app.py

import streamlit as st
import torch
import torch.nn as nn
from torch.nn import functional as F
import tiktoken
import sys
import os
import logging
import warnings
from dataclasses import dataclass
import math

class MLP(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.gelu    = nn.GELU(approximate='tanh')
        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        return x


class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANGPT_SCALE_INIT = 1
        # regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size))

    def forward(self, x):
        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
        att = F.softmax(att, dim=-1)
        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)

        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
        # output projection
        y = self.c_proj(y)
        return y



class Block(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


@dataclass
class GPTConfig:
    block_size: int = 1024 # max sequence length
    vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
    n_layer: int = 12 # number of layers
    n_head: int = 12 # number of heads
    n_embd: int = 768 # embedding dimension


class GPT(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.config = config

        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = nn.LayerNorm(config.n_embd),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        # weight sharing
        self.transformer.wte.weight = self.lm_head.weight

        # weight initialization
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, 'NANGPT_SCALE_INIT'):
                std *= (2 * self.config.n_layer) ** -0.5
            torch.nn.init.normal_(module.weight, mean = 0.0, std = std)
            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 print_num_parameters(self):
        num_params = sum(p.numel() for p in self.parameters())
        print(f"Number of model parameters: {num_params}")

    def forward(self, idx, targets=None):
        # idx is of shape (B, T)
        B, T = idx.size()
        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
        # forward the token and posisition embeddings
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
        x = tok_emb + pos_emb
        # forward the blocks of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    @classmethod
    def from_pretrained(cls, model_type):
        """Loads pretrained GPT-2 model weights from huggingface"""
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
        }[model_type]
        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
        # create a from-scratch initialized minGPT model
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # this means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model


# Configure logging and warnings
logging.getLogger('streamlit').setLevel(logging.ERROR)
warnings.filterwarnings('ignore', message='.*torch.classes.*')
warnings.filterwarnings('ignore', category=FutureWarning)

# Add the project root to Python path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))



@st.cache_resource
def load_model():
    device = "cpu"
    config = GPTConfig()
    model = GPT(config)
    
    # Load the trained weights from root directory
    checkpoint = torch.load('trained_model_quantized.pt', map_location=device, weights_only=True)
    
    # Handle pruned weights
    state_dict = checkpoint['model_state_dict']
    new_state_dict = {}
    
    for key in model.state_dict().keys():
        if key.endswith('.weight'):
            # Check if this is a pruned weight
            orig_key = key[:-7] + '.weight_orig' if key.endswith('.weight') else key
            mask_key = key[:-7] + '.weight_mask' if key.endswith('.weight') else key
            
            if orig_key in state_dict and mask_key in state_dict:
                # Reconstruct the pruned weight
                new_state_dict[key] = state_dict[orig_key] * state_dict[mask_key]
            else:
                # Use the weight as is
                new_state_dict[key] = state_dict[key] if key in state_dict else model.state_dict()[key]
        else:
            # Copy non-weight parameters as is
            new_state_dict[key] = state_dict[key] if key in state_dict else model.state_dict()[key]
    
    # Load the processed state dict
    model.load_state_dict(new_state_dict)
    
    # Convert back to float32 for inference
    model = model.float()
    model.to(device)
    model.eval()
    
    return model, device

def generate_text(model, prompt, max_length=100, num_return_sequences=1, device='cpu'):
    tokenizer = tiktoken.get_encoding('gpt2')
    input_tokens = tokenizer.encode(prompt)
    x = torch.tensor(input_tokens).unsqueeze(0).repeat(num_return_sequences, 1)
    x = x.to(device)
    
    # Calculate final length (input length + requested additional tokens)
    input_length = x.size(1)
    target_length = input_length + max_length
    
    # Generate text
    with torch.no_grad():
        while x.size(1) < target_length:
            logits = model(x)[0]
            next_token_logits = logits[:, -1, :]
            probs = torch.softmax(next_token_logits, dim=-1)
            next_token = torch.multinomial(probs, num_samples=1)
            x = torch.cat((x, next_token), dim=1)
    
    # Print token information once before generating sequences
    st.text(f"Size of Input tokens: {input_length}, Additional tokens to be predicted: {max_length}, Total tokens to be generated: {x.size(1)}")
    
    # Decode generated sequences
    generated_texts = []
    for i in range(num_return_sequences):
        tokens = x[i].tolist()
        text = tokenizer.decode(tokens)
        generated_texts.append(text)
    
    return generated_texts

# Streamlit UI
st.title("GPT Text Generator")

# Load model
model, device = load_model()

# Input form
prompt = st.text_area("Enter your prompt:", "Once upon a time")
max_length = st.slider("Predict additional text of length:", min_value=1, max_value=50, value=5)
num_sequences = st.slider("Number of sequences to generate:", 1, 5, 1)

if st.button("Generate"):
    with st.spinner("Generating text..."):
        generated_texts = generate_text(
            model=model,
            prompt=prompt,
            max_length=max_length,
            num_return_sequences=num_sequences,
            device=device
        )
        
        # Display results
        for i, text in enumerate(generated_texts, 1):
            st.write(f"\nSequence {i}:")
            st.write(text)