add llama3 tokenizer and support for custom tokenizer

litellm/utils.py

@@ -3687,6 +3687,10 @@ def _select_tokenizer(model: str):
     elif "llama-2" in model.lower() or "replicate" in model.lower():
         tokenizer = Tokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
         return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
+    # llama3
+    elif "llama-3" in model.lower():
+        tokenizer = Tokenizer.from_pretrained("Xenova/llama-3-tokenizer")
+        return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
     # default - tiktoken
     else:
         return {"type": "openai_tokenizer", "tokenizer": encoding}
@@ -3881,10 +3885,41 @@ def calculage_img_tokens(
         tile_tokens = (base_tokens * 2) * tiles_needed_high_res
         total_tokens = base_tokens + tile_tokens
         return total_tokens
+    
+
+def create_pretrained_tokenizer(
+    identifier: str, 
+    revision="main", 
+    auth_token: Optional[str] = None
+):
+    """
+    Creates a tokenizer from an existing file on a HuggingFace repository to be used with `token_counter`.
+
+    Args:
+    identifier (str): The identifier of a Model on the Hugging Face Hub, that contains a tokenizer.json file
+    revision (str, defaults to main): A branch or commit id
+    auth_token (str, optional, defaults to None): An optional auth token used to access private repositories on the Hugging Face Hub
+    """
+
+    tokenizer = Tokenizer.from_pretrained(identifier, revision=revision, auth_token=auth_token)
+    return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
+
+
+def create_tokenizer(json: str):
+    """
+    Creates a tokenizer from a valid JSON string for use with `token_counter`.
+
+    Args:
+    json (str): A valid JSON string representing a previously serialized tokenizer
+    """
+
+    tokenizer = Tokenizer.from_str(json)
+    return {"type": "huggingface_tokenizer", "tokenizer": tokenizer}
 
 
 def token_counter(
     model="",
+    custom_tokenizer: Optional[dict] = None,
     text: Optional[Union[str, List[str]]] = None,
     messages: Optional[List] = None,
     count_response_tokens: Optional[bool] = False,
@@ -3894,13 +3929,14 @@ def token_counter(
 
     Args:
     model (str): The name of the model to use for tokenization. Default is an empty string.
+    tokenizer (Optional[dict]): A custom tokenizer created with the `create_pretrained_tokenizer` or `create_tokenizer` method. Must be a dictionary with a string value for `type` and Tokenizer for `tokenizer`. Default is None.
     text (str): The raw text string to be passed to the model. Default is None.
     messages (Optional[List[Dict[str, str]]]): Alternative to passing in text. A list of dictionaries representing messages with "role" and "content" keys. Default is None.
 
     Returns:
     int: The number of tokens in the text.
     """
-    # use tiktoken, anthropic, cohere or llama2's tokenizer depending on the model
+    # use tiktoken, anthropic, cohere, llama2, or llama3's tokenizer depending on the model
     is_tool_call = False
     num_tokens = 0
     if text == None:
@@ -3942,8 +3978,8 @@ def token_counter(
     elif isinstance(text, str):
         count_response_tokens = True  # user just trying to count tokens for a text. don't add the chat_ml +3 tokens to this
 
-    if model is not None:
+    if model is not None or custom_tokenizer is not None:
-        tokenizer_json = _select_tokenizer(model=model)
+        tokenizer_json = custom_tokenizer or _select_tokenizer(model=model)
         if tokenizer_json["type"] == "huggingface_tokenizer":
             print_verbose(
                 f"Token Counter - using hugging face token counter, for model={model}"