Bugfix: Check cache keys as prefix to prompt tokens

llama_cpp/llama.py

@@ -4,7 +4,7 @@ import uuid
 import time
 import math
 import multiprocessing
-from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque
+from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque, Tuple
 from collections import deque
 
 from . import llama_cpp
@@ -15,15 +15,34 @@ class LlamaCache:
     """Cache for a llama.cpp model."""
 
     def __init__(self):
-        self.cache_state: Dict[Sequence[llama_cpp.llama_token], "LlamaState"] = dict()
+        self.cache_state: Dict[Tuple[llama_cpp.llama_token, ...], "LlamaState"] = dict()
+
+    def _sorted_keys(self) -> List[Tuple[llama_cpp.llama_token, ...]]:
+        return [
+            key
+            for _, key in sorted(
+                ((len(key), key) for key in self.cache_state.keys()), reverse=True
+            )
+        ]
+
+    def _find_key(
+        self, key: Tuple[llama_cpp.llama_token, ...]
+    ) -> Optional[Tuple[llama_cpp.llama_token, ...]]:
+        for k in self._sorted_keys():
+            if key[: len(k)] == k:
+                return k
+        return None
 
     def __getitem__(
         self, key: Sequence[llama_cpp.llama_token]
     ) -> Optional["LlamaState"]:
-        return self.cache_state.get(tuple(key), None)
+        _key = self._find_key(tuple(key))
+        if _key is None:
+            return None
+        return self.cache_state[_key]
 
     def __contains__(self, key: Sequence[llama_cpp.llama_token]) -> bool:
-        return tuple(key) in self.cache_state
+        return self._find_key(tuple(key)) is not None
 
     def __setitem__(self, key: Sequence[llama_cpp.llama_token], value: "LlamaState"):
         self.cache_state = dict()  # NOTE: Currently limit to one cache entry.
@@ -295,7 +314,7 @@ class Llama:
         if (
             reset
             and len(self.eval_tokens) > 0
-            and self.eval_tokens == tokens[: len(self.eval_tokens)]
+            and tuple(self.eval_tokens) == tuple(tokens[: len(self.eval_tokens)])
         ):
             if self.verbose:
                 print("generate cache hit", file=sys.stderr)
@@ -438,6 +457,8 @@ class Llama:
 
             if self.cache and len(completion_tokens) == 0:
                 if prompt_tokens not in self.cache:
+                    if self.verbose:
+                        print("cache miss", file=sys.stderr)
                     self.cache[prompt_tokens] = self.save_state()
 
             completion_tokens.append(token)