Upload gpt.py

gpt.py

@@ -0,0 +1,198 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from dataclasses import dataclass
+
+
+@dataclass
+class GPTConfig:
+    vocab_size: int
+    block_size: int
+    n_embd: int
+    n_head: int
+    n_layer: int
+    dropout: float = 0.0
+    device: str = "cpu" # "cpu" or "cuda" or "mps"
+
+
+class Head(nn.Module):
+    """one head of self-attention"""
+
+    def __init__(self, config, head_size):
+        super().__init__()
+        self.key = nn.Linear(config.n_embd, head_size, bias=False)
+        self.query = nn.Linear(config.n_embd, head_size, bias=False)
+        self.value = nn.Linear(config.n_embd, head_size, bias=False)
+        self.register_buffer(
+            "tril", torch.tril(torch.ones(config.block_size, config.block_size))
+        )
+        self.dropout = nn.Dropout(config.dropout)
+        self.config = config
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x)  # (B,T,hs)
+        q = self.query(x)  # (B,T,hs)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2, -1) * C**-0.5  # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf"))  # (B, T, T)
+        wei = F.softmax(wei, dim=-1)  # (B, T, T)
+        wei = self.dropout(wei)
+        v = self.value(x)  # (B,T,hs)
+        out = wei @ v  # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+        return out
+
+
+class MultiHeadAttention(nn.Module):
+    """multiple heads of self-attention in parallel"""
+
+    def __init__(self, config, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList(
+            [Head(config, head_size) for _ in range(config.n_head)]
+        )
+        self.proj = nn.Linear(config.n_embd, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        return out
+
+
+class FeedFoward(nn.Module):
+    """a simple linear layer followed by a non-linearity"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(config.n_embd, 4 * config.n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * config.n_embd, config.n_embd),
+            nn.Dropout(config.dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Block(nn.Module):
+    """Transformer block: communication followed by computation"""
+
+    def __init__(self, config):
+        super().__init__()
+        head_size = config.n_embd // config.n_head
+        self.sa = MultiHeadAttention(config, head_size)
+        self.ffwd = FeedFoward(config)
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        # each token directly reads off the logits for the next token from a lookup table
+        self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd)  
+        self.position_embedding_table = nn.Embedding(config.block_size, config.n_embd)
+        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)  # final layer norm
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(idx)  # (B,T,C)
+        pos_emb = self.position_embedding_table(
+            torch.arange(T, device=idx.device)
+        )  # (T,C)
+        x = tok_emb + pos_emb  # (B,T,C)
+        x = self.blocks(x)  # (B,T,C)
+        x = self.ln_f(x)  # (B,T,C)
+        logits = self.lm_head(x)  # (B,T,vocab_size)
+
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B * T, C)
+            targets = targets.view(B * T)
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens, stop_token_id=None):
+        for _ in range(max_new_tokens):
+            # crop context to the last block_size tokens
+            idx_cond = idx[:, -self.config.block_size :]
+
+            # get the predictions
+            logits, _ = self(idx_cond)
+
+            # get probabilities
+            logits = logits[:, -1, :]
+            probs = F.softmax(logits, dim=-1)
+
+            _, idx_next = torch.topk(probs, k=1, dim=-1)
+
+            # append to the sequence and keep going
+            idx = torch.cat((idx, idx_next), dim=1)
+
+            # stopping rule to avoid unnecesary inference
+            if stop_token_id is not None and idx_next.item() == stop_token_id:
+                # We hit '$', so we stop inference
+                return idx
+            # -------------------------------
+
+        return idx
+
+    def train_step(self, optimizer, idx, target_idx, importance_weight=1.0):
+        """
+        Single training step for RL correction.
+        idx: (B, T) tensor of context inputs
+        target_idx: (B, 1) tensor (or scalar tensor) of the target token to predict
+        importance_weight: float multiplier for the loss
+        """
+        self.train()
+        optimizer.zero_grad()
+
+        # 1. Forward Pass
+        # We only care about the last token prediction for the loss
+        # The input 'idx' should be the full context up to the target
+
+        logits, _ = self(idx)
+
+        # Get the logits for the VERY LAST token (the one we are trying to predict)
+        # logits shape: (B, T, V) -> we want (B, -1, V)
+        last_token_logits = logits[:, -1, :]  # Shape: (B, VocabSize)
+
+        # 2. Loss Calculation
+        # target_idx should be (B) or (1)
+        if target_idx.dim() == 2:
+            target_idx = target_idx.squeeze(-1)
+
+        loss = F.cross_entropy(last_token_logits, target_idx, reduction="none")
+
+        # Apply importance weight
+        weighted_loss = loss * importance_weight
+        final_loss = weighted_loss.mean()
+
+        # 3. Update
+        final_loss.backward()
+
+        # Clip gradients to prevent explosion during online updates
+        torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=0.5)
+
+        optimizer.step()
+
+        # Return probs for visualization
+        with torch.no_grad():
+            probs = F.softmax(last_token_logits, dim=-1)
+
+        return final_loss.item(), probs