Update model.py

model.py

@@ -1,483 +1,449 @@
-# GPT-3 Paper 
-# add cosing delay  
-import os
-import math
-import time
-import inspect
-from dataclasses import dataclass
-import torch
-import torch.nn as nn
-import tiktoken
-from torch.nn import functional as F
-
-
-class CausalSelfAttention(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-
-        #assertion to ensure the embedding dimension is divisible by the number of heads (important for reshaping later).
-        assert config.n_embd % config.n_head == 0
-
-        # key, query, value projections for all heads, but in a batch. Each vector has the same dimension (C) as the input embedding.
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
-
-        
-        # output projection find the meaning?
-        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):
-        # x is tokenised version of input.txt
-        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'))# find what is it???
-
-        # att = F.softmax(att, dim=-1)
-        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        
-
-
-        ## This function combines the dot product, scaling, and softmax operations into a single step.
-        y = F.scaled_dot_product_attention(q, k, v, is_causal = True) # Flash attention
-
-        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):
-# MLP (Multi-Layer Perceptron)
-## This class implements a simple multi-layer perceptron (MLP) sub-module. 
-## It's often used within transformers for non-linear transformations.
-
-    def __init__(self, config):
-        #sqeeze and expand
-        super().__init__()
-        #c_fc: Projects the input (x) to a dimension four times larger than the embedding dimension (n_embd).
-        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd)
-
-        # GELU (Gaussian Error Linear Unit) activation function for non-linearity. 
-        #Here, an approximate version using tanh is used.
-        self.gelu    = nn.GELU(approximate='tanh')
-
-        # Projects the output back to the original embedding dimension (n_embd).
-        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
-        self.c_proj.NANOGPT_SCALE_INIT = 1
-
-    def forward(self, x):
-
-      #Takes the input (x).
-      # Applies the linear layer (c_fc), followed by the GELU activation.
-      # Applies the final projection layer (c_proj).
-      # Returns the transformed output.
-        x = self.c_fc(x)
-        x = self.gelu(x)
-        x = self.c_proj(x)
-        return x
-
-class Block(nn.Module):
-  # This class combines the CausalSelfAttention layer (explained previously) and the MLP layer to form a single transformer block. 
-  # The input is processed through the attention layer, followed by layer normalization and an MLP, and 
-  # then again with layer normalization.
-
-    def __init__(self, config):
-        super().__init__()
-        
-        #ln_1: A layer normalization layer applied before the causal self-attention.
-        #attn: An instance of the CausalSelfAttention class (explained previously).
-        #mlp: An instance of the MLP class (explained previously).
-        
-        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):
-      # Takes the input (x).
-      # Performs a residual connection with the output from the causal self-attention layer (attn), preceded by layer normalization (ln_1).
-      # Performs another residual connection with the output from the MLP layer (mlp), preceded by layer normalization (ln_2).
-      # Returns the final output after the second residual connection.
-        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 = 50304 # 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
-
-
-
-        # Creates a transformer module dictionary containing several key components:
-        #wte: Word token embedding layer (nn.Embedding). Maps each word index to its corresponding embedding vector.
-        #wpe: Positional embedding layer (nn.Embedding). Adds positional information to the word embeddings.
-        #h: A module list containing multiple Block instances (explained earlier). These are the core processing units of the transformer.
-        #ln_f: Final layer normalization layer (nn.LayerNorm) applied to the output of the transformer blocks.
-
-        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),
-        ))
-
-
-        #Creates the language modeling head (lm_head), a linear layer that projects the final hidden state from the 
-        #transformer to the vocabulary size, predicting the next word in the sequence.
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-
-        # weight sharing Implements weight sharing between the word token embedding layer (wte) 
-        #and the language modeling head (lm_head). This reduces the number of parameters and encourages 
-        #the model to learn a meaningful representation for words that can be used for both embedding and prediction.
-        self.transformer.wte.weight = self.lm_head.weight
-
-        # weight initialization
-        #Initializes the weights of the model using a custom function (_init_weights).
-        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 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
-
-    def configure_optimizers(self, weight_decay, learning_rate, device_type):
-        # 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]
-        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
-        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)
-        
-        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 device_type == "cuda"
-        
-        print(f"using fused AdamW: {use_fused}")
-        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
-        return optimizer
-
-# model = GPT.from_pretrained('gpt2')
-
-device = 'cpu'
-if torch.cuda.is_available():
-    device = 'cuda'
-elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-    device = "mps"
-print(f"using device: {device}")
-
-# SEED
-torch.manual_seed(1337)
-if torch.cuda.is_available():
-    torch.cuda.manual_seed(1337)
-
-# STOP
-# num_return_sequences = 5
-# max_length = 30
-
-
-
-import tiktoken
-
-class DataLoaderLite:
-    def __init__(self, B, T):
-        self.B = B
-        self.T = T
-
-        # at init load tokens from disk and store them in memory
-        with open('input.txt', 'r') as f:
-            text = f.read()
-        enc = tiktoken.get_encoding('gpt2') 
-        tokens = enc.encode(text)
-        self.tokens = torch.tensor(tokens)
-        print(f'loaded {len(self.tokens)} tokens')
-        print(f'1 epoch = {len(self.tokens) // (B * T)} batches')
-
-        # state
-        self.current_position = 0
-    
-    def next_batch(self):
-        B, T = self.B, self.T
-        buf = self.tokens[self.current_position: self.current_position + B * T + 1]
-        x = (buf[:-1]).view(B, T) # inputs
-        y = (buf[1:]).view(B, T) # targets
-        # advance the position in the tensor
-        self.current_position += B*T
-        # if loading the next batch would be out of bounds, reset
-        if self.current_position + (B * T + 1) > len(self.tokens):
-            self.current_position = 0
-        return x, y
-
-# CHANGES IN CURRENT CODE
-torch.set_float32_matmul_precision('high')
-model = GPT(GPTConfig())
-model.to(device)
-# model = torch.compile(model)
-
-# CODE UPDATE HERE
-max_lr = 6e-4 
-min_lr = max_lr * 0.1
-# warmup_steps = 100
-# # max_steps = 50
-
-def get_lr(it,warmup_steps, max_steps):
-    if it < warmup_steps:
-        return max_lr * (it + 1) / warmup_steps
-    if it > max_steps:
-        return min_lr
-    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
-    assert 0 <= decay_ratio <=1
-    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
-    return min_lr + coeff * (max_lr - min_lr)
-
-
-# NEW CODE
-import time
-train_loader = DataLoaderLite(B = 8, T = 512)
-
-# train_loader = DataLoaderLite(B = B, T = T)
-x, y = train_loader.next_batch()
-x.shape, y.shape
-
-def run_train (max_steps = 50 ,warmup_steps = 100, PATH = "/content/drive/MyDrive/S21/gpt_124M.pth" ):
-  # optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4, betas=(0.9, 0.95), eps=1e-8)
-  optimizer = model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device_type=device)
-  for step in range(max_steps):
-      t0 = time.time()
-      x, y = train_loader.next_batch()
-      x, y = x.to(device), y.to(device)
-      optimizer.zero_grad()
-      # NEW CODE ADDED HERE
-      with torch.autocast(device_type=device, dtype=torch.bfloat16):
-          logits, loss = model(x, y) 
-      loss.backward()
-      norm = torch.nn.utils.clip_grad_norm(model.parameters(), 1.0)
-      # NEW CODE
-      lr = get_lr(step, max_steps = 50 ,warmup_steps = 100)
-      for param_group in optimizer.param_groups:
-          param_group['lr'] = lr
-          
-      optimizer.step()
-      torch.cuda.synchronize() 
-      t1 = time.time()
-      dt = (t1 - t0) * 1000
-      tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
-      print(f'step{step} | loss: {loss.item()} | dt: {dt:.2f}ms | tok/sec: {tokens_per_sec: .2f} | norm: {norm:.2f}')
-  print(loss)
-  torch.save(model.state_dict(), PATH)
-  return model
-
-def load_fromsaved(PATH = "/content/drive/MyDrive/S21/gpt_124M.pth" ):
-
-  # Create a new GPT model instance
-  model = GPT(GPTConfig())
-  model.to(device)
-
-  # Load the saved weights into the model
-  model.load_state_dict(torch.load(PATH))
-  
-
-  # Print confirmation message
-  print("Loaded model weights from:", PATH)
-  model.to(device)
-
-  return model 
-
-
-def gen_text(model,start_tokens, max_length=100, num_return_sequences=10 ):
-  """
-  Generates text using the loaded GPT model.
-
-  Args:
-      model: The GPT model to use for generation.
-      start_tokens (optional): A list of token IDs to use as the starting prompt.
-      max_length: The maximum length of the generated text.
-      num_return_sequences: The number of text sequences to generate.
-
-  Returns:
-      None
-  """
-  decoded_texts = ''
-  enc = tiktoken.get_encoding('gpt2') 
-  tokens = enc.encode(start_tokens)
-  tokens = torch.tensor(tokens, dtype= torch.long) # (8,) #check tiktoken app
-  tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
-  x = tokens.to(device)
-
-  # Set random seeds for consistent generation across runs
-  torch.manual_seed(42)
-  torch.cuda.manual_seed(42)
-
-  while x.size(1) < max_length:
-        # forward the model to get the logits
-        with torch.no_grad():
-            logits = model(x)[0] # (B, T, vocab_size)
-            # take the logits at the last position
-            logits = logits[:, -1, :] # (B, vocab_size)
-            # get the probabilities
-            probs = F.softmax(logits, dim=-1)
-            # do top-k sampling of 50 (huggingface pipeline default)
-            # topk_probs here becomes (5, 50), topk_indices is (5, 50)
-            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
-            # select a token from the top-k probabilities
-            # note: multinomial does not demand the input to sum to 1
-            ix = torch.multinomial(topk_probs, 1) # (B, 1)
-            # gather the corresponding indices
-            xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
-            # append to the sequence
-            x = torch.cat((x, xcol), dim=1)
-
-    # print the generated text
-  for i in range(num_return_sequences):
-        tokens = x[i, :max_length].tolist()
-        decoded = enc.decode(tokens)
-        print(">", decoded)
-  #       decoded_texts.append(decoded)
-  #   # Join all the decoded texts into a single string and print it
-  # final_decoded_text = "".join(decoded_texts)
-  # print(final_decoded_text)
-  # return final_decoded_text
-
-
-  
-# def gen_text(model,x = x, max_length = 100, num_return_sequences=10):
-#   torch.manual_seed(42)
-#   torch.cuda.manual_seed(42)
-#   while x.size(1) < max_length:
-#       # forward the model to get the logits
-#       with torch.no_grad():
-#           logits = model(x)[0] # (B, T, vocab_size)
-#           # take the logits at the last position
-#           logits = logits[:, -1, :] # (B, vocab_size)
-#           # get the probabilities
-#           probs = F.softmax(logits, dim=-1)
-#           # do top-k sampling of 50 (huggingface pipeline default)
-#           # topk_probs here becomes (5, 50), topk_indices is (5, 50)
-#           topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
-#           # select a token from the top-k probabilities
-#           # note: multinomial does not demand the input to sum to 1
-#           ix = torch.multinomial(topk_probs, 1) # (B, 1)
-#           # gather the corresponding indices
-#           xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
-#           # append to the sequence
-#           x = torch.cat((x, xcol), dim=1)
-
-#   # print the generated text
-#   for i in range(num_return_sequences):
-#       tokens = x[i, :max_length].tolist()
-#       decoded = enc.decode(tokens)
-#       print(">", decoded)
+# GPT-3 Paper 
+import os
+import math
+import time
+import inspect
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+import tiktoken
+from torch.nn import functional as F
+
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+
+        #assertion to ensure the embedding dimension is divisible by the number of heads (important for reshaping later).
+        assert config.n_embd % config.n_head == 0
+
+        # key, query, value projections for all heads, but in a batch. Each vector has the same dimension (C) as the input embedding.
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+
+        
+        # output projection find the meaning?
+        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):
+        # x is tokenised version of input.txt
+        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'))# find what is it???
+
+        # att = F.softmax(att, dim=-1)
+        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        
+
+
+        ## This function combines the dot product, scaling, and softmax operations into a single step.
+        y = F.scaled_dot_product_attention(q, k, v, is_causal = True) # Flash attention
+
+        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):
+# MLP (Multi-Layer Perceptron)
+## This class implements a simple multi-layer perceptron (MLP) sub-module. 
+## It's often used within transformers for non-linear transformations.
+
+    def __init__(self, config):
+        #sqeeze and expand
+        super().__init__()
+        #c_fc: Projects the input (x) to a dimension four times larger than the embedding dimension (n_embd).
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd)
+
+        # GELU (Gaussian Error Linear Unit) activation function for non-linearity. 
+        #Here, an approximate version using tanh is used.
+        self.gelu    = nn.GELU(approximate='tanh')
+
+        # Projects the output back to the original embedding dimension (n_embd).
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+
+      #Takes the input (x).
+      # Applies the linear layer (c_fc), followed by the GELU activation.
+      # Applies the final projection layer (c_proj).
+      # Returns the transformed output.
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+class Block(nn.Module):
+  # This class combines the CausalSelfAttention layer (explained previously) and the MLP layer to form a single transformer block. 
+  # The input is processed through the attention layer, followed by layer normalization and an MLP, and 
+  # then again with layer normalization.
+
+    def __init__(self, config):
+        super().__init__()
+        
+        #ln_1: A layer normalization layer applied before the causal self-attention.
+        #attn: An instance of the CausalSelfAttention class (explained previously).
+        #mlp: An instance of the MLP class (explained previously).
+        
+        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):
+      # Takes the input (x).
+      # Performs a residual connection with the output from the causal self-attention layer (attn), preceded by layer normalization (ln_1).
+      # Performs another residual connection with the output from the MLP layer (mlp), preceded by layer normalization (ln_2).
+      # Returns the final output after the second residual connection.
+        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 = 50304 # 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
+
+
+
+        # Creates a transformer module dictionary containing several key components:
+        #wte: Word token embedding layer (nn.Embedding). Maps each word index to its corresponding embedding vector.
+        #wpe: Positional embedding layer (nn.Embedding). Adds positional information to the word embeddings.
+        #h: A module list containing multiple Block instances (explained earlier). These are the core processing units of the transformer.
+        #ln_f: Final layer normalization layer (nn.LayerNorm) applied to the output of the transformer blocks.
+
+        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),
+        ))
+
+
+        #Creates the language modeling head (lm_head), a linear layer that projects the final hidden state from the 
+        #transformer to the vocabulary size, predicting the next word in the sequence.
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # weight sharing Implements weight sharing between the word token embedding layer (wte) 
+        #and the language modeling head (lm_head). This reduces the number of parameters and encourages 
+        #the model to learn a meaningful representation for words that can be used for both embedding and prediction.
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # weight initialization
+        #Initializes the weights of the model using a custom function (_init_weights).
+        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 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
+
+    def configure_optimizers(self, weight_decay, learning_rate, device_type):
+        # 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]
+        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
+        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)
+        
+        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 device_type == "cuda"
+        
+        print(f"using fused AdamW: {use_fused}")
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
+        return optimizer
+
+# model = GPT.from_pretrained('gpt2')
+
+device = 'cpu'
+if torch.cuda.is_available():
+    device = 'cuda'
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    device = "mps"
+print(f"using device: {device}")
+
+# SEED
+torch.manual_seed(1337)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed(1337)
+
+# STOP
+# num_return_sequences = 5
+# max_length = 30
+
+
+
+import tiktoken
+
+class DataLoaderLite:
+    def __init__(self, B, T):
+        self.B = B
+        self.T = T
+
+        # at init load tokens from disk and store them in memory
+        with open('input.txt', 'r') as f:
+            text = f.read()
+        enc = tiktoken.get_encoding('gpt2') 
+        tokens = enc.encode(text)
+        self.tokens = torch.tensor(tokens)
+        print(f'loaded {len(self.tokens)} tokens')
+        print(f'1 epoch = {len(self.tokens) // (B * T)} batches')
+
+        # state
+        self.current_position = 0
+    
+    def next_batch(self):
+        B, T = self.B, self.T
+        buf = self.tokens[self.current_position: self.current_position + B * T + 1]
+        x = (buf[:-1]).view(B, T) # inputs
+        y = (buf[1:]).view(B, T) # targets
+        # advance the position in the tensor
+        self.current_position += B*T
+        # if loading the next batch would be out of bounds, reset
+        if self.current_position + (B * T + 1) > len(self.tokens):
+            self.current_position = 0
+        return x, y
+
+# CHANGES IN CURRENT CODE
+torch.set_float32_matmul_precision('high')
+model = GPT(GPTConfig())
+model.to(device)
+# model = torch.compile(model)
+
+# CODE UPDATE HERE
+max_lr = 6e-4 
+min_lr = max_lr * 0.1
+# warmup_steps = 100
+# # max_steps = 50
+
+def get_lr(it,warmup_steps, max_steps):
+    if it < warmup_steps:
+        return max_lr * (it + 1) / warmup_steps
+    if it > max_steps:
+        return min_lr
+    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
+    assert 0 <= decay_ratio <=1
+    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+    return min_lr + coeff * (max_lr - min_lr)
+
+
+# NEW CODE
+import time
+train_loader = DataLoaderLite(B = 8, T = 512)
+
+# train_loader = DataLoaderLite(B = B, T = T)
+x, y = train_loader.next_batch()
+x.shape, y.shape
+
+def run_train (max_steps = 50 ,warmup_steps = 100, PATH = "/content/drive/MyDrive/S21/gpt_124M.pth" ):
+  # optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4, betas=(0.9, 0.95), eps=1e-8)
+  optimizer = model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device_type=device)
+  for step in range(max_steps):
+      t0 = time.time()
+      x, y = train_loader.next_batch()
+      x, y = x.to(device), y.to(device)
+      optimizer.zero_grad()
+      # NEW CODE ADDED HERE
+      with torch.autocast(device_type=device, dtype=torch.bfloat16):
+          logits, loss = model(x, y) 
+      loss.backward()
+      norm = torch.nn.utils.clip_grad_norm(model.parameters(), 1.0)
+      # NEW CODE
+      lr = get_lr(step, max_steps = 50 ,warmup_steps = 100)
+      for param_group in optimizer.param_groups:
+          param_group['lr'] = lr
+          
+      optimizer.step()
+      torch.cuda.synchronize() 
+      t1 = time.time()
+      dt = (t1 - t0) * 1000
+      tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
+      print(f'step{step} | loss: {loss.item()} | dt: {dt:.2f}ms | tok/sec: {tokens_per_sec: .2f} | norm: {norm:.2f}')
+  print(loss)
+  torch.save(model.state_dict(), PATH)
+  return model
+
+def load_fromsaved(PATH = "/content/drive/MyDrive/S21/gpt_124M.pth" ):
+
+  # Create a new GPT model instance
+  model = GPT(GPTConfig())
+  model.to(device)
+
+  # Load the saved weights into the model
+  model.load_state_dict(torch.load(PATH))
+  
+
+  # Print confirmation message
+  print("Loaded model weights from:", PATH)
+  model.to(device)
+
+  return model 
+
+
+def gen_text(model,start_tokens, max_length=100, num_return_sequences=10 ):
+  """
+  Generates text using the loaded GPT model.
+
+  Args:
+      model: The GPT model to use for generation.
+      start_tokens (optional): A list of token IDs to use as the starting prompt.
+      max_length: The maximum length of the generated text.
+      num_return_sequences: The number of text sequences to generate.
+
+  Returns:
+      None
+  """
+  decoded_texts = ''
+  enc = tiktoken.get_encoding('gpt2') 
+  tokens = enc.encode(start_tokens)
+  tokens = torch.tensor(tokens, dtype= torch.long) # (8,) #check tiktoken app
+  tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
+  x = tokens.to(device)
+
+  # Set random seeds for consistent generation across runs
+  torch.manual_seed(42)
+  torch.cuda.manual_seed(42)
+  generated_text = ""
+  while x.size(1) < max_length:
+        # forward the model to get the logits
+        with torch.no_grad():
+            logits = model(x)[0] # (B, T, vocab_size)
+            # take the logits at the last position
+            logits = logits[:, -1, :] # (B, vocab_size)
+            # get the probabilities
+            probs = F.softmax(logits, dim=-1)
+            # do top-k sampling of 50 (huggingface pipeline default)
+            # topk_probs here becomes (5, 50), topk_indices is (5, 50)
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+            # select a token from the top-k probabilities
+            # note: multinomial does not demand the input to sum to 1
+            ix = torch.multinomial(topk_probs, 1) # (B, 1)
+            # gather the corresponding indices
+            xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
+            # append to the sequence
+            x = torch.cat((x, xcol), dim=1)
+
+  # print the generated text
+  for i in range(num_return_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = enc.decode(tokens)
+        print(">", decoded)
+        generated_text += decoded
+  return generated_text