Configurable Chat Formats (ggml-org#711)

* Add configurable default chat completion format.

* Remove chat_template file to avoid circular import

* Update llama_types

* Add chat format

llama_cpp/llama.py

@@ -24,6 +24,7 @@
 from . import llama_cpp
 from .llama_types import *
 from .llama_grammar import LlamaGrammar
+from . import llama_chat_format
 
 import numpy as np
 import numpy.typing as npt
@@ -243,6 +244,8 @@ def __init__(
         lora_path: Optional[str] = None,
         # Backend Params
         numa: bool = False,
+        # Chat Format Params
+        chat_format: str = "llama-2",
         # Misc
         verbose: bool = True,
         # Extra Params
@@ -273,6 +276,7 @@ def __init__(
             lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
             numa: Enable NUMA support. (NOTE: The initial value of this parameter is used for the remainder of the program as this value is set in llama_backend_init)
+            chat_format: String specifying the chat format to use when calling create_chat_completion.
             verbose: Print verbose output to stderr.
             kwargs: Unused keyword arguments (for additional backwards compatibility).
 
@@ -388,6 +392,8 @@ def __init__(
 
         if self.verbose:
             print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
+
+        self.chat_format = chat_format
 
         self._n_vocab = self.n_vocab()
         self._n_ctx = self.n_ctx()
@@ -1565,9 +1571,21 @@ def _convert_text_completion_chunks_to_chat(
                 ],
             }
 
+    def _convert_completion_to_chat(
+        self,
+        completion_or_chunks: Union[Completion, Iterator[CompletionChunk]],
+        stream: bool = False,
+    ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
+        if stream:
+            chunks: Iterator[CompletionChunk] = completion_or_chunks  # type: ignore
+            return self._convert_text_completion_chunks_to_chat(chunks)
+        else:
+            completion: Completion = completion_or_chunks  # type: ignore
+            return self._convert_text_completion_to_chat(completion)
+
     def create_chat_completion(
         self,
-        messages: List[ChatCompletionMessage],
+        messages: List[ChatCompletionRequestMessage],
         functions: Optional[List[ChatCompletionFunction]] = None,
         function_call: Optional[Union[str, ChatCompletionFunctionCall]] = None,
         temperature: float = 0.2,
@@ -1602,26 +1620,28 @@ def create_chat_completion(
         Returns:
             Generated chat completion or a stream of chat completion chunks.
         """
-        stop = (
-            stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
-        )
-        chat_history = "".join(
-            f'### {"Human" if message["role"] == "user" else "Assistant"}:{message["content"]}'
-            for message in messages
+
+        format = llama_chat_format.get_chat_format(self.chat_format)
+        result = format(
+            messages=messages,
         )
-        PROMPT = chat_history + "### Assistant:"
-        PROMPT_STOP = ["### Assistant:", "### Human:"]
-        completion_or_chunks = self(
-            prompt=PROMPT,
-            stop=PROMPT_STOP + stop,
+        prompt = result.prompt
+        if result.stop is not None:
+            stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
+            rstop = result.stop if isinstance(result.stop, list) else [result.stop]
+            stop = stop + rstop
+
+        completion_or_chunks = self.create_completion(
+            prompt=prompt,
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
             stream=stream,
+            stop=stop,
             max_tokens=max_tokens,
-            repeat_penalty=repeat_penalty,
             presence_penalty=presence_penalty,
             frequency_penalty=frequency_penalty,
+            repeat_penalty=repeat_penalty,
             tfs_z=tfs_z,
             mirostat_mode=mirostat_mode,
             mirostat_tau=mirostat_tau,
@@ -1630,12 +1650,7 @@ def create_chat_completion(
             logits_processor=logits_processor,
             grammar=grammar,
         )
-        if stream:
-            chunks: Iterator[CompletionChunk] = completion_or_chunks  # type: ignore
-            return self._convert_text_completion_chunks_to_chat(chunks)
-        else:
-            completion: Completion = completion_or_chunks  # type: ignore
-            return self._convert_text_completion_to_chat(completion)
+        return self._convert_completion_to_chat(completion_or_chunks, stream=stream)  # type: ignore
 
     def __del__(self):
         if hasattr(self, "model") and self.model is not None:
@@ -1675,6 +1690,8 @@ def __getstate__(self):
             lora_path=self.lora_path,
             # Backend Params
             numa=self.numa,
+            # Chat Format Params
+            chat_format=self.chat_format,
             # Misc
             verbose=self.verbose,
         )
@@ -1708,6 +1725,8 @@ def __setstate__(self, state):
             lora_path=state["lora_path"],
             # Backend Params
             numa=state["numa"],
+            # Chat Format Params
+            chat_format=state["chat_format"],
             # Misc
             verbose=state["verbose"],
         )