baalajimaestro/llama.cpp
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fix: Functionary bug fixes (#1385)

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* fix completion tokens tracking, prompt forming

* fix 'function_call' and 'tool_calls' depending on 'functions' and 'tools', incompatibility with python 3.8

* Updated README

* fix for openai server compatibility

---------

Co-authored-by: Andrei <abetlen@gmail.com>
This commit is contained in:
Jeffrey Fong 2024-04-28 08:49:52 +08:00 committed by GitHub
parent e6bbfb863c
commit f178636e1b
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2 changed files with 57 additions and 41 deletions
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2
README.md
View file

@ -484,6 +484,8 @@ Due to discrepancies between llama.cpp and HuggingFace's tokenizers, it is requi
tokenizer=LlamaHFTokenizer.from_pretrained("meetkai/functionary-small-v2.2-GGUF")
)
```
**NOTE**: There is no need to provide the default system messages used in Functionary as they are added automatically in the Functionary chat handler. Thus, the messages should contain just the chat messages and/or system messages that provide additional context for the model (e.g.: datetime, etc.).
</details>
### Multi-modal Models

96
llama_cpp/llama_chat_format.py
View file

@ -1828,27 +1828,35 @@ def functionary_v1_v2_chat_handler(
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 functions is not None:
if tool_choice == "none":
all_messages.append(
llama_types.ChatCompletionRequestSystemMessage(
role="system", content=generate_schema_from_functions(functions)
role="system", content=generate_schema_from_functions([])
)
)
elif 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"
]
),
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(
@ -1888,7 +1896,7 @@ def functionary_v1_v2_chat_handler(
function_call = "auto"
prompt = prepare_messages_for_inference(
messages, tokenizer, version, functions, tools
messages, tokenizer, version, functions, tools, function_call
)
# If no tools/functions are provided
@ -1985,17 +1993,12 @@ def functionary_v1_v2_chat_handler(
content = ""
function_calls, function_bodies = [], []
completion_tokens = 0
if version == "v1":
# If no or "auto" tool_choice/function_call
if isinstance(function_call, str) and function_call == "auto":
stops = ["\n", END_ASSISTANT_TOKEN]
# If tool_choice/function_call is "none"
elif isinstance(function_call, str) and function_call == "none":
prompt = prepare_messages_for_inference(
messages, tokenizer, version, [], []
)
stops = 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"
@ -2009,12 +2012,15 @@ def functionary_v1_v2_chat_handler(
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 (
@ -2032,23 +2038,14 @@ def functionary_v1_v2_chat_handler(
)
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 "none"
if isinstance(function_call, str) and function_call == "none":
prompt = (
prepare_messages_for_inference(messages, tokenizer, version, [], [])
+ "all\n<|content|>"
)
stops = [STOP_TOKEN, FROM_TOKEN]
completion = create_completion(stop=stops)
completion["choices"][0]["text"] = completion["choices"][0]["text"].strip()
return _convert_completion_to_chat(completion, stream=stream) # type: ignore
# If tool_choice/function_call is provided
elif isinstance(function_call, dict):
if isinstance(function_call, dict):
prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
function_call = function_call["name"]
function_calls.append(function_call)
@ -2056,6 +2053,7 @@ def functionary_v1_v2_chat_handler(
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":
@ -2065,6 +2063,7 @@ def functionary_v1_v2_chat_handler(
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|>"
@ -2077,12 +2076,23 @@ def functionary_v1_v2_chat_handler(
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":
content += completion_text.removesuffix("\n<|from|>assistant\n").removesuffix("\n<|from|> assistant\n")
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:
cleaned_completion_text = completion_text.removesuffix("\n<|from|>assistant\n").removesuffix("\n<|from|> assistant\n").strip()
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
@ -2092,6 +2102,7 @@ def functionary_v1_v2_chat_handler(
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:
@ -2120,12 +2131,16 @@ def functionary_v1_v2_chat_handler(
)
# TODO: support stream mode
function_call_dict: Union[Dict[str, str], Dict[Literal["function_call"], llama_types.ChatCompletionRequestAssistantMessageFunctionCall]] = {
"function_call": {
"name": tool_calls[0]["function"]["name"],
"arguments": tool_calls[0]["function"]["arguments"],
}
} if len(tool_calls) == 1 else {}
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",
@ -2138,7 +2153,6 @@ def functionary_v1_v2_chat_handler(
"message": {
"role": "assistant",
"content": None if content == "" else content,
"tool_calls": tool_calls,
**function_call_dict,
},
"finish_reason": "tool_calls" if len(tool_calls) > 0 else "stop",