[Evals API][4/n] evals with generation meta-reference impl (#303)

* wip

* dataset validation

* test_scoring

* cleanup

* clean up test

* comments

* error checking

* dataset client

* test client:

* datasetio client

* clean up

* basic scoring function works

* scorer wip

* equality scorer

* score batch impl

* score batch

* update scoring test

* refactor

* validate scorer input

* address comments

* evals with generation

* add all rows scores to ScoringResult

* minor typing

* bugfix

* scoring function def rename

* rebase name

* refactor

* address comments

* Update iOS inference instructions for new quantization

* Small updates to quantization config

* Fix score threshold in faiss

* Bump version to 0.0.45

* Handle both ipv6 and ipv4 interfaces together

* update manifest for build templates

* Update getting_started.md

* chatcompletion & completion input type validation

* inclusion->subsetof

* error checking

* scoring_function -> scoring_fn rename, scorer -> scoring_fn rename

* address comments

* [Evals API][5/n] fixes to generate openapi spec (#323)

* generate openapi

* typing comment, dataset -> dataset_id

* remove custom type

* sample eval run.yaml

---------

Co-authored-by: Dalton Flanagan <6599399+dltn@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

llama_stack/providers/tests/eval/test_eval.py

@@ -0,0 +1,79 @@
+# 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.apis.common.type_system import *  # noqa: F403
+from llama_stack.apis.datasetio import *  # noqa: F403
+from llama_stack.apis.eval.eval import ModelCandidate
+from llama_stack.distribution.datatypes import *  # noqa: F403
+
+from llama_models.llama3.api import SamplingParams
+
+from llama_stack.providers.tests.datasetio.test_datasetio import register_dataset
+from llama_stack.providers.tests.resolver import resolve_impls_for_test
+
+# How to run this test:
+#
+# 1. Ensure you have a conda with the right dependencies installed. This is a bit tricky
+#    since it depends on the provider you are testing. On top of that you need
+#    `pytest` and `pytest-asyncio` installed.
+#
+# 2. Copy and modify the provider_config_example.yaml depending on the provider you are testing.
+#
+# 3. Run:
+#
+# ```bash
+# PROVIDER_ID=<your_provider> \
+#   PROVIDER_CONFIG=provider_config.yaml \
+#   pytest -s llama_stack/providers/tests/eval/test_eval.py \
+#   --tb=short --disable-warnings
+# ```
+
+
+@pytest_asyncio.fixture(scope="session")
+async def eval_settings():
+    impls = await resolve_impls_for_test(
+        Api.eval, deps=[Api.datasetio, Api.scoring, Api.inference]
+    )
+    return {
+        "eval_impl": impls[Api.eval],
+        "scoring_impl": impls[Api.scoring],
+        "datasets_impl": impls[Api.datasets],
+    }
+
+
+@pytest.mark.asyncio
+async def test_eval(eval_settings):
+    datasets_impl = eval_settings["datasets_impl"]
+    await register_dataset(
+        datasets_impl,
+        for_generation=True,
+        dataset_id="test_dataset_for_eval",
+    )
+
+    response = await datasets_impl.list_datasets()
+    assert len(response) == 1
+
+    eval_impl = eval_settings["eval_impl"]
+    response = await eval_impl.evaluate_batch(
+        dataset_id=response[0].identifier,
+        candidate=ModelCandidate(
+            model="Llama3.2-1B-Instruct",
+            sampling_params=SamplingParams(),
+        ),
+        scoring_functions=["subset_of"],
+    )
+    assert response.job_id == "0"
+    job_status = await eval_impl.job_status(response.job_id)
+
+    assert job_status and job_status.value == "completed"
+
+    eval_response = await eval_impl.job_result(response.job_id)
+
+    assert eval_response is not None
+    assert len(eval_response.generations) == 5
+    assert "subset_of" in eval_response.scores