360 lines
11 KiB
Python
360 lines
11 KiB
Python
"""Chat completion utilities for OAI server."""
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import asyncio
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import pathlib
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from asyncio import CancelledError
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from copy import deepcopy
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from typing import List, Optional
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from fastapi import HTTPException, Request
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from jinja2 import TemplateError
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from loguru import logger
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from common import model
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from common.networking import (
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get_generator_error,
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handle_request_disconnect,
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handle_request_error,
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request_disconnect_loop,
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)
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from common.utils import unwrap
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from endpoints.OAI.types.chat_completion import (
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ChatCompletionLogprobs,
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ChatCompletionLogprob,
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ChatCompletionMessage,
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ChatCompletionRequest,
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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 endpoints.OAI.types.common import UsageStats
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from endpoints.OAI.utils.completion import _stream_collector
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def _create_response(
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request_id: str, generations: List[dict], model_name: Optional[str]
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):
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"""Create a chat completion response from the provided text."""
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prompt_tokens = unwrap(generations[-1].get("prompt_tokens"), 0)
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completion_tokens = unwrap(generations[-1].get("generated_tokens"), 0)
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choices = []
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for index, generation in enumerate(generations):
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message = ChatCompletionMessage(
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role="assistant", content=unwrap(generation.get("text"), "")
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)
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logprob_response = None
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token_probs = unwrap(generation.get("token_probs"), {})
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if token_probs:
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logprobs = unwrap(generation.get("logprobs"), [])
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collected_token_probs = []
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for index, token in enumerate(token_probs.keys()):
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top_logprobs = [
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ChatCompletionLogprob(token=token, logprob=logprob)
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for token, logprob in logprobs[index].items()
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]
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collected_token_probs.append(
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ChatCompletionLogprob(
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token=token,
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logprob=token_probs[token],
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top_logprobs=top_logprobs,
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)
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)
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logprob_response = ChatCompletionLogprobs(content=collected_token_probs)
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choice = ChatCompletionRespChoice(
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index=index,
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finish_reason=generation.get("finish_reason"),
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message=message,
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logprobs=logprob_response,
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)
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choices.append(choice)
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response = ChatCompletionResponse(
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id=f"chatcmpl-{request_id}",
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choices=choices,
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model=unwrap(model_name, ""),
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usage=UsageStats(
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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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)
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return response
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def _create_stream_chunk(
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request_id: str,
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generation: Optional[dict] = None,
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model_name: Optional[str] = None,
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is_usage_chunk: bool = False,
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):
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"""Create a chat completion stream chunk from the provided text."""
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index = generation.get("index")
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choices = []
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usage_stats = None
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if is_usage_chunk:
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prompt_tokens = unwrap(generation.get("prompt_tokens"), 0)
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completion_tokens = unwrap(generation.get("generated_tokens"), 0)
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usage_stats = UsageStats(
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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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elif "finish_reason" in generation:
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choice = ChatCompletionStreamChoice(
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index=index,
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finish_reason=generation.get("finish_reason"),
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)
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choices.append(choice)
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else:
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message = ChatCompletionMessage(
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role="assistant", content=unwrap(generation.get("text"), "")
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)
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logprob_response = None
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token_probs = unwrap(generation.get("token_probs"), {})
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if token_probs:
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logprobs = unwrap(generation.get("logprobs"), {})
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top_logprobs = [
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ChatCompletionLogprob(token=token, logprob=logprob)
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for token, logprob in logprobs.items()
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]
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generated_token = next(iter(token_probs))
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token_prob_response = ChatCompletionLogprob(
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token=generated_token,
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logprob=token_probs[generated_token],
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top_logprobs=top_logprobs,
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)
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logprob_response = ChatCompletionLogprobs(content=[token_prob_response])
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choice = ChatCompletionStreamChoice(
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index=index,
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delta=message,
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logprobs=logprob_response,
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)
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choices.append(choice)
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chunk = ChatCompletionStreamChunk(
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id=f"chatcmpl-{request_id}",
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choices=choices,
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model=unwrap(model_name, ""),
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usage=usage_stats,
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)
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return chunk
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def format_prompt_with_template(data: ChatCompletionRequest):
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"""
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Compile the prompt and get any additional stop strings from the template.
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Template stop strings can be overriden by sampler overrides if force is true.
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"""
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try:
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special_tokens_dict = model.container.get_special_tokens(
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unwrap(data.add_bos_token, True),
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unwrap(data.ban_eos_token, False),
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)
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# Deal with list in messages.content
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# Just replace the content list with the very first text message
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for message in data.messages:
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if message["role"] == "user" and isinstance(message["content"], list):
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message["content"] = next(
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(
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content["text"]
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for content in message["content"]
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if content["type"] == "text"
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),
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"",
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)
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# Overwrite any protected vars with their values
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data.template_vars.update(
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{
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"messages": data.messages,
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"add_generation_prompt": data.add_generation_prompt,
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**special_tokens_dict,
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}
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)
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prompt, template_stop_strings = model.container.prompt_template.render(
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data.template_vars
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)
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# Append response prefix if present
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if data.response_prefix:
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if data.add_generation_prompt:
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prompt += data.response_prefix
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else:
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logger.warning(
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"Could not add response prefix because "
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"add_generation_prompt is False"
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)
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# Removes the starting BOS token if present
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# This is to prevent add_bos_token from adding multiple bos tokens
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bos_token = special_tokens_dict.get("bos_token")
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if bos_token and prompt.startswith(bos_token):
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prompt = prompt.removeprefix(bos_token)
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# Append template stop strings
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if isinstance(data.stop, str):
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data.stop = [data.stop] + template_stop_strings
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else:
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data.stop += template_stop_strings
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return prompt
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except KeyError as exc:
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error_message = handle_request_error(
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"Could not find a Conversation from prompt template "
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f"'{model.container.prompt_template.name}'. "
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"Check your spelling?",
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).error.message
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raise HTTPException(400, error_message) from exc
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except TemplateError as exc:
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error_message = handle_request_error(f"TemplateError: {str(exc)}").error.message
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raise HTTPException(400, error_message) from exc
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async def stream_generate_chat_completion(
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prompt: str, data: ChatCompletionRequest, request: Request, model_path: pathlib.Path
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):
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"""Generator for the generation process."""
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abort_event = asyncio.Event()
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gen_queue = asyncio.Queue()
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gen_tasks: List[asyncio.Task] = []
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disconnect_task = asyncio.create_task(request_disconnect_loop(request))
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try:
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logger.info(f"Received chat completion streaming request {request.state.id}")
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gen_params = data.to_gen_params()
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for n in range(0, data.n):
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if n > 0:
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task_gen_params = deepcopy(gen_params)
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else:
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task_gen_params = gen_params
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gen_task = asyncio.create_task(
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_stream_collector(
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n,
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gen_queue,
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prompt,
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request.state.id,
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abort_event,
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**task_gen_params,
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)
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)
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gen_tasks.append(gen_task)
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# Consumer loop
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while True:
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if disconnect_task.done():
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abort_event.set()
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handle_request_disconnect(
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f"Chat completion generation {request.state.id} cancelled by user."
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)
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generation = await gen_queue.get()
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# Stream collector will push an exception to the queue if it fails
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if isinstance(generation, Exception):
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raise generation
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response = _create_stream_chunk(
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request.state.id, generation, model_path.name
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)
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yield response.model_dump_json()
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# Check if all tasks are completed
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if all(task.done() for task in gen_tasks) and gen_queue.empty():
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# Send a usage chunk
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if data.stream_options and data.stream_options.include_usage:
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usage_chunk = _create_stream_chunk(
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request.state.id,
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generation,
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model_path.name,
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is_usage_chunk=True,
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)
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yield usage_chunk.model_dump_json()
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logger.info(
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f"Finished chat completion streaming request {request.state.id}"
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)
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yield "[DONE]"
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break
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except CancelledError:
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# Get out if the request gets disconnected
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if not disconnect_task.done():
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abort_event.set()
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handle_request_disconnect("Chat completion generation cancelled by user.")
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except Exception:
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yield get_generator_error(
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"Chat completion aborted. Please check the server console."
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)
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async def generate_chat_completion(
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prompt: str, data: ChatCompletionRequest, request: Request, model_path: pathlib.Path
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):
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gen_tasks: List[asyncio.Task] = []
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gen_params = data.to_gen_params()
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try:
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for n in range(0, data.n):
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# Deepcopy gen params above the first index
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# to ensure nested structures aren't shared
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if n > 0:
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task_gen_params = deepcopy(gen_params)
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else:
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task_gen_params = gen_params
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gen_tasks.append(
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asyncio.create_task(
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model.container.generate(
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prompt, request.state.id, **task_gen_params
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)
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)
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)
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generations = await asyncio.gather(*gen_tasks)
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response = _create_response(request.state.id, generations, model_path.name)
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logger.info(f"Finished chat completion request {request.state.id}")
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return response
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except Exception as exc:
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error_message = handle_request_error(
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f"Chat completion {request.state.id} aborted. "
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"Maybe the model was unloaded? "
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"Please check the server console."
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).error.message
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# Server error if there's a generation exception
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raise HTTPException(503, error_message) from exc
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