Add cache implementation using llama state

llama_cpp/llama.py

@@ -12,12 +12,22 @@ from .llama_types import *
 
 
 class LlamaCache:
-    """Cache for a llama.cpp model.
+    """Cache for a llama.cpp model."""
 
-    NOTE: This implementation currently only tells the Llama class to avoid reprocessing bytes and continue from the last
-    completion. It does not actually cache the results."""
+    def __init__(self):
+        self.cache_state: Dict[Sequence[llama_cpp.llama_token], "LlamaState"] = dict()
 
-    pass
+    def __getitem__(
+        self, key: Sequence[llama_cpp.llama_token]
+    ) -> Optional["LlamaState"]:
+        return self.cache_state.get(tuple(key), None)
+
+    def __contains__(self, key: Sequence[llama_cpp.llama_token]) -> bool:
+        return tuple(key) in self.cache_state
+
+    def __setitem__(self, key: Sequence[llama_cpp.llama_token], value: "LlamaState"):
+        self.cache_state = dict()  # NOTE: Currently limit to one cache entry.
+        self.cache_state[tuple(key)] = value
 
 
 class LlamaState:
@@ -100,13 +110,7 @@ class Llama:
         self.eval_tokens: Deque[llama_cpp.llama_token] = deque(maxlen=n_ctx)
         self.eval_logits: Deque[List[float]] = deque(maxlen=n_ctx)
 
-        ### HACK: This is a hack to work around the fact that the llama.cpp API does not yet support
-        ###       saving and restoring state, this allows us to continue a completion if the last
-        ###       completion_bytes is a prefix to the prompt passed in. However this is actually incorrect
-        ###       because it does not take into account stop tokens which have been processed by the model.
-        self._completion_bytes: List[bytes] = []
-        self._cache: Optional[LlamaCache] = None
-        ###
+        self.cache: Optional[LlamaCache] = None
 
         self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
 
@@ -182,7 +186,7 @@ class Llama:
         Args:
             cache: The cache to set.
         """
-        self._cache = cache
+        self.cache = cache
 
     def reset(self):
         """Reset the model state."""
@@ -287,10 +291,9 @@ class Llama:
             The generated tokens.
         """
         assert self.ctx is not None
-        ### HACK
+
         if (
             reset
-            and self._cache
             and len(self.eval_tokens) > 0
             and self.eval_tokens == tokens[: len(self.eval_tokens)]
         ):
@@ -298,7 +301,7 @@ class Llama:
                 print("generate cache hit", file=sys.stderr)
             reset = False
             tokens = tokens[len(self.eval_tokens) :]
-        ###
+
         if reset:
             self.reset()
         while True:
@@ -415,20 +418,10 @@ class Llama:
                 "logprobs is not supported for models created with logits_all=False"
             )
 
-        ### HACK
-        reset: bool = True
-        _prompt: bytes = prompt.encode("utf-8")
-        _completion: bytes = b"".join(self._completion_bytes)
-        if len(_completion) and self._cache and _prompt.startswith(_completion):
+        if self.cache and prompt_tokens in self.cache:
             if self.verbose:
-                print("completion cache hit", file=sys.stderr)
-            reset = False
-            _prompt = _prompt[len(_completion) :]
-            prompt_tokens = self.tokenize(b" " + _prompt)
-            self._completion_bytes.append(_prompt)
-        else:
-            self._completion_bytes = [prompt.encode("utf-8")]
-        ###
+                print("cache hit", file=sys.stderr)
+            self.load_state(self.cache[prompt_tokens])
 
         finish_reason = "length"
         for token in self.generate(
@@ -437,12 +430,16 @@ class Llama:
             top_p=top_p,
             temp=temperature,
             repeat_penalty=repeat_penalty,
-            reset=reset,
         ):
             if token == llama_cpp.llama_token_eos():
                 text = self.detokenize(completion_tokens)
                 finish_reason = "stop"
                 break
+
+            if self.cache and len(completion_tokens) == 0:
+                if prompt_tokens not in self.cache:
+                    self.cache[prompt_tokens] = self.save_state()
+
             completion_tokens.append(token)
 
             all_text = self.detokenize(completion_tokens)
@@ -467,9 +464,6 @@ class Llama:
                             break
                 text = all_text[: len(all_text) - longest]
                 returned_characters += len(text[start:])
-                ### HACK
-                self._completion_bytes.append(text[start:])
-                ###
                 yield {
                     "id": completion_id,
                     "object": "text_completion",
@@ -491,9 +485,6 @@ class Llama:
                 break
 
         if stream:
-            ### HACK
-            self._completion_bytes.append(text[returned_characters:])
-            ###
             yield {
                 "id": completion_id,
                 "object": "text_completion",
@@ -510,9 +501,6 @@ class Llama:
             }
             return
 
-        ### HACK
-        self._completion_bytes.append(text)
-        ###
         text_str = text.decode("utf-8")
 
         if echo: