refactor: use simpler BaseModel for Sequential, compact

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "torcharc"
-version = "2.1.1"
+version = "2.1.2"
 description = "Build PyTorch models by specifying architectures."
 readme = "README.md"
 requires-python = ">=3.12"

torcharc/validator/modules.py

@@ -1,5 +1,5 @@
 # Pydantic validation for modules spec
-from pydantic import BaseModel, Field, RootModel, field_validator
+from pydantic import BaseModel, ConfigDict, Field, RootModel, field_validator
 from torch import nn
 
 
@@ -47,7 +47,7 @@ def build(self) -> nn.Module:
         return cls(**kwargs)
 
 
-class SequentialSpec(RootModel):
+class SequentialSpec(BaseModel):
     """
     Sequential spec where key = Sequential and value = list of NNSpecs.
     E.g.
@@ -61,7 +61,9 @@ class SequentialSpec(RootModel):
             out_features: 10
     """
 
-    root: dict[str, list[NNSpec]] = Field(
+    model_config = ConfigDict(extra="forbid")
+
+    Sequential: list[NNSpec] = Field(
         description="Sequential module spec where value is a list of NNSpec.",
         examples=[
             {
@@ -74,20 +76,9 @@ class SequentialSpec(RootModel):
         ],
     )
 
-    @field_validator("root", mode="before")
-    def is_single_key_dict(value: dict) -> dict:
-        return NNSpec.is_single_key_dict(value)
-
-    @field_validator("root", mode="before")
-    def key_is_sequential(value: dict) -> dict:
-        assert (
-            next(iter(value)) == "Sequential"
-        ), "Key must be 'Sequential' if using SequentialSpec."
-        return value
-
     def build(self) -> nn.Sequential:
         """Build nn.Sequential from sequential spec."""
-        nn_specs = next(iter(self.root.values()))
+        nn_specs = self.Sequential
         return nn.Sequential(*[nn_spec.build() for nn_spec in nn_specs])
 
 
@@ -138,7 +129,7 @@ class CompactValueSpec(BaseModel):
     )
 
 
-class CompactSpec(RootModel):
+class CompactSpec(BaseModel):
     """
     Higher level compact spec that expands into Sequential spec. This is useful for architecture search.
     Compact spec has the format:
@@ -174,7 +165,9 @@ class CompactSpec(RootModel):
                 p: 0.1
     """
 
-    root: dict[str, CompactValueSpec] = Field(
+    model_config = ConfigDict(extra="forbid")
+
+    compact: CompactValueSpec = Field(
         description="Higher level compact spec that expands into Sequential spec.",
         examples=[
             {
@@ -201,17 +194,6 @@ class CompactSpec(RootModel):
         ],
     )
 
-    @field_validator("root", mode="before")
-    def is_single_key_dict(value: dict) -> dict:
-        return NNSpec.is_single_key_dict(value)
-
-    @field_validator("root", mode="before")
-    def key_is_compact(value: dict) -> dict:
-        assert (
-            next(iter(value)) == "compact"
-        ), "Key must be 'compact' if using CompactSpec."
-        return value
-
     def __expand_spec(self, compact_layer: dict) -> list[dict]:
         class_name = compact_layer["type"]
         keys = compact_layer["keys"]
@@ -223,7 +205,7 @@ def __expand_spec(self, compact_layer: dict) -> list[dict]:
         return nn_specs
 
     def expand_to_sequential_spec(self) -> SequentialSpec:
-        compact_spec = next(iter(self.root.values())).model_dump()
+        compact_spec = self.compact.model_dump()
         prelayer = compact_spec.get("prelayer")
         postlayer = compact_spec.get("postlayer")
         nn_specs = []