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LoRA patching optimization (#6439)

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* allow model patcher to optimize away the unpatching step when feasible

* remove lazy_offloading functionality

* allow model patcher to optimize away the unpatching step when feasible

* remove lazy_offloading functionality

* do not save original weights if there is a CPU copy of state dict

* Update invokeai/backend/model_manager/load/load_base.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* documentation fixes added during penultimate review

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
This commit is contained in:
Lincoln Stein
2024-06-06 09:53:35 -04:00
committed by GitHub
parent 1c5c3cdbd6
commit 2871676f79
7 changed files with 146 additions and 48 deletions
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50
docs/contributing/MODEL_MANAGER.md
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@ -1367,12 +1367,20 @@ the in-memory loaded model:
| `model` | AnyModel | The instantiated model (details below) |
| `locker` | ModelLockerBase | A context manager that mediates the movement of the model into VRAM |
Because the loader can return multiple model types, it is typed to
return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
models. The others are obvious.
### get_model_by_key(key, [submodel]) -> LoadedModel
The `get_model_by_key()` method will retrieve the model using its
unique database key. For example:
loaded_model = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
`get_model_by_key()` may raise any of the following exceptions:
* `UnknownModelException` -- key not in database
* `ModelNotFoundException` -- key in database but model not found at path
* `NotImplementedException` -- the loader doesn't know how to load this type of model
### Using the Loaded Model in Inference
`LoadedModel` acts as a context manager. The context loads the model
into the execution device (e.g. VRAM on CUDA systems), locks the model
@ -1380,17 +1388,33 @@ in the execution device for the duration of the context, and returns
the model. Use it like this:
```
model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
with model_info as vae:
loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
with loaded_model as vae:
image = vae.decode(latents)[0]
```
`get_model_by_key()` may raise any of the following exceptions:
The object returned by the LoadedModel context manager is an
`AnyModel`, which is a Union of `ModelMixin`, `torch.nn.Module`,
`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
models. The others are obvious.
In addition, you may call `LoadedModel.model_on_device()`, a context
manager that returns a tuple of the model's state dict in CPU and the
model itself in VRAM. It is used to optimize the LoRA patching and
unpatching process:
```
loaded_model_= loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
with loaded_model.model_on_device() as (state_dict, vae):
image = vae.decode(latents)[0]
```
Since not all models have state dicts, the `state_dict` return value
can be None.
* `UnknownModelException` -- key not in database
* `ModelNotFoundException` -- key in database but model not found at path
* `NotImplementedException` -- the loader doesn't know how to load this type of model
### Emitting model loading events
When the `context` argument is passed to `load_model_*()`, it will