remove need for cache key computation during runtime

evoprompt/models.py

-import functools
+import hashlib
 import inspect
+import json
 import logging
 import warnings
 from abc import ABC, abstractmethod
@@ -35,11 +36,6 @@ class LLMModel(ABC):
             raise ValueError("Model %s does not exist", name)
         return cls.models[name](options=options, **kwargs)
 
-    @functools.lru_cache
-    def _compute_cache_key(self, name, **kwargs):
-        # we use a tuple of the model name, the options, and the kwargs as the cache key
-        return (name,) + tuple((key, value) for key, value in kwargs.items())
-
     def __init__(self, options: Namespace, **kwargs):
         self.usage = ModelUsage()
 
@@ -120,17 +116,28 @@ class LLMModel(ABC):
 
         if use_cache:
             # use cached function call
-            cache_key = self._compute_cache_key(
-                self.__class__.__name__, **self.options.__dict__, **self.kwargs
-            )
-            return self._call_model_cached(model_completion_fn, cache_key, **kwargs)
+            return self._call_model_cached(model_completion_fn, **kwargs)
         else:
             return model_completion_fn(**kwargs)
 
-    def _call_model_cached(self, func, cache_key, *args, **kwargs):
+    def _call_model_cached(self, func, *args, **kwargs):
         # `cache_key` is added to the cache key (e.g., to distinguish between different models), but it is not used in the function
         return func(*args, **kwargs)
 
+    @property
+    def model_cache_key(self):
+        unique_options_key = json.dumps(
+            vars(self.options),
+            sort_keys=True,
+        ) + json.dumps(
+            self.kwargs,
+            sort_keys=True,
+        )
+        cache_key = (
+            str(self.model_name) + hashlib.sha1(unique_options_key.encode()).hexdigest()
+        )
+        return cache_key
+
     @classmethod
     @abstractmethod
     def register_arguments(cls, parser: ArgumentParser):
@@ -196,7 +203,6 @@ class Llama(LLMModel):
         messages: list[dict[str, str]],
         history: list[dict[str, str]] | None = None,
     ):
-
         if system_message is not None:
             prompt = system_message + prompt
         return {"prompt": prompt}
@@ -216,10 +222,6 @@ class Llama(LLMModel):
         usage = ModelUsage(**response["usage"])
         return response_text, usage
 
-    @property
-    def model_cache_key(self):
-        return self.model_name
-
     @classmethod
     def register_arguments(cls, parser: ArgumentParser):
         group = parser.add_argument_group(f"{cls.__name__} model arguments")
@@ -326,10 +328,6 @@ class OpenAI(LLMModel):
         usage = ModelUsage(**response.usage.__dict__)
         return response_text, usage
 
-    @property
-    def model_cache_key(self):
-        return self.model_name
-
     @classmethod
     def register_arguments(cls, parser: ArgumentParser):
         group = parser.add_argument_group("OpenAI model arguments")