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app.py

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+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) 

requirements.txt

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+torch
+transformers
+tiktoken
+torchsummary
+gradio
+streamlit

trained_model_quantized.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3d71eeb703354e72af3b0205521e19e34d59fbc166bada1c5136a95fac0881e
+size 548146590