Configurable Chat Formats (#711)
* Add configurable default chat completion format. * Remove chat_template file to avoid circular import * Update llama_types * Add chat format
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Andrei
GitHub
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@ -24,6 +24,7 @@ import ctypes
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from . import llama_cpp
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from .llama_types import *
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from .llama_grammar import LlamaGrammar
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from . import llama_chat_format
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import numpy as np
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import numpy.typing as npt
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@ -243,6 +244,8 @@ class Llama:
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lora_path: Optional[str] = None,
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# Backend Params
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numa: bool = False,
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# Chat Format Params
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chat_format: str = "llama-2",
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# Misc
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verbose: bool = True,
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# Extra Params
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@ -273,6 +276,7 @@ class Llama:
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lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
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lora_path: Path to a LoRA file to apply to the model.
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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)
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chat_format: String specifying the chat format to use when calling create_chat_completion.
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verbose: Print verbose output to stderr.
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kwargs: Unused keyword arguments (for additional backwards compatibility).
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@ -388,6 +392,8 @@ class Llama:
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if self.verbose:
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print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
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self.chat_format = chat_format
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self._n_vocab = self.n_vocab()
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self._n_ctx = self.n_ctx()
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@ -1565,9 +1571,21 @@ class Llama:
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],
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}
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def _convert_completion_to_chat(
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self,
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completion_or_chunks: Union[Completion, Iterator[CompletionChunk]],
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stream: bool = False,
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) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
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if stream:
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chunks: Iterator[CompletionChunk] = completion_or_chunks # type: ignore
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return self._convert_text_completion_chunks_to_chat(chunks)
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else:
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completion: Completion = completion_or_chunks # type: ignore
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return self._convert_text_completion_to_chat(completion)
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def create_chat_completion(
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self,
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messages: List[ChatCompletionMessage],
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messages: List[ChatCompletionRequestMessage],
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functions: Optional[List[ChatCompletionFunction]] = None,
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function_call: Optional[Union[str, ChatCompletionFunctionCall]] = None,
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temperature: float = 0.2,
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@ -1602,26 +1620,28 @@ class Llama:
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Returns:
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Generated chat completion or a stream of chat completion chunks.
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"""
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stop = (
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stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
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format = llama_chat_format.get_chat_format(self.chat_format)
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result = format(
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messages=messages,
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)
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chat_history = "".join(
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f'### {"Human" if message["role"] == "user" else "Assistant"}:{message["content"]}'
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for message in messages
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)
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PROMPT = chat_history + "### Assistant:"
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PROMPT_STOP = ["### Assistant:", "### Human:"]
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completion_or_chunks = self(
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prompt=PROMPT,
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stop=PROMPT_STOP + stop,
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prompt = result.prompt
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if result.stop is not None:
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stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
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rstop = result.stop if isinstance(result.stop, list) else [result.stop]
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stop = stop + rstop
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completion_or_chunks = self.create_completion(
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prompt=prompt,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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stream=stream,
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stop=stop,
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max_tokens=max_tokens,
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repeat_penalty=repeat_penalty,
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presence_penalty=presence_penalty,
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frequency_penalty=frequency_penalty,
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repeat_penalty=repeat_penalty,
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tfs_z=tfs_z,
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mirostat_mode=mirostat_mode,
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mirostat_tau=mirostat_tau,
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@ -1630,12 +1650,7 @@ class Llama:
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logits_processor=logits_processor,
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grammar=grammar,
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)
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if stream:
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chunks: Iterator[CompletionChunk] = completion_or_chunks # type: ignore
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return self._convert_text_completion_chunks_to_chat(chunks)
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else:
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completion: Completion = completion_or_chunks # type: ignore
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return self._convert_text_completion_to_chat(completion)
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return self._convert_completion_to_chat(completion_or_chunks, stream=stream) # type: ignore
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def __del__(self):
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if hasattr(self, "model") and self.model is not None:
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@ -1675,6 +1690,8 @@ class Llama:
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lora_path=self.lora_path,
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# Backend Params
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numa=self.numa,
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# Chat Format Params
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chat_format=self.chat_format,
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# Misc
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verbose=self.verbose,
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)
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@ -1708,6 +1725,8 @@ class Llama:
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lora_path=state["lora_path"],
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# Backend Params
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numa=state["numa"],
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# Chat Format Params
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chat_format=state["chat_format"],
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# Misc
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verbose=state["verbose"],
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)
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292
llama_cpp/llama_chat_format.py
Normal file
292
llama_cpp/llama_chat_format.py
Normal file
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@ -0,0 +1,292 @@
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import dataclasses
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from typing import Any, Dict, List, Optional, Tuple, Union, Protocol
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from . import llama_types
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def _get_system_message(
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messages: List[llama_types.ChatCompletionRequestMessage],
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) -> str:
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"""Get the first system message."""
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for message in messages:
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if message["role"] == "system":
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return message["content"] or ""
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return ""
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def _map_roles(
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messages: List[llama_types.ChatCompletionRequestMessage], role_map: Dict[str, str]
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) -> List[Tuple[str, Optional[str]]]:
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"""Map the message roles."""
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output: List[Tuple[str, Optional[str]]] = []
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for message in messages:
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role = message["role"]
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if role in role_map:
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output.append((role_map[role], message["content"]))
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return output
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def _format_llama2(
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system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
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) -> str:
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"""Format the prompt with the llama2 style."""
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ret = system_message + sep
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for role, message in messages:
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if message:
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ret += message + " "
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else:
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ret += role + " "
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return ret
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def _format_add_colon_single(
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system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
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) -> str:
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"""Format the prompt with the add-colon-single style."""
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ret = system_message + sep
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for role, message in messages:
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if message:
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ret += role + ": " + message + sep
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else:
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ret += role + ":"
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return ret
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def _format_add_colon_two(
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system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
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) -> str:
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"""Format the prompt with the add-colon-two style."""
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seps = [sep, sep2]
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ret = system_message + seps[0]
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for i, (role, message) in enumerate(messages):
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if message:
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ret += role + ": " + message + seps[i % 2]
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else:
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ret += role + ":"
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return ret
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def _format_no_colon_single(
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system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
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) -> str:
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"""Format the prompt with the no-colon-single style."""
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ret = system_message
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for role, message in messages:
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if message:
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ret += role + message + sep
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else:
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ret += role
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return ret
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def _format_add_colon_space_single(
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system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
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) -> str:
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"""Format the prompt with the add-colon-space-single style."""
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ret = system_message + sep
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for role, message in messages:
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if message:
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ret += role + ": " + message + sep
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else:
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ret += role + ": " # must be end with a space
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return ret
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@dataclasses.dataclass
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class ChatFormatterResponse:
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prompt: str
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stop: Optional[Union[str, List[str]]] = None
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class ChatFormatter(Protocol):
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def __call__(
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self,
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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...
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_CHAT_FORMATS: Dict[str, ChatFormatter] = {}
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def register_chat_format(name: str):
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def decorator(f: ChatFormatter):
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_CHAT_FORMATS[name] = f
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return f
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return decorator
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def get_chat_format(name: str):
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try:
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return _CHAT_FORMATS[name]
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except KeyError:
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raise ValueError(
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f"Invalid chat format: {name} (valid formats: {list(_CHAT_FORMATS.keys())})"
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)
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@register_chat_format("llama-2")
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def format_llama2(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n"
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_roles = dict(user="[INST]", assistant="[/INST]")
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_sep = "\n\n"
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system_message = _get_system_message(messages)
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system_message = _system_template.format(system_message=system_message)
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_llama2(system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("alpaca")
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def format_alpaca(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_roles = dict(user="### Instruction", assistant="### Response")
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_sep = "\n\n"
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_sep2 = "</s>"
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system_message = _get_system_message(messages)
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_messages = _map_roles(messages, _roles)
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_prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("vicuna")
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def format(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_system_message = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
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_roles = dict(user="USER", assistant="ASSISTANT")
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_sep = " "
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_sep2 = "</s>"
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system_message = _system_message
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("oasst_llama")
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def format_oasst_llama(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n"
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_roles = dict(user="<|prompter|>", assistant="<|assistant|>")
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_sep = "</s>"
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system_message = _get_system_message(messages)
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system_message = _system_template.format(system_message=system_message)
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_no_colon_single(system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("openbuddy")
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def format_openbuddy(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_system_message = """Consider a conversation between User (a human) and Assistant (named Buddy).
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Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team. GitHub: https://github.com/OpenBuddy/OpenBuddy
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Buddy cannot access the Internet.
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Buddy can fluently speak the user's language (e.g. English, Chinese).
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Buddy can generate poems, stories, code, essays, songs, parodies, and more.
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Buddy possesses vast knowledge about the world, history, and culture.
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Buddy's responses are always safe, creative, high-quality, human-like, and interesting.
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Buddy strictly refuses to discuss political, NSFW, or other unsafe topics.
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User: Hi.
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Assistant: Hi, I'm Buddy, your AI assistant. How can I help you today?"""
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_roles = dict(user="User", assistant="Assistant")
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_sep = "\n"
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system_message = _system_message
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_add_colon_single(system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("redpajama-incite")
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def format_redpajama_incite(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_system_message = _get_system_message(messages)
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_roles = dict(user="<human>", assistant="<bot>")
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_sep = "\n"
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_stop = "<human>"
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system_message = _system_message
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_add_colon_single(system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt, stop=_stop)
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@register_chat_format("snoozy")
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def format_snoozy(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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system_template = "### Instruction:\n{system_message}"
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default_system_message = "The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response."
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_system_message = _get_system_message(messages)
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_system_message = (
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_system_message if _system_message != "" else default_system_message
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)
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system_message = system_template.format(system_message=_system_message)
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_roles = dict(user="### Prompt", assistant="### Response")
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_sep = "\n"
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_stop = "###"
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system_message = _system_message
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_add_colon_single(system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt, stop=_stop)
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@register_chat_format("phind")
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def format_phind(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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_roles = dict(user="### User Message", assistant="### Assistant")
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_sep = "\n\n"
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_system_message = "### System Prompt\nYou are an intelligent programming assistant."
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_messages = _map_roles(messages, _roles)
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_messages.append((_roles["assistant"], None))
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_prompt = _format_add_colon_single(_system_message, _messages, _sep)
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return ChatFormatterResponse(prompt=_prompt)
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@register_chat_format("open-orca")
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def format_open_orca(
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse:
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system_template = "{system_message}"
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system_message = (
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"You are a helpful assistant. Please answer truthfully and write out your "
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)
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"thinking step by step to be sure you get the right answer. If you make a mistake or encounter "
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"an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
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"aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
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"and physicist. You will also act as the most appropriate type of expert to answer any particular "
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"question or solve the relevant problem; state which expert type your are, if so. Also think of "
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"any particular named expert that would be ideal to answer the relevant question or solve the "
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"relevant problem; name and act as them, if appropriate."
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roles = ("User", "Assistant")
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sep = "<|end_of_turn|>\n"
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# stop_token_ids=[32000, 32001], # "<|end_of_turn|>"
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stop_str = "User"
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system_message = system_template.format(system_message=system_message)
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_messages = _map_roles(messages, dict(zip(roles, roles)))
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_messages.append((roles[1], None))
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_prompt = _format_add_colon_space_single(system_message, _messages, sep)
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return ChatFormatterResponse(prompt=_prompt, stop=stop_str)
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