Partial Loading PR1: Tidy ModelCache (#7492)

## Summary

This PR tidies up the model cache code in preparation for further
refactoring to support partial loading of models onto the GPU. **These
code changes should not change the functional behavior in any way.**

Changes:
- Remove the `ModelCacheBase` class. `ModelCache` is the only
implementation, so there is no benefit to the separate abstract class.
- Split `CacheRecord` and `CacheStats` out into their own files.
- Remove the `ModelLocker` class. This extra layer of indirection was
not providing any benefit. Locking is now done directly with the
`ModelCache`.
- Tidy up relative imports that were contributing to circular import
issues.
- Pull the 'submodel' concern out of the `ModelCache`. The `ModelCache`
should not need to be aware of the model manager submodel system.
- Delete unused properties from the `ModelCache` (e.g.
`.lazy_offloading`, `.storage_device`, etc.)

## QA Instructions

I ran smoke tests with a variety of SD1, SDXL and FLUX models. No change
to behavior is expected.

## Merge Plan

<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_

Author: RyanJDick

docs/contributing/MODEL_MANAGER.md

@@ -1364,7 +1364,6 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
-| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
 ### get_model_by_key(key, [submodel]) -> LoadedModel
 

invokeai/app/api/routers/model_manager.py

@@ -37,7 +37,7 @@
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -20,7 +20,7 @@
     NodeExecutionStatsSummary,
 )
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load.model_cache import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 
 # Size of 1GB in bytes.
 GB = 2**30

invokeai/app/services/model_load/model_load_base.py

@@ -7,7 +7,7 @@
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
 class ModelLoadServiceBase(ABC):
@@ -24,7 +24,7 @@ def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubMo
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
 
     @abstractmethod

invokeai/app/services/model_load/model_load_default.py

@@ -18,7 +18,7 @@
     ModelLoaderRegistry,
     ModelLoaderRegistryBase,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -30,7 +30,7 @@ class ModelLoadService(ModelLoadServiceBase):
     def __init__(
         self,
         app_config: InvokeAIAppConfig,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
         registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
     ):
         """Initialize the model load service."""
@@ -45,7 +45,7 @@ def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
@@ -78,9 +78,8 @@ def load_model_from_path(
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         cache_key = str(model_path)
-        ram_cache = self.ram_cache
         try:
-            return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+            return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)
         except IndexError:
             pass
 
@@ -109,5 +108,5 @@ def diffusers_load_directory(directory: Path) -> AnyModel:
         )
         assert loader is not None
         raw_model = loader(model_path)
-        ram_cache.put(key=cache_key, model=raw_model)
-        return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+        self._ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)

invokeai/app/services/model_manager/model_manager_default.py

@@ -16,7 +16,8 @@
 from invokeai.app.services.model_load.model_load_default import ModelLoadService
 from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordServiceBase
-from invokeai.backend.model_manager.load import ModelCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 

invokeai/backend/model_manager/load/__init__.py

@@ -8,7 +8,7 @@
 
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_default import ModelCache
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
 
 # This registers the subclasses that implement loaders of specific model types

invokeai/backend/model_manager/load/load_base.py

@@ -5,7 +5,6 @@
 
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
 from typing import Any, Dict, Generator, Optional, Tuple
@@ -18,19 +17,17 @@
     AnyModelConfig,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
-@dataclass
 class LoadedModelWithoutConfig:
-    """
-    Context manager object that mediates transfer from RAM<->VRAM.
+    """Context manager object that mediates transfer from RAM<->VRAM.
 
     This is a context manager object that has two distinct APIs:
 
     1. Older API (deprecated):
-    Use the LoadedModel object directly as a context manager.
-    It will move the model into VRAM (on CUDA devices), and
+    Use the LoadedModel object directly as a context manager.  It will move the model into VRAM (on CUDA devices), and
     return the model in a form suitable for passing to torch.
     Example:
     ```
@@ -40,13 +37,9 @@ class LoadedModelWithoutConfig:
     ```
 
     2. Newer API (recommended):
-    Call the LoadedModel's `model_on_device()` method in a
-    context. It returns a tuple consisting of a copy of
-    the model's state dict in CPU RAM followed by a copy
-    of the model in VRAM. The state dict is provided to allow
-    LoRAs and other model patchers to return the model to
-    its unpatched state without expensive copy and restore
-    operations.
+    Call the LoadedModel's `model_on_device()` method in a context. It returns a tuple consisting of a copy of the
+    model's state dict in CPU RAM followed by a copy of the model in VRAM. The state dict is provided to allow LoRAs and
+    other model patchers to return the model to its unpatched state without expensive copy and restore operations.
 
     Example:
     ```
@@ -55,43 +48,42 @@ class LoadedModelWithoutConfig:
         image = vae.decode(latents)[0]
     ```
 
-    The state_dict should be treated as a read-only object and
-    never modified. Also be aware that some loadable models do
-    not have a state_dict, in which case this value will be None.
+    The state_dict should be treated as a read-only object and never modified. Also be aware that some loadable models
+    do not have a state_dict, in which case this value will be None.
     """
 
-    _locker: ModelLockerBase
+    def __init__(self, cache_record: CacheRecord, cache: ModelCache):
+        self._cache_record = cache_record
+        self._cache = cache
 
     def __enter__(self) -> AnyModel:
-        """Context entry."""
-        self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         return self.model
 
     def __exit__(self, *args: Any, **kwargs: Any) -> None:
-        """Context exit."""
-        self._locker.unlock()
+        self._cache.unlock(self._cache_record.key)
 
     @contextmanager
     def model_on_device(self) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
         """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device."""
-        locked_model = self._locker.lock()
+        self._cache.lock(self._cache_record.key)
         try:
-            state_dict = self._locker.get_state_dict()
-            yield (state_dict, locked_model)
+            yield (self._cache_record.state_dict, self._cache_record.model)
         finally:
-            self._locker.unlock()
+            self._cache.unlock(self._cache_record.key)
 
     @property
     def model(self) -> AnyModel:
         """Return the model without locking it."""
-        return self._locker.model
+        return self._cache_record.model
 
 
-@dataclass
 class LoadedModel(LoadedModelWithoutConfig):
     """Context manager object that mediates transfer from RAM<->VRAM."""
 
-    config: Optional[AnyModelConfig] = None
+    def __init__(self, config: Optional[AnyModelConfig], cache_record: CacheRecord, cache: ModelCache):
+        super().__init__(cache_record=cache_record, cache=cache)
+        self.config = config
 
 
 # TODO(MM2):
@@ -110,7 +102,7 @@ def __init__(
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         pass
@@ -138,6 +130,6 @@ def get_size_fs(
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         pass

invokeai/backend/model_manager/load/load_default.py

@@ -14,7 +14,8 @@
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
@@ -28,7 +29,7 @@ def __init__(
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         self._app_config = app_config
@@ -54,22 +55,22 @@ def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubMo
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
         with skip_torch_weight_init():
-            locker = self._load_and_cache(model_config, submodel_type)
-        return LoadedModel(config=model_config, _locker=locker)
+            cache_record = self._load_and_cache(model_config, submodel_type)
+        return LoadedModel(config=model_config, cache_record=cache_record, cache=self._ram_cache)
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         return self._ram_cache
 
     def _get_model_path(self, config: AnyModelConfig) -> Path:
         model_base = self._app_config.models_path
         return (model_base / config.path).resolve()
 
-    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
+    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> CacheRecord:
         stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
         except IndexError:
             pass
 
@@ -78,16 +79,11 @@ def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubMod
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
-            config.key,
-            submodel_type=submodel_type,
+            get_model_cache_key(config.key, submodel_type),
             model=loaded_model,
         )
 
-        return self._ram_cache.get(
-            key=config.key,
-            submodel_type=submodel_type,
-            stats_name=stats_name,
-        )
+        return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
 
     def get_size_fs(
         self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None

invokeai/backend/model_manager/load/model_cache/__init__.py

@@ -1,6 +0,0 @@
-"""Init file for ModelCache."""
-
-from .model_cache_base import ModelCacheBase, CacheStats  # noqa F401
-from .model_cache_default import ModelCache  # noqa F401
-
-_all__ = ["ModelCacheBase", "ModelCache", "CacheStats"]

invokeai/backend/model_manager/load/model_cache/cache_record.py

@@ -0,0 +1,50 @@
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import torch
+
+
+@dataclass
+class CacheRecord:
+    """
+    Elements of the cache:
+
+    key: Unique key for each model, same as used in the models database.
+    model: Model in memory.
+    state_dict: A read-only copy of the model's state dict in RAM. It will be
+                used as a template for creating a copy in the VRAM.
+    size: Size of the model
+    loaded: True if the model's state dict is currently in VRAM
+
+    Before a model is executed, the state_dict template is copied into VRAM,
+    and then injected into the model. When the model is finished, the VRAM
+    copy of the state dict is deleted, and the RAM version is reinjected
+    into the model.
+
+    The state_dict should be treated as a read-only attribute. Do not attempt
+    to patch or otherwise modify it. Instead, patch the copy of the state_dict
+    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
+    context manager call `model_on_device()`.
+    """
+
+    key: str
+    model: Any
+    device: torch.device
+    state_dict: Optional[Dict[str, torch.Tensor]]
+    size: int
+    loaded: bool = False
+    _locks: int = 0
+
+    def lock(self) -> None:
+        """Lock this record."""
+        self._locks += 1
+
+    def unlock(self) -> None:
+        """Unlock this record."""
+        self._locks -= 1
+        assert self._locks >= 0
+
+    @property
+    def locked(self) -> bool:
+        """Return true if record is locked."""
+        return self._locks > 0

invokeai/backend/model_manager/load/model_cache/cache_stats.py

@@ -0,0 +1,15 @@
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass
+class CacheStats(object):
+    """Collect statistics on cache performance."""
+
+    hits: int = 0  # cache hits
+    misses: int = 0  # cache misses
+    high_watermark: int = 0  # amount of cache used
+    in_cache: int = 0  # number of models in cache
+    cleared: int = 0  # number of models cleared to make space
+    cache_size: int = 0  # total size of cache
+    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)