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config.json

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+{
+  "architectures": [
+    "MyGPTForCausalLM"
+  ],
+  "context_length": 256,
+  "drop_rate": 0.1,
+  "dtype": "float32",
+  "emb_dim": 256,
+  "model_type": "my_gpt",
+  "n_heads": 4,
+  "n_layers": 12,
+  "qkv_bias": false,
+  "transformers_version": "5.1.0",
+  "vocab_size": 50257,
+
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256
+}

configuration_my_gpt.py

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+from transformers import PretrainedConfig
+
+class MyGPTConfig(PretrainedConfig):
+    model_type = "my_gpt"
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        context_length=256,
+        emb_dim=256,
+        n_heads=4,
+        n_layers=12,
+        drop_rate=0.1,
+        qkv_bias=False,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.context_length = context_length
+        self.emb_dim = emb_dim
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.drop_rate = drop_rate
+        self.qkv_bias = qkv_bias

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2315b9bb1ea7a701548cf7b78919b30120aa8f3be7eb3f84e917be93bc35d67
+size 144226200

modeling_my_gpt.py

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+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from configuration_my_gpt import MyGPTConfig
+from untrained_model import GPTModel
+
+import os
+import sys
+
+curr_dir = os.getcwd()
+parent_dir = os.path.dirname(curr_dir)
+
+sys.path.insert(0, parent_dir)
+
+
+class MyGPTForCausalLM(PreTrainedModel):
+    config_class = MyGPTConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        # Import your original GPTModel
+        self.model = GPTModel({
+            "vocab_size": config.vocab_size,
+            "context_length": config.context_length,
+            "emb_dim": config.emb_dim,
+            "n_heads": config.n_heads,
+            "n_layers": config.n_layers,
+            "drop_rate": config.drop_rate,
+            "qkv_bias": config.qkv_bias
+        })
+
+        self.post_init()
+
+    def forward(self, input_ids, labels=None):
+        logits = self.model(input_ids)
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1)
+            )
+
+        return {
+            "loss": loss,
+            "logits": logits,
+        }

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

untrained_model.py

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+import tiktoken
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MaxNLocator
+import numpy as np
+
+
+
+
+class GPTDatasetV1(Dataset):
+    def __init__(self, txt, tokenizer, max_length, stride):
+        self.input_ids = []
+        self.target_ids = []
+
+        # Tokenize the entire text
+        token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
+
+        # Use a sliding window to chunk the book into overlapping sequences of max_length
+        for i in range(0, len(token_ids) - max_length, stride):
+            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 create_dataloader_v1(txt, batch_size=4, max_length=256,
+                         stride=128, shuffle=True, drop_last=True, num_workers=0):
+    # Initialize the tokenizer
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    # Create dataset
+    dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
+
+    # Create dataloader
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
+
+    return dataloader
+
+
+
+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)
+
+        # We 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 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
+
+
+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 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 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 attention 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 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
+
+
+def generate_text_simple(model, idx, max_new_tokens, context_size):
+    # idx is (B, T) array of indices in the current context
+    for _ in range(max_new_tokens):
+
+        # Crop current context if it exceeds the supported context size
+        # E.g., if LLM supports only 5 tokens, and the context size is 10
+        # then only the last 5 tokens are used as context
+        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_token, vocab_size) becomes (batch, vocab_size)
+        logits = logits[:, -1, :]
+
+        # Get the idx of the vocab entry with the highest logits value
+        idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch, 1)
+
+        # Append sampled index to the running sequence
+        idx = torch.cat((idx, idx_next), dim=1)  # (batch, n_tokens+1)
+
+    return idx
+
+
+def generate(model, idx, max_new_tokens, context_size, 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
+
+            # New (not in book): numerical stability tip to get equivalent results on mps device
+            # subtract rowwise max before softmax
+            logits = logits - logits.max(dim=-1, keepdim=True).values
+
+            # 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 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
+
+        # Same as before: append sampled index to the running sequence
+        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)
+
+    return idx
+
+
+def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
+                       eval_freq, eval_iter, start_context, tokenizer):
+    # Initialize lists to track losses and tokens seen
+    train_losses, val_losses, track_tokens_seen = [], [], []
+    tokens_seen, global_step = 0, -1
+
+    # Main training loop
+    for epoch in range(num_epochs):
+        model.train()  # Set model to training mode
+
+        for input_batch, target_batch in train_loader:
+            optimizer.zero_grad()  # Reset loss gradients from previous batch iteration
+            loss = calc_loss_batch(input_batch, target_batch, model, device)
+            loss.backward()  # Calculate loss gradients
+            optimizer.step()  # Update model weights using loss gradients
+            tokens_seen += input_batch.numel()
+            global_step += 1
+
+            # Optional evaluation step
+            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(f"Ep {epoch+1} (Step {global_step:06d}): "
+                      f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
+
+        # Print a sample text after each epoch
+        generate_and_print_sample(
+            model, tokenizer, device, start_context
+        )
+
+    return train_losses, val_losses, track_tokens_seen
+
+
+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 assign(left, right):
+    if left.shape != right.shape:
+        raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
+    return torch.nn.Parameter(torch.tensor(right))
+
+
+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 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 plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
+    fig, ax1 = plt.subplots(figsize=(5, 3))
+
+    # Plot training and validation loss against epochs
+    ax1.plot(epochs_seen, train_losses, label="Training loss")
+    ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
+    ax1.set_xlabel("Epochs")
+    ax1.set_ylabel("Loss")
+    ax1.legend(loc="upper right")
+    ax1.xaxis.set_major_locator(MaxNLocator(integer=True))  # only show integer labels on x-axis
+
+    # Create a second x-axis for tokens seen
+    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
+    ax2.plot(tokens_seen, train_losses, alpha=0)  # Invisible plot for aligning ticks
+    ax2.set_xlabel("Tokens seen")
+
+    fig.tight_layout()  # Adjust layout to make room
+    plt.savefig("loss-plot.pdf")
+    plt.show()
+
+def main():
+    GPT_CONFIG_124M = {
+        "vocab_size": 50257,     # Vocabulary size
+        "context_length": 1024,  # Context length
+        "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
+    }
+
+    torch.manual_seed(123)
+    model = GPTModel(GPT_CONFIG_124M)
+    model.eval()  # disable dropout
+
+    start_context = "Hi, there"
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+    encoded = tokenizer.encode(start_context)
+    encoded_tensor = torch.tensor(encoded).unsqueeze(0)
+
+    print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
+    print("\nInput text:", start_context)
+    print("Encoded input text:", encoded)
+    print("encoded_tensor.shape:", encoded_tensor.shape)
+
+    out = generate_text_simple(
+        model=model,
+        idx=encoded_tensor,
+        max_new_tokens=10,
+        context_size=GPT_CONFIG_124M["context_length"]
+    )
+    decoded_text = tokenizer.decode(out.squeeze(0).tolist())
+
+    print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
+    print("\nOutput:", out)
+    print("Output length:", len(out[0]))
+    print("Output text:", decoded_text)
+
+
+if __name__ == "__main__":
+    main()