add the application and model file

app.py

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+import torch
+import tiktoken
+import gradio as gr
+from model import GPT, GPTConfig
+from torch.nn import functional as F
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Set device
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Load the tokenizer
+TOKENIZER = tiktoken.get_encoding('gpt2')
+
+# Load untrained model
+UNTRAINED_MODEL = GPT(GPTConfig)
+UNTRAINED_MODEL.to(device)
+UNTRAINED_MODEL.eval()
+
+# Load fine-tuned model 
+TRAINED_MODEL = GPT(GPTConfig)
+checkpoint = torch.load("log/model_19072.pt", weights_only=False)
+TRAINED_MODEL.load_state_dict(checkpoint["model"])
+TRAINED_MODEL.to(device)
+TRAINED_MODEL.eval()
+
+
+def generate_text(input, model, num_sequences, max_length):
+    tokens = TOKENIZER.encode(input)
+    tokens = torch.tensor(tokens, dtype=torch.long) 
+    tokens = tokens.unsqueeze(0).repeat(num_sequences, 1) 
+    x = tokens.to(device)
+
+    sentences = []
+    while x.size(1) < max_length:
+        with torch.no_grad():
+            logits, loss = model(x)
+            logits = logits[:, -1, :] 
+            probs = F.softmax(logits, dim=-1)
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+            ix = torch.multinomial(topk_probs, 1) 
+            xcol = torch.gather(topk_indices, -1, ix) 
+            x = torch.cat((x, xcol), dim=1)
+
+    for i in range(num_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = TOKENIZER.decode(tokens)
+        sentences.append(decoded) 
+
+    return sentences
+
+
+def gradio_fn(prompt, num_sequences=1, max_length=30):
+    """Generate text using both models."""
+    untrained_texts = generate_text(prompt, UNTRAINED_MODEL, num_sequences, max_length)
+    untrained_output = "\n\n".join(f"> {s}" for s in untrained_texts)
+
+    trained_texts = generate_text(prompt, TRAINED_MODEL, num_sequences, max_length)
+    trained_output = "\n\n".join(f"> {s}" for s in trained_texts)
+
+    return untrained_output, trained_output
+
+# Gradio interface
+def main():
+    interface = gr.Interface(
+        fn=gradio_fn,
+        inputs=[
+            gr.Textbox(label="Enter your prompt here:"),
+            gr.Slider(minimum=1, maximum=10, step=1, label="Number of Generations"),
+            gr.Slider(minimum=10, maximum=100, step=10, label="Max Length"),
+        ],
+        outputs=[
+            gr.Textbox(label="Generated Text (Untrained Model)"),
+            gr.Textbox(label="Generated Text (Trained Model)"),
+        ],
+        title="GPT-2  Text Generator",
+        description="Generate text an untrained and a trained GPT-2 model."
+    )
+
+    interface.launch(share=True)
+
+if __name__ == "__main__":
+    main()
+
+

model.py

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+import torch
+import inspect
+import torch.nn as nn
+from dataclasses import dataclass
+from torch.nn import functional as F
+
+# Model Architecture ================================================================================================================
+
+@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 in the multihead attention
+    n_embd: int = 768  # embedding dimension
+    dropout: float = 0.1
+
+
+# FLASH ATTENTION
+# Flash attention is a kernel fusion operation of the attention operation. 
+# It was found out manually. It cannot be found by compilers Because it requires an algorithmic rewrite of how attention is implemented.
+# Though it performs more operations, it is faster than regular attention because it is mindful of the memory hierarchy and has high AI.
+# It avoids read and write operations. It never materializes the large NxN attention matrix which reduces AI.
+# It relies on the online softmax trick which incrementally calculates softmax without having to materialize the inputs to the softmax.
+
+# This is a combination of attention and multi-head attention.
+# There are 1024 tokens in a sequence each emitting 3 vectors - Q, K, V.
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # Key, Query and 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.NANOGPT_SCALE_INIT = 1
+        # Regularization
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        # not really a 'bias', more of a mask, but following the OpenAI/HF naming though
+        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, 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)
+
+        # Attention (materializes the large (T,T) matrix for all the queries and keys)
+        # att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        # att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))  # Only looks at previous tokens
+        # att = F.softmax(att, dim=-1)
+        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        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 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()
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+# In the GPT-3 paper, the LayerNorm layers are applied 
+# before the linear and attention layers.
+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
+
+
+# A final Layernorm is added before the final linear head.
+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), # Embedding
+            wpe = nn.Embedding(config.block_size, config.n_embd), # Position embeddings
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), # Transformer blocks
+            ln_f = nn.LayerNorm(config.n_embd), # Final layer norm (GPT3)
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # Weight Sharing Scheme
+        self.transformer.wte.weight = self.lm_head.weight
+        # init params
+        self.apply(self._init_weights)
+
+    # Weight Initialization  
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, 'NANOGPT_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 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:
+            # Flatten out multidiemntsional input for cross entropy. 
+            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
+    
+    # The parameters are divided into decay and nondecay params.
+    # It is common to not decay bias and 1 dimensional tensors.
+    def configure_optimizers(self, weight_decay, learning_rate, device, master_process):
+        # start with all of the candidate parameters (that require grad)
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
+        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
+        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
+        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2] # Embeddings and weights in matmul
+        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2] # 1D tensors like LayerNorms, biases
+        optim_groups = [
+            {'params': decay_params, 'weight_decay': weight_decay},
+            {'params': nodecay_params, 'weight_decay': 0.0}
+        ]
+        num_decay_params = sum(p.numel() for p in decay_params)
+        num_nodecay_params = sum(p.numel() for p in nodecay_params)
+        if master_process:
+            print(f"Num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
+            print(f"Num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
+        # Create AdamW optimizer and use the fused version if it is available
+        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
+        use_fused = fused_available and 'cuda' in device # Fuses the kernels used in the updation of parameters to make it faster
+        if master_process:
+            print(f"Using fused AdamW: {use_fused} \n")
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
+        return optimizer