d23fefbecd
use_as_default was not being properly applied into model overrides. For compartmentalization's sake, apply all overrides in a single function to avoid clutter. In addition, fix where the traditional /v1/model/load endpoint checks for draft options. These can be applied via an inline config, so let any failures fallthrough. Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
import pathlib
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from asyncio import CancelledError
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from typing import Optional
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from common import model
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from common.networking import get_generator_error, handle_request_disconnect
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from common.tabby_config import config
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from endpoints.core.types.model import (
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ModelCard,
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ModelList,
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ModelLoadRequest,
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ModelLoadResponse,
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)
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def get_model_list(model_path: pathlib.Path, draft_model_path: Optional[str] = None):
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"""Get the list of models from the provided path."""
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# Convert the provided draft model path to a pathlib path for
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# 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) # pylint: disable=no-member
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return model_card_list
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async def get_current_model_list(model_type: str = "model"):
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"""
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Gets the current model in list format and with path only.
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Unified for fetching both models and embedding models.
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"""
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current_models = []
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model_path = None
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# Make sure the model container exists
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match model_type:
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case "model":
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if model.container:
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model_path = model.container.model_dir
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case "draft":
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if model.container:
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model_path = model.container.draft_model_dir
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case "embedding":
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if model.embeddings_container:
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model_path = model.embeddings_container.model_dir
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if model_path:
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current_models.append(ModelCard(id=model_path.name))
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return ModelList(data=current_models)
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def get_current_model():
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"""Gets the current model with all parameters."""
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model_card = model.container.model_info()
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return model_card
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def get_dummy_models():
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if config.model.dummy_model_names:
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return [ModelCard(id=dummy_id) for dummy_id in config.model.dummy_model_names]
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else:
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return [ModelCard(id="gpt-3.5-turbo")]
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async def stream_model_load(
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data: ModelLoadRequest,
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model_path: pathlib.Path,
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):
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"""Request generation wrapper for the loading process."""
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# Get trimmed load data
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load_data = data.model_dump(exclude_none=True)
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# Set the draft model directory
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load_data.setdefault("draft_model", {})["draft_model_dir"] = (
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config.draft_model.draft_model_dir
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)
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load_status = model.load_model_gen(
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model_path, skip_wait=data.skip_queue, **load_data
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)
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try:
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async for module, modules, model_type in load_status:
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if module != 0:
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response = ModelLoadResponse(
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model_type=model_type,
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module=module,
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modules=modules,
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status="processing",
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)
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yield response.model_dump_json()
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if module == modules:
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response = ModelLoadResponse(
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model_type=model_type,
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module=module,
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modules=modules,
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status="finished",
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)
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yield response.model_dump_json()
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except CancelledError:
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# Get out if the request gets disconnected
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handle_request_disconnect(
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"Model load cancelled by user. "
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"Please make sure to run unload to free up resources."
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)
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except Exception as exc:
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yield get_generator_error(str(exc))
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