forked from phoenix-oss/llama-stack-mirror
a6fa3aa5a2
# What does this PR do? - as title [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan CI ``` pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb ``` <img width="587" alt="image" src="https://github.com/user-attachments/assets/4a25f493-501e-43f4-9836-d9802223a93a" /> [//]: # (## Documentation)
89 lines
3 KiB
Python
89 lines
3 KiB
Python
# 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 uuid
|
|
|
|
import pytest
|
|
|
|
from ..datasetio.test_datasetio import register_dataset
|
|
|
|
# How to run this test:
|
|
#
|
|
# LLAMA_STACK_CONFIG="template-name" pytest -v tests/integration/eval
|
|
|
|
|
|
@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
|
|
def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
|
|
register_dataset(llama_stack_client, for_generation=True, dataset_id="test_dataset_for_eval")
|
|
response = llama_stack_client.datasets.list()
|
|
assert any(x.identifier == "test_dataset_for_eval" for x in response)
|
|
|
|
rows = llama_stack_client.datasets.iterrows(
|
|
dataset_id="test_dataset_for_eval",
|
|
limit=3,
|
|
)
|
|
assert len(rows.data) == 3
|
|
|
|
scoring_functions = [
|
|
scoring_fn_id,
|
|
]
|
|
benchmark_id = str(uuid.uuid4())
|
|
llama_stack_client.benchmarks.register(
|
|
benchmark_id=benchmark_id,
|
|
dataset_id="test_dataset_for_eval",
|
|
scoring_functions=scoring_functions,
|
|
)
|
|
list_benchmarks = llama_stack_client.benchmarks.list()
|
|
assert any(x.identifier == benchmark_id for x in list_benchmarks)
|
|
|
|
response = llama_stack_client.eval.evaluate_rows(
|
|
benchmark_id=benchmark_id,
|
|
input_rows=rows.data,
|
|
scoring_functions=scoring_functions,
|
|
benchmark_config={
|
|
"eval_candidate": {
|
|
"type": "model",
|
|
"model": text_model_id,
|
|
"sampling_params": {
|
|
"temperature": 0.0,
|
|
},
|
|
},
|
|
},
|
|
)
|
|
|
|
assert len(response.generations) == 3
|
|
assert scoring_fn_id in response.scores
|
|
|
|
|
|
@pytest.mark.parametrize("scoring_fn_id", ["basic::subset_of"])
|
|
def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
|
|
register_dataset(llama_stack_client, for_generation=True, dataset_id="test_dataset_for_eval_2")
|
|
benchmark_id = str(uuid.uuid4())
|
|
llama_stack_client.benchmarks.register(
|
|
benchmark_id=benchmark_id,
|
|
dataset_id="test_dataset_for_eval_2",
|
|
scoring_functions=[scoring_fn_id],
|
|
)
|
|
|
|
response = llama_stack_client.eval.run_eval(
|
|
benchmark_id=benchmark_id,
|
|
benchmark_config={
|
|
"eval_candidate": {
|
|
"type": "model",
|
|
"model": text_model_id,
|
|
"sampling_params": {
|
|
"temperature": 0.0,
|
|
},
|
|
},
|
|
},
|
|
)
|
|
assert response.job_id == "0"
|
|
job_status = llama_stack_client.eval.jobs.status(job_id=response.job_id, benchmark_id=benchmark_id)
|
|
assert job_status and job_status == "completed"
|
|
|
|
eval_response = llama_stack_client.eval.jobs.retrieve(job_id=response.job_id, benchmark_id=benchmark_id)
|
|
assert eval_response is not None
|
|
assert len(eval_response.generations) == 5
|
|
assert scoring_fn_id in eval_response.scores
|