fix tests after registration migration & rename meta-reference -> basic / llm_as_judge provider (#424)

* rename meta-reference -> basic

* config rename

* impl rename

* rename llm_as_judge, fix test

* util

* rebase

* naming fix

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

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import Dict
+
+from llama_stack.distribution.datatypes import Api, ProviderSpec
+
+from .config import BasicScoringConfig
+
+
+async def get_provider_impl(
+    config: BasicScoringConfig,
+    deps: Dict[Api, ProviderSpec],
+):
+    from .scoring import BasicScoringImpl
+
+    impl = BasicScoringImpl(
+        config,
+        deps[Api.datasetio],
+        deps[Api.datasets],
+    )
+    await impl.initialize()
+    return impl

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

@@ -3,7 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from llama_stack.apis.scoring import *  # noqa: F401, F403
+from pydantic import BaseModel
 
 
-class MetaReferenceScoringConfig(BaseModel): ...
+class BasicScoringConfig(BaseModel): ...

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

@@ -11,44 +11,33 @@ from llama_stack.apis.scoring_functions import *  # noqa: F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
 from llama_stack.apis.datasetio import *  # noqa: F403
 from llama_stack.apis.datasets import *  # noqa: F403
-from llama_stack.apis.inference.inference import Inference
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 
-from .config import MetaReferenceScoringConfig
+from .config import BasicScoringConfig
 from .scoring_fn.equality_scoring_fn import EqualityScoringFn
-from .scoring_fn.llm_as_judge_scoring_fn import LlmAsJudgeScoringFn
 from .scoring_fn.regex_parser_scoring_fn import RegexParserScoringFn
 from .scoring_fn.subset_of_scoring_fn import SubsetOfScoringFn
 
 FIXED_FNS = [EqualityScoringFn, SubsetOfScoringFn, RegexParserScoringFn]
 
-LLM_JUDGE_FNS = [LlmAsJudgeScoringFn]
 
-
-class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
+class BasicScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
     def __init__(
         self,
-        config: MetaReferenceScoringConfig,
+        config: BasicScoringConfig,
         datasetio_api: DatasetIO,
         datasets_api: Datasets,
-        inference_api: Inference,
     ) -> None:
         self.config = config
         self.datasetio_api = datasetio_api
         self.datasets_api = datasets_api
-        self.inference_api = inference_api
         self.scoring_fn_id_impls = {}
 
     async def initialize(self) -> None:
-        for x in FIXED_FNS:
-            impl = x()
+        for fn in FIXED_FNS:
+            impl = fn()
             for fn_defs in impl.get_supported_scoring_fn_defs():
                 self.scoring_fn_id_impls[fn_defs.identifier] = impl
-        for x in LLM_JUDGE_FNS:
-            impl = x(inference_api=self.inference_api)
-            for fn_defs in impl.get_supported_scoring_fn_defs():
-                self.scoring_fn_id_impls[fn_defs.identifier] = impl
-                self.llm_as_judge_fn = impl
 
     async def shutdown(self) -> None: ...
 
@@ -61,8 +50,8 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
 
         for f in scoring_fn_defs_list:
             assert f.identifier.startswith(
-                "meta-reference"
-            ), "All meta-reference scoring fn must have identifier prefixed with 'meta-reference'! "
+                "basic"
+            ), "All basic scoring fn must have identifier prefixed with 'basic'! "
 
         return scoring_fn_defs_list
 
@@ -70,18 +59,18 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         raise NotImplementedError("Register scoring function not implemented yet")
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        if not dataset_def.schema or len(dataset_def.schema) == 0:
             raise ValueError(
                 f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
             )
 
         for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
+            if required_column not in dataset_def.schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.dataset_schema[required_column].type != "string":
+            if dataset_def.schema[required_column].type != "string":
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
                 )

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

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from .base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/equality.py

@@ -9,10 +9,10 @@ from llama_stack.apis.scoring_functions import ScoringFn
 
 
 equality = ScoringFn(
-    identifier="meta-reference::equality",
+    identifier="basic::equality",
     description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
     params=None,
-    provider_id="meta-reference",
+    provider_id="basic",
     provider_resource_id="equality",
     return_type=NumberType(),
 )

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/regex_parser_multiple_choice_answer.py

@@ -57,10 +57,10 @@ MULTILINGUAL_ANSWER_PATTERN_TEMPLATE = (
 )
 
 regex_parser_multiple_choice_answer = ScoringFn(
-    identifier="meta-reference::regex_parser_multiple_choice_answer",
+    identifier="basic::regex_parser_multiple_choice_answer",
     description="Extract answer from response matching Answer: [the_answer_letter], and compare with expected result",
     return_type=NumberType(),
-    provider_id="meta-reference",
+    provider_id="basic",
     provider_resource_id="regex-parser-multiple-choice-answer",
     params=RegexParserScoringFnParams(
         parsing_regexes=[

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/subset_of.py

@@ -9,9 +9,9 @@ from llama_stack.apis.scoring_functions import ScoringFn
 
 
 subset_of = ScoringFn(
-    identifier="meta-reference::subset_of",
+    identifier="basic::subset_of",
     description="Returns 1.0 if the expected is included in generated, 0.0 otherwise.",
     return_type=NumberType(),
-    provider_id="meta-reference",
+    provider_id="basic",
     provider_resource_id="subset-of",
 )

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

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 import re
 
-from .base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403

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

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from .base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403

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

@@ -63,18 +63,18 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
         )
 
     async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
-        dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
-        if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        if not dataset_def.schema or len(dataset_def.schema) == 0:
             raise ValueError(
                 f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
             )
 
         for required_column in ["generated_answer", "expected_answer", "input_query"]:
-            if required_column not in dataset_def.dataset_schema:
+            if required_column not in dataset_def.schema:
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column."
                 )
-            if dataset_def.dataset_schema[required_column].type != "string":
+            if dataset_def.schema[required_column].type != "string":
                 raise ValueError(
                     f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
                 )

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

@@ -7,16 +7,16 @@ from typing import Dict
 
 from llama_stack.distribution.datatypes import Api, ProviderSpec
 
-from .config import MetaReferenceScoringConfig
+from .config import LlmAsJudgeScoringConfig
 
 
 async def get_provider_impl(
-    config: MetaReferenceScoringConfig,
+    config: LlmAsJudgeScoringConfig,
     deps: Dict[Api, ProviderSpec],
 ):
-    from .scoring import MetaReferenceScoringImpl
+    from .scoring import LlmAsJudgeScoringImpl
 
-    impl = MetaReferenceScoringImpl(
+    impl = LlmAsJudgeScoringImpl(
         config, deps[Api.datasetio], deps[Api.datasets], deps[Api.inference]
     )
     await impl.initialize()

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

@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from pydantic import BaseModel
+
+
+class LlmAsJudgeScoringConfig(BaseModel): ...

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

@@ -0,0 +1,131 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import Any, Dict, List, Optional
+
+from llama_stack.apis.datasetio import DatasetIO
+from llama_stack.apis.datasets import Datasets
+from llama_stack.apis.inference.inference import Inference
+
+from llama_stack.apis.scoring import (
+    ScoreBatchResponse,
+    ScoreResponse,
+    Scoring,
+    ScoringResult,
+)
+from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
+from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
+
+from .config import LlmAsJudgeScoringConfig
+from .scoring_fn.llm_as_judge_scoring_fn import LlmAsJudgeScoringFn
+
+
+LLM_JUDGE_FNS = [LlmAsJudgeScoringFn]
+
+
+class LlmAsJudgeScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
+    def __init__(
+        self,
+        config: LlmAsJudgeScoringConfig,
+        datasetio_api: DatasetIO,
+        datasets_api: Datasets,
+        inference_api: Inference,
+    ) -> None:
+        self.config = config
+        self.datasetio_api = datasetio_api
+        self.datasets_api = datasets_api
+        self.inference_api = inference_api
+        self.scoring_fn_id_impls = {}
+
+    async def initialize(self) -> None:
+        for fn in LLM_JUDGE_FNS:
+            impl = fn(inference_api=self.inference_api)
+            for fn_defs in impl.get_supported_scoring_fn_defs():
+                self.scoring_fn_id_impls[fn_defs.identifier] = impl
+                self.llm_as_judge_fn = impl
+
+    async def shutdown(self) -> None: ...
+
+    async def list_scoring_functions(self) -> List[ScoringFn]:
+        scoring_fn_defs_list = [
+            fn_def
+            for impl in self.scoring_fn_id_impls.values()
+            for fn_def in impl.get_supported_scoring_fn_defs()
+        ]
+
+        for f in scoring_fn_defs_list:
+            assert f.identifier.startswith(
+                "llm-as-judge"
+            ), "All llm-as-judge scoring fn must have identifier prefixed with 'llm-as-judge'! "
+
+        return scoring_fn_defs_list
+
+    async def register_scoring_function(self, function_def: ScoringFn) -> None:
+        raise NotImplementedError("Register scoring function not implemented yet")
+
+    async def validate_scoring_input_dataset_schema(self, dataset_id: str) -> None:
+        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
+        if not dataset_def.schema or len(dataset_def.schema) == 0:
+            raise ValueError(
+                f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
+            )
+
+        for required_column in ["generated_answer", "expected_answer", "input_query"]:
+            if required_column not in dataset_def.schema:
+                raise ValueError(
+                    f"Dataset {dataset_id} does not have a '{required_column}' column."
+                )
+            if dataset_def.schema[required_column].type != "string":
+                raise ValueError(
+                    f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
+                )
+
+    async def score_batch(
+        self,
+        dataset_id: str,
+        scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
+        save_results_dataset: bool = False,
+    ) -> ScoreBatchResponse:
+        await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
+        all_rows = await self.datasetio_api.get_rows_paginated(
+            dataset_id=dataset_id,
+            rows_in_page=-1,
+        )
+        res = await self.score(
+            input_rows=all_rows.rows,
+            scoring_functions=scoring_functions,
+        )
+        if save_results_dataset:
+            # TODO: persist and register dataset on to server for reading
+            # self.datasets_api.register_dataset()
+            raise NotImplementedError("Save results dataset not implemented yet")
+
+        return ScoreBatchResponse(
+            results=res.results,
+        )
+
+    async def score(
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
+    ) -> ScoreResponse:
+        res = {}
+        for scoring_fn_id in scoring_functions.keys():
+            if scoring_fn_id not in self.scoring_fn_id_impls:
+                raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
+            scoring_fn = self.scoring_fn_id_impls[scoring_fn_id]
+            scoring_fn_params = scoring_functions.get(scoring_fn_id, None)
+            score_results = await scoring_fn.score(
+                input_rows, scoring_fn_id, scoring_fn_params
+            )
+            agg_results = await scoring_fn.aggregate(score_results)
+            res[scoring_fn_id] = ScoringResult(
+                score_rows=score_results,
+                aggregated_results=agg_results,
+            )
+
+        return ScoreResponse(
+            results=res,
+        )

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

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_base.py

@@ -9,9 +9,9 @@ from llama_stack.apis.scoring_functions import ScoringFn
 
 
 llm_as_judge_base = ScoringFn(
-    identifier="meta-reference::llm_as_judge_base",
+    identifier="llm-as-judge::llm_as_judge_base",
     description="Llm As Judge Scoring Function",
     return_type=NumberType(),
-    provider_id="meta-reference",
+    provider_id="llm-as-judge",
     provider_resource_id="llm-as-judge-base",
 )

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

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 from llama_stack.apis.inference.inference import Inference
 
-from .base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403

llama_stack/providers/inline/scoring/meta_reference/scoring_fn/base_scoring_fn.py

@@ -1,61 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List
-from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
-from llama_stack.apis.scoring import *  # noqa: F401, F403
-
-
-class BaseScoringFn(ABC):
-    """
-    Base interface class for all meta-reference scoring_fns.
-    Each scoring_fn needs to implement the following methods:
-    - score_row(self, row)
-    - aggregate(self, scoring_fn_results)
-    """
-
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-        self.supported_fn_defs_registry = {}
-
-    def __str__(self) -> str:
-        return self.__class__.__name__
-
-    def get_supported_scoring_fn_defs(self) -> List[ScoringFn]:
-        return [x for x in self.supported_fn_defs_registry.values()]
-
-    def register_scoring_fn_def(self, scoring_fn: ScoringFn) -> None:
-        if scoring_fn.identifier in self.supported_fn_defs_registry:
-            raise ValueError(
-                f"Scoring function def with identifier {scoring_fn.identifier} already exists."
-            )
-        self.supported_fn_defs_registry[scoring_fn.identifier] = scoring_fn
-
-    @abstractmethod
-    async def score_row(
-        self,
-        input_row: Dict[str, Any],
-        scoring_fn_identifier: Optional[str] = None,
-        scoring_params: Optional[ScoringFnParams] = None,
-    ) -> ScoringResultRow:
-        raise NotImplementedError()
-
-    @abstractmethod
-    async def aggregate(
-        self, scoring_results: List[ScoringResultRow]
-    ) -> Dict[str, Any]:
-        raise NotImplementedError()
-
-    async def score(
-        self,
-        input_rows: List[Dict[str, Any]],
-        scoring_fn_identifier: Optional[str] = None,
-        scoring_params: Optional[ScoringFnParams] = None,
-    ) -> List[ScoringResultRow]:
-        return [
-            await self.score_row(input_row, scoring_fn_identifier, scoring_params)
-            for input_row in input_rows
-        ]