evals with generation

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,
+        include_generated_answer=False,
+        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="Llama3.1-8B-Instruct",
+            sampling_params=SamplingParams(),
+        ),
+        scoring_functions=["inclusion"],
+    )
+    assert response.job_id == "0"
+    job_status = await eval_impl.job_status(response.job_id)
+
+    assert 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 "inclusion" in eval_response.scores