __init__(self, cfg):
        super().__init__()
        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
        self.drop_emb = nn.Dropout(cfg["drop_rate"])

        self.trf_blocks = nn.Sequential(
            *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
        )

        self.final_norm = LayerNorm(cfg["emb_dim"])
        self.out_head = nn.Linear(
            cfg["emb_dim"], cfg["vocab_size"], bias=False
        )
    
    def forward(self, in_idx):
        batch_size, seq_len = in_idx.shape
        tok_embeds = self.tok_emb(in_idx)
        pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
        x = self.drop_emb(x)
        x = self.trf_blocks(x)
        x = self.final_norm(x)
        logits = self.out_head(x)
        return logits

class TransformerBlock(nn.Module):

    def __init__(self, cfg):
        super().__init__()
        self.att = MultiHeadAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            context_length=cfg["context_length"],
            num_heads=cfg["n_heads"],
            dropout=cfg["drop_rate"],
            qkv_bias=cfg["qkv_bias"]
        )
        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg["emb_dim"])
        self.norm2 = LayerNorm(cfg["emb_dim"])
        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
    
    def forward(self, x):
        # Shortcut connection for attnetion block
        shortcut = x
        x = self.norm1(x)
        x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
        x = self.drop_shortcut(x)
        x = x + shortcut # Add the original input back

        # Shortcut connection for feed forward block
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut # Add the original input back

        return x

class TransformerBlock(nn.Module):

    def __init__(self, cfg):
        super().__init__()
        self.att = MultiHeadAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            context_length=cfg["context_length"],
            num_heads=cfg["n_heads"],
            dropout=cfg["drop_rate"],
            qkv_bias=cfg["qkv_bias"]
        )
        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg["emb_dim"])
        self.norm2 = LayerNorm(cfg["emb_dim"])
        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
    
    def forward(self, x):
        # Shortcut connection for attnetion block
        shortcut = x
        x = self.norm1(x)
        x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
        x = self.drop_shortcut(x)
        x = x + shortcut # Add the original input back

        # Shortcut connection for feed forward block
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut # Add the original input back

        return x

class MultiHeadAttention(nn.Module):

    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
        super().__init__()
        assert (d_out % num_heads == 0), \
            "d_out must be divisible by num_heads"
        
        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim

        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
        self.dropout = nn.Dropout(dropout)
        self.register_buffer(
            "mask",
            torch.triu(torch.ones(context_length, context_length),
                       diagonal=1)
        )
    
    def forward(self, x):
        b, num_tokens, d_in = x.shape

        keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
        queries = self.W_query(x)
        values = self.W_value(x)
        
        # implicitly split the matrix by adding a `num_heads` dimension
        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
        values = values.view(b, num_tokens, self.num_heads, self.head_dim)
        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)

        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
        keys = keys.transpose(1, 2)
        queries = queries.transpose(1, 2)
        values = values.transpose(1, 2)

        # Compute scaled dot-product attention (aka self-attention) with a causal mask
        attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head

        # Original mask truncated to the number of tokens and converted to boolean
        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]

        # Use the mask to fill attention scores
        attn_scores.masked_fill_(mask_bool, -torch.inf)

        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
        attn_weights = self.dropout(attn_weights)

        # Shape: (b, num_tokens, num_heads, head_dim)
        context_vec = (attn_weights @ values).transpose(1, 2)

        # Combine heads, where self.d_out = self.num_heads * self.head_dim
        context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
        context_vec = self.out_proj(context_vec) # optional projection

        return context_vec
    
class FeedForward(nn.Module):

    def __init__(self, cfg):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
            GELU(),
            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"])
        )
    
    def forward(self, x):
        return self.layers(x)

class GELU(nn.Module):

    def __init__(self):
        super().__init__()
    
    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(
            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
            (x + 0.044715 * torch.pow(x, 3))
        ))

class LayerNorm(nn.Module):

    def __init__(self, emb_dim):
        super().__init__()
        self.eps = 1e-5
        self.scale = nn.Parameter(torch.ones(emb_dim))
        self.shift = nn.Parameter(torch.zeros(emb_dim))
    
    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        var = x.var(dim=-1, keepdim=True, unbiased=False)
        norm_x = (x - mean) / torch.sqrt(var + self.eps)
        return self.scale * norm_x + self.shift




GPT_CONFIG_124M = {
    "vocab_size": 50257,   # Vocabulary size
    "context_length": 256, # Shortended context length (orig: 1024)
    "emb_dim": 768,        # Embedding dimension
    "n_heads": 12,         # Number of attention heads
    "n_layers": 12,        # Number of layers
    "drop_rate": 0.1,      # Dropout rate
    "qkv_bias": False      # Query-key-value bias
}

model = GPTModel(GPT_CONFIG_124M)

def generate(model, idx, max_new_tokens, context_size, tokenizer, text_to_token_ids, temperature=0.0, top_k=None, eos_id=None):

    # For-loop is the same as before: Get logits, and only focus on last time step
    for _ in range(max_new_tokens):
        idx_cond = idx[:, -context_size:]
        with torch.no_grad():
            logits = model(idx_cond)
        logits = logits[:, -1, :]

        # New: Filter logits with top_k sampling
        if top_k is not None:
            # Keep only top_k values
            top_logits, _ = torch.topk(logits, top_k)
            min_val = top_logits[:, -1]
            logits = torch.where(logits < min_val, torch.tensor(float("-inf")).to(logits.device), logits)
        
        # New: Apply temperature scaling
        if temperature > 0.0:
            logits = logits / temperature

            # Apply softmax to get probabilities
            probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)

            # Sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
        
        # Otherwise, same as before: get the idx of the vocab entry with the highest logits value
        else:
            idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
        
        if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
            break
        
        # if idx_next == text_to_token_ids(".", tokenizer):
        if idx_next == "tensor([[13]])":
            # idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)
            print("\nperiod\n")
        
        # if idx_next == text_to_token_ids("?", tokenizer):
        if idx_next == "tensor([[30]])":
            # idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)
            print("\nperiod\n")
        
        # if idx_next == text_to_token_ids("!", tokenizer):
        if idx_next == "tensor([[0]])":
            # idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)
            print("\nperiod\n")
        
        # print(idx_next)
        # print("----")
        # print(idx_next + text_to_token_ids("Meow.", tokenizer))
        # test = idx_next + text_to_token_ids("Meow.", tokenizer)
        # print("------")
        # print(token_ids_to_text(idx_next, tokenizer))
        # Same as before: append sampled index to the running sequence
        idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
    # new_idx = re.sub(".", ". Meow.", idx)
    
    # return new_idx
    return idx

def text_to_token_ids(text, tokenizer):
    encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
    encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
    return encoded_tensor

def token_ids_to_text(token_ids, tokenizer):
    flat = token_ids.squeeze(0) # remove batch dimension
    return tokenizer.decode(flat.tolist())

def train_model(model, train_loader, val_loader, optimizer, device,
                n_epochs, eval_freq, eval_iter, start_context, tokenizer,
                warmup_steps, initial_lr=3e-05, min_lr=1e-6):
    
    train_losses, val_losses, track_tokens_seen, track_lrs = [], [], [], []
    tokens_seen, global_step = 0, -1

    # Retrieve the maximum learning rate from the optimizer
    peak_lr = optimizer.param_groups[0]["lr"]

    # Calculate the total number of iterations in the training process
    total_training_steps = len(train_loader) * n_epochs

    # Calculate the learning rate increment during the warmup phase
    lr_increment = (peak_lr - initial_lr) / warmup_steps

    for epoch in range(n_epochs):
        model.train()
        for input_batch, target_batch in train_loader:
            optimizer.zero_grad()
            global_step += 1

            # Adjust the learning rate based on the current phase (warmup or cosine annealing)
            if global_step < warmup_steps:
                # Linear warmup
                lr = initial_lr + global_step * lr_increment
            else:
                # Cosine annealing after warmup
                progress = ((global_step - warmup_steps) /
                            (total_training_steps - warmup_steps))
                lr = min_lr + (peak_lr - min_lr) * 0.5 * (1 + math.cos(math.pi * progress))
            
            # Apply the calculated learning rate to the optimizer
            for param_group in optimizer.param_groups:
                param_group["lr"] = lr
            track_lrs.append(lr) # Store the current learning rate

            # Calculate and backpropagate the loss
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            loss.backward()

            # Apply gradient clipping after the warmup phase to avoid exploding gradients
            if global_step > warmup_steps:
                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            
            optimizer.step()
            tokens_seen += input_batch.numel()

            # Periodically evaluate the model on the training and validation sets
            if global_step % eval_freq == 0:
                train_loss, val_loss = evaluate_model(
                    model, train_loader, val_loader,
                    device, eval_iter
                )
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                track_tokens_seen.append(tokens_seen)
                # Print the current losses
                print(f"Ep {epoch+1} (Iter {global_step:06d}): "
                      f"Train loss {train_loss:.3f}, "
                      f"Val loss {val_loss:.3f}"
                )

        # Generate and print a sample from the model to monitor progress
        generate_and_print_sample(
            model, tokenizer, device, start_context
        )
    
    return train_losses, val_losses, track_tokens_seen, track_lrs

def create_dataloader_v1(txt, batch_size=4, max_length=256, stride=128, shuffle=True, drop_last=True, num_workers=0):
    tokenizer = tiktoken.get_encoding("gpt2") # A - Initalize the tokenizer
    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride) # B - Create dataset
    dataloader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=shuffle,
        drop_last=drop_last, # C - drop_last=True drops the last batch if it is shorter than the specified batch_size to prevent loss spikes during training
        num_workers=0 # D - The number of CPU processes to use for preprocessing
    )

    return dataloader



class GPTDatasetV1(Dataset):
    def __init__(self, txt, tokenizer, max_length, stride):
        self.tokenizer = tokenizer
        self.input_ids = []
        self.target_ids = []

        token_ids = tokenizer.encode(txt) # A

        for i in range(0, len(token_ids) - max_length, stride): # B
            input_chunk = token_ids[i:i + max_length]
            target_chunk = token_ids[i + 1: i +max_length + 1]
            self.input_ids.append(torch.tensor(input_chunk))
            self.target_ids.append(torch.tensor(target_chunk))
    
    def __len__(self):
        return len(self.input_ids)
    
    def __getitem__(self, idx):
        return self.input_ids[idx], self.target_ids[idx]


def evaluate_model(model, train_loader, val_loader, device, eval_iter):
    model.eval()
    with torch.no_grad():
        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
    model.train()
    return train_loss, val_loss

def generate_and_print_sample(model, tokenizer, device, start_context):
    model.eval()
    context_size = model.pos_emb.weight.shape[0]
    encoded = text_to_token_ids(start_context, tokenizer).to(device)
    with torch.no_grad():
        token_ids = generate_text_simple(
            model=model, idx=encoded,
            max_new_tokens=50, context_size=context_size
        )
    decoded_text = token_ids_to_text(token_ids, tokenizer)
    print(decoded_text.replace("\n", " ")) # Compact print format
    model.train()

def calc_loss_batch(input_batch, target_batch, model, device):
    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
    logits = model(input_batch)
    loss = torch.nn.functional.cross_entropy(logits.flatten(0, 1), target_batch.flatten())
    return loss

def calc_loss_loader(data_loader, model, device, num_batches=None):
    total_loss = 0.
    if len(data_loader) == 0:
        return float("nan")
    elif num_batches is None:
        num_batches = len(data_loader)
    else:
        # Reduce the number of batches to match the total number of batches in the data loader
        # if num_batches exceeds the number of batches in the data loader
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            total_loss += loss.item()
        else:
            break
    return total_loss / num_batches

def generate_text_simple(model, idx, max_new_tokens, context_size):
    # idx is (batch, n_tokens) array of indices in the current context
    for _ in range(max_new_tokens):

        # Crop current context if it exceeds the supported context size
        idx_cond = idx[:, -context_size:]

        # get the predictions
        with torch.no_grad():
            logits = model(idx_cond)
        
        # Focus only on the last time step
        # (batch, n_tokens, vocab_size) becomes (batch, vocab_size)
        logits = logits[:, -1, :]

        # apply softmax to get the probabilities
        probas = torch.softmax(logits, dim=-1) # (batch, vocab_size)

        # Get the idx of the vocab entry with the highest probability value
        idx_next = torch.argmax(probas, dim=-1, keepdim=True) # (batch, 1)

        # if idx_next == text_to_token_ids(".", tokenizer):
        #     idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)
        
        # if idx_next == text_to_token_ids("?", tokenizer):
        #     idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)
        
        # if idx_next == text_to_token_ids("!", tokenizer):
        #     idx_next = idx_next + text_to_token_ids("Meow.", tokenizer)

        # Append sampled index to the running sequence
        idx = torch.cat((idx, idx_next), dim=1) # (batch , n_tokens+1)

    return idx

def main(input_text, max_new_tokens):

    tokenizer = tiktoken.get_encoding("gpt2")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if torch.cuda.is_available():
        device = torch.device("cuda")
    elif torch.backends.mps.is_available():
        device = torch.device("mps")
    else:
        device = torch.device("cpu")

    checkpoint = torch.load("model_and_optimizer.pth", weights_only=True)

    model = GPTModel(GPT_CONFIG_124M)
    model.load_state_dict(checkpoint["model_state_dict"])

    optimizer = torch.optim.AdamW(model.parameters(), lr=0.0005, weight_decay=0.1)
    optimizer.load_state_dict(checkpoint["optimizer_state_dict"])

    # weights = torch.load("model_and_optimizer.pth", map_location=torch.device(device))
    # weights = torch.load("model_and_optimizer.pth", weights_only=False)

    # model = GPTModel({
    # "vocab_size": 50257,   # Vocabulary size
    # "context_length": 512, # Shortened context length (orig: 1024)
    # "emb_dim": 768,        # Embedding dimension
    # "n_heads": 12,         # Number of attention heads
    # "n_layers": 12,        # Number of layers
    # "drop_rate": 0.3,      # Dropout rate
    # "qkv_bias": False      # Query-key-value bias
    # }).to(device)
    # model.load_state_dict(weights['model_state_dict'])
    model.eval()

    context_size = model.pos_emb.weight.shape[0]
    encoded = torch.tensor(tokenizer.encode(input_text.strip())).unsqueeze(0).to(device)

    with torch.no_grad():
        token_ids = generate(
            model=model, idx=encoded,
            max_new_tokens=max_new_tokens, context_size=context_size,
            top_k=25, temperature=1.4, text_to_token_ids=text_to_token_ids, tokenizer=tokenizer
        )
        new_thing = re.sub(".", ". Meow.", token_ids)
        # return tokenizer.decode(token_ids.squeeze(0).tolist())
        return tokenizer.decode(new_thing.squeeze(0).tolist())

# if __name__ == "__main__":
#     gr.Interface(fn=main, inputs=[gr.Textbox(label='Starting context'), gr.Number(label="Maximum output tokens")], outputs=[gr.Textbox(label="Response:")], title="CatGPT", article="Meow").launch()

# thing_old = gr.Interface(fn=main, theme=gr.themes.Soft(primary_hue="pink", secondary_hue="stone"), inputs=[gr.Textbox(label='Starting context'), gr.Number(label="Maximum output tokens")], outputs=[gr.Textbox(label="Response:")], title="CatGPT", article="Meow")
thing = gr.Interface(fn=main, 
                     theme='ParityError/Anime', 
                     inputs=[gr.Textbox(label='Starting context'), 
                     gr.Number(label="Maximum output tokens")], 
                     outputs=[gr.Textbox(label="Response:")], 
                     title="CatGPT", 
                     article="Meow")

if __name__ == "__main__":
    thing.launch()