Commit Graph

14 Commits

Author SHA1 Message Date
psychedelicious
c238a7f18b feat(api): chore: pydantic & fastapi upgrade
Upgrade pydantic and fastapi to latest.

- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1

**Big Changes**

There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.

**Invocations**

The biggest change relates to invocation creation, instantiation and validation.

Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.

Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.

With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.

This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.

In the end, this implementation is cleaner.

**Invocation Fields**

In pydantic v2, you can no longer directly add or remove fields from a model.

Previously, we did this to add the `type` field to invocations.

**Invocation Decorators**

With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.

A similar technique is used for `invocation_output()`.

**Minor Changes**

There are a number of minor changes around the pydantic v2 models API.

**Protected `model_` Namespace**

All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".

Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.

```py
class IPAdapterModelField(BaseModel):
    model_name: str = Field(description="Name of the IP-Adapter model")
    base_model: BaseModelType = Field(description="Base model")

    model_config = ConfigDict(protected_namespaces=())
```

**Model Serialization**

Pydantic models no longer have `Model.dict()` or `Model.json()`.

Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.

**Model Deserialization**

Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.

Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.

```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```

**Field Customisation**

Pydantic `Field`s no longer accept arbitrary args.

Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.

**Schema Customisation**

FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.

This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised

The specific aren't important, but this does present additional surface area for bugs.

**Performance Improvements**

Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.

I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-10-17 14:59:25 +11:00
Ryan Dick
399ebe443e Fix IP-Adapter calculation of memory footprint. 2023-09-25 18:28:10 -04:00
Ryan Dick
343df03a92 isort 2023-09-15 13:18:00 -04:00
Ryan Dick
990ce9a1da Lookup IP-Adapter linked image encoder from disk instead of storing in model config metadata. 2023-09-14 23:06:57 -04:00
Ryan Dick
c2f074dc2f Fix python static checks. 2023-09-14 16:48:47 -04:00
Ryan Dick
94c186bb4c Fix bug in IPAdapter.to(...). 2023-09-14 15:45:25 -04:00
Ryan Dick
a22c8cb3a1 Improve robustness of check for IPAdapter vs IPAdapterPlus. 2023-09-14 15:25:41 -04:00
Ryan Dick
388554448a Add CLIP Vision model to IP-Adapter info and use this to infer which model to use. 2023-09-14 11:57:53 -04:00
Ryan Dick
d5160648d0 Add support for downloading IP-Adapter models from HF. 2023-09-14 11:18:43 -04:00
Ryan Dick
77d135967f Update IPAdapterModel to respect requested torch_dtype. 2023-09-13 21:06:42 -04:00
Ryan Dick
ebf26687cb (minor) Remove unnecessary TODO. 2023-09-13 21:03:42 -04:00
Ryan Dick
1c8991a3df Use CLIPVisionModel under model management for IP-Adapter. 2023-09-13 19:10:02 -04:00
Ryan Dick
3ee9a21647 Initial (barely) working version of IP-Adapter model management. 2023-09-13 08:27:24 -04:00
Ryan Dick
163ece9aee Initial skeleton for IPAdapter model management. 2023-09-13 08:27:24 -04:00