from __future__ import annotations
import os
import json
import ctypes
import dataclasses
import random
import string
from contextlib import ExitStack
from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union, Protocol, cast
import jinja2
import numpy as np
import numpy.typing as npt
import llama_cpp.llama as llama
import llama_cpp.llama_types as llama_types
import llama_cpp.llama_grammar as llama_grammar
from ._logger import logger
from ._utils import suppress_stdout_stderr, Singleton
### Common Chat Templates and Special Tokens ###
# Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
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 %}"
CHATML_BOS_TOKEN = ""
CHATML_EOS_TOKEN = "<|im_end|>"
# Source: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
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 %}"
MISTRAL_INSTRUCT_BOS_TOKEN = ""
MISTRAL_INSTRUCT_EOS_TOKEN = ""
# Source: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/blob/main/tokenizer_config.json
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 %}"
# Source: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
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 %}"
### Chat Completion Handler ###
class LlamaChatCompletionHandler(Protocol):
"""Base Protocol for a llama chat completion handler.
Very generic protocol that can be used to implement any chat format.
The only hard requirement is that it must return a ChatCompletion when
stream=False and an iterator of ChatCompletionChunks when stream=True."""
def __call__(
self,
*,
# llama.cpp instance
llama: llama.Llama,
# openai api parameters
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,
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,
model: Optional[str] = None,
logit_bias: Optional[Dict[str, float]] = None,
# llama.cpp parameters
min_p: float = 0.05,
typical_p: float = 1.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
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],
]: ...
class LlamaChatCompletionHandlerNotFoundException(Exception):
pass
class LlamaChatCompletionHandlerRegistry(Singleton):
_chat_handlers: Dict[str, LlamaChatCompletionHandler] = {}
def register_chat_completion_handler(
self,
name: str,
chat_handler: LlamaChatCompletionHandler,
overwrite: bool = False,
):
if not overwrite and name in self._chat_handlers:
raise ValueError(
f"Formatter with name '{name}' is already registered. Use `overwrite=True` to overwrite it."
)
self._chat_handlers[name] = chat_handler
def unregister_chat_handler(self, name: str):
if name in self._chat_handlers:
del self._chat_handlers[name]
else:
raise ValueError(f"No formatter registered under the name '{name}'.")
def get_chat_completion_handler_by_name(
self, name: str
) -> LlamaChatCompletionHandler:
try:
chat_handler = self._chat_handlers[name]
return chat_handler
except KeyError:
raise LlamaChatCompletionHandlerNotFoundException(
f"Invalid chat handler: {name} (valid formats: {list(self._chat_handlers.keys())})"
)
def get_chat_completion_handler(name: str) -> LlamaChatCompletionHandler:
return LlamaChatCompletionHandlerRegistry().get_chat_completion_handler_by_name(
name
)
def register_chat_completion_handler(name: str):
def decorator(f: LlamaChatCompletionHandler):
LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(name, f)
return f
return decorator
### Chat Formatter ###
@dataclasses.dataclass
class ChatFormatterResponse:
"""Dataclass that stores completion parameters for a given chat format and
create_chat_completion request.
prompt contains the formatted prompt generated from the chat format and messages.
stop contains the stop token or list of stop tokens to use for the chat format."""
prompt: str
stop: Optional[Union[str, List[str]]] = None
stopping_criteria: Optional[llama.StoppingCriteriaList] = None
class ChatFormatter(Protocol):
"""Base Protocol for a chat formatter. A chat formatter is a function that
takes a list of messages and returns a chat format response which can be used
to generate a completion. The response can also include a stop token or list
of stop tokens to use for the completion."""
def __call__(
self,
*,
messages: List[llama_types.ChatCompletionRequestMessage],
**kwargs: Any,
) -> ChatFormatterResponse: ...
class Jinja2ChatFormatter(ChatFormatter):
def __init__(
self,
template: str,
eos_token: str,
bos_token: str,
add_generation_prompt: bool = True,
stop_token_ids: Optional[List[int]] = None,
):
"""A chat formatter that uses jinja2 templates to format the prompt."""
self.template = template
self.eos_token = eos_token
self.bos_token = bos_token
self.add_generation_prompt = add_generation_prompt
self.stop_token_ids = set(stop_token_ids) if stop_token_ids is not None else None
self._environment = jinja2.Environment(
loader=jinja2.BaseLoader(),
trim_blocks=True,
lstrip_blocks=True,
).from_string(self.template)
def __call__(
self,
*,
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,
**kwargs: Any,
) -> ChatFormatterResponse:
def raise_exception(message: str):
raise ValueError(message)
prompt = self._environment.render(
messages=messages,
eos_token=self.eos_token,
bos_token=self.bos_token,
raise_exception=raise_exception,
add_generation_prompt=self.add_generation_prompt,
functions=functions,
function_call=function_call,
tools=tools,
tool_choice=tool_choice,
)
stopping_criteria = None
if self.stop_token_ids is not None:
def stop_on_last_token(
tokens: npt.NDArray[np.intc],
logits: npt.NDArray[np.single]
) -> bool:
return tokens[-1] in self.stop_token_ids
stopping_criteria = llama.StoppingCriteriaList([stop_on_last_token])
return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token], stopping_criteria=stopping_criteria)
def to_chat_handler(self) -> LlamaChatCompletionHandler:
return chat_formatter_to_chat_completion_handler(self)
def _convert_text_completion_to_chat(
completion: llama_types.Completion,
) -> llama_types.ChatCompletion:
assert "usage" in completion
return {
"id": "chat" + completion["id"],
"object": "chat.completion",
"created": completion["created"],
"model": completion["model"],
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": completion["choices"][0]["text"],
},
"logprobs": completion["choices"][0]["logprobs"],
"finish_reason": completion["choices"][0]["finish_reason"],
}
],
"usage": completion["usage"],
}
def _convert_text_completion_chunks_to_chat(
chunks: Iterator[llama_types.CreateCompletionStreamResponse],
) -> Iterator[llama_types.ChatCompletionChunk]:
for i, chunk in enumerate(chunks):
if i == 0:
yield {
"id": "chat" + chunk["id"],
"model": chunk["model"],
"created": chunk["created"],
"object": "chat.completion.chunk",
"choices": [
{
"index": 0,
"delta": {
"role": "assistant",
},
"logprobs": None,
"finish_reason": None,
}
],
}
yield {
"id": "chat" + chunk["id"],
"model": chunk["model"],
"created": chunk["created"],
"object": "chat.completion.chunk",
"choices": [
{
"index": 0,
"delta": (
{
"content": chunk["choices"][0]["text"],
}
if chunk["choices"][0]["finish_reason"] is None
else {}
),
"logprobs": chunk["choices"][0]["logprobs"],
"finish_reason": chunk["choices"][0]["finish_reason"],
}
],
}
def _convert_completion_to_chat(
completion_or_chunks: Union[
llama_types.CreateCompletionResponse,
Iterator[llama_types.CreateCompletionStreamResponse],
],
stream: bool = False,
) -> Union[
llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk]
]:
if stream:
chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore
return _convert_text_completion_chunks_to_chat(chunks)
else:
completion: llama_types.Completion = completion_or_chunks # type: ignore
return _convert_text_completion_to_chat(completion)
def _convert_completion_to_chat_function(
tool_name: str,
completion_or_chunks: Union[
llama_types.CreateCompletionResponse,
Iterator[llama_types.CreateCompletionStreamResponse],
],
stream: bool,
):
if not stream:
completion: llama_types.CreateCompletionResponse = completion_or_chunks # type: ignore
assert "usage" in completion
tool_id = "call_" + "_0_" + tool_name + "_" + completion["id"]
# TODO: Fix for legacy function calls
chat_completion: 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": tool_name,
"arguments": completion["choices"][0]["text"],
},
"tool_calls": [
{
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": completion["choices"][0]["text"],
},
}
],
},
"logprobs": completion["choices"][0]["logprobs"],
"finish_reason": "tool_calls",
}
],
"usage": completion["usage"],
}
return chat_completion
else:
chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore
def _stream_response_to_function_stream(
chunks: Iterator[llama_types.CreateCompletionStreamResponse],
) -> Iterator[llama_types.CreateChatCompletionStreamResponse]:
# blank first message
first = True
id_ = None
created = None
model = None
tool_id = None
for chunk in chunks:
if first:
id_ = "chat" + chunk["id"]
created = chunk["created"]
model = chunk["model"]
tool_id = "call_" + "_0_" + tool_name + "_" + chunk["id"]
yield {
"id": id_,
"object": "chat.completion.chunk",
"created": created,
"model": model,
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": None,
"delta": {
"role": "assistant",
"content": None,
"function_call": None,
"tool_calls": None,
},
}
],
}
yield {
"id": "chat" + chunk["id"],
"object": "chat.completion.chunk",
"created": chunk["created"],
"model": chunk["model"],
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": None,
"content": None,
"function_call": {
"name": tool_name,
"arguments": chunk["choices"][0]["text"],
},
"tool_calls": [
{
"index": 0,
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": chunk["choices"][0]["text"],
},
}
],
},
}
],
}
first = False
continue
assert tool_id is not None
yield {
"id": "chat" + chunk["id"],
"object": "chat.completion.chunk",
"created": chunk["created"],
"model": chunk["model"],
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": None,
"content": None,
"function_call": {
"name": tool_name,
"arguments": chunk["choices"][0]["text"],
},
"tool_calls": [
{
"index": 0,
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": chunk["choices"][0][
"text"
],
},
}
],
},
}
],
}
if id_ is not None and created is not None and model is not None:
yield {
"id": id_,
"object": "chat.completion.chunk",
"created": created,
"model": model,
"choices": [
{
"index": 0,
"finish_reason": "tool_calls",
"logprobs": None,
"delta": {
"role": None,
"content": None,
"function_call": None,
"tool_calls": None,
},
}
],
}
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 = "[INST] <>\n{system_message}\n<>"
_roles = dict(user="[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, " ", "") + "[/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 = ""
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 = ""
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] <>\n{system_message}\n<>\n\n"
_roles = dict(user="<|prompter|>", assistant="<|assistant|>")
_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-2")
def format_baichuan2(
messages: List[llama_types.ChatCompletionRequestMessage],
**kwargs: Any,
) -> ChatFormatterResponse:
_system_template = "{system_message}"
_roles = dict(user="", assistant="")
_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="", assistant="")
_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="", assistant="")
_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("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="\n<|assistant|>")
_sep = " "
system_template = """<|system|>{system_message}"""
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 = ""
_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 = ""
eos = ""
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 = ""
_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 = "{role}\n{content}"
_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"{role}\n"
# Response template
_prompt += "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="user\n", assistant="model\n")
_sep = "\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\n"
else:
return f"user:\n{msg['content']}\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']}\n"
elif "function_call" in msg:
return f"assistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}\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']}\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:", ""],
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:", ""],
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"]
)
elif function_call is not None:
pass
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,
)
if stream is False:
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
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(prompt, stop, grammar):
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=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 completion
content = ""
function_calls, function_bodies = [], []
completion_tokens = 0
def generate_streaming(tools, functions, function_call, prompt):
assert version == "v2", "Streaming for v1 is not supported"
chunk_id, chunk_created = None, None
# If tool_choice/function_call is provided
if isinstance(function_call, dict):
prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
grammar = get_grammar(function_call["name"])
stops = [STOP_TOKEN, FROM_TOKEN]
tool_id = "".join([random.choice(string.ascii_letters + string.digits) for _ in range(24)])
completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
completion_text = ""
first = True
for chunk in completion:
# Yield the tool/function name first
if first:
if tools is not None:
func_call_dict = {
"tool_calls": [
{
"index": 0,
"id": "call_" + tool_id,
"type": "function",
"function": {"name": function_call["name"], "arguments": ""},
}
]
}
else:
func_call_dict = {"function_call": {"name": function_call["name"], "arguments": ""}}
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk["id"],
object="chat.completion.chunk",
created=chunk["created"],
model=chunk["model"],
choices=[
{"index": 0, "logprobs": None, "delta": {"role": None, "content": None, **func_call_dict}}
],
)
first = False
if tools is not None:
func_call_dict = {
"tool_calls": [
{
"index": 0,
"id": "call_" + tool_id,
"type": "function",
"function": {
"name": None,
"arguments": chunk["choices"][0]["text"].rstrip(),
},
}
]
}
else:
func_call_dict = {"function_call": {"name": None, "arguments": chunk["choices"][0]["text"].rstrip()}}
if len(chunk["choices"][0]["text"].rstrip()) > 0:
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk["id"],
object="chat.completion.chunk",
created=chunk["created"],
model=chunk["model"],
choices=[
{
"index": 0,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": None,
"content": None,
**func_call_dict,
},
}
],
)
# Yield tool_call/function_call stop message
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk["id"],
object="chat.completion.chunk",
created=chunk["created"],
model=chunk["model"],
choices=[
{
"index": 0,
"finish_reason": "tool_calls" if tools is not None else "function_call",
"logprobs": None,
"delta": {
"role": None, "content": None, "function_call": None, "tool_calls": None
},
}
],
)
# If "auto" or no tool_choice/function_call
elif isinstance(function_call, str) and function_call == "auto":
tool_index = 0
while True:
# Generate function name first
grammar = None
stops = CONTENT_TOKEN
completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
completion_text = ""
for chunk in completion:
completion_text += chunk["choices"][0]["text"]
if chunk_id is None:
chunk_id = chunk["id"]
if chunk_created is None:
chunk_created = chunk["created"]
function_name = completion_text.strip()
if function_name == "all":
prompt += "all\n<|content|>"
# Yield the first empty message for content
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
model=chunk["model"],
created=chunk_created,
object="chat.completion.chunk",
choices=[
{
"index": 0,
"delta": {"role": "assistant", "content": ""},
"logprobs": None,
"finish_reason": None,
}
],
)
else:
prompt += f"{function_name}\n<|content|>"
grammar = get_grammar(function_name)
tool_id = "".join([random.choice(string.ascii_letters + string.digits) for _ in range(24)])
if tools is not None:
func_call_dict = {
"tool_calls": [
{
"index": tool_index,
"id": "call_" + tool_id,
"type": "function",
"function": {"name": function_name, "arguments": ""},
}
]
}
else:
func_call_dict = {"function_call": {"name": function_name, "arguments": ""}}
# Stream function name
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
object="chat.completion.chunk",
created=chunk_created,
model=chunk["model"],
choices=[
{
"index": 0,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": "assistant",
"content": None,
**func_call_dict,
},
}
],
)
# Generate content
stops = [RECIPIENT_TOKEN, STOP_TOKEN]
completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
if function_name == "all":
completion_text = ""
stop_sequence, buffer, is_end = "\n<|from|>assistant\n<|recipient|>", [], False
for i, chunk in enumerate(completion):
completion_text += chunk["choices"][0]["text"]
if is_end:
buffer.append(chunk["choices"][0]["text"].strip(" "))
if stop_sequence.startswith("".join(buffer)):
continue
else:
buffer.pop()
while len(buffer) > 0:
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
object="chat.completion.chunk",
created=chunk_created,
model=chunk["model"],
choices=[
{
"index": 0,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": "assistant", "content": buffer.pop(0)
},
}
],
)
is_end = False
elif chunk["choices"][0]["text"] == "\n":
is_end = True
buffer.append(chunk["choices"][0]["text"].strip(" "))
continue
if len(buffer) == 0 and len(chunk["choices"][0]["text"]) > 0:
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
object="chat.completion.chunk",
created=chunk_created,
model=chunk["model"],
choices=[
{
"index": 0,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": "assistant",
"content": chunk["choices"][0]["text"] if i > 0 else chunk["choices"][0]["text"].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:
# Yield stop message
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
model=chunk["model"],
created=chunk_created,
object="chat.completion.chunk",
choices=[
{
"index": 0,
"delta": {},
"logprobs": None,
"finish_reason": "stop",
}
],
)
break
else:
# Check whether the model wants to generate another turn
completion_text = ""
for chunk in completion:
completion_text += chunk["choices"][0]["text"]
if len(chunk["choices"][0]["text"].rstrip()) > 0:
if tools is not None:
func_call_dict = {
"tool_calls": [
{
"index": tool_index,
"id": "call_" + tool_id,
"type": "function",
"function": {
"name": None,
"arguments": chunk["choices"][0]["text"].rstrip(),
},
}
]
}
else:
func_call_dict = {"function_call": {"name": None, "arguments": chunk["choices"][0]["text"].rstrip()}}
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
object="chat.completion.chunk",
created=chunk_created,
model=chunk["model"],
choices=[
{
"index": 0,
"logprobs": chunk["choices"][0]["logprobs"],
"delta": {
"role": None,
"content": None,
**func_call_dict,
},
}
],
)
prompt += completion_text.strip()
grammar = None
completion = create_completion(prompt=prompt, stop=stops, grammar=grammar)
completion_text += "".join([chunk["choices"][0]["text"] for chunk in completion])
if ("<|from|> assistant" in completion_text or "<|from|>assistant" in completion_text) and tools is not None:
prompt += "\n<|from|>assistant\n<|recipient|>"
tool_index += 1
else:
# Yield tool_call/function_call stop message
yield llama_types.CreateChatCompletionStreamResponse(
id="chat" + chunk_id,
object="chat.completion.chunk",
created=chunk_created,
model=chunk["model"],
choices=[
{
"index": 0,
"finish_reason": "tool_calls" if tools is not None else "function_call",
"logprobs": None,
"delta": {
"role": None, "content": None, "function_call": None, "tool_calls": None
},
}
],
)
break
if stream is not False:
return generate_streaming(
tools=tools, functions=functions, function_call=function_call, prompt=prompt
)
else:
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: Optional[str] = "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 == "" and self.DEFAULT_SYSTEM_MESSAGE is not None:
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
# ###
# <|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"\n\n{chat_history}Question: {question}\n\nAnswer:"
CHAT_FORMAT = (
"{% for message in messages %}"
"{% if message.role == 'user' %}"
"{% if message.content is iterable %}"
#
"{% 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: \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 %}"
#
"{% 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
#
# 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 %}"
)
class Llama3VisionAlpha(Llava15ChatHandler):
# question = "" + q
# prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
DEFAULT_SYSTEM_MESSAGE = None
CHAT_FORMAT = (
"{% for message in messages %}"
"<|start_header_id|>"
"{% if message.role == 'user' %}"
"user<|end_header_id|>\n\n"
"{% if message.content is iterable %}"
#
"{% for content in message.content %}"
"{% if content.type == 'image_url' %}"
"{% if content.image_url is string %}"
"{{ content.image_url }}"
"{% endif %}"
"{% if content.image_url is mapping %}"
"{{ content.image_url.url }}"
"{% 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' %}"
"assistant<|end_header_id|>\n\n"
"{{ message.content }}"
"{% endif %}"
"<|eot_id|>"
"{% endfor %}"
# Generation prompt
"{% if add_generation_prompt %}"
"<|start_header_id|>assistant<|end_header_id|>\n\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"
"\n\nTo respond with one or more function calls begin the message with 'functions.:', use the following format:"
"\n\nfunctions.:"
'\n{ "arg1": "value1", "arg2": "value2" }'
"\nfunctions.:"
'\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")