c8371e0f50
For multiple generations in the same request, nested arrays kept their original reference, resulting in duplications. This will occur with any collection type. For optimization purposes, a deepcopy isn't run for the first iteration since original references are created. This is not the most elegant solution, but it works for the described cases. Signed-off-by: kingbri <bdashore3@proton.me>
275 lines
8.7 KiB
Python
275 lines
8.7 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 uuid import uuid4
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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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def _create_response(generations: List[dict], model_name: Optional[str]):
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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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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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const_id: str,
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generation: Optional[dict] = None,
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model_name: Optional[str] = None,
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):
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"""Create a chat completion stream chunk from the provided text."""
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logprob_response = None
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if "finish_reason" in generation:
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choice = ChatCompletionStreamChoice(
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finish_reason=generation.get("finish_reason")
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)
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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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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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delta=message,
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logprobs=logprob_response,
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)
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chunk = ChatCompletionStreamChunk(
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id=const_id, choices=[choice], model=unwrap(model_name, "")
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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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# 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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try:
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const_id = f"chatcmpl-{uuid4().hex}"
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new_generation = model.container.generate_gen(
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prompt, abort_event, **data.to_gen_params()
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)
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# Create a background task to avoid blocking the loop
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disconnect_task = asyncio.create_task(request_disconnect_loop(request))
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async for generation in new_generation:
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# Sometimes this fires, and sometimes a CancelledError will fire
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# Keep both implementations in to avoid the headache
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if disconnect_task.done():
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abort_event.set()
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handle_request_disconnect("Completion generation cancelled by user.")
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response = _create_stream_chunk(const_id, generation, model_path.name)
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yield response.model_dump_json()
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# Break if the generation is finished
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if "finish_reason" in generation:
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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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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, 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(prompt, **task_gen_params)
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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(generations, model_path.name)
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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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"Chat completion aborted. 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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