feat(gpt): implement prefill phase for efficient prompt processing with KV-caching

Add prefill phase that processes entire prompt in single forward pass before generation loop, extracting logits only for last token position and populating KV-cache

nanochat/gpt.py

@@ -22,7 +22,6 @@ import torch.nn.functional as F
 from nanochat.common import get_dist_info, print0
 from nanochat.muon import Muon, DistMuon
 from nanochat.adamw import DistAdamW
-from nanochat.engine import KVCache
 
 @dataclass
 class GPTConfig:
@@ -305,16 +304,13 @@ class GPT(nn.Module):
         if temperature > 0:
             rng = torch.Generator(device=device)
             rng.manual_seed(seed)
-        # Initialize KV cache for efficient generation
-        kv_length_hint = len(tokens) + max_tokens
-        kv_cache = KVCache(
-            batch_size=1,
-            num_heads=self.config.n_kv_head,
-            seq_len=kv_length_hint,
-            head_dim=self.config.n_embd // self.config.n_head,
-            num_layers=self.config.n_layer
-        )
         ids = torch.tensor([tokens], dtype=torch.long, device=device) # add batch dim
+        
+        # Prefill phase: process entire prompt in a single forward pass
+        logits = self.forward(ids, kv_cache=kv_cache)  # (B, T_prompt, vocab_size)
+        logits = logits[:, -1, :]  # (B, vocab_size) - only need last token's logits
+        
+        # Incremental decoding: generate tokens one at a time using cached K/V pairs
         for _ in range(max_tokens):
             logits = self.forward(ids) # (B, T, vocab_size)
             logits = logits[:, -1, :] # (B, vocab_size)