fix eval

llama_stack/apis/eval_tasks/eval_tasks.py

@@ -35,9 +35,9 @@ class EvalTasks(Protocol):
     async def list_eval_tasks(self) -> List[EvalTaskDefWithProvider]: ...
 
     @webmethod(route="/eval_tasks/get", method="GET")
-    async def get_eval_tasks(self, name: str) -> Optional[EvalTaskDefWithProvider]: ...
+    async def get_eval_task(self, name: str) -> Optional[EvalTaskDefWithProvider]: ...
 
     @webmethod(route="/eval_tasks/register", method="POST")
-    async def register_eval_tasks(
-        self, function_def: EvalTaskDefWithProvider
+    async def register_eval_task(
+        self, eval_task_def: EvalTaskDefWithProvider
     ) -> None: ...

llama_stack/distribution/distribution.py

@@ -43,6 +43,10 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
             routing_table_api=Api.scoring_functions,
             router_api=Api.scoring,
         ),
+        AutoRoutedApiInfo(
+            routing_table_api=Api.eval_tasks,
+            router_api=Api.eval,
+        ),
     ]
 
 

llama_stack/distribution/resolver.py

@@ -15,6 +15,7 @@ from llama_stack.apis.agents import Agents
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.eval import Eval
+from llama_stack.apis.eval_tasks import EvalTasks
 from llama_stack.apis.inference import Inference
 from llama_stack.apis.inspect import Inspect
 from llama_stack.apis.memory import Memory
@@ -46,6 +47,7 @@ def api_protocol_map() -> Dict[Api, Any]:
         Api.scoring: Scoring,
         Api.scoring_functions: ScoringFunctions,
         Api.eval: Eval,
+        Api.eval_tasks: EvalTasks,
     }
 
 
@@ -56,6 +58,7 @@ def additional_protocols_map() -> Dict[Api, Any]:
         Api.safety: (ShieldsProtocolPrivate, Shields),
         Api.datasetio: (DatasetsProtocolPrivate, Datasets),
         Api.scoring: (ScoringFunctionsProtocolPrivate, ScoringFunctions),
+        Api.eval_tasks: (EvalTasksProtocolPrivate, EvalTasks),
     }
 
 

llama_stack/distribution/routers/__init__.py

@@ -12,6 +12,7 @@ from llama_stack.distribution.store import DistributionRegistry
 
 from .routing_tables import (
     DatasetsRoutingTable,
+    EvalTasksRoutingTable,
     MemoryBanksRoutingTable,
     ModelsRoutingTable,
     ScoringFunctionsRoutingTable,
@@ -31,6 +32,7 @@ async def get_routing_table_impl(
         "shields": ShieldsRoutingTable,
         "datasets": DatasetsRoutingTable,
         "scoring_functions": ScoringFunctionsRoutingTable,
+        "eval_tasks": EvalTasksRoutingTable,
     }
 
     if api.value not in api_to_tables:
@@ -44,6 +46,7 @@ async def get_routing_table_impl(
 async def get_auto_router_impl(api: Api, routing_table: RoutingTable, _deps) -> Any:
     from .routers import (
         DatasetIORouter,
+        EvalRouter,
         InferenceRouter,
         MemoryRouter,
         SafetyRouter,
@@ -56,6 +59,7 @@ async def get_auto_router_impl(api: Api, routing_table: RoutingTable, _deps) ->
         "safety": SafetyRouter,
         "datasetio": DatasetIORouter,
         "scoring": ScoringRouter,
+        "eval": EvalRouter,
     }
     if api.value not in api_to_routers:
         raise ValueError(f"API {api.value} not found in router map")

llama_stack/distribution/routers/routers.py

@@ -14,6 +14,7 @@ from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.apis.safety import *  # noqa: F403
 from llama_stack.apis.datasetio import *  # noqa: F403
 from llama_stack.apis.scoring import *  # noqa: F403
+from llama_stack.apis.eval import *  # noqa: F403
 
 
 class MemoryRouter(Memory):
@@ -252,3 +253,49 @@ class ScoringRouter(Scoring):
             res.update(score_response.results)
 
         return ScoreResponse(results=res)
+
+
+class EvalRouter(Eval):
+    def __init__(
+        self,
+        routing_table: RoutingTable,
+    ) -> None:
+        self.routing_table = routing_table
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def run_benchmark_eval(
+        self,
+        benchmark_id: str,
+        eval_task_config: BenchmarkEvalTaskConfig,
+    ) -> Job:
+        pass
+
+    async def run_eval(
+        self,
+        eval_task_def: EvalTaskDef,
+        eval_task_config: EvalTaskConfig,
+    ) -> Job:
+        pass
+
+    @webmethod(route="/eval/evaluate_rows", method="POST")
+    async def evaluate_rows(
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: List[str],
+        eval_task_config: EvalTaskConfig,  # type: ignore
+    ) -> EvaluateResponse:
+        pass
+
+    async def job_status(self, job_id: str) -> Optional[JobStatus]:
+        pass
+
+    async def job_cancel(self, job_id: str) -> None:
+        pass
+
+    async def job_result(self, job_id: str) -> EvaluateResponse:
+        pass

llama_stack/distribution/routers/routing_tables.py

@@ -12,6 +12,8 @@ from llama_stack.apis.models import *  # noqa: F403
 from llama_stack.apis.shields import *  # noqa: F403
 from llama_stack.apis.memory_banks import *  # noqa: F403
 from llama_stack.apis.datasets import *  # noqa: F403
+from llama_stack.apis.eval_tasks import *  # noqa: F403
+
 
 from llama_stack.distribution.store import DistributionRegistry
 from llama_stack.distribution.datatypes import *  # noqa: F403
@@ -40,6 +42,8 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> None:
         await p.register_dataset(obj)
     elif api == Api.scoring:
         await p.register_scoring_function(obj)
+    elif api == Api.eval:
+        await p.register_eval_task(obj)
     else:
         raise ValueError(f"Unknown API {api} for registering object with provider")
 
@@ -103,6 +107,11 @@ class CommonRoutingTableImpl(RoutingTable):
                 scoring_functions = await p.list_scoring_functions()
                 await add_objects(scoring_functions, pid, ScoringFnDefWithProvider)
 
+            elif api == Api.eval:
+                p.eval_task_store = self
+                eval_tasks = await p.list_eval_tasks()
+                await add_objects(eval_tasks, pid, EvalTaskDefWithProvider)
+
     async def shutdown(self) -> None:
         for p in self.impls_by_provider_id.values():
             await p.shutdown()
@@ -121,6 +130,8 @@ class CommonRoutingTableImpl(RoutingTable):
                 return ("DatasetIO", "dataset")
             elif isinstance(self, ScoringFunctionsRoutingTable):
                 return ("Scoring", "scoring_function")
+            elif isinstance(self, EvalTasksRoutingTable):
+                return ("Eval", "eval_task")
             else:
                 raise ValueError("Unknown routing table type")
 
@@ -246,7 +257,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
         await self.register_object(dataset_def)
 
 
-class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
+class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
     async def list_scoring_functions(self) -> List[ScoringFnDefWithProvider]:
         return await self.get_all_with_type("scoring_fn")
 
@@ -259,3 +270,14 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
         self, function_def: ScoringFnDefWithProvider
     ) -> None:
         await self.register_object(function_def)
+
+
+class EvalTasksRoutingTable(CommonRoutingTableImpl, EvalTasks):
+    async def list_eval_tasks(self) -> List[ScoringFnDefWithProvider]:
+        return await self.get_all_with_type("eval_task")
+
+    async def get_eval_task(self, name: str) -> Optional[EvalTaskDefWithProvider]:
+        return await self.get_object_by_identifier(name)
+
+    async def register_eval_task(self, eval_task_def: EvalTaskDefWithProvider) -> None:
+        await self.register_object(eval_task_def)

llama_stack/providers/datatypes.py

@@ -11,6 +11,7 @@ from llama_models.schema_utils import json_schema_type
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.datasets import DatasetDef
+from llama_stack.apis.eval_tasks import EvalTaskDef
 from llama_stack.apis.memory_banks import MemoryBankDef
 from llama_stack.apis.models import ModelDef
 from llama_stack.apis.scoring_functions import ScoringFnDef
@@ -34,6 +35,7 @@ class Api(Enum):
     memory_banks = "memory_banks"
     datasets = "datasets"
     scoring_functions = "scoring_functions"
+    eval_tasks = "eval_tasks"
 
     # built-in API
     inspect = "inspect"
@@ -69,6 +71,12 @@ class ScoringFunctionsProtocolPrivate(Protocol):
     async def register_scoring_function(self, function_def: ScoringFnDef) -> None: ...
 
 
+class EvalTasksProtocolPrivate(Protocol):
+    async def list_eval_tasks(self) -> List[EvalTaskDef]: ...
+
+    async def register_eval_task(self, eval_task_def: EvalTaskDef) -> None: ...
+
+
 @json_schema_type
 class ProviderSpec(BaseModel):
     api: Api

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

@@ -6,13 +6,16 @@
 from enum import Enum
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 
+from .....apis.common.job_types import Job
+from .....apis.eval.eval import BenchmarkEvalTaskConfig
 from llama_stack.apis.common.type_system import *  # noqa: F403
-from llama_stack.apis.common.job_types import Job
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
-from llama_stack.apis.eval import Eval, EvalCandidate, EvaluateResponse, JobStatus
+from llama_stack.apis.eval import *  # noqa: F403
+from llama_stack.apis.eval_tasks import EvalTaskDef
 from llama_stack.apis.inference import Inference
 from llama_stack.apis.scoring import Scoring
+from llama_stack.providers.datatypes import EvalTasksProtocolPrivate
 
 from .config import MetaReferenceEvalConfig
 
@@ -25,7 +28,7 @@ class ColumnName(Enum):
     generated_answer = "generated_answer"
 
 
-class MetaReferenceEvalImpl(Eval):
+class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
     def __init__(
         self,
         config: MetaReferenceEvalConfig,
@@ -47,6 +50,10 @@ class MetaReferenceEvalImpl(Eval):
 
     async def shutdown(self) -> None: ...
 
+    async def list_eval_tasks(self) -> List[EvalTaskDefWithProvider]:
+        print("HHHH")
+        return []
+
     async def validate_eval_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:
@@ -70,12 +77,22 @@ class MetaReferenceEvalImpl(Eval):
                 f"Dataset {dataset_id} does not have a correct input schema in {expected_schemas}"
             )
 
-    async def evaluate_batch(
+    async def run_benchmark_eval(
         self,
-        dataset_id: str,
-        candidate: EvalCandidate,
-        scoring_functions: List[str],
+        benchmark_id: str,
+        eval_task_config: BenchmarkEvalTaskConfig,
     ) -> Job:
+        raise NotImplementedError("Benchmark eval is not implemented yet")
+
+    async def run_eval(
+        self,
+        eval_task_def: EvalTaskDef,
+        eval_task_config: EvalTaskConfig,
+    ) -> Job:
+        dataset_id = eval_task_def.dataset_id
+        candidate = eval_task_config.eval_candidate
+        scoring_functions = eval_task_def.scoring_functions
+
         await self.validate_eval_input_dataset_schema(dataset_id=dataset_id)
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
@@ -93,12 +110,13 @@ class MetaReferenceEvalImpl(Eval):
         self.jobs[job_id] = res
         return Job(job_id=job_id)
 
-    async def evaluate(
+    async def evaluate_rows(
         self,
         input_rows: List[Dict[str, Any]],
-        candidate: EvalCandidate,
         scoring_functions: List[str],
+        eval_task_config: EvalTaskConfig,
     ) -> EvaluateResponse:
+        candidate = eval_task_config.eval_candidate
         if candidate.type == "agent":
             raise NotImplementedError(
                 "Evaluation with generation has not been implemented for agents"

llama_stack/providers/tests/conftest.py

@@ -151,4 +151,5 @@ pytest_plugins = [
     "llama_stack.providers.tests.agents.fixtures",
     "llama_stack.providers.tests.datasetio.fixtures",
     "llama_stack.providers.tests.scoring.fixtures",
+    "llama_stack.providers.tests.eval.fixtures",
 ]

llama_stack/providers/tests/eval/conftest.py

@@ -0,0 +1,72 @@
+# 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.
+
+import pytest
+
+from ..conftest import get_provider_fixture_overrides
+
+from ..datasetio.fixtures import DATASETIO_FIXTURES
+from ..inference.fixtures import INFERENCE_FIXTURES
+from ..scoring.fixtures import SCORING_FIXTURES
+from .fixtures import EVAL_FIXTURES
+
+DEFAULT_PROVIDER_COMBINATIONS = [
+    pytest.param(
+        {
+            "eval": "meta_reference",
+            "scoring": "meta_reference",
+            "datasetio": "meta_reference",
+            "inference": "fireworks",
+        },
+        id="meta_reference_eval_fireworks_inference",
+        marks=pytest.mark.meta_reference_eval_fireworks_inference,
+    ),
+    pytest.param(
+        {
+            "eval": "meta_reference",
+            "scoring": "meta_reference",
+            "datasetio": "meta_reference",
+            "inference": "together",
+        },
+        id="meta_reference_eval_together_inference",
+        marks=pytest.mark.meta_reference_eval_together_inference,
+    ),
+]
+
+
+def pytest_configure(config):
+    for fixture_name in [
+        "meta_reference_eval_fireworks_inference",
+        "meta_reference_eval_together_inference",
+    ]:
+        config.addinivalue_line(
+            "markers",
+            f"{fixture_name}: marks tests as {fixture_name} specific",
+        )
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--inference-model",
+        action="store",
+        default="Llama3.2-3B-Instruct",
+        help="Specify the inference model to use for testing",
+    )
+
+
+def pytest_generate_tests(metafunc):
+    if "eval_stack" in metafunc.fixturenames:
+        available_fixtures = {
+            "eval": EVAL_FIXTURES,
+            "scoring": SCORING_FIXTURES,
+            "datasetio": DATASETIO_FIXTURES,
+            "inference": INFERENCE_FIXTURES,
+        }
+        combinations = (
+            get_provider_fixture_overrides(metafunc.config, available_fixtures)
+            or DEFAULT_PROVIDER_COMBINATIONS
+        )
+        metafunc.parametrize("eval_stack", combinations, indirect=True)

llama_stack/providers/tests/eval/fixtures.py

@@ -0,0 +1,62 @@
+# 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.
+
+import pytest
+import pytest_asyncio
+
+from llama_stack.distribution.datatypes import Api, Provider
+
+from llama_stack.providers.tests.resolver import resolve_impls_for_test_v2
+from ..conftest import ProviderFixture, remote_stack_fixture
+
+
+@pytest.fixture(scope="session")
+def eval_remote() -> ProviderFixture:
+    return remote_stack_fixture()
+
+
+@pytest.fixture(scope="session")
+def eval_meta_reference() -> ProviderFixture:
+    return ProviderFixture(
+        providers=[
+            Provider(
+                provider_id="meta-reference",
+                provider_type="meta-reference",
+                config={},
+            )
+        ],
+    )
+
+
+EVAL_FIXTURES = ["meta_reference", "remote"]
+
+
+@pytest_asyncio.fixture(scope="session")
+async def eval_stack(request):
+    fixture_dict = request.param
+
+    providers = {}
+    provider_data = {}
+    for key in ["datasetio", "eval", "scoring", "inference"]:
+        fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
+        providers[key] = fixture.providers
+        if fixture.provider_data:
+            provider_data.update(fixture.provider_data)
+
+    impls = await resolve_impls_for_test_v2(
+        [Api.eval, Api.datasetio, Api.inference, Api.scoring],
+        providers,
+        provider_data,
+    )
+
+    return (
+        impls[Api.eval],
+        impls[Api.eval_tasks],
+        impls[Api.scoring],
+        impls[Api.scoring_functions],
+        impls[Api.datasetio],
+        impls[Api.datasets],
+    )

llama_stack/providers/tests/eval/test_eval.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.
+
+
+import pytest
+
+# How to run this test:
+#
+# pytest llama_stack/providers/tests/eval/test_eval.py
+#   -m "meta_reference"
+#   -v -s --tb=short --disable-warnings
+
+
+class Testeval:
+    @pytest.mark.asyncio
+    async def test_eval_tasks_list(self, eval_stack):
+        # NOTE: this needs you to ensure that you are starting from a clean state
+        # but so far we don't have an unregister API unfortunately, so be careful
+        _, eval_tasks_impl, _, _, _, _ = eval_stack
+        response = await eval_tasks_impl.list_eval_tasks()
+        assert isinstance(response, list)
+        print(response)

llama_stack/providers/tests/scoring/conftest.py

@@ -21,12 +21,24 @@ DEFAULT_PROVIDER_COMBINATIONS = [
         },
         id="meta_reference_scoring_fireworks_inference",
         marks=pytest.mark.meta_reference_scoring_fireworks_inference,
-    )
+    ),
+    pytest.param(
+        {
+            "scoring": "meta_reference",
+            "datasetio": "meta_reference",
+            "inference": "together",
+        },
+        id="meta_reference_scoring_together_inference",
+        marks=pytest.mark.meta_reference_scoring_together_inference,
+    ),
 ]
 
 
 def pytest_configure(config):
-    for fixture_name in ["meta_reference_scoring_fireworks_inference"]:
+    for fixture_name in [
+        "meta_reference_scoring_fireworks_inference",
+        "meta_reference_scoring_together_inference",
+    ]:
         config.addinivalue_line(
             "markers",
             f"{fixture_name}: marks tests as {fixture_name} specific",