aef411bed5
Chat completions require a finish reason to be provided in the OAI spec once the streaming is completed. This is different from a non- streaming chat completion response. Also fix some errors that were raised from the endpoint. References #15 Signed-off-by: kingbri <bdashore3@proton.me>
133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
import os, pathlib
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from OAI.types.completion import CompletionResponse, CompletionRespChoice, UsageStats
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from OAI.types.chat_completion import (
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ChatCompletionMessage,
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ChatCompletionRespChoice,
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ChatCompletionStreamChunk,
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ChatCompletionResponse,
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ChatCompletionStreamChoice
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)
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from OAI.types.common import UsageStats
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from OAI.types.model import ModelList, ModelCard
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from packaging import version
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from typing import Optional, List
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# Check fastchat
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try:
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import fastchat
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from fastchat.model.model_adapter import get_conversation_template
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_fastchat_available = True
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except ImportError:
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_fastchat_available = False
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def create_completion_response(text: str, prompt_tokens: int, completion_tokens: int, model_name: Optional[str]):
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choice = CompletionRespChoice(
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finish_reason = "Generated",
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text = text
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)
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response = CompletionResponse(
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choices = [choice],
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model = model_name or "",
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usage = UsageStats(prompt_tokens = prompt_tokens,
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completion_tokens = completion_tokens,
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total_tokens = prompt_tokens + completion_tokens)
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)
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return response
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def create_chat_completion_response(text: str, prompt_tokens: int, completion_tokens: int, model_name: Optional[str]):
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message = ChatCompletionMessage(
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role = "assistant",
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content = text
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)
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choice = ChatCompletionRespChoice(
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finish_reason = "Generated",
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message = message
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)
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response = ChatCompletionResponse(
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choices = [choice],
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model = model_name or "",
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usage = UsageStats(prompt_tokens = prompt_tokens,
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completion_tokens = completion_tokens,
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total_tokens = prompt_tokens + completion_tokens)
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)
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return response
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def create_chat_completion_stream_chunk(const_id: str,
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text: Optional[str] = None,
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model_name: Optional[str] = None,
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finish_reason: Optional[str] = None):
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if finish_reason:
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message = {}
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else:
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message = ChatCompletionMessage(
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role = "assistant",
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content = text
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)
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# The finish reason can be None
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choice = ChatCompletionStreamChoice(
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finish_reason = finish_reason,
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delta = message
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)
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chunk = ChatCompletionStreamChunk(
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id = const_id,
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choices = [choice],
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model = model_name or ""
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)
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return chunk
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def get_model_list(model_path: pathlib.Path, draft_model_path: Optional[str]):
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# Convert the draft model path to a pathlib path for equality comparisons
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if draft_model_path:
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draft_model_path = pathlib.Path(draft_model_path).resolve()
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model_card_list = ModelList()
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for path in model_path.iterdir():
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# Don't include the draft models path
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if path.is_dir() and path != draft_model_path:
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model_card = ModelCard(id = path.name)
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model_card_list.data.append(model_card)
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return model_card_list
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def get_chat_completion_prompt(model_path: str, messages: List[ChatCompletionMessage]):
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# Check if fastchat is available
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if not _fastchat_available:
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raise ModuleNotFoundError(
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"Fastchat must be installed to parse these chat completion messages.\n"
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"Please run the following command: pip install fschat[model_worker]"
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)
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if version.parse(fastchat.__version__) < version.parse("0.2.23"):
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raise ImportError(
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"Parsing these chat completion messages requires fastchat 0.2.23 or greater. "
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f"Current version: {fastchat.__version__}\n"
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"Please upgrade fastchat by running the following command: "
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"pip install -U fschat[model_worker]"
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)
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conv = get_conversation_template(model_path)
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for message in messages:
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msg_role = message.role
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if msg_role == "system":
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conv.set_system_message(message.content)
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elif msg_role == "user":
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conv.append_message(conv.roles[0], message.content)
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elif msg_role == "assistant":
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conv.append_message(conv.roles[1], message.content)
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else:
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raise ValueError(f"Unknown role: {msg_role}")
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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return prompt
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