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diff --git a/‎llama_cpp/llama.py
Copy file name to clipboardExpand all lines: llama_cpp/llama.py
+65-4Lines changed: 65 additions & 4 deletions b/‎llama_cpp/llama.py
Copy file name to clipboardExpand all lines: llama_cpp/llama.py
+65-4Lines changed: 65 additions & 4 deletions
diff --git a/‎llama_cpp/server/__main__.py
Copy file name to clipboardExpand all lines: llama_cpp/server/__main__.py
+4-1Lines changed: 4 additions & 1 deletion b/‎llama_cpp/server/__main__.py
Copy file name to clipboardExpand all lines: llama_cpp/server/__main__.py
+4-1Lines changed: 4 additions & 1 deletion
@@ -11,6 +11,15 @@
 from .llama_types import *
 
 
+class LlamaCache:
+    """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."""
+
+    pass
+
+
 class Llama:
     """High-level Python wrapper for a llama.cpp model."""
 
@@ -82,6 +91,14 @@ def __init__(
         self.n_past = 0
         self.all_logits: List[List[float]] = []  # TODO: Use an array instead of a list.
 
+        ### 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.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
 
         if not os.path.exists(model_path):
@@ -135,6 +152,14 @@ def detokenize(self, tokens: List[llama_cpp.llama_token]) -> bytes:
             output += llama_cpp.llama_token_to_str(self.ctx, token)
         return output
 
+    def set_cache(self, cache: Optional[LlamaCache]):
+        """Set the cache.
+
+        Args:
+            cache: The cache to set.
+        """
+        self._cache = cache
+
     def reset(self):
         """Reset the model state."""
         self.last_n_tokens_data.extend(
@@ -245,6 +270,17 @@ def generate(
             The generated tokens.
         """
         assert self.ctx is not None
+        ### HACK
+        if (
+            reset
+            and self._cache
+            and len(self.tokens) > 0
+            and self.tokens == tokens[: len(self.tokens)]
+        ):
+            if self.verbose:
+                print("generate cache hit", file=sys.stderr)
+            reset = False
+        ###
         if reset:
             self.reset()
         while True:
@@ -361,13 +397,29 @@ def _create_completion(
                 "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.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")]
+        ###
+
         finish_reason = "length"
         for token in self.generate(
             prompt_tokens,
             top_k=top_k,
             top_p=top_p,
             temp=temperature,
             repeat_penalty=repeat_penalty,
+            reset=reset,
         ):
             if token == llama_cpp.llama_token_eos():
                 text = self.detokenize(completion_tokens)
@@ -397,6 +449,9 @@ def _create_completion(
                             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",
@@ -418,6 +473,9 @@ def _create_completion(
                 break
 
         if stream:
+            ### HACK
+            self._completion_bytes.append(text[returned_characters:])
+            ###
             yield {
                 "id": completion_id,
                 "object": "text_completion",
@@ -434,13 +492,16 @@ def _create_completion(
             }
             return
 
-        text = text.decode("utf-8")
+        ### HACK
+        self._completion_bytes.append(text)
+        ###
+        text_str = text.decode("utf-8")
 
         if echo:
-            text = prompt + text
+            text_str = prompt + text_str
 
         if suffix is not None:
-            text = text + suffix
+            text_str = text_str + suffix
 
         logprobs_or_none: Optional[CompletionLogprobs] = None
         if logprobs is not None:
@@ -493,7 +554,7 @@ def _create_completion(
             "model": self.model_path,
             "choices": [
                 {
-                    "text": text,
+                    "text": text_str,
                     "index": 0,
                     "logprobs": logprobs_or_none,
                     "finish_reason": finish_reason,
 
@@ -35,6 +35,7 @@ class Settings(BaseSettings):
     embedding: bool = True
     last_n_tokens_size: int = 64
     logits_all: bool = False
+    cache: bool = False  # WARNING: This is an experimental feature
 
 
 app = FastAPI(
@@ -60,6 +61,9 @@ class Settings(BaseSettings):
     n_ctx=settings.n_ctx,
     last_n_tokens_size=settings.last_n_tokens_size,
 )
+if settings.cache:
+    cache = llama_cpp.LlamaCache()
+    llama.set_cache(cache)
 llama_lock = Lock()
 
 
@@ -68,7 +72,6 @@ def get_llama():
         yield llama
 
 
-
 class CreateCompletionRequest(BaseModel):
     prompt: Union[str, List[str]]
     suffix: Optional[str] = Field(None)