move DataSchemaValidatorMixin into standalone utils (#720)

# What does this PR do?

- there's no value in keeping data schema validation logic in a
DataSchemaValidatorMixin
- move into data schema validation logic into standalone utils

## Test Plan
```
pytest -v -s -m llm_as_judge_scoring_together_inference scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct
pytest -v -s -m basic_scoring_together_inference scoring/test_scoring.py
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py

pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py
```



## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -18,8 +18,8 @@ from llama_stack.providers.datatypes import EvalTasksProtocolPrivate
 
 from llama_stack.providers.utils.common.data_schema_validator import (
     ColumnName,
-    DataSchemaValidatorMixin,
     get_valid_schemas,
+    validate_dataset_schema,
 )
 from llama_stack.providers.utils.kvstore import kvstore_impl
 
@@ -31,7 +31,10 @@ from .config import MetaReferenceEvalConfig
 EVAL_TASKS_PREFIX = "eval_tasks:"
 
 
-class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate, DataSchemaValidatorMixin):
+class MetaReferenceEvalImpl(
+    Eval,
+    EvalTasksProtocolPrivate,
+):
     def __init__(
         self,
         config: MetaReferenceEvalConfig,
@@ -85,7 +88,7 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate, DataSchemaValidatorM
         candidate = task_config.eval_candidate
         scoring_functions = task_def.scoring_functions
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        self.validate_dataset_schema(
+        validate_dataset_schema(
             dataset_def.dataset_schema, get_valid_schemas(Api.eval.value)
         )
         all_rows = await self.datasetio_api.get_rows_paginated(

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -18,8 +18,8 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
 from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.utils.common.data_schema_validator import (
-    DataSchemaValidatorMixin,
     get_valid_schemas,
+    validate_dataset_schema,
 )
 from .config import BasicScoringConfig
 from .scoring_fn.equality_scoring_fn import EqualityScoringFn
@@ -30,7 +30,8 @@ FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn, RegexParserScoringFn]
 
 
 class BasicScoringImpl(
-    Scoring, ScoringFunctionsProtocolPrivate, DataSchemaValidatorMixin
+    Scoring,
+    ScoringFunctionsProtocolPrivate,
 ):
     def __init__(
         self,
@@ -75,7 +76,7 @@ class BasicScoringImpl(
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        self.validate_dataset_schema(
+        validate_dataset_schema(
             dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
         )
 

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -35,8 +35,9 @@ from llama_stack.distribution.datatypes import Api
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.utils.common.data_schema_validator import (
-    DataSchemaValidatorMixin,
     get_valid_schemas,
+    validate_dataset_schema,
+    validate_row_schema,
 )
 
 from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_metrics
@@ -111,7 +112,6 @@ class BraintrustScoringImpl(
     Scoring,
     ScoringFunctionsProtocolPrivate,
     NeedsRequestProviderData,
-    DataSchemaValidatorMixin,
 ):
     def __init__(
         self,
@@ -171,7 +171,7 @@ class BraintrustScoringImpl(
         await self.set_api_key()
 
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        self.validate_dataset_schema(
+        validate_dataset_schema(
             dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
         )
 
@@ -194,7 +194,7 @@ class BraintrustScoringImpl(
     async def score_row(
         self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None
     ) -> ScoringResultRow:
-        self.validate_row_schema(input_row, get_valid_schemas(Api.scoring.value))
+        validate_row_schema(input_row, get_valid_schemas(Api.scoring.value))
         await self.set_api_key()
         assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None"
         expected_answer = input_row["expected_answer"]

llama_stack/providers/inline/scoring/llm_as_judge/scoring.py

@@ -19,8 +19,8 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
 from llama_stack.distribution.datatypes import Api
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.utils.common.data_schema_validator import (
-    DataSchemaValidatorMixin,
     get_valid_schemas,
+    validate_dataset_schema,
 )
 
 from .config import LlmAsJudgeScoringConfig
@@ -31,7 +31,8 @@ LLM_JUDGE_FNS = [LlmAsJudgeScoringFn]
 
 
 class LlmAsJudgeScoringImpl(
-    Scoring, ScoringFunctionsProtocolPrivate, DataSchemaValidatorMixin
+    Scoring,
+    ScoringFunctionsProtocolPrivate,
 ):
     def __init__(
         self,
@@ -79,7 +80,7 @@ class LlmAsJudgeScoringImpl(
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        self.validate_dataset_schema(
+        validate_dataset_schema(
             dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
         )
 

llama_stack/providers/utils/common/data_schema_validator.py

@@ -62,26 +62,24 @@ def get_valid_schemas(api_str: str):
         raise ValueError(f"Invalid API string: {api_str}")
 
 
-class DataSchemaValidatorMixin:
+def validate_dataset_schema(
-    def validate_dataset_schema(
+    dataset_schema: Dict[str, Any],
-        self,
+    expected_schemas: List[Dict[str, Any]],
-        dataset_schema: Dict[str, Any],
+):
-        expected_schemas: List[Dict[str, Any]],
+    if dataset_schema not in expected_schemas:
-    ):
-        if dataset_schema not in expected_schemas:
-            raise ValueError(
-                f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}"
-            )
-
-    def validate_row_schema(
-        self,
-        input_row: Dict[str, Any],
-        expected_schemas: List[Dict[str, Any]],
-    ):
-        for schema in expected_schemas:
-            if all(key in input_row for key in schema):
-                return
-
         raise ValueError(
-            f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}"
+            f"Dataset {dataset_schema} does not have a correct input schema in {expected_schemas}"
         )
+
+
+def validate_row_schema(
+    input_row: Dict[str, Any],
+    expected_schemas: List[Dict[str, Any]],
+):
+    for schema in expected_schemas:
+        if all(key in input_row for key in schema):
+            return
+
+    raise ValueError(
+        f"Input row {input_row} does not match any of the expected schemas in {expected_schemas}"
+    )