Support kwargs predictors (#2345)

This change allows a predictor input to be defined exclusively as
 `**kwargs`. This is an experimental change that allows a model
to be used as a proxy where any inputs will be passed through
directly without modification.


```py
import json

def predict(**kwargs) -> str:
  return json.dumps(kwargs)
```

This change restricts the use of `**kwargs` to functions/methods
that accept only that as an argument to avoid the complexity of
having to support both dynamic and fixed inputs.

The OpenAPI schema for such a model will be:

```json
{
   "properties": {},
   "additionalProperties": true,
   "type": "object",
   "title": "Input"
}
```

Author: philandstuff

.tool-versions

@@ -1 +1,2 @@
 golang 1.24.2
+python 3.13.2

python/cog/predictor.py

@@ -231,13 +231,38 @@ def validate_input_type(
 
 
 def get_input_create_model_kwargs(signature: inspect.Signature) -> Dict[str, Any]:
-    create_model_kwargs = {}
+    create_model_kwargs: Dict[str, Any] = {
+        "__base__": BaseInput,
+        "__config__": None,
+    }
 
     order = 0
 
     for name, parameter in signature.parameters.items():
         InputType = parameter.annotation
 
+        if parameter.kind == inspect.Parameter.VAR_POSITIONAL:
+            raise TypeError(f"Unsupported variadic positional parameter *{name}.")
+
+        if parameter.kind == inspect.Parameter.VAR_KEYWORD:
+            if order != 0:
+                raise TypeError(f"Unsupported variadic keyword parameter **{name}")
+
+            class ExtraKeywordInput(BaseInput):
+                if PYDANTIC_V2:
+                    model_config = pydantic.ConfigDict(extra="allow")
+                else:
+
+                    class Config:
+                        extra = "allow"
+
+            create_model_kwargs["__base__"] = ExtraKeywordInput
+            name = "__pydantic_extra__"
+            InputType = Dict[str, Any]
+
+            create_model_kwargs[name] = (InputType, Input())
+            continue
+
         validate_input_type(InputType, name)
 
         # if no default is specified, create an empty, required input
@@ -325,8 +350,6 @@ class Input(BaseModel):
 
     return create_model(
         "Input",
-        __config__=None,
-        __base__=BaseInput,
         __module__=__name__,
         __validators__=None,
         **get_input_create_model_kwargs(signature),
@@ -431,8 +454,6 @@ class TrainingInput(BaseModel):
 
     return create_model(
         "TrainingInput",
-        __config__=None,
-        __base__=BaseInput,
         __module__=__name__,
         __validators__=None,
         **get_input_create_model_kwargs(signature),

python/tests/server/fixtures/input_kwargs.py

@@ -0,0 +1,8 @@
+from typing import Dict
+
+from cog import BasePredictor
+
+
+class Predictor(BasePredictor):
+    def predict(self, **kwargs) -> Dict:
+        return kwargs

python/tests/server/test_http_input.py

@@ -39,6 +39,18 @@ def test_empty_input(client, match):
     assert resp.json() == match({"status": "succeeded", "output": "foobar"})
 
 
+@uses_predictor("input_kwargs")
+def test_kwargs_input(client, match):
+    """Check we support kwargs input fields"""
+    input = {"animal": "giraffe", "no": 5}
+    resp = client.post("/predictions", json={"input": input})
+    assert resp.json() == match({"status": "succeeded"})
+    assert resp.status_code == 200
+
+    result = resp.json()["output"]
+    assert result == input
+
+
 @uses_predictor("input_integer")
 def test_good_int_input(client, match):
     resp = client.post("/predictions", json={"input": {"num": 3}})