3197 lines
122 KiB
Python
3197 lines
122 KiB
Python
from __future__ import annotations
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import os
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import json
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import ctypes
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import dataclasses
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import random
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import string
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from contextlib import ExitStack
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from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union, Protocol, cast
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import jinja2
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import numpy as np
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import numpy.typing as npt
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import llama_cpp.llama as llama
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import llama_cpp.llama_types as llama_types
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import llama_cpp.llama_grammar as llama_grammar
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from ._logger import logger
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from ._utils import suppress_stdout_stderr, Singleton
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### Common Chat Templates and Special Tokens ###
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# Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
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CHATML_CHAT_TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
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CHATML_BOS_TOKEN = "<s>"
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CHATML_EOS_TOKEN = "<|im_end|>"
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# Source: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
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MISTRAL_INSTRUCT_CHAT_TEMPLATE = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
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MISTRAL_INSTRUCT_BOS_TOKEN = "<s>"
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MISTRAL_INSTRUCT_EOS_TOKEN = "</s>"
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# Source: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/blob/main/tokenizer_config.json
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MIXTRAL_INSTRUCT_CHAT_TEMPLATE = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
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# Source: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
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LLAMA3_INSTRUCT_CHAT_TEMPLATE = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
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### Chat Completion Handler ###
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class LlamaChatCompletionHandler(Protocol):
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"""Base Protocol for a llama chat completion handler.
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Very generic protocol that can be used to implement any chat format.
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The only hard requirement is that it must return a ChatCompletion when
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stream=False and an iterator of ChatCompletionChunks when stream=True."""
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def __call__(
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self,
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*,
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# llama.cpp instance
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llama: llama.Llama,
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# openai api parameters
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messages: List[llama_types.ChatCompletionRequestMessage],
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functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
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function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
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tools: Optional[List[llama_types.ChatCompletionTool]] = None,
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tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
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temperature: float = 0.2,
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top_p: float = 0.95,
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top_k: int = 40,
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stream: bool = False,
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stop: Optional[Union[str, List[str]]] = [],
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seed: Optional[int] = None,
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response_format: Optional[
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llama_types.ChatCompletionRequestResponseFormat
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] = None,
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max_tokens: Optional[int] = None,
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presence_penalty: float = 0.0,
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frequency_penalty: float = 0.0,
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repeat_penalty: float = 1.1,
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model: Optional[str] = None,
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logit_bias: Optional[Dict[str, float]] = None,
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# llama.cpp parameters
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min_p: float = 0.05,
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typical_p: float = 1.0,
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tfs_z: float = 1.0,
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mirostat_mode: int = 0,
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mirostat_tau: float = 5.0,
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mirostat_eta: float = 0.1,
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logits_processor: Optional[llama.LogitsProcessorList] = None,
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grammar: Optional[llama.LlamaGrammar] = None,
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logprobs: Optional[bool] = None,
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top_logprobs: Optional[int] = None,
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**kwargs, # type: ignore
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) -> Union[
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llama_types.CreateChatCompletionResponse,
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Iterator[llama_types.CreateChatCompletionStreamResponse],
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]: ...
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class LlamaChatCompletionHandlerNotFoundException(Exception):
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pass
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class LlamaChatCompletionHandlerRegistry(Singleton):
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_chat_handlers: Dict[str, LlamaChatCompletionHandler] = {}
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def register_chat_completion_handler(
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self,
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name: str,
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chat_handler: LlamaChatCompletionHandler,
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overwrite: bool = False,
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):
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if not overwrite and name in self._chat_handlers:
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raise ValueError(
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f"Formatter with name '{name}' is already registered. Use `overwrite=True` to overwrite it."
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)
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self._chat_handlers[name] = chat_handler
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def unregister_chat_handler(self, name: str):
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if name in self._chat_handlers:
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del self._chat_handlers[name]
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else:
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raise ValueError(f"No formatter registered under the name '{name}'.")
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def get_chat_completion_handler_by_name(
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self, name: str
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) -> LlamaChatCompletionHandler:
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try:
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chat_handler = self._chat_handlers[name]
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return chat_handler
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except KeyError:
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raise LlamaChatCompletionHandlerNotFoundException(
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f"Invalid chat handler: {name} (valid formats: {list(self._chat_handlers.keys())})"
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)
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def get_chat_completion_handler(name: str) -> LlamaChatCompletionHandler:
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return LlamaChatCompletionHandlerRegistry().get_chat_completion_handler_by_name(
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name
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)
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def register_chat_completion_handler(name: str):
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def decorator(f: LlamaChatCompletionHandler):
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LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(name, f)
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return f
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return decorator
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### Chat Formatter ###
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@dataclasses.dataclass
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class ChatFormatterResponse:
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"""Dataclass that stores completion parameters for a given chat format and
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create_chat_completion request.
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prompt contains the formatted prompt generated from the chat format and messages.
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stop contains the stop token or list of stop tokens to use for the chat format."""
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prompt: str
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stop: Optional[Union[str, List[str]]] = None
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stopping_criteria: Optional[llama.StoppingCriteriaList] = None
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class ChatFormatter(Protocol):
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"""Base Protocol for a chat formatter. A chat formatter is a function that
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takes a list of messages and returns a chat format response which can be used
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to generate a completion. The response can also include a stop token or list
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of stop tokens to use for the completion."""
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def __call__(
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self,
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*,
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messages: List[llama_types.ChatCompletionRequestMessage],
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**kwargs: Any,
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) -> ChatFormatterResponse: ...
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class Jinja2ChatFormatter(ChatFormatter):
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def __init__(
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self,
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template: str,
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eos_token: str,
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bos_token: str,
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add_generation_prompt: bool = True,
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stop_token_ids: Optional[List[int]] = None,
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):
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"""A chat formatter that uses jinja2 templates to format the prompt."""
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self.template = template
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self.eos_token = eos_token
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self.bos_token = bos_token
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self.add_generation_prompt = add_generation_prompt
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self.stop_token_ids = set(stop_token_ids) if stop_token_ids is not None else None
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self._environment = jinja2.Environment(
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loader=jinja2.BaseLoader(),
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trim_blocks=True,
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lstrip_blocks=True,
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).from_string(self.template)
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def __call__(
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self,
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*,
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messages: List[llama_types.ChatCompletionRequestMessage],
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functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
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function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
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tools: Optional[List[llama_types.ChatCompletionTool]] = None,
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tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
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**kwargs: Any,
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) -> ChatFormatterResponse:
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def raise_exception(message: str):
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raise ValueError(message)
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prompt = self._environment.render(
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messages=messages,
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eos_token=self.eos_token,
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bos_token=self.bos_token,
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raise_exception=raise_exception,
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add_generation_prompt=self.add_generation_prompt,
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functions=functions,
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function_call=function_call,
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tools=tools,
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tool_choice=tool_choice,
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)
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stopping_criteria = None
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if self.stop_token_ids is not None:
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def stop_on_last_token(
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tokens: npt.NDArray[np.intc],
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logits: npt.NDArray[np.single]
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) -> bool:
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return tokens[-1] in self.stop_token_ids
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stopping_criteria = llama.StoppingCriteriaList([stop_on_last_token])
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return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token], stopping_criteria=stopping_criteria)
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def to_chat_handler(self) -> LlamaChatCompletionHandler:
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return chat_formatter_to_chat_completion_handler(self)
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def _convert_text_completion_to_chat(
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completion: llama_types.Completion,
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) -> llama_types.ChatCompletion:
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assert "usage" in completion
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return {
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"id": "chat" + completion["id"],
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"object": "chat.completion",
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"created": completion["created"],
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"model": completion["model"],
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": completion["choices"][0]["text"],
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},
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"logprobs": completion["choices"][0]["logprobs"],
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"finish_reason": completion["choices"][0]["finish_reason"],
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}
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],
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"usage": completion["usage"],
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}
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def _convert_text_completion_chunks_to_chat(
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chunks: Iterator[llama_types.CreateCompletionStreamResponse],
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) -> Iterator[llama_types.ChatCompletionChunk]:
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for i, chunk in enumerate(chunks):
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if i == 0:
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yield {
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"id": "chat" + chunk["id"],
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"model": chunk["model"],
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"created": chunk["created"],
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"object": "chat.completion.chunk",
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"choices": [
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{
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"index": 0,
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"delta": {
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"role": "assistant",
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},
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"logprobs": None,
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"finish_reason": None,
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}
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],
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}
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yield {
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"id": "chat" + chunk["id"],
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"model": chunk["model"],
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"created": chunk["created"],
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"object": "chat.completion.chunk",
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"choices": [
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{
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"index": 0,
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"delta": (
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{
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"content": chunk["choices"][0]["text"],
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}
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if chunk["choices"][0]["finish_reason"] is None
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else {}
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),
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"logprobs": chunk["choices"][0]["logprobs"],
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"finish_reason": chunk["choices"][0]["finish_reason"],
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}
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],
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}
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def _convert_completion_to_chat(
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completion_or_chunks: Union[
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llama_types.CreateCompletionResponse,
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Iterator[llama_types.CreateCompletionStreamResponse],
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],
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stream: bool = False,
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) -> Union[
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llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk]
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]:
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if stream:
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chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore
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return _convert_text_completion_chunks_to_chat(chunks)
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else:
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completion: llama_types.Completion = completion_or_chunks # type: ignore
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return _convert_text_completion_to_chat(completion)
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def _convert_completion_to_chat_function(
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tool_name: str,
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completion_or_chunks: Union[
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llama_types.CreateCompletionResponse,
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Iterator[llama_types.CreateCompletionStreamResponse],
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],
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stream: bool,
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):
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if not stream:
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completion: llama_types.CreateCompletionResponse = completion_or_chunks # type: ignore
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assert "usage" in completion
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tool_id = "call_" + "_0_" + tool_name + "_" + completion["id"]
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# TODO: Fix for legacy function calls
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chat_completion: llama_types.CreateChatCompletionResponse = {
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"id": "chat" + completion["id"],
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"object": "chat.completion",
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"created": completion["created"],
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"model": completion["model"],
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": None,
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"function_call": {
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"name": tool_name,
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"arguments": completion["choices"][0]["text"],
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},
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"tool_calls": [
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{
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"id": tool_id,
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": completion["choices"][0]["text"],
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},
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}
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],
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},
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"logprobs": completion["choices"][0]["logprobs"],
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"finish_reason": "tool_calls",
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}
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],
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"usage": completion["usage"],
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}
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return chat_completion
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else:
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chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore
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def _stream_response_to_function_stream(
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chunks: Iterator[llama_types.CreateCompletionStreamResponse],
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) -> Iterator[llama_types.CreateChatCompletionStreamResponse]:
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# blank first message
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first = True
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id_ = None
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created = None
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model = None
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tool_id = None
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for chunk in chunks:
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if first:
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id_ = "chat" + chunk["id"]
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created = chunk["created"]
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model = chunk["model"]
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tool_id = "call_" + "_0_" + tool_name + "_" + chunk["id"]
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yield {
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"id": id_,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model,
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"choices": [
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{
|
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"index": 0,
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"finish_reason": None,
|
|
"logprobs": None,
|
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"delta": {
|
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"role": "assistant",
|
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"content": None,
|
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"function_call": None,
|
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"tool_calls": None,
|
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},
|
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}
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],
|
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}
|
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yield {
|
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"id": "chat" + chunk["id"],
|
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"object": "chat.completion.chunk",
|
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"created": chunk["created"],
|
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"model": chunk["model"],
|
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"choices": [
|
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{
|
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"index": 0,
|
|
"finish_reason": None,
|
|
"logprobs": chunk["choices"][0]["logprobs"],
|
|
"delta": {
|
|
"role": None,
|
|
"content": None,
|
|
"function_call": {
|
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"name": tool_name,
|
|
"arguments": chunk["choices"][0]["text"],
|
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},
|
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"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": tool_id,
|
|
"type": "function",
|
|
"function": {
|
|
"name": tool_name,
|
|
"arguments": chunk["choices"][0]["text"],
|
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},
|
|
}
|
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],
|
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},
|
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}
|
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],
|
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}
|
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first = False
|
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continue
|
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assert tool_id is not None
|
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yield {
|
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"id": "chat" + chunk["id"],
|
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"object": "chat.completion.chunk",
|
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"created": chunk["created"],
|
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"model": chunk["model"],
|
|
"choices": [
|
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{
|
|
"index": 0,
|
|
"finish_reason": None,
|
|
"logprobs": chunk["choices"][0]["logprobs"],
|
|
"delta": {
|
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"role": None,
|
|
"content": None,
|
|
"function_call": {
|
|
"name": tool_name,
|
|
"arguments": chunk["choices"][0]["text"],
|
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},
|
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"tool_calls": [
|
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{
|
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"index": 0,
|
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"id": tool_id,
|
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"type": "function",
|
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"function": {
|
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"name": tool_name,
|
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"arguments": chunk["choices"][0][
|
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"text"
|
|
],
|
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},
|
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}
|
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],
|
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},
|
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}
|
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],
|
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}
|
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|
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if id_ is not None and created is not None and model is not None:
|
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yield {
|
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"id": id_,
|
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"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
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"choices": [
|
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{
|
|
"index": 0,
|
|
"finish_reason": "tool_calls",
|
|
"logprobs": None,
|
|
"delta": {
|
|
"role": None,
|
|
"content": None,
|
|
"function_call": None,
|
|
"tool_calls": None,
|
|
},
|
|
}
|
|
],
|
|
}
|
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|
|
return _stream_response_to_function_stream(chunks)
|
|
|
|
|
|
|
|
def chat_formatter_to_chat_completion_handler(
|
|
chat_formatter: ChatFormatter,
|
|
) -> LlamaChatCompletionHandler:
|
|
def chat_completion_handler(
|
|
*,
|
|
llama: llama.Llama,
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
|
|
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
min_p: float = 0.05,
|
|
typical_p: float = 1.0,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
seed: Optional[int] = None,
|
|
response_format: Optional[
|
|
llama_types.ChatCompletionRequestResponseFormat
|
|
] = None,
|
|
max_tokens: Optional[int] = None,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
logits_processor: Optional[llama.LogitsProcessorList] = None,
|
|
grammar: Optional[llama.LlamaGrammar] = None,
|
|
logit_bias: Optional[Dict[str, float]] = None,
|
|
logprobs: Optional[bool] = None,
|
|
top_logprobs: Optional[int] = None,
|
|
**kwargs, # type: ignore
|
|
) -> Union[
|
|
llama_types.CreateChatCompletionResponse,
|
|
Iterator[llama_types.CreateChatCompletionStreamResponse],
|
|
]:
|
|
result = chat_formatter(
|
|
messages=messages,
|
|
functions=functions,
|
|
function_call=function_call,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
)
|
|
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
|
|
|
|
stopping_criteria = None
|
|
if result.stopping_criteria is not None:
|
|
stopping_criteria = result.stopping_criteria
|
|
|
|
if response_format is not None and response_format["type"] == "json_object":
|
|
grammar = _grammar_for_response_format(response_format, verbose=llama.verbose)
|
|
|
|
# Convert legacy functions to tools
|
|
if functions is not None:
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": function,
|
|
}
|
|
for function in functions
|
|
]
|
|
|
|
# Convert legacy function_call to tool_choice
|
|
if function_call is not None:
|
|
if isinstance(function_call, str) and (
|
|
function_call == "none" or function_call == "auto"
|
|
):
|
|
tool_choice = function_call
|
|
if isinstance(function_call, dict) and "name" in function_call:
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {
|
|
"name": function_call["name"],
|
|
},
|
|
}
|
|
|
|
tool = None
|
|
if tool_choice is not None and isinstance(tool_choice, dict) and tools is not None:
|
|
name = tool_choice["function"]["name"]
|
|
tool = next((t for t in tools if t["function"]["name"] == name), None)
|
|
if tool is None:
|
|
raise ValueError(f"Tool choice '{name}' not found in tools.")
|
|
schema = tool["function"]["parameters"]
|
|
try:
|
|
# create grammar from json schema
|
|
grammar = llama_grammar.LlamaGrammar.from_json_schema(
|
|
json.dumps(schema), verbose=llama.verbose
|
|
)
|
|
except Exception as e:
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
|
|
completion_or_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
logprobs=top_logprobs if logprobs else None,
|
|
stream=stream,
|
|
stop=stop,
|
|
seed=seed,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
stopping_criteria=stopping_criteria,
|
|
grammar=grammar,
|
|
logit_bias=logit_bias,
|
|
)
|
|
if tool is not None:
|
|
tool_name = tool["function"]["name"]
|
|
return _convert_completion_to_chat_function(
|
|
tool_name, completion_or_chunks, stream
|
|
)
|
|
return _convert_completion_to_chat(completion_or_chunks, stream=stream)
|
|
|
|
return chat_completion_handler
|
|
|
|
|
|
def hf_autotokenizer_to_chat_formatter(
|
|
pretrained_model_name_or_path: Union[str, os.PathLike[str]]
|
|
) -> ChatFormatter:
|
|
# https://huggingface.co/docs/transformers/main/chat_templating
|
|
# https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
|
|
# https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
|
|
from transformers import AutoTokenizer # type: ignore
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path) # type: ignore
|
|
|
|
def format_autotokenizer(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
tokenizer.use_default_system_prompt = False # type: ignore
|
|
prompt: str = tokenizer.apply_chat_template(messages, tokenize=False) # type: ignore
|
|
assert isinstance(prompt, str)
|
|
# Return formatted prompt and eos token by default
|
|
return ChatFormatterResponse(prompt=prompt, stop=tokenizer.eos_token)
|
|
|
|
return format_autotokenizer
|
|
|
|
|
|
def hf_autotokenizer_to_chat_completion_handler(
|
|
pretrained_model_name_or_path: Union[str, os.PathLike[str]]
|
|
) -> LlamaChatCompletionHandler:
|
|
chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path)
|
|
return chat_formatter_to_chat_completion_handler(chat_formatter)
|
|
|
|
|
|
def hf_tokenizer_config_to_chat_formatter(
|
|
tokenizer_config: Dict[str, Any],
|
|
add_generation_prompt: bool = True,
|
|
) -> ChatFormatter:
|
|
assert isinstance(tokenizer_config, dict)
|
|
|
|
assert "chat_template" in tokenizer_config
|
|
assert isinstance(tokenizer_config["chat_template"], str)
|
|
chat_template = tokenizer_config["chat_template"]
|
|
|
|
assert "bos_token" in tokenizer_config
|
|
assert isinstance(tokenizer_config["bos_token"], str)
|
|
bos_token = tokenizer_config["bos_token"]
|
|
|
|
assert "eos_token" in tokenizer_config
|
|
assert isinstance(tokenizer_config["eos_token"], str)
|
|
eos_token = tokenizer_config["eos_token"]
|
|
|
|
env = jinja2.Environment(
|
|
loader=jinja2.BaseLoader(),
|
|
trim_blocks=True,
|
|
lstrip_blocks=True,
|
|
).from_string(chat_template)
|
|
|
|
def format_tokenizer_config(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
# TODO: veryify this is correct
|
|
# Add a blank assistant message to the end of the messages to prompt the model to generate a response
|
|
if add_generation_prompt:
|
|
messages = [
|
|
*messages,
|
|
llama_types.ChatCompletionRequestAssistantMessage(
|
|
role="assistant", content=""
|
|
),
|
|
]
|
|
prompt = env.render(
|
|
messages=messages,
|
|
bos_token=bos_token,
|
|
eos_token=eos_token,
|
|
)
|
|
return ChatFormatterResponse(prompt=prompt, stop=[eos_token, bos_token])
|
|
|
|
return format_tokenizer_config
|
|
|
|
|
|
def hf_tokenizer_config_to_chat_completion_handler(
|
|
tokenizer_config: Dict[str, Any],
|
|
add_generation_prompt: bool = True,
|
|
) -> LlamaChatCompletionHandler:
|
|
chat_formatter = hf_tokenizer_config_to_chat_formatter(
|
|
tokenizer_config, add_generation_prompt=add_generation_prompt
|
|
)
|
|
return chat_formatter_to_chat_completion_handler(chat_formatter)
|
|
|
|
|
|
def guess_chat_format_from_gguf_metadata(metadata: Dict[str, str]) -> Optional[str]:
|
|
if "tokenizer.chat_template" not in metadata:
|
|
return None
|
|
|
|
if metadata["tokenizer.chat_template"] == CHATML_CHAT_TEMPLATE:
|
|
return "chatml"
|
|
|
|
if (metadata["tokenizer.chat_template"] == MISTRAL_INSTRUCT_CHAT_TEMPLATE or
|
|
metadata["tokenizer.chat_template"] == MIXTRAL_INSTRUCT_CHAT_TEMPLATE):
|
|
return "mistral-instruct"
|
|
|
|
if metadata["tokenizer.chat_template"] == LLAMA3_INSTRUCT_CHAT_TEMPLATE:
|
|
return "llama-3"
|
|
|
|
return None
|
|
|
|
|
|
### Utility functions for formatting chat prompts ###
|
|
# TODO: Replace these with jinja2 templates
|
|
|
|
|
|
def _get_system_message(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
) -> str:
|
|
"""Get the first system message."""
|
|
for message in messages:
|
|
if message["role"] == "system":
|
|
return message["content"] or ""
|
|
return ""
|
|
|
|
|
|
def _map_roles(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
role_map: Dict[str, str],
|
|
) -> List[Tuple[str, Optional[str]]]:
|
|
"""Map the message roles."""
|
|
output: List[Tuple[str, Optional[str]]] = []
|
|
for message in messages:
|
|
role = message["role"]
|
|
if role in role_map:
|
|
content: str | None = (
|
|
message["content"] if isinstance(message["content"], str) else None
|
|
)
|
|
output.append((role_map[role], content))
|
|
return output
|
|
|
|
|
|
def _format_llama2(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
|
|
) -> str:
|
|
"""Format the prompt with the llama2 style."""
|
|
seps = [sep, sep2]
|
|
ret = system_message + sep
|
|
for i, (role, message) in enumerate(messages):
|
|
if system_message and i == 0:
|
|
m = message or ""
|
|
ret += m + seps[i % 2]
|
|
elif message:
|
|
ret += role + message + " " + seps[i % 2]
|
|
else:
|
|
ret += role + " "
|
|
return ret
|
|
|
|
|
|
def _format_add_colon_single(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
|
|
) -> str:
|
|
"""Format the prompt with the add-colon-single style."""
|
|
ret = system_message + sep
|
|
for role, message in messages:
|
|
if message:
|
|
ret += role + ": " + message + sep
|
|
else:
|
|
ret += role + ":"
|
|
return ret
|
|
|
|
|
|
def _format_add_colon_two(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
|
|
) -> str:
|
|
"""Format the prompt with the add-colon-two style."""
|
|
seps = [sep, sep2]
|
|
ret = system_message + seps[0]
|
|
for i, (role, message) in enumerate(messages):
|
|
if message:
|
|
ret += role + ": " + message + seps[i % 2]
|
|
else:
|
|
ret += role + ":"
|
|
return ret
|
|
|
|
|
|
def _format_no_colon_single(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
|
|
) -> str:
|
|
"""Format the prompt with the no-colon-single style."""
|
|
ret = system_message
|
|
for role, message in messages:
|
|
if message:
|
|
ret += role + message + sep
|
|
else:
|
|
ret += role
|
|
return ret
|
|
|
|
|
|
def _format_add_colon_space_single(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
|
|
) -> str:
|
|
"""Format the prompt with the add-colon-space-single style."""
|
|
ret = system_message + sep
|
|
for role, message in messages:
|
|
if message:
|
|
ret += role + ": " + message + sep
|
|
else:
|
|
ret += role + ": " # must be end with a space
|
|
return ret
|
|
|
|
|
|
def _format_chatml(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
|
|
) -> str:
|
|
"""Format the prompt with the chatml style."""
|
|
ret = "" if system_message == "" else system_message + sep + "\n"
|
|
for role, message in messages:
|
|
if message:
|
|
ret += role + "\n" + message + sep + "\n"
|
|
else:
|
|
ret += role + "\n"
|
|
return ret
|
|
|
|
|
|
def _format_chatglm3(
|
|
system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
|
|
) -> str:
|
|
"""Format the prompt with the chatglm3 style."""
|
|
ret = ""
|
|
if system_message:
|
|
ret += system_message
|
|
for role, message in messages:
|
|
if message:
|
|
ret += role + "\n" + " " + message
|
|
else:
|
|
ret += role
|
|
return ret
|
|
|
|
def _grammar_for_json(verbose:bool=False):
|
|
return llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF, verbose=verbose)
|
|
|
|
def _grammar_for_json_schema(
|
|
schema: str,
|
|
verbose: bool = False,
|
|
fallback_to_json: bool = True
|
|
):
|
|
try:
|
|
return llama_grammar.LlamaGrammar.from_json_schema(schema, verbose=verbose)
|
|
except Exception as e:
|
|
if fallback_to_json:
|
|
return _grammar_for_json(verbose=verbose)
|
|
else:
|
|
raise e
|
|
|
|
def _grammar_for_response_format(
|
|
response_format: llama_types.ChatCompletionRequestResponseFormat,
|
|
verbose: bool = False
|
|
):
|
|
if response_format["type"] != "json_object":
|
|
return None
|
|
|
|
if "schema" in response_format:
|
|
return _grammar_for_json_schema(
|
|
json.dumps(response_format["schema"]), verbose=verbose
|
|
)
|
|
else:
|
|
return _grammar_for_json(verbose=verbose)
|
|
|
|
### Chat Formats ###
|
|
|
|
|
|
def register_chat_format(name: str):
|
|
def decorator(f: ChatFormatter):
|
|
chat_completion_handler = chat_formatter_to_chat_completion_handler(f)
|
|
LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(
|
|
name, chat_completion_handler
|
|
)
|
|
return f
|
|
|
|
return decorator
|
|
|
|
|
|
# see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
|
|
# system prompt is "embedded" in the first message
|
|
@register_chat_format("llama-2")
|
|
def format_llama2(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_template = "<s>[INST] <<SYS>>\n{system_message}\n<</SYS>>"
|
|
_roles = dict(user="<s>[INST]", assistant="[/INST]")
|
|
_messages = _map_roles(messages, _roles)
|
|
system_message = _get_system_message(messages)
|
|
if system_message:
|
|
system_message = _system_template.format(system_message=system_message)
|
|
_prompt = _format_llama2(system_message, _messages, " ", "</s>") + "[/INST]"
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
# Chat format for Llama-3 models, see more details at:
|
|
# https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py#L202-L229
|
|
@register_chat_format("llama-3")
|
|
def format_llama3(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(
|
|
system="<|start_header_id|>system<|end_header_id|>\n\n",
|
|
user="<|start_header_id|>user<|end_header_id|>\n\n",
|
|
assistant="<|start_header_id|>assistant<|end_header_id|>\n\n",
|
|
)
|
|
_begin_token = "<|begin_of_text|>"
|
|
_sep = "<|eot_id|>"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(_begin_token, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
@register_chat_format("alpaca")
|
|
def format_alpaca(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(user="### Instruction", assistant="### Response")
|
|
_sep = "\n\n"
|
|
_sep2 = "</s>"
|
|
system_message = _get_system_message(messages)
|
|
_messages = _map_roles(messages, _roles)
|
|
_prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("qwen")
|
|
def format_qwen(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
|
|
system_message = "You are a helpful assistant."
|
|
system_template = "<|im_start|>system\n{system_message}"
|
|
system_message = system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_sep = "<|im_end|>"
|
|
_prompt = _format_chatml(system_message, _messages, _sep)
|
|
_sep2 = "<|endoftext|>"
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep2)
|
|
|
|
|
|
@register_chat_format("vicuna")
|
|
def format(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_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."
|
|
_roles = dict(user="USER", assistant="ASSISTANT")
|
|
_sep = " "
|
|
_sep2 = "</s>"
|
|
system_message = _system_message
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("oasst_llama")
|
|
def format_oasst_llama(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n"
|
|
_roles = dict(user="<|prompter|>", assistant="<|assistant|>")
|
|
_sep = "</s>"
|
|
system_message = _get_system_message(messages)
|
|
system_message = _system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("baichuan-2")
|
|
def format_baichuan2(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_template = "{system_message}"
|
|
_roles = dict(user="<reserved_106>", assistant="<reserved_107>")
|
|
_sep = ""
|
|
system_message = _get_system_message(messages)
|
|
system_message = _system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("baichuan")
|
|
def format_baichuan(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_template = "{system_message}"
|
|
_roles = dict(user="<reserved_102>", assistant="<reserved_103>")
|
|
_sep = ""
|
|
system_message = _get_system_message(messages)
|
|
system_message = _system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("openbuddy")
|
|
def format_openbuddy(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_message = """You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
|
|
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
|
|
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
|
|
You can speak fluently in many languages, for example: English, Chinese.
|
|
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
|
|
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
|
|
|
|
"""
|
|
_roles = dict(user="User", assistant="Assistant")
|
|
_sep = "\n"
|
|
system_message = _system_message
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("redpajama-incite")
|
|
def format_redpajama_incite(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_system_message = _get_system_message(messages)
|
|
_roles = dict(user="<human>", assistant="<bot>")
|
|
_sep = "\n"
|
|
_stop = "<human>"
|
|
system_message = _system_message
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_stop)
|
|
|
|
|
|
@register_chat_format("snoozy")
|
|
def format_snoozy(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = "### Instruction:\n{system_message}"
|
|
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."
|
|
_system_message = _get_system_message(messages)
|
|
_system_message = (
|
|
_system_message if _system_message != "" else default_system_message
|
|
)
|
|
system_message = system_template.format(system_message=_system_message)
|
|
_roles = dict(user="### Prompt", assistant="### Response")
|
|
_sep = "\n"
|
|
_stop = "###"
|
|
system_message = _system_message
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_stop)
|
|
|
|
|
|
@register_chat_format("phind")
|
|
def format_phind(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(user="### User Message", assistant="### Assistant")
|
|
_sep = "\n\n"
|
|
_system_message = "### System Prompt\nYou are an intelligent programming assistant."
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_single(_system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("intel")
|
|
def format_intel(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(user="### User:", assistant="### Assistant:")
|
|
_sep = "\n"
|
|
_system_message = "### System:\n{system_message}"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_add_colon_single(_system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("open-orca")
|
|
def format_open_orca(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = "{system_message}"
|
|
system_message = (
|
|
"You are a helpful assistant. Please answer truthfully and write out your "
|
|
"thinking step by step to be sure you get the right answer. If you make a mistake or encounter "
|
|
"an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
|
|
"aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
|
|
"and physicist. You will also act as the most appropriate type of expert to answer any particular "
|
|
"question or solve the relevant problem; state which expert type your are, if so. Also think of "
|
|
"any particular named expert that would be ideal to answer the relevant question or solve the "
|
|
"relevant problem; name and act as them, if appropriate."
|
|
)
|
|
roles = ("User", "Assistant")
|
|
sep = "<|end_of_turn|>\n"
|
|
# stop_token_ids=[32000, 32001], # "<|end_of_turn|>"
|
|
stop_str = "User"
|
|
system_message = system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, dict(zip(roles, roles)))
|
|
_messages.append((roles[1], None))
|
|
_prompt = _format_add_colon_space_single(system_message, _messages, sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=stop_str)
|
|
|
|
|
|
@register_chat_format("mistrallite")
|
|
def format_mistrallite(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_roles = dict(user="<|prompter|>", assistant="</s>\n<|assistant|>")
|
|
_sep = " "
|
|
system_template = """<|system|>{system_message}</s>"""
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt)
|
|
|
|
|
|
@register_chat_format("zephyr")
|
|
def format_zephyr(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = """<|system|>
|
|
{system_message}"""
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_roles = dict(user="<|user|>\n", assistant="<|assistant|>\n")
|
|
_sep = "</s>"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_chatml(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
@register_chat_format("pygmalion")
|
|
def format_pygmalion(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = """<|system|>{system_message}"""
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_roles = dict(user="<|user|>", assistant="<|model|>")
|
|
_sep = "\n"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_chatml(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
@register_chat_format("chatml")
|
|
def format_chatml(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = """<|im_start|>system
|
|
{system_message}"""
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
|
|
_sep = "<|im_end|>"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_chatml(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
@register_chat_format("mistral-instruct")
|
|
def format_mistral_instruct(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
bos = "<s>"
|
|
eos = "</s>"
|
|
stop = eos
|
|
prompt = bos
|
|
for message in messages:
|
|
if (
|
|
message["role"] == "user"
|
|
and message["content"] is not None
|
|
and isinstance(message["content"], str)
|
|
):
|
|
prompt += "[INST] " + message["content"]
|
|
elif (
|
|
message["role"] == "assistant"
|
|
and message["content"] is not None
|
|
):
|
|
prompt += " [/INST]" + message["content"] + eos
|
|
prompt += " [/INST]"
|
|
return ChatFormatterResponse(prompt=prompt, stop=stop)
|
|
|
|
|
|
@register_chat_format("chatglm3")
|
|
def format_chatglm3(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = """<|system|>
|
|
{system_message}"""
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_roles = dict(user="<|user|>", assistant="<|assistant|>")
|
|
_sep = "</s>"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_chatglm3(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
@register_chat_format("openchat")
|
|
def format_openchat(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_template = "{system_message}<|end_of_turn|>"
|
|
system_message = _get_system_message(messages)
|
|
system_message = system_template.format(system_message=system_message)
|
|
_roles = dict(
|
|
user="GPT4 Correct User: ", assistant="<|end_of_turn|>GPT4 Correct Assistant: "
|
|
)
|
|
_sep = "<|end_of_turn|>"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_chatml(system_message, _messages, _sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
# Chat format for Saiga models, see more details and available models:
|
|
# https://huggingface.co/collections/IlyaGusev/saiga2-saigamistral-6505d4ccc3d1e53166b636cd
|
|
@register_chat_format("saiga")
|
|
def format_saiga(
|
|
messages: list[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
_message_template = "<s>{role}\n{content}</s>"
|
|
_roles = dict(user="user", bot="bot", system="system")
|
|
_messages = _map_roles(messages, _roles)
|
|
|
|
_prompt = ""
|
|
for role, content in _messages:
|
|
if content:
|
|
_prompt += _message_template.format(role=role, content=content)
|
|
else:
|
|
_prompt += f"<s>{role}\n"
|
|
# Response template
|
|
_prompt += "<s>bot"
|
|
return ChatFormatterResponse(prompt=_prompt.strip())
|
|
|
|
|
|
# Chat format for Google's Gemma models, see more details and available models:
|
|
# https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b
|
|
@register_chat_format("gemma")
|
|
def format_gemma(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
**kwargs: Any,
|
|
) -> ChatFormatterResponse:
|
|
system_message = _get_system_message(messages)
|
|
if system_message != "":
|
|
logger.debug(
|
|
"`role='system'` messages are not allowed on Google's Gemma models."
|
|
)
|
|
_roles = dict(user="<start_of_turn>user\n", assistant="<start_of_turn>model\n")
|
|
_sep = "<end_of_turn>\n"
|
|
_messages = _map_roles(messages, _roles)
|
|
_messages.append((_roles["assistant"], None))
|
|
_prompt = _format_no_colon_single(system_message="", messages=_messages, sep=_sep)
|
|
return ChatFormatterResponse(prompt=_prompt, stop=_sep)
|
|
|
|
|
|
# Tricky chat formats that require custom chat handlers
|
|
|
|
|
|
@register_chat_completion_handler("functionary")
|
|
def functionary_chat_handler(
|
|
llama: llama.Llama,
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
|
|
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
min_p: float = 0.05,
|
|
typical_p: float = 1.0,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
|
|
max_tokens: Optional[int] = None,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
logits_processor: Optional[llama.LogitsProcessorList] = None,
|
|
grammar: Optional[llama.LlamaGrammar] = None,
|
|
**kwargs, # type: ignore
|
|
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
|
|
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. The assistant calls functions with appropriate input when necessary"""
|
|
|
|
def generate_type_definition(
|
|
param: Dict[str, llama_types.JsonType], indent_level: int, shared_defs
|
|
) -> str:
|
|
indent = " " * indent_level
|
|
if "$ref" in param:
|
|
# Reference to a shared definition
|
|
ref_name = param["$ref"].split("/")[
|
|
-1
|
|
] # Extract the type name from the reference
|
|
return ref_name
|
|
elif param.get("type") == "array":
|
|
items = param.get("items", {})
|
|
item_type = generate_type_definition(items, indent_level + 1, shared_defs)
|
|
return f"Array<{item_type}>"
|
|
elif param.get("type") == "object":
|
|
properties = param.get("properties", {})
|
|
nested_schema = "{\n"
|
|
for nested_param_name, nested_param in properties.items():
|
|
nested_param_type = generate_type_definition(
|
|
nested_param, indent_level + 1, shared_defs
|
|
)
|
|
nested_schema += (
|
|
f"{indent} {nested_param_name}: {nested_param_type},\n"
|
|
)
|
|
nested_schema += indent + "}"
|
|
return nested_schema
|
|
elif "enum" in param:
|
|
# Enum type
|
|
return " | ".join([f'"{enum_value}"' for enum_value in param["enum"]])
|
|
else:
|
|
# Simple type
|
|
return param.get("type", "any")
|
|
|
|
def generate_shared_definitions(shared_defs, indent_level: int) -> str:
|
|
indent = " " * indent_level
|
|
shared_definitions = ""
|
|
for def_name, def_properties in shared_defs.items():
|
|
shared_definitions += f"{indent}type {def_name} = "
|
|
if def_properties.get("type") == "object":
|
|
shared_definitions += generate_type_definition(
|
|
def_properties, indent_level, shared_defs
|
|
)
|
|
elif "enum" in def_properties:
|
|
# Enum type
|
|
shared_definitions += " | ".join(
|
|
[f'"{enum_value}"' for enum_value in def_properties["enum"]]
|
|
)
|
|
shared_definitions += ";\n"
|
|
return shared_definitions
|
|
|
|
def generate_schema_from_functions(functions, namespace="functions") -> str:
|
|
schema = (
|
|
"// Supported function definitions that should be called when necessary.\n"
|
|
)
|
|
schema += f"namespace {namespace} {{\n\n"
|
|
|
|
# Generate shared definitions
|
|
shared_definitions = {}
|
|
for function in functions:
|
|
parameters = function.get("parameters", {})
|
|
shared_definitions.update(parameters.get("$defs", {}))
|
|
|
|
schema += generate_shared_definitions(shared_definitions, 1)
|
|
|
|
for function in functions:
|
|
function_name = function["name"]
|
|
description = function.get("description", "")
|
|
parameters = function.get("parameters", {})
|
|
required_params = parameters.get("required", [])
|
|
|
|
schema += f" // {description}\n"
|
|
schema += f" type {function_name} = (_: {{\n"
|
|
|
|
for param_name, param in parameters.get("properties", {}).items():
|
|
param_description = param.get("description", "")
|
|
param_type = generate_type_definition(param, 2, shared_definitions)
|
|
optional_indicator = "" if param_name in required_params else "?"
|
|
schema += f" // {param_description}\n"
|
|
schema += f" {param_name}{optional_indicator}: {param_type},\n"
|
|
schema += " }) => any;\n\n"
|
|
|
|
schema += "}} // namespace {}\n".format(namespace)
|
|
return schema
|
|
|
|
def prepare_messages_for_inference(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunctions]] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
):
|
|
all_messages: List[llama_types.ChatCompletionRequestMessage] = []
|
|
if functions is not None:
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system", content=generate_schema_from_functions(functions)
|
|
)
|
|
)
|
|
|
|
if tools is not None:
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system",
|
|
content=generate_schema_from_functions(
|
|
[
|
|
tool["function"]
|
|
for tool in tools
|
|
if tool["type"] == "function"
|
|
]
|
|
),
|
|
)
|
|
)
|
|
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system", content=SYSTEM_MESSAGE
|
|
)
|
|
)
|
|
|
|
for message in messages:
|
|
# Function call responses
|
|
if message["role"] == "function" and "name" in message:
|
|
message["name"] = f"functions.{message['name']}"
|
|
# Function call requests by assistant
|
|
if "function_call" in message:
|
|
message["function_call"][
|
|
"name"
|
|
] = f"functions.{message['function_call']['name']}"
|
|
all_messages.append(message)
|
|
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestAssistantMessage(
|
|
role="assistant", content=None
|
|
)
|
|
)
|
|
|
|
def message_to_str(msg: llama_types.ChatCompletionRequestMessage):
|
|
if msg["role"] == "system":
|
|
return f"system:\n{msg['content']}\n"
|
|
|
|
elif msg["role"] == "function" and "name" in msg:
|
|
return f"function name={msg['name']}:\n{msg['content']}\n"
|
|
elif msg["role"] == "function" and "function_call" in msg:
|
|
return f"function name={msg['function_call']['name']}:\n{msg['function_call']['arguments']}\n"
|
|
elif msg["role"] == "tool":
|
|
if msg["content"] is not None:
|
|
return f"function name={msg['tool_call_id']}:\n{msg['content']}\n"
|
|
else:
|
|
return f"function name={msg['tool_call_id']}\n"
|
|
elif msg["role"] == "user":
|
|
if msg["content"] is None:
|
|
return "user:\n</s></s>\n"
|
|
else:
|
|
return f"user:\n</s>{msg['content']}</s>\n"
|
|
elif msg["role"] == "assistant":
|
|
if msg["content"] is not None and "function_call" in msg:
|
|
return f"assistant:\n{msg['content']}\nassistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
|
|
elif "function_call" in msg:
|
|
return f"assistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
|
|
elif "tool_calls" in msg and len(msg["tool_calls"]) > 0:
|
|
for tool_call in msg[
|
|
"tool_calls"
|
|
]: # NOTE: probably doesn't work with the functionary model
|
|
return f"assistant to={tool_call['id']}:\n{tool_call['function']['arguments']}</s>\n"
|
|
elif msg["content"] is None:
|
|
return "assistant"
|
|
else:
|
|
return f"assistant:\n{msg['content']}\n"
|
|
else:
|
|
raise ValueError(f"Unsupported role: {msg['role']}")
|
|
|
|
return "".join([message_to_str(msg) for msg in all_messages])
|
|
|
|
if tools is not None:
|
|
functions = [tool["function"] for tool in tools if tool["type"] == "function"]
|
|
|
|
if tool_choice is not None:
|
|
function_call = (
|
|
tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
|
|
)
|
|
|
|
prompt = prepare_messages_for_inference(messages, functions, tools)
|
|
|
|
if function_call is None and (functions is None or len(functions) == 0):
|
|
completion_or_completion_chunks = llama.create_completion(
|
|
prompt=prompt + ":\n",
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=stream,
|
|
stop=["user:", "</s>"],
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
)
|
|
return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore
|
|
|
|
if function_call is None or (
|
|
isinstance(function_call, str) and function_call == "auto"
|
|
):
|
|
stop = "\n"
|
|
completion: llama_types.Completion = llama.create_completion(
|
|
prompt=prompt, stop=stop, stream=False
|
|
) # type: ignore
|
|
completion_text = completion["choices"][0]["text"]
|
|
# strip " to=functions." and ending ":"
|
|
function_call = completion_text.split(".")[-1][:-1]
|
|
new_prompt = prompt + completion_text + stop
|
|
elif isinstance(function_call, str) and function_call != "none":
|
|
new_prompt = prompt + f":\n"
|
|
elif isinstance(function_call, dict):
|
|
new_prompt = prompt + f" to=functions.{function_call['name']}:\n"
|
|
function_call = function_call["name"]
|
|
else:
|
|
new_prompt = prompt + f":\n"
|
|
|
|
function_body = None
|
|
for function in functions or []:
|
|
if function["name"] == function_call:
|
|
function_body = function["parameters"]
|
|
break
|
|
for tool in tools or []:
|
|
if tool["type"] == "function" and tool["function"]["name"] == function_call:
|
|
function_body = tool["function"]["parameters"]
|
|
break
|
|
|
|
if function_body is not None:
|
|
try:
|
|
with suppress_stdout_stderr(disable=llama.verbose):
|
|
grammar_text = llama_grammar.json_schema_to_gbnf(
|
|
json.dumps(function_body)
|
|
)
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.json_schema_to_gbnf(json.dumps(function_body)),
|
|
verbose=llama.verbose,
|
|
)
|
|
print(grammar_text)
|
|
except Exception as e:
|
|
if llama.verbose:
|
|
print(
|
|
"Failed to parse function body as JSON schema, falling back to default grammar"
|
|
)
|
|
print(e)
|
|
with suppress_stdout_stderr(disable=llama.verbose):
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF,
|
|
verbose=llama.verbose,
|
|
)
|
|
else:
|
|
with suppress_stdout_stderr(disable=llama.verbose):
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
|
|
completion: llama_types.Completion = llama.create_completion(
|
|
prompt=new_prompt,
|
|
stop=["user:", "</s>"],
|
|
stream=False,
|
|
grammar=grammar,
|
|
max_tokens=max_tokens,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
) # type: ignore
|
|
|
|
assert "usage" in completion
|
|
assert isinstance(function_call, str)
|
|
assert stream is False # TODO: support stream mode
|
|
|
|
if llama.verbose:
|
|
print(new_prompt)
|
|
print(completion["choices"][0]["text"])
|
|
|
|
# TODO: support stream mode
|
|
return llama_types.CreateChatCompletionResponse(
|
|
id="chat" + completion["id"],
|
|
object="chat.completion",
|
|
created=completion["created"],
|
|
model=completion["model"],
|
|
choices=[
|
|
{
|
|
"index": 0,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": None,
|
|
"function_call": {
|
|
"name": function_call,
|
|
"arguments": completion["choices"][0]["text"],
|
|
},
|
|
"tool_calls": [
|
|
{
|
|
"id": function_call,
|
|
"type": "function",
|
|
"function": {
|
|
"name": function_call,
|
|
"arguments": completion["choices"][0]["text"],
|
|
},
|
|
}
|
|
],
|
|
},
|
|
"logprobs": completion["choices"][0]["logprobs"],
|
|
"finish_reason": "tool_calls",
|
|
}
|
|
],
|
|
usage=completion["usage"],
|
|
)
|
|
|
|
|
|
@register_chat_completion_handler("functionary-v1")
|
|
@register_chat_completion_handler("functionary-v2")
|
|
def functionary_v1_v2_chat_handler(
|
|
llama: llama.Llama,
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
|
|
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
min_p: float = 0.05,
|
|
typical_p: float = 1.0,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
|
|
max_tokens: Optional[int] = None,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
logits_processor: Optional[llama.LogitsProcessorList] = None,
|
|
grammar: Optional[llama.LlamaGrammar] = None,
|
|
**kwargs, # type: ignore
|
|
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
|
|
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. The assistant calls functions with appropriate input when necessary"""
|
|
|
|
tokenizer = llama.tokenizer_
|
|
assert hasattr(
|
|
tokenizer, "hf_tokenizer"
|
|
), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class"
|
|
from transformers import AutoTokenizer
|
|
|
|
if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens:
|
|
version = "v1"
|
|
END_SYSTEM_TOKEN = "<|END_OF_SYSTEM|>"
|
|
END_USER_TOKEN = "<|END_OF_USER|>"
|
|
END_ASSISTANT_TOKEN = "<|END_OF_ASSISTANT|>"
|
|
END_FUNCTION_RESULT_TOKEN = "<|END_OF_FUNCTION_RESULT|>"
|
|
START_FUNCTION_CALL_TOKEN = "<|START_OF_FUNCTION_CALL|>"
|
|
END_FUNCTION_CALL_TOKEN = "<|END_OF_FUNCTION_CALL|>"
|
|
else:
|
|
version = "v2"
|
|
RECIPIENT_TOKEN = "<|recipient|>"
|
|
FROM_TOKEN = "<|from|>"
|
|
STOP_TOKEN = "<|stop|>"
|
|
CONTENT_TOKEN = "<|content|>"
|
|
|
|
def generate_type_definition(
|
|
param: Dict[str, llama_types.JsonType], indent_level: int, shared_defs
|
|
) -> str:
|
|
indent = " " * indent_level
|
|
if "$ref" in param:
|
|
# Reference to a shared definition
|
|
ref_name = param["$ref"].split("/")[
|
|
-1
|
|
] # Extract the type name from the reference
|
|
return ref_name
|
|
elif param.get("type") == "array":
|
|
items = param.get("items", {})
|
|
item_type = generate_type_definition(items, indent_level + 1, shared_defs)
|
|
return f"Array<{item_type}>"
|
|
elif param.get("type") == "object":
|
|
properties = param.get("properties", {})
|
|
nested_schema = "{\n"
|
|
for nested_param_name, nested_param in properties.items():
|
|
nested_param_type = generate_type_definition(
|
|
nested_param, indent_level + 1, shared_defs
|
|
)
|
|
nested_schema += (
|
|
f"{indent} {nested_param_name}: {nested_param_type},\n"
|
|
)
|
|
nested_schema += indent + "}"
|
|
return nested_schema
|
|
elif "enum" in param:
|
|
# Enum type
|
|
return " | ".join([f'"{enum_value}"' for enum_value in param["enum"]])
|
|
else:
|
|
# Simple type
|
|
return param.get("type", "any")
|
|
|
|
def generate_shared_definitions(shared_defs, indent_level: int) -> str:
|
|
indent = " " * indent_level
|
|
shared_definitions = ""
|
|
for def_name, def_properties in shared_defs.items():
|
|
shared_definitions += f"{indent}type {def_name} = "
|
|
if def_properties.get("type") == "object":
|
|
shared_definitions += generate_type_definition(
|
|
def_properties, indent_level, shared_defs
|
|
)
|
|
elif "enum" in def_properties:
|
|
# Enum type
|
|
shared_definitions += " | ".join(
|
|
[f'"{enum_value}"' for enum_value in def_properties["enum"]]
|
|
)
|
|
shared_definitions += ";\n"
|
|
return shared_definitions
|
|
|
|
def generate_schema_from_functions(functions, namespace="functions") -> str:
|
|
schema = (
|
|
"// Supported function definitions that should be called when necessary.\n"
|
|
)
|
|
schema += f"namespace {namespace} {{\n\n"
|
|
|
|
# Generate shared definitions
|
|
shared_definitions = {}
|
|
for function in functions:
|
|
parameters = function.get("parameters", {})
|
|
shared_definitions.update(parameters.get("$defs", {}))
|
|
|
|
schema += generate_shared_definitions(shared_definitions, 1)
|
|
|
|
for function in functions:
|
|
function_name = function["name"]
|
|
description = function.get("description", "")
|
|
parameters = function.get("parameters", {})
|
|
required_params = parameters.get("required", [])
|
|
|
|
schema += f"// {description}\n"
|
|
schema += f"type {function_name} = (_: {{\n"
|
|
|
|
for param_name, param in parameters.get("properties", {}).items():
|
|
param_description = param.get("description", "")
|
|
param_type = generate_type_definition(param, 2, shared_definitions)
|
|
optional_indicator = "" if param_name in required_params else "?"
|
|
schema += f"// {param_description}\n"
|
|
schema += f"{param_name}{optional_indicator}: {param_type},\n"
|
|
schema += "}) => any;\n\n"
|
|
|
|
schema += "}} // namespace {}".format(namespace)
|
|
return schema
|
|
|
|
def prepare_messages_for_inference(
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
tokenizer: AutoTokenizer,
|
|
version: Literal["v1", "v2"],
|
|
functions: Optional[List[llama_types.ChatCompletionFunctions]] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Union[Dict, str] = "auto",
|
|
):
|
|
all_messages: List[llama_types.ChatCompletionRequestMessage] = []
|
|
if tool_choice == "none":
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system", content=generate_schema_from_functions([])
|
|
)
|
|
)
|
|
else:
|
|
if functions is not None:
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system", content=generate_schema_from_functions(functions)
|
|
)
|
|
)
|
|
elif tools is not None and tool_choice != "none":
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system",
|
|
content=generate_schema_from_functions(
|
|
[
|
|
tool["function"]
|
|
for tool in tools
|
|
if tool["type"] == "function"
|
|
]
|
|
),
|
|
)
|
|
)
|
|
|
|
all_messages.append(
|
|
llama_types.ChatCompletionRequestSystemMessage(
|
|
role="system", content=SYSTEM_MESSAGE
|
|
)
|
|
)
|
|
|
|
for message in messages:
|
|
# Function call responses
|
|
if message["role"] == "function" and "name" in message:
|
|
message["name"] = f"functions.{message['name']}"
|
|
# Function call requests by assistant
|
|
if "function_call" in message:
|
|
message["function_call"][
|
|
"name"
|
|
] = f"functions.{message['function_call']['name']}"
|
|
all_messages.append(message)
|
|
|
|
if version == "v1":
|
|
suffix = "assistant:\n"
|
|
else:
|
|
suffix = "<|from|>assistant\n<|recipient|>"
|
|
|
|
return (
|
|
tokenizer.hf_tokenizer.apply_chat_template(all_messages, tokenize=False)
|
|
+ suffix
|
|
)
|
|
|
|
if tools is not None:
|
|
functions = [tool["function"] for tool in tools if tool["type"] == "function"]
|
|
|
|
if tool_choice is not None:
|
|
function_call = (
|
|
tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
|
|
)
|
|
else:
|
|
function_call = "auto"
|
|
|
|
prompt = prepare_messages_for_inference(
|
|
messages, tokenizer, version, functions, tools, function_call
|
|
)
|
|
|
|
# If no tools/functions are provided
|
|
if function_call == "none" or functions is None or len(functions) == 0:
|
|
if version == "v1":
|
|
stop = END_ASSISTANT_TOKEN
|
|
else:
|
|
stop = STOP_TOKEN
|
|
prompt += "all\n<|content|>"
|
|
|
|
completion_or_completion_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=stream,
|
|
stop=stop,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
)
|
|
completion_or_completion_chunks["choices"][0]["text"] = completion_or_completion_chunks["choices"][0]["text"].lstrip()
|
|
return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore
|
|
|
|
assert stream is False # TODO: support stream mode
|
|
|
|
def get_grammar(function_call):
|
|
function_body = None
|
|
for function in functions or []:
|
|
if function["name"] == function_call:
|
|
function_body = function["parameters"]
|
|
break
|
|
for tool in tools or []:
|
|
if tool["type"] == "function" and tool["function"]["name"] == function_call:
|
|
function_body = tool["function"]["parameters"]
|
|
break
|
|
|
|
try:
|
|
with suppress_stdout_stderr(disable=llama.verbose):
|
|
grammar_text = llama_grammar.json_schema_to_gbnf(
|
|
json.dumps(function_body)
|
|
)
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.json_schema_to_gbnf(json.dumps(function_body))
|
|
)
|
|
print(grammar_text)
|
|
except Exception as e:
|
|
if llama.verbose:
|
|
print(
|
|
"Failed to parse function body as JSON schema, falling back to default grammar"
|
|
)
|
|
print(e)
|
|
with suppress_stdout_stderr(disable=llama.verbose):
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
|
|
return grammar
|
|
|
|
def create_completion(stop):
|
|
completion = cast(llama_types.Completion, llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=False,
|
|
stop=stop,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
))
|
|
|
|
return completion
|
|
|
|
content = ""
|
|
function_calls, function_bodies = [], []
|
|
completion_tokens = 0
|
|
|
|
if version == "v1":
|
|
# If no or "auto" tool_choice/function_call
|
|
if isinstance(function_call, str) and function_call == "auto":
|
|
stops = ["\n", END_ASSISTANT_TOKEN]
|
|
# If tool_choice/function_call is provided
|
|
elif isinstance(function_call, dict):
|
|
prompt += f"{START_FUNCTION_CALL_TOKEN}{function_call['name']}:\n"
|
|
stops = END_FUNCTION_CALL_TOKEN
|
|
function_call = function_call["name"]
|
|
function_calls.append(function_call)
|
|
grammar = get_grammar(function_call)
|
|
else:
|
|
prompt = prompt
|
|
stops = ["\n", END_ASSISTANT_TOKEN]
|
|
|
|
completion = create_completion(stop=stops)
|
|
completion_text = completion["choices"][0]["text"]
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
|
|
|
|
# If the generation does not involve a function call
|
|
if (
|
|
START_FUNCTION_CALL_TOKEN not in prompt
|
|
and START_FUNCTION_CALL_TOKEN not in completion_text
|
|
):
|
|
completion["usage"]["completion_tokens"] = completion_tokens
|
|
return _convert_completion_to_chat(completion, stream=stream) # type: ignore
|
|
# If the generation involves a function call in completion, generate the parameters
|
|
elif (
|
|
START_FUNCTION_CALL_TOKEN not in prompt
|
|
and START_FUNCTION_CALL_TOKEN in completion_text
|
|
):
|
|
prompt += (
|
|
completion_text.replace(
|
|
f"{START_FUNCTION_CALL_TOKEN} ", START_FUNCTION_CALL_TOKEN
|
|
)
|
|
+ "\n"
|
|
)
|
|
function_calls.append(
|
|
completion_text.split(START_FUNCTION_CALL_TOKEN)[-1][:-1].strip()
|
|
)
|
|
grammar = get_grammar(function_calls[-1])
|
|
completion = create_completion(stop=END_FUNCTION_CALL_TOKEN)
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
function_bodies.append(completion["choices"][0]["text"].strip())
|
|
# If the prompt involves a function call, just append generated parameters to function_bodies
|
|
else:
|
|
function_bodies.append(completion_text.strip())
|
|
else:
|
|
# If tool_choice/function_call is provided
|
|
if isinstance(function_call, dict):
|
|
prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
|
|
function_call = function_call["name"]
|
|
function_calls.append(function_call)
|
|
grammar = get_grammar(function_call)
|
|
stops = [STOP_TOKEN, FROM_TOKEN]
|
|
completion = create_completion(stop=stops)
|
|
completion_text = completion["choices"][0]["text"]
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
function_bodies.append(completion_text.strip())
|
|
# If "auto" or no tool_choice/function_call
|
|
elif isinstance(function_call, str) and function_call == "auto":
|
|
while True:
|
|
# Generate function name first
|
|
grammar = None
|
|
stops = CONTENT_TOKEN
|
|
completion = create_completion(stop=stops)
|
|
completion_text = completion["choices"][0]["text"]
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
function_name = completion_text.strip()
|
|
if function_name == "all":
|
|
prompt += "all\n<|content|>"
|
|
else:
|
|
function_call = completion_text.strip()
|
|
prompt += f"{function_call}\n<|content|>"
|
|
function_calls.append(function_call)
|
|
grammar = get_grammar(function_call)
|
|
# Generate content
|
|
stops = [RECIPIENT_TOKEN, STOP_TOKEN]
|
|
completion = create_completion(stop=stops)
|
|
completion_text = completion["choices"][0]["text"]
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
if function_name == "all":
|
|
if completion_text.endswith("\n<|from|>assistant\n"):
|
|
content += completion_text[:-len("\n<|from|>assistant\n")]
|
|
if completion_text.endswith("\n<|from|> assistant\n"):
|
|
content += completion_text[-len("\n<|from|> assistant\n")]
|
|
else:
|
|
content += completion_text
|
|
content = content.lstrip()
|
|
# Check whether the model wants to generate another turn
|
|
if "<|from|> assistant" in completion_text or "<|from|>assistant" in completion_text:
|
|
if completion_text.endswith("\n<|from|>assistant\n"):
|
|
cleaned_completion_text = completion_text[:-len("\n<|from|>assistant\n")].strip()
|
|
elif completion_text.endswith("\n<|from|> assistant\n"):
|
|
cleaned_completion_text = completion_text[-len("\n<|from|> assistant\n")].strip()
|
|
else:
|
|
cleaned_completion_text = completion_text.strip()
|
|
prompt += f"{cleaned_completion_text}\n<|from|>assistant\n<|recipient|>"
|
|
else:
|
|
break
|
|
else:
|
|
function_bodies.append(completion_text.strip())
|
|
# Check whether the model wants to generate another turn
|
|
prompt += completion_text.strip()
|
|
grammar = None
|
|
completion = create_completion(stop=stops)
|
|
completion_tokens += completion["usage"]["completion_tokens"]
|
|
if "<|from|> assistant" in completion["choices"][0]["text"] or "<|from|>assistant" in completion["choices"][0]["text"]:
|
|
prompt += "\n<|from|>assistant\n<|recipient|>"
|
|
else:
|
|
break
|
|
|
|
assert "usage" in completion
|
|
assert len(function_calls) == len(function_bodies)
|
|
|
|
tool_calls: List[llama_types.ChatCompletionMessageToolCall] = []
|
|
for function_call, function_body in zip(function_calls, function_bodies):
|
|
tool_calls.append(
|
|
{
|
|
"id": "call_"
|
|
+ "".join(
|
|
[
|
|
random.choice(string.ascii_letters + string.digits)
|
|
for _ in range(24)
|
|
]
|
|
),
|
|
"type": "function",
|
|
"function": {
|
|
"name": function_call,
|
|
"arguments": function_body,
|
|
},
|
|
}
|
|
)
|
|
|
|
# TODO: support stream mode
|
|
function_call_dict: Union[Dict[str, str], Dict[Literal["function_call"], llama_types.ChatCompletionRequestAssistantMessageFunctionCall]] = {}
|
|
if len(tool_calls) > 0:
|
|
if tools is not None:
|
|
function_call_dict["tool_calls"] = tool_calls
|
|
else:
|
|
function_call_dict["function_call"] = {
|
|
"name": tool_calls[0]["function"]["name"],
|
|
"arguments": tool_calls[0]["function"]["arguments"],
|
|
}
|
|
completion["usage"]["completion_tokens"] = completion_tokens
|
|
return llama_types.CreateChatCompletionResponse(
|
|
id="chat" + completion["id"],
|
|
object="chat.completion",
|
|
created=completion["created"],
|
|
model=completion["model"],
|
|
choices=[
|
|
{
|
|
"index": 0,
|
|
"logprobs": completion["choices"][0]["logprobs"],
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": None if content == "" else content,
|
|
**function_call_dict,
|
|
},
|
|
"finish_reason": "tool_calls" if len(tool_calls) > 0 else "stop",
|
|
}
|
|
],
|
|
usage=completion["usage"],
|
|
)
|
|
|
|
|
|
class Llava15ChatHandler:
|
|
DEFAULT_SYSTEM_MESSAGE = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
|
|
|
|
CHAT_FORMAT = (
|
|
"{% for message in messages %}"
|
|
"{% if message.role == 'system' %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
"{% if message.role == 'user' %}"
|
|
"{% if message.content is string %}"
|
|
"\nUSER: {{ message.content }}"
|
|
"{% endif %}"
|
|
"{% if message.content is iterable %}"
|
|
"\nUSER: "
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'image_url' and content.image_url is string %}"
|
|
"{{ content.image_url }}"
|
|
"{% endif %}"
|
|
"{% if content.type == 'image_url' and content.image_url is mapping %}"
|
|
"{{ content.image_url.url }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'text' %}"
|
|
"{{ content.text }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% endif %}"
|
|
"{% endif %}"
|
|
"{% if message.role == 'assistant' and message.content is not none %}"
|
|
"\nASSISTANT: {{ message.content }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
"{% if add_generation_prompt %}"
|
|
"\nASSISTANT: "
|
|
"{% endif %}"
|
|
)
|
|
|
|
def __init__(self, clip_model_path: str, verbose: bool = True):
|
|
import llama_cpp.llava_cpp as llava_cpp
|
|
|
|
self.clip_model_path = clip_model_path
|
|
self.verbose = verbose
|
|
|
|
self._llava_cpp = llava_cpp # TODO: Fix
|
|
self._exit_stack = ExitStack()
|
|
self._last_image_embed: Optional[llava_cpp.CtypesPointer[llava_cpp.llava_image_embed]] = None
|
|
self._last_image_hash: Optional[int] = None
|
|
|
|
if not os.path.exists(clip_model_path):
|
|
raise ValueError(f"Clip model path does not exist: {clip_model_path}")
|
|
|
|
with suppress_stdout_stderr(disable=self.verbose):
|
|
clip_ctx = self._llava_cpp.clip_model_load(
|
|
self.clip_model_path.encode(), 0
|
|
)
|
|
|
|
if clip_ctx is None:
|
|
raise ValueError(f"Failed to load clip model: {clip_model_path}")
|
|
|
|
self.clip_ctx = clip_ctx
|
|
|
|
def clip_free():
|
|
with suppress_stdout_stderr(disable=self.verbose):
|
|
self._llava_cpp.clip_free(self.clip_ctx)
|
|
|
|
self._exit_stack.callback(clip_free)
|
|
|
|
def last_image_embed_free():
|
|
with suppress_stdout_stderr(disable=self.verbose):
|
|
if self._last_image_embed is not None:
|
|
self._llava_cpp.llava_image_embed_free(self._last_image_embed)
|
|
self._last_image_embed = None
|
|
|
|
self._exit_stack.callback(last_image_embed_free)
|
|
|
|
def load_image(self, image_url: str) -> bytes:
|
|
return self._load_image(image_url)
|
|
|
|
def __call__(
|
|
self,
|
|
*,
|
|
llama: llama.Llama,
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
|
|
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
min_p: float = 0.05,
|
|
typical_p: float = 1.0,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
seed: Optional[int] = None,
|
|
response_format: Optional[
|
|
llama_types.ChatCompletionRequestResponseFormat
|
|
] = None,
|
|
max_tokens: Optional[int] = None,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
logits_processor: Optional[llama.LogitsProcessorList] = None,
|
|
grammar: Optional[llama.LlamaGrammar] = None,
|
|
logit_bias: Optional[Dict[str, float]] = None,
|
|
logprobs: Optional[bool] = None,
|
|
top_logprobs: Optional[int] = None,
|
|
**kwargs, # type: ignore
|
|
) -> Union[
|
|
llama_types.CreateChatCompletionResponse,
|
|
Iterator[llama_types.CreateChatCompletionStreamResponse],
|
|
]:
|
|
assert self.clip_ctx is not None
|
|
|
|
system_prompt = _get_system_message(messages)
|
|
if system_prompt == "":
|
|
messages = [llama_types.ChatCompletionRequestSystemMessage(role="system", content=self.DEFAULT_SYSTEM_MESSAGE)] + messages
|
|
|
|
image_urls = self.get_image_urls(messages)
|
|
template = jinja2.Template(self.CHAT_FORMAT)
|
|
text = template.render(messages=messages, add_generation_prompt=True)
|
|
split_text = self.split_text_on_image_urls(text, image_urls)
|
|
|
|
def embed_image_bytes(image_bytes: bytes):
|
|
if self._last_image_embed is not None and self._last_image_hash is not None and hash(image_bytes) == self._last_image_hash:
|
|
return self._last_image_embed
|
|
with suppress_stdout_stderr(disable=self.verbose):
|
|
embed = (
|
|
self._llava_cpp.llava_image_embed_make_with_bytes(
|
|
self.clip_ctx,
|
|
llama.context_params.n_threads_batch,
|
|
(ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)),
|
|
len(image_bytes),
|
|
)
|
|
)
|
|
self._last_image_embed = embed
|
|
self._last_image_hash = hash(image_bytes)
|
|
return embed
|
|
|
|
# Evaluate prompt
|
|
llama.reset()
|
|
for i, (type_, value) in enumerate(split_text):
|
|
if type_ == "text":
|
|
tokens = llama.tokenize(value.encode("utf8"), add_bos=i == 0)
|
|
if llama.n_tokens + len(tokens) > llama.n_ctx():
|
|
raise ValueError("Prompt exceeds n_ctx") # TODO: Fix
|
|
llama.eval(tokens)
|
|
else:
|
|
image_bytes = self.load_image(value)
|
|
embed = embed_image_bytes(image_bytes)
|
|
if llama.n_tokens + embed.contents.n_image_pos > llama.n_ctx():
|
|
raise ValueError("Prompt exceeds n_ctx") # TODO: Fix
|
|
n_past = ctypes.c_int(llama.n_tokens)
|
|
n_past_p = ctypes.pointer(n_past)
|
|
with suppress_stdout_stderr(disable=self.verbose):
|
|
self._llava_cpp.llava_eval_image_embed(
|
|
llama.ctx,
|
|
embed,
|
|
llama.n_batch,
|
|
n_past_p,
|
|
)
|
|
llama.n_tokens = n_past.value
|
|
|
|
# Get prompt tokens to avoid a cache miss
|
|
prompt = llama.input_ids[: llama.n_tokens].tolist()
|
|
|
|
if response_format is not None and response_format["type"] == "json_object":
|
|
grammar = _grammar_for_response_format(response_format)
|
|
|
|
# Convert legacy functions to tools
|
|
if functions is not None:
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": function,
|
|
}
|
|
for function in functions
|
|
]
|
|
|
|
# Convert legacy function_call to tool_choice
|
|
if function_call is not None:
|
|
if isinstance(function_call, str) and (
|
|
function_call == "none" or function_call == "auto"
|
|
):
|
|
tool_choice = function_call
|
|
if isinstance(function_call, dict) and "name" in function_call:
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {
|
|
"name": function_call["name"],
|
|
},
|
|
}
|
|
|
|
tool = None
|
|
if tool_choice is not None and isinstance(tool_choice, dict) and tools is not None:
|
|
name = tool_choice["function"]["name"]
|
|
tool = next((t for t in tools if t["function"]["name"] == name), None)
|
|
if tool is None:
|
|
raise ValueError(f"Tool choice '{name}' not found in tools.")
|
|
schema = tool["function"]["parameters"]
|
|
try:
|
|
# create grammar from json schema
|
|
grammar = llama_grammar.LlamaGrammar.from_json_schema(
|
|
json.dumps(schema), verbose=llama.verbose
|
|
)
|
|
except Exception as e:
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
|
|
completion_or_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
logprobs=top_logprobs if logprobs else None,
|
|
stream=stream,
|
|
stop=stop,
|
|
seed=seed,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
logit_bias=logit_bias,
|
|
)
|
|
if tool is not None:
|
|
tool_name = tool["function"]["name"]
|
|
return _convert_completion_to_chat_function(
|
|
tool_name, completion_or_chunks, stream
|
|
)
|
|
return _convert_completion_to_chat(completion_or_chunks, stream=stream)
|
|
|
|
@staticmethod
|
|
def _load_image(image_url: str) -> bytes:
|
|
# TODO: Add Pillow support for other image formats beyond (jpg, png)
|
|
if image_url.startswith("data:"):
|
|
import base64
|
|
|
|
image_bytes = base64.b64decode(image_url.split(",")[1])
|
|
return image_bytes
|
|
else:
|
|
import urllib.request
|
|
|
|
with urllib.request.urlopen(image_url) as f:
|
|
image_bytes = f.read()
|
|
return image_bytes
|
|
|
|
@staticmethod
|
|
def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]):
|
|
image_urls: List[str] = []
|
|
for message in messages:
|
|
if message["role"] == "user":
|
|
if message["content"] is None:
|
|
continue
|
|
for content in message["content"]:
|
|
if isinstance(content, dict) and "type" in content:
|
|
if content["type"] == "image_url":
|
|
if (
|
|
isinstance(content["image_url"], dict)
|
|
and "url" in content["image_url"]
|
|
):
|
|
image_urls.append(content["image_url"]["url"])
|
|
else:
|
|
image_urls.append(content["image_url"])
|
|
return image_urls
|
|
|
|
@staticmethod
|
|
def split_text_on_image_urls(text: str, image_urls: List[str]):
|
|
def find_first(s: str, substrs: List[str]):
|
|
for i, substr in enumerate(substrs):
|
|
pos = s.find(substr)
|
|
if pos != -1:
|
|
return pos, i
|
|
return None, None
|
|
|
|
split_text: List[Tuple[Literal["text", "image_url"], str]] = []
|
|
remaining = text
|
|
while remaining:
|
|
# Find first image_url
|
|
pos, i = find_first(remaining, image_urls)
|
|
if pos is not None and i is not None:
|
|
if pos > 0:
|
|
split_text.append(("text", remaining[:pos]))
|
|
split_text.append(("image_url", image_urls[i]))
|
|
remaining = remaining[pos + len(image_urls[i]) :]
|
|
else:
|
|
split_text.append(("text", remaining))
|
|
remaining = ""
|
|
return split_text
|
|
|
|
@classmethod
|
|
def from_pretrained(
|
|
cls,
|
|
repo_id: str,
|
|
filename: Optional[str],
|
|
local_dir: Optional[Union[str, os.PathLike[str]]] = None,
|
|
local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
|
|
cache_dir: Optional[Union[str, os.PathLike[str]]] = None,
|
|
**kwargs: Any,
|
|
) -> "Llava15ChatHandler":
|
|
import fnmatch
|
|
from pathlib import Path
|
|
try:
|
|
from huggingface_hub import hf_hub_download, HfFileSystem # type: ignore
|
|
from huggingface_hub.utils import validate_repo_id # type: ignore
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Llama.from_pretrained requires the huggingface-hub package. "
|
|
"You can install it with `pip install huggingface-hub`."
|
|
)
|
|
|
|
validate_repo_id(repo_id)
|
|
|
|
hffs = HfFileSystem()
|
|
|
|
files = [
|
|
file["name"] if isinstance(file, dict) else file
|
|
for file in hffs.ls(repo_id) # type: ignore
|
|
]
|
|
|
|
# split each file into repo_id, subfolder, filename
|
|
file_list: List[str] = []
|
|
for file in files:
|
|
rel_path = Path(file).relative_to(repo_id)
|
|
file_list.append(str(rel_path))
|
|
|
|
matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)] # type: ignore
|
|
|
|
if len(matching_files) == 0:
|
|
raise ValueError(
|
|
f"No file found in {repo_id} that match {filename}\n\n"
|
|
f"Available Files:\n{json.dumps(file_list)}"
|
|
)
|
|
|
|
if len(matching_files) > 1:
|
|
raise ValueError(
|
|
f"Multiple files found in {repo_id} matching {filename}\n\n"
|
|
f"Available Files:\n{json.dumps(files)}"
|
|
)
|
|
|
|
(matching_file,) = matching_files
|
|
|
|
subfolder = str(Path(matching_file).parent)
|
|
filename = Path(matching_file).name
|
|
|
|
# download the file
|
|
hf_hub_download(
|
|
repo_id=repo_id,
|
|
filename=filename,
|
|
subfolder=subfolder,
|
|
local_dir=cast(Union[str, Path, None], local_dir),
|
|
local_dir_use_symlinks=local_dir_use_symlinks,
|
|
cache_dir=cast(Union[str, Path, None], cache_dir),
|
|
)
|
|
|
|
if local_dir is None:
|
|
model_path = hf_hub_download(
|
|
repo_id=repo_id,
|
|
filename=filename,
|
|
subfolder=subfolder,
|
|
local_dir=local_dir,
|
|
local_dir_use_symlinks=local_dir_use_symlinks,
|
|
cache_dir=cast(Union[str, Path, None], cache_dir),
|
|
local_files_only=True,
|
|
)
|
|
else:
|
|
model_path = os.path.join(local_dir, filename)
|
|
|
|
return cls(
|
|
clip_model_path=model_path,
|
|
**kwargs,
|
|
)
|
|
|
|
class ObsidianChatHandler(Llava15ChatHandler):
|
|
# Prompt Format
|
|
# The model followed ChatML format. However, with ### as the seperator
|
|
|
|
# <|im_start|>user
|
|
# What is this sign about?\n<image>
|
|
# ###
|
|
# <|im_start|>assistant
|
|
# The sign is about bullying, and it is placed on a black background with a red background.
|
|
# ###
|
|
|
|
CHAT_FORMAT = (
|
|
"{% for message in messages %}"
|
|
# System message
|
|
"{% if message.role == 'system' %}"
|
|
"<|im_start|>system\n"
|
|
"{{ message.content }}\n"
|
|
"###\n"
|
|
"{% endif %}"
|
|
# User message
|
|
"{% if message.role == 'user' %}"
|
|
"<|im_start|>user\n"
|
|
"{% if message.content is string %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
"{% if message.content is iterable %}"
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'image_url' and content.image_url is string %}"
|
|
"{{ content.image_url }}"
|
|
"{% endif %}"
|
|
"{% if content.type == 'image_url' and content.image_url is mapping %}"
|
|
"{{ content.image_url.url }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'text' %}"
|
|
"{{ content.text }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% endif %}"
|
|
"###\n"
|
|
"{% endif %}"
|
|
# Assistant message
|
|
"{% if message.role == 'assistant' %}"
|
|
"<|im_start|>assistant\n"
|
|
"{{ message.content }}"
|
|
"###\n"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
# Generation prompt
|
|
"{% if add_generation_prompt %}"
|
|
"<|im_start|>assistant\n"
|
|
"{% endif %}"
|
|
)
|
|
|
|
class MoondreamChatHandler(Llava15ChatHandler):
|
|
# Chat Format:
|
|
# f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
|
|
CHAT_FORMAT = (
|
|
"{% for message in messages %}"
|
|
"{% if message.role == 'user' %}"
|
|
"{% if message.content is iterable %}"
|
|
|
|
# <image>
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'image_url' %}"
|
|
"{% if content.image_url is string %}"
|
|
"{{ content.image_url }}\n\n"
|
|
"{% endif %}"
|
|
"{% if content.image_url is mapping %}"
|
|
"{{ content.image_url.url }}\n\n"
|
|
"{% endif %}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
# Question:
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'text' %}"
|
|
"Question: {{ content.text }}\n\n"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% endif %}"
|
|
|
|
# Question:
|
|
"{% if message.content is string %}"
|
|
"Question: {{ message.content }}\n\n"
|
|
"{% endif %}"
|
|
|
|
"{% endif %}"
|
|
|
|
# Answer:
|
|
"{% if message.role == 'assistant' %}"
|
|
"Answer:{{ message.content }}\n\n"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
# Generation prompt
|
|
"{% if add_generation_prompt %}"
|
|
"Answer:"
|
|
"{% endif %}"
|
|
)
|
|
|
|
class Llava16ChatHandler(Llava15ChatHandler):
|
|
DEFAULT_SYSTEM_MESSAGE = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. "
|
|
|
|
# Example prompt
|
|
# "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
|
|
|
|
CHAT_FORMAT = (
|
|
"{% for message in messages %}"
|
|
"{% if message.role == 'system' %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
"{% if message.role == 'user' %}"
|
|
"{% if message.content is iterable %}"
|
|
|
|
# <image>
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'image_url' %}"
|
|
"{% if content.image_url is string %}"
|
|
"{{ content.image_url }}\n"
|
|
"{% endif %}"
|
|
"{% if content.image_url is mapping %}"
|
|
"{{ content.image_url.url }}\n"
|
|
"{% endif %}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
# Question:
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'text' %}"
|
|
"{{ content.text }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% endif %}"
|
|
|
|
# Question:
|
|
"{% if message.content is string %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
|
|
"{% endif %}"
|
|
|
|
# Answer:
|
|
"{% if message.role == 'assistant' %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
# Generation prompt
|
|
"{% if add_generation_prompt %}"
|
|
"Answer:"
|
|
"{% endif %}"
|
|
)
|
|
|
|
class NanoLlavaChatHandler(Llava15ChatHandler):
|
|
# Prompt Format
|
|
# The model follow the ChatML standard, however, without \n at the end of <|im_end|>:
|
|
|
|
# <|im_start|>system
|
|
# Answer the question<|im_end|><|im_start|>user
|
|
# <image>
|
|
# What is the picture about?<|im_end|><|im_start|>assistant
|
|
|
|
CHAT_FORMAT = (
|
|
"{% for message in messages %}"
|
|
# System message
|
|
"{% if message.role == 'system' %}"
|
|
"<|im_start|>system\n"
|
|
"{{ message.content }}"
|
|
"<|im_end|>"
|
|
"{% endif %}"
|
|
# User message
|
|
"{% if message.role == 'user' %}"
|
|
"<|im_start|>user\n"
|
|
"{% if message.content is string %}"
|
|
"{{ message.content }}"
|
|
"{% endif %}"
|
|
"{% if message.content is iterable %}"
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'image_url' and content.image_url is string %}"
|
|
"{{ content.image_url }}"
|
|
"{% endif %}"
|
|
"{% if content.type == 'image_url' and content.image_url is mapping %}"
|
|
"{{ content.image_url.url }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% for content in message.content %}"
|
|
"{% if content.type == 'text' %}"
|
|
"{{ content.text }}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
|
|
"{% endif %}"
|
|
"<|im_end|>"
|
|
"{% endif %}"
|
|
# Assistant message
|
|
"{% if message.role == 'assistant' %}"
|
|
"<|im_start|>assistant\n"
|
|
"{{ message.content }}"
|
|
"<|im_end|>"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
# Generation prompt
|
|
"{% if add_generation_prompt %}"
|
|
"<|im_start|>assistant\n"
|
|
"{% endif %}"
|
|
)
|
|
|
|
|
|
@register_chat_completion_handler("chatml-function-calling")
|
|
def chatml_function_calling(
|
|
llama: llama.Llama,
|
|
messages: List[llama_types.ChatCompletionRequestMessage],
|
|
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
|
|
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
|
|
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
|
|
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
min_p: float = 0.05,
|
|
typical_p: float = 1.0,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
|
|
max_tokens: Optional[int] = None,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
logits_processor: Optional[llama.LogitsProcessorList] = None,
|
|
grammar: Optional[llama.LlamaGrammar] = None,
|
|
logprobs: Optional[bool] = None,
|
|
top_logprobs: Optional[int] = None,
|
|
**kwargs, # type: ignore
|
|
) -> Union[
|
|
llama_types.CreateChatCompletionResponse,
|
|
Iterator[llama_types.CreateChatCompletionStreamResponse],
|
|
]:
|
|
print(logprobs)
|
|
function_calling_template = (
|
|
"{% for message in messages %}"
|
|
"<|im_start|>{{ message.role }}\n"
|
|
# System message
|
|
"{% if message.role == 'system' %}"
|
|
"{{ message.content }}"
|
|
"{% if tool_calls %}"
|
|
"\n\nYou have access to the following functions:\n"
|
|
"{% for tool in tools %}"
|
|
"\nfunctions.{{ tool.function.name }}:\n"
|
|
"{{ tool.function.parameters | tojson }}"
|
|
"\n{% endfor %}"
|
|
"\n\nYou can respond to users messages with either a single message or one or more function calls."
|
|
"\n\nTo respond with a message begin the message with 'message:', use the following format:"
|
|
"\n\nmessage:"
|
|
"\n<message>"
|
|
"\n\nTo respond with one or more function calls begin the message with 'functions.<function_name>:', use the following format:"
|
|
"\n\nfunctions.<function_name>:"
|
|
'\n{ "arg1": "value1", "arg2": "value2" }'
|
|
"\nfunctions.<function_name>:"
|
|
'\n{ "arg1": "value1", "arg2": "value2" }'
|
|
"{% endif %}"
|
|
"<|im_end|>\n"
|
|
"{% endif %}"
|
|
# User message
|
|
"{% if message.role == 'user' %}"
|
|
"{{ message.content }}"
|
|
"<|im_end|>\n"
|
|
"{% endif %}"
|
|
# Assistant message
|
|
"{% if message.role == 'assistant' %}"
|
|
## Reglar message
|
|
"{% if message.content and message.content | length > 0 %}"
|
|
"{% if tool_calls %}"
|
|
"message:\n"
|
|
"{% endif %}"
|
|
"{{ message.content }}"
|
|
"<|im_end|>\n"
|
|
"{% endif %}"
|
|
## Function calls
|
|
"{% if 'tool_calls' in message %}"
|
|
"{% for tool_call in message.tool_calls %}"
|
|
"functions.{{ tool_call.function.name }}:\n"
|
|
"{{ tool_call.function.arguments }}"
|
|
"{% endfor %}"
|
|
"<|im_end|>\n"
|
|
"{% endif %}"
|
|
"{% endif %}"
|
|
"{% endfor %}"
|
|
"{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
|
)
|
|
template_renderer = jinja2.Environment(
|
|
loader=jinja2.BaseLoader(),
|
|
autoescape=jinja2.select_autoescape(["html", "xml"]),
|
|
undefined=jinja2.StrictUndefined,
|
|
).from_string(function_calling_template)
|
|
|
|
# Convert legacy functions to tools
|
|
if functions is not None:
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": function,
|
|
}
|
|
for function in functions
|
|
]
|
|
|
|
# Convert legacy function_call to tool_choice
|
|
if function_call is not None:
|
|
if isinstance(function_call, str) and (
|
|
function_call == "none" or function_call == "auto"
|
|
):
|
|
tool_choice = function_call
|
|
if isinstance(function_call, dict) and "name" in function_call:
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {
|
|
"name": function_call["name"],
|
|
},
|
|
}
|
|
|
|
stop = [stop, "<|im_end|>"] if isinstance(stop, str) else stop + ["<|im_end|>"] if stop else ["<|im_end|>"]
|
|
|
|
# Case 1: No tool choice by user
|
|
if (
|
|
tool_choice is None
|
|
or (isinstance(tool_choice, str) and tool_choice == "none")
|
|
or tools is None
|
|
or len(tools) == 0
|
|
):
|
|
prompt = template_renderer.render(
|
|
messages=messages,
|
|
tools=[],
|
|
tool_calls=None,
|
|
add_generation_prompt=True,
|
|
)
|
|
|
|
if response_format is not None and response_format["type"] == "json_object":
|
|
grammar = _grammar_for_response_format(response_format)
|
|
|
|
return _convert_completion_to_chat(
|
|
llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=stream,
|
|
stop=stop,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
logprobs=top_logprobs if logprobs else None,
|
|
),
|
|
stream=stream,
|
|
)
|
|
|
|
# Case 2: Tool choice by user
|
|
if isinstance(tool_choice, dict):
|
|
tool_name = tool_choice["function"]["name"]
|
|
tool = next(
|
|
(tool for tool in tools if tool["function"]["name"] == tool_name), None
|
|
)
|
|
if tool is None:
|
|
raise ValueError(f"Tool with name '{tool_name}' not found in tools")
|
|
prompt = template_renderer.render(
|
|
messages=messages,
|
|
tools=tools,
|
|
tool_calls=True,
|
|
add_generation_prompt=True,
|
|
)
|
|
prompt += f"functions.{tool_name}:\n"
|
|
try:
|
|
grammar = llama_grammar.LlamaGrammar.from_json_schema(
|
|
json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
|
|
)
|
|
except Exception as e:
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
if llama.verbose:
|
|
print(
|
|
"Failed to parse function body as JSON schema, falling back to default grammar"
|
|
)
|
|
print(e)
|
|
completion_or_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=stream,
|
|
stop=stop,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
)
|
|
return _convert_completion_to_chat_function(
|
|
tool_name, completion_or_chunks, stream
|
|
)
|
|
|
|
# Case 3: Automatic tool choice
|
|
assert isinstance(tool_choice, str) and tool_choice == "auto"
|
|
function_names = " | ".join(
|
|
[f'''"functions.{tool['function']['name']}:"''' for tool in tools]
|
|
)
|
|
initial_gbnf_tool_grammar = (
|
|
"""root ::= functions | "message:"\n"""
|
|
f"""functions ::= {function_names}\n"""
|
|
)
|
|
follow_up_gbnf_tool_grammar = (
|
|
"""root ::= functions | "<|im_end|>"\n"""
|
|
f"""functions ::= {function_names}\n"""
|
|
)
|
|
prompt = template_renderer.render(
|
|
messages=messages,
|
|
tools=tools,
|
|
tool_calls=True,
|
|
add_generation_prompt=True,
|
|
)
|
|
completion_or_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=0,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=False,
|
|
stop=[":"],
|
|
max_tokens=None,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=llama_grammar.LlamaGrammar.from_string(
|
|
initial_gbnf_tool_grammar, verbose=llama.verbose
|
|
),
|
|
)
|
|
completion: llama_types.CreateCompletionResponse = completion_or_chunks # type: ignore
|
|
text = completion["choices"][0]["text"]
|
|
if "message" in text:
|
|
return _convert_completion_to_chat(
|
|
llama.create_completion(
|
|
prompt=prompt + "message:\n",
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=stream,
|
|
stop=["<|im_end|>"],
|
|
logprobs=top_logprobs if logprobs else None,
|
|
max_tokens=None,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=llama_grammar.LlamaGrammar.from_string(
|
|
follow_up_gbnf_tool_grammar, verbose=llama.verbose
|
|
),
|
|
),
|
|
stream=stream,
|
|
)
|
|
|
|
# One or more function calls
|
|
tool_name = text[len("functions.") :]
|
|
tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
|
|
if not stream:
|
|
completions: List[llama_types.CreateCompletionResponse] = []
|
|
completions_tool_name: List[str] = []
|
|
while tool is not None:
|
|
prompt += f"functions.{tool_name}:\n"
|
|
try:
|
|
grammar = llama_grammar.LlamaGrammar.from_json_schema(
|
|
json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
|
|
)
|
|
except Exception as e:
|
|
grammar = llama_grammar.LlamaGrammar.from_string(
|
|
llama_grammar.JSON_GBNF, verbose=llama.verbose
|
|
)
|
|
if llama.verbose:
|
|
print(
|
|
"Failed to parse function body as JSON schema, falling back to default grammar"
|
|
)
|
|
print(e)
|
|
completion_or_chunks = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=False,
|
|
stop=stop,
|
|
max_tokens=None,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=grammar,
|
|
)
|
|
completion_or_chunks = cast(llama_types.CreateCompletionResponse, completion_or_chunks)
|
|
completions.append(completion_or_chunks)
|
|
completions_tool_name.append(tool_name)
|
|
prompt += completion_or_chunks["choices"][0]["text"]
|
|
prompt += "\n"
|
|
|
|
response = llama.create_completion(
|
|
prompt=prompt,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
min_p=min_p,
|
|
typical_p=typical_p,
|
|
stream=False,
|
|
stop=stop,
|
|
max_tokens=None,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
logits_processor=logits_processor,
|
|
grammar=llama_grammar.LlamaGrammar.from_string(
|
|
follow_up_gbnf_tool_grammar, verbose=llama.verbose
|
|
),
|
|
)
|
|
response = cast(llama_types.CreateCompletionResponse, response)
|
|
|
|
tool_name = response["choices"][0]["text"][len("functions.") :]
|
|
tool = next(
|
|
(tool for tool in tools if tool["function"]["name"] == tool_name), None
|
|
)
|
|
|
|
# Merge completions
|
|
function_call_dict: Union[Dict[str, str], Dict[Literal["function_call"], llama_types.ChatCompletionRequestAssistantMessageFunctionCall]] = {
|
|
"function_call": {
|
|
"name": tool_name,
|
|
"arguments": completions[0]["choices"][0]["text"],
|
|
}
|
|
} if len(completions) == 1 else {}
|
|
return {
|
|
"id": "chat" + completion["id"],
|
|
"object": "chat.completion",
|
|
"created": completion["created"],
|
|
"model": completion["model"],
|
|
"choices": [
|
|
{
|
|
"finish_reason": "tool_calls",
|
|
"index": 0,
|
|
"logprobs": completion["choices"][0]["logprobs"],
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": None,
|
|
"tool_calls": [
|
|
{
|
|
"id": "call_"
|
|
+ f"_{i}_"
|
|
+ tool_name
|
|
+ "_"
|
|
+ completion["id"],
|
|
"type": "function",
|
|
"function": {
|
|
"name": tool_name,
|
|
"arguments": completion["choices"][0]["text"],
|
|
},
|
|
}
|
|
for i, (tool_name, completion) in enumerate(
|
|
zip(completions_tool_name, completions)
|
|
)
|
|
],
|
|
**function_call_dict
|
|
},
|
|
}
|
|
],
|
|
"usage": {
|
|
"completion_tokens": sum(
|
|
completion["usage"]["completion_tokens"] if "usage" in completion else 0
|
|
for completion in completions
|
|
),
|
|
"prompt_tokens": sum(
|
|
completion["usage"]["prompt_tokens"] if "usage" in completion else 0
|
|
for completion in completions
|
|
),
|
|
"total_tokens": sum(
|
|
completion["usage"]["total_tokens"] if "usage" in completion else 0
|
|
for completion in completions
|
|
),
|
|
},
|
|
}
|
|
|
|
raise ValueError("Automatic streaming tool choice is not supported") |