forked from phoenix-oss/llama-stack-mirror
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import uuid
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from pathlib import Path
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import pytest
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from ..datasets.test_datasets import data_url_from_file
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# How to run this test:
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#
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# LLAMA_STACK_CONFIG="template-name" pytest -v tests/integration/eval
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@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
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dataset = llama_stack_client.datasets.register(
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purpose="eval/messages-answer",
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source={
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"type": "uri",
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"uri": data_url_from_file(Path(__file__).parent.parent / "datasets" / "test_dataset.csv"),
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},
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)
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response = llama_stack_client.datasets.list()
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assert any(x.identifier == dataset.identifier for x in response)
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rows = llama_stack_client.datasets.iterrows(
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dataset_id=dataset.identifier,
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limit=3,
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)
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assert len(rows.data) == 3
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scoring_functions = [
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scoring_fn_id,
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]
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benchmark_id = str(uuid.uuid4())
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llama_stack_client.benchmarks.register(
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benchmark_id=benchmark_id,
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dataset_id=dataset.identifier,
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scoring_functions=scoring_functions,
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)
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list_benchmarks = llama_stack_client.benchmarks.list()
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assert any(x.identifier == benchmark_id for x in list_benchmarks)
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response = llama_stack_client.eval.evaluate_rows(
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benchmark_id=benchmark_id,
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input_rows=rows.data,
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scoring_functions=scoring_functions,
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benchmark_config={
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"eval_candidate": {
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"type": "model",
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"model": text_model_id,
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"sampling_params": {
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"temperature": 0.0,
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},
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},
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},
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)
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assert len(response.generations) == 3
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assert scoring_fn_id in response.scores
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@pytest.mark.parametrize("scoring_fn_id", ["basic::subset_of"])
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@pytest.mark.skip(reason="TODO(xiyan): fix this")
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def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
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dataset = llama_stack_client.datasets.register(
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purpose="eval/messages-answer",
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source={
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"type": "uri",
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"uri": data_url_from_file(Path(__file__).parent.parent / "datasets" / "test_dataset.csv"),
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},
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)
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benchmark_id = str(uuid.uuid4())
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llama_stack_client.benchmarks.register(
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benchmark_id=benchmark_id,
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dataset_id=dataset.identifier,
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scoring_functions=[scoring_fn_id],
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)
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response = llama_stack_client.eval.run_eval(
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benchmark_id=benchmark_id,
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benchmark_config={
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"eval_candidate": {
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"type": "model",
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"model": text_model_id,
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"sampling_params": {
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"temperature": 0.0,
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},
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},
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},
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)
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assert response.job_id == "0"
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job_status = llama_stack_client.eval.jobs.status(job_id=response.job_id, benchmark_id=benchmark_id)
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assert job_status and job_status.status == "completed"
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eval_response = llama_stack_client.eval.jobs.retrieve(job_id=response.job_id, benchmark_id=benchmark_id)
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assert eval_response is not None
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assert len(eval_response.generations) == 5
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assert scoring_fn_id in eval_response.scores
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