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https://github.com/invoke-ai/InvokeAI.git
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e93f4d632d
* introduce new abstraction layer for GPU devices * add unit test for device abstraction * fix ruff * convert TorchDeviceSelect into a stateless class * move logic to select context-specific execution device into context API * add mock hardware environments to pytest * remove dangling mocker fixture * fix unit test for running on non-CUDA systems * remove unimplemented get_execution_device() call * remove autocast precision * Multiple changes: 1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to context.models.get_execution_device(). 2. Rename TorchDeviceSelect to TorchDevice 3. Added back the legacy public API defined in `invocation_api`, including choose_precision(). 4. Added a config file migration script to accommodate removal of precision=autocast. * add deprecation warnings to choose_torch_device() and choose_precision() * fix test crash * remove app_config argument from choose_torch_device() and choose_torch_dtype() --------- Co-authored-by: Lincoln Stein <lstein@gmail.com>
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
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"""Implementation of ModelManagerServiceBase."""
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from typing import Optional
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import torch
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from typing_extensions import Self
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from invokeai.app.services.invoker import Invoker
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from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
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from invokeai.backend.util.devices import TorchDevice
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from invokeai.backend.util.logging import InvokeAILogger
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from ..config import InvokeAIAppConfig
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from ..download import DownloadQueueServiceBase
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from ..events.events_base import EventServiceBase
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from ..model_install import ModelInstallService, ModelInstallServiceBase
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from ..model_load import ModelLoadService, ModelLoadServiceBase
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from ..model_records import ModelRecordServiceBase
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from .model_manager_base import ModelManagerServiceBase
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class ModelManagerService(ModelManagerServiceBase):
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"""
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The ModelManagerService handles various aspects of model installation, maintenance and loading.
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It bundles three distinct services:
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model_manager.store -- Routines to manage the database of model configuration records.
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model_manager.install -- Routines to install, move and delete models.
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model_manager.load -- Routines to load models into memory.
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"""
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def __init__(
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self,
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store: ModelRecordServiceBase,
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install: ModelInstallServiceBase,
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load: ModelLoadServiceBase,
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):
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self._store = store
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self._install = install
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self._load = load
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@property
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def store(self) -> ModelRecordServiceBase:
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return self._store
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@property
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def install(self) -> ModelInstallServiceBase:
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return self._install
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@property
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def load(self) -> ModelLoadServiceBase:
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return self._load
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def start(self, invoker: Invoker) -> None:
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for service in [self._store, self._install, self._load]:
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if hasattr(service, "start"):
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service.start(invoker)
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def stop(self, invoker: Invoker) -> None:
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for service in [self._store, self._install, self._load]:
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if hasattr(service, "stop"):
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service.stop(invoker)
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@classmethod
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def build_model_manager(
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cls,
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app_config: InvokeAIAppConfig,
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model_record_service: ModelRecordServiceBase,
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download_queue: DownloadQueueServiceBase,
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events: EventServiceBase,
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execution_device: Optional[torch.device] = None,
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) -> Self:
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"""
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Construct the model manager service instance.
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For simplicity, use this class method rather than the __init__ constructor.
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"""
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logger = InvokeAILogger.get_logger(cls.__name__)
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logger.setLevel(app_config.log_level.upper())
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ram_cache = ModelCache(
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max_cache_size=app_config.ram,
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max_vram_cache_size=app_config.vram,
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lazy_offloading=app_config.lazy_offload,
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logger=logger,
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execution_device=execution_device or TorchDevice.choose_torch_device(),
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)
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convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
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loader = ModelLoadService(
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app_config=app_config,
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ram_cache=ram_cache,
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convert_cache=convert_cache,
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registry=ModelLoaderRegistry,
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)
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installer = ModelInstallService(
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app_config=app_config,
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record_store=model_record_service,
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download_queue=download_queue,
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event_bus=events,
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)
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return cls(store=model_record_service, install=installer, load=loader)
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