fix: Fix and optimize functionary chat handler (#1282)
* fix functionary chat logic * further fixes --------- Co-authored-by: Andrei <abetlen@gmail.com>
This commit is contained in:
Jeffrey Fong
GitHub
parent
8d298b4750
commit
8a60c7bc8c
1 changed files with 65 additions and 66 deletions
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@ -1596,13 +1596,15 @@ def functionary_v1_v2_chat_handler(
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function_call = (
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tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
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)
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else:
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function_call = "auto"
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prompt = prepare_messages_for_inference(
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messages, tokenizer, version, functions, tools
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)
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# If no tools/functions are provided
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if function_call is None and (functions is None or len(functions) == 0):
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if function_call == "none" or functions is None or len(functions) == 0:
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if version == "v1":
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stop = END_ASSISTANT_TOKEN
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else:
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@ -1630,6 +1632,7 @@ def functionary_v1_v2_chat_handler(
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logits_processor=logits_processor,
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grammar=grammar,
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)
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completion_or_completion_chunks["choices"][0]["text"] = completion_or_completion_chunks["choices"][0]["text"].lstrip()
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return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore
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assert stream is False # TODO: support stream mode
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@ -1692,13 +1695,12 @@ def functionary_v1_v2_chat_handler(
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return completion
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content = ""
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function_calls, function_bodies = [], []
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if version == "v1":
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# If no or "auto" tool_choice/function_call
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if function_call is None or (
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isinstance(function_call, str) and function_call == "auto"
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):
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if isinstance(function_call, str) and function_call == "auto":
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stops = ["\n", END_ASSISTANT_TOKEN]
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# If tool_choice/function_call is "none"
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elif isinstance(function_call, str) and function_call == "none":
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@ -1747,70 +1749,67 @@ def functionary_v1_v2_chat_handler(
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else:
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function_bodies.append(completion_text.strip())
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else:
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# Loop until all parallel function calls are generated
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while True:
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# If no or "auto" tool_choice/function_call
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if function_call is None or (
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isinstance(function_call, str) and function_call == "auto"
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):
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grammar = None
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stops = CONTENT_TOKEN
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# If tool_choice/function_call is "none"
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elif isinstance(function_call, str) and function_call == "none":
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if isinstance(function_call, str) and function_call == "none":
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prompt = (
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prepare_messages_for_inference(messages, tokenizer, version, [], [])
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+ "all\n<|content|>"
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)
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stops = STOP_TOKEN
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stops = [STOP_TOKEN, FROM_TOKEN]
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completion = create_completion(stop=stops)
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completion["choices"][0]["text"] = completion["choices"][0]["text"].strip()
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return _convert_completion_to_chat(completion, stream=stream) # type: ignore
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# If tool_choice/function_call is provided
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elif isinstance(function_call, dict):
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prompt += f"{function_call['name']}\n{CONTENT_TOKEN}"
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stops = STOP_TOKEN
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function_call = function_call["name"]
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function_calls.append(function_call)
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grammar = get_grammar(function_call)
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else:
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prompt = prompt
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stops = STOP_TOKEN
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stops = [STOP_TOKEN, FROM_TOKEN]
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completion = create_completion(stop=stops)
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completion_text = completion["choices"][0]["text"]
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# If the generation does not involve a function call
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if prompt.endswith("all\n<|content|>") and not completion_text.startswith(
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"all"
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):
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return _convert_completion_to_chat(completion, stream=stream) # type: ignore
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# Generate model response if the model decides not to call any function
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elif prompt.endswith(RECIPIENT_TOKEN) and completion_text.startswith("all"):
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prompt += completion_text + CONTENT_TOKEN
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completion = create_completion(stop=STOP_TOKEN)
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return _convert_completion_to_chat(completion, stream=stream) # type: ignore
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# Generate parameters if model decides to call a function
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elif prompt.endswith(RECIPIENT_TOKEN):
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function_calls.append(completion_text[:-1])
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grammar = get_grammar(function_calls[-1])
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completion = create_completion(stop=[STOP_TOKEN, "\n"])
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function_bodies.append(completion["choices"][0]["text"].strip())
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prompt += f"{function_calls[-1]}\n{CONTENT_TOKEN}{function_bodies[-1]}"
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function_bodies.append(completion_text.strip())
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# If "auto" or no tool_choice/function_call
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elif isinstance(function_call, str) and function_call == "auto":
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while True:
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# Generate function name first
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grammar = None
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# Try to generate the beginning of next turn
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# If empty completion, break from loop
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next_turn_completion_text = create_completion(
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stop=[STOP_TOKEN, RECIPIENT_TOKEN]
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)["choices"][0]["text"]
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if len(next_turn_completion_text) > 0:
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prompt += f"\n{FROM_TOKEN}assistant\n{RECIPIENT_TOKEN}"
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stops = CONTENT_TOKEN
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completion = create_completion(stop=stops)
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completion_text = completion["choices"][0]["text"]
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function_name = completion_text.strip()
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if function_name == "all":
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prompt += "all\n<|content|>"
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else:
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function_call = completion_text.strip()
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prompt += f"{function_call}\n<|content|>"
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function_calls.append(function_call)
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grammar = get_grammar(function_call)
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# Generate content
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stops = [RECIPIENT_TOKEN, STOP_TOKEN]
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completion = create_completion(stop=stops)
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completion_text = completion["choices"][0]["text"]
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if function_name == "all":
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content += completion_text.removesuffix("\n<|from|>assistant\n").removesuffix("\n<|from|> assistant\n")
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content = content.lstrip()
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# Check whether the model wants to generate another turn
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if "<|from|> assistant" in completion_text or "<|from|>assistant" in completion_text:
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cleaned_completion_text = completion_text.removesuffix("\n<|from|>assistant\n").removesuffix("\n<|from|> assistant\n").strip()
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prompt += f"{cleaned_completion_text}\n<|from|>assistant\n<|recipient|>"
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else:
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break
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# Break from loop if tool_choice/function_call is provided as a dict
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else:
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function_bodies.append(completion_text.strip())
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# Check whether the model wants to generate another turn
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prompt += completion_text.strip()
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grammar = None
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completion = create_completion(stop=stops)
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if "<|from|> assistant" in completion["choices"][0]["text"] or "<|from|>assistant" in completion["choices"][0]["text"]:
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prompt += "\n<|from|>assistant\n<|recipient|>"
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else:
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break
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assert "usage" in completion
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assert len(function_calls) > 0
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assert len(function_calls) == len(function_bodies)
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tool_calls = []
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@ -1843,14 +1842,14 @@ def functionary_v1_v2_chat_handler(
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"index": 0,
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"message": {
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"role": "assistant",
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"content": None,
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"content": None if content == "" else content,
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"function_call": {
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"name": tool_calls[0]["function"]["name"],
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"arguments": tool_calls[0]["function"]["arguments"],
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} if len(tool_calls) > 0 else None,
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"tool_calls": tool_calls if len(tool_calls) > 0 else None,
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},
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"tool_calls": tool_calls,
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},
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"finish_reason": "tool_calls",
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"finish_reason": "tool_calls" if len(tool_calls) > 0 else "stop",
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}
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],
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usage=completion["usage"],
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