Upload model.py

model.py

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+"""
+nano GPT: A tiny GPT model built from scratch in pure PyTorch.
+
+This is a step-by-step tutorial implementation following Andrej Karpathy's
+build-nanogpt approach. Every piece is explicit and commented.
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from dataclasses import dataclass
+
+
+# ---------------------------------------------------------------------------
+# Step 1: Configuration
+# ---------------------------------------------------------------------------
+# We define all hyperparameters in a single dataclass so they are easy to
+# tweak without hunting through the code.
+
+@dataclass
+class GPTConfig:
+    block_size: int = 256      # maximum sequence length (context length)
+    vocab_size: int = 65       # number of unique characters in our dataset
+    n_layer: int = 4           # number of transformer blocks
+    n_head: int = 4            # number of attention heads per block
+    n_embd: int = 256          # embedding dimension (hidden size)
+    dropout: float = 0.0       # dropout probability (0 for small overfit-prone runs)
+
+
+# ---------------------------------------------------------------------------
+# Step 2: Causal Self-Attention
+# ---------------------------------------------------------------------------
+# This is the heart of the transformer. For each token we compute three
+# vectors: Query, Key, and Value.
+#
+#   Query: "What am I looking for?"
+#   Key:   "What do I contain?"
+#   Value: "What information do I have?"
+#
+# We then compute attention scores = Q @ K.T, mask future tokens so the
+# model cannot "cheat" by looking ahead, and take a weighted sum of Values.
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0, "n_embd must be divisible by n_head"
+
+        # One linear layer projects input into Q, K, V concatenated together.
+        # Output shape: (B, T, 3 * n_embd)
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+
+        # Output projection back to n_embd
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+
+        # Register a causal mask (lower-triangular) so we never attend to future tokens.
+        # We do this once at init instead of recomputing every forward pass.
+        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: torch.Tensor) -> torch.Tensor:
+        B, T, C = x.size()  # batch, sequence length, embedding dim
+
+        # 1. Compute Q, K, V
+        qkv = self.c_attn(x)                     # (B, T, 3*C)
+        q, k, v = qkv.split(self.n_embd, dim=2)  # each (B, T, C)
+
+        # 2. Reshape into (B, n_head, T, head_size) for multi-head attention
+        head_size = C // self.n_head
+        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)   # (B, nh, T, hs)
+        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)   # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)   # (B, nh, T, hs)
+
+        # 3. Compute attention scores: (B, nh, T, hs) @ (B, nh, hs, T) -> (B, nh, T, T)
+        # We scale by 1/sqrt(head_size) to keep gradients stable.
+        att = (q @ k.transpose(-2, -1)) * (1.0 / (head_size ** 0.5))
+
+        # 4. Apply causal mask: set future positions to -inf so softmax gives 0
+        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))
+
+        # 5. Softmax to get probability distribution over past tokens
+        att = F.softmax(att, dim=-1)
+
+        # 6. Weighted sum of values: (B, nh, T, T) @ (B, nh, T, hs) -> (B, nh, T, hs)
+        y = att @ v
+
+        # 7. Concatenate heads back together: (B, nh, T, hs) -> (B, T, nh*hs) = (B, T, C)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+
+        # 8. Final output projection
+        y = self.c_proj(y)
+        return y
+
+
+# ---------------------------------------------------------------------------
+# Step 3: Feed-Forward Network (MLP)
+# ---------------------------------------------------------------------------
+# After attention, each token gets its own private "thinking" step through
+# a simple two-layer MLP with a GELU non-linearity.
+
+class MLP(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        # Expand by 4x (common in transformers) then project back down
+        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.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+
+# ---------------------------------------------------------------------------
+# Step 4: Transformer Block
+# ---------------------------------------------------------------------------
+# A block = Attention -> Add & Norm -> MLP -> Add & Norm
+# We use **pre-norm**: normalize BEFORE applying attention/MLP.
+# This is what modern models (GPT-2, GPT-3, Llama, etc.) use.
+
+class Block(nn.Module):
+    def __init__(self, config: GPTConfig):
+        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: torch.Tensor) -> torch.Tensor:
+        # Pre-norm residual connections
+        x = x + self.attn(self.ln_1(x))   # attention branch
+        x = x + self.mlp(self.ln_2(x))    # MLP branch
+        return x
+
+
+# ---------------------------------------------------------------------------
+# Step 5: Full GPT Model
+# ---------------------------------------------------------------------------
+# Putting it all together:
+#   1. Token embedding table (wte): maps character index -> vector
+#   2. Position embedding table (wpe): maps position index -> vector
+#   3. Stack of N transformer blocks
+#   4. Final layer norm
+#   5. Language model head: projects back to vocab_size logits
+
+class GPT(nn.Module):
+    def __init__(self, config: GPTConfig):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict({
+            "wte": nn.Embedding(config.vocab_size, config.n_embd),      # token embeddings
+            "wpe": nn.Embedding(config.block_size, config.n_embd),      # position embeddings
+            "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 tying: share the token embedding weights with the output projection.
+        # This saves parameters and often improves training.
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # Initialize weights
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            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: torch.Tensor,
+        targets: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """
+        idx:    (B, T)  integer token indices
+        targets:(B, T)  integer targets for next-token prediction (optional)
+        returns: logits (B, T, vocab_size), loss (scalar or None)
+        """
+        B, T = idx.size()
+        assert T <= self.config.block_size, f"Sequence length {T} exceeds block_size {self.config.block_size}"
+
+        # 1. Token + position embeddings
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)          # (T,)
+        tok_emb = self.transformer.wte(idx)                                     # (B, T, C)
+        pos_emb = self.transformer.wpe(pos)                                     # (T, C)
+        x = tok_emb + pos_emb                                                   # (B, T, C)
+
+        # 2. Pass through transformer blocks
+        for block in self.transformer.h:
+            x = block(x)
+
+        # 3. Final layer norm
+        x = self.transformer.ln_f(x)
+
+        # 4. Project to vocabulary logits
+        logits = self.lm_head(x)                                                # (B, T, vocab_size)
+
+        # 5. Compute cross-entropy loss if targets are provided
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(
+                logits.view(-1, logits.size(-1)),
+                targets.view(-1),
+                ignore_index=-1,
+            )
+
+        return logits, loss
+
+    def generate(
+        self,
+        idx: torch.Tensor,
+        max_new_tokens: int,
+        temperature: float = 1.0,
+        top_k: int | None = None,
+    ) -> torch.Tensor:
+        """
+        Generate new tokens autoregressively.
+        idx: (B, T) starting token indices
+        """
+        for _ in range(max_new_tokens):
+            # Crop to block_size so we never exceed context length
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+
+            # Forward pass
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :]  # take logits for the last token only: (B, vocab_size)
+
+            # Optional top-k sampling
+            if top_k is not None:
+                v, _ = torch.topk(logits, top_k, dim=-1)
+                logits[logits < v[:, [-1]]] = float("-inf")
+
+            # Apply temperature and softmax
+            probs = F.softmax(logits / temperature, dim=-1)
+
+            # Sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)  # (B, 1)
+            idx = torch.cat((idx, idx_next), dim=1)              # (B, T+1)
+
+        return idx