mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
2b7d70ba86
* wip * scoring fn api * eval api * eval task * evaluate api update * pre commit * unwrap context -> config * config field doc * typo * naming fix * separate benchmark / app eval * api name * rename * wip tests * wip * datasetio test * delete unused * fixture * scoring resolve * fix scoring register * scoring test pass * score batch * scoring fix * fix eval * test eval works * huggingface provider * datasetdef files * mmlu scoring fn * test wip * remove type ignore * api refactor * add default task_eval_id for routing * add eval_id for jobs * remove type ignore * huggingface provider * wip huggingface register * only keep 1 run_eval * fix optional * register task required * register task required * delete old tests * fix * mmlu loose * refactor * msg * fix tests * move benchmark task def to file * msg * gen openapi * openapi gen * move dataset to hf llamastack repo * remove todo * refactor * add register model to unit test * rename * register to client * delete preregistered dataset/eval task * comments * huggingface -> remote adapter * openapi gen
210 lines
7.6 KiB
Python
210 lines
7.6 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 pytest
|
|
|
|
from llama_models.llama3.api import SamplingParams, URL
|
|
|
|
from llama_stack.apis.common.type_system import ChatCompletionInputType, StringType
|
|
|
|
from llama_stack.apis.datasetio.datasetio import DatasetDefWithProvider
|
|
|
|
from llama_stack.apis.eval.eval import (
|
|
AppEvalTaskConfig,
|
|
BenchmarkEvalTaskConfig,
|
|
EvalTaskDefWithProvider,
|
|
ModelCandidate,
|
|
)
|
|
from llama_stack.distribution.datatypes import Api
|
|
from llama_stack.providers.tests.datasetio.test_datasetio import register_dataset
|
|
|
|
|
|
# How to run this test:
|
|
#
|
|
# pytest llama_stack/providers/tests/eval/test_eval.py
|
|
# -m "meta_reference_eval_together_inference_huggingface_datasetio"
|
|
# -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[Api.eval_tasks]
|
|
response = await eval_tasks_impl.list_eval_tasks()
|
|
assert isinstance(response, list)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_eval_evaluate_rows(self, eval_stack):
|
|
eval_impl, eval_tasks_impl, datasetio_impl, datasets_impl, models_impl = (
|
|
eval_stack[Api.eval],
|
|
eval_stack[Api.eval_tasks],
|
|
eval_stack[Api.datasetio],
|
|
eval_stack[Api.datasets],
|
|
eval_stack[Api.models],
|
|
)
|
|
for model_id in ["Llama3.2-3B-Instruct", "Llama3.1-8B-Instruct"]:
|
|
await models_impl.register_model(
|
|
model_id=model_id,
|
|
provider_id="",
|
|
)
|
|
await register_dataset(
|
|
datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval"
|
|
)
|
|
response = await datasets_impl.list_datasets()
|
|
|
|
rows = await datasetio_impl.get_rows_paginated(
|
|
dataset_id="test_dataset_for_eval",
|
|
rows_in_page=3,
|
|
)
|
|
assert len(rows.rows) == 3
|
|
|
|
scoring_functions = [
|
|
"meta-reference::llm_as_judge_8b_correctness",
|
|
"meta-reference::equality",
|
|
]
|
|
task_id = "meta-reference::app_eval"
|
|
task_def = EvalTaskDefWithProvider(
|
|
identifier=task_id,
|
|
dataset_id="test_dataset_for_eval",
|
|
scoring_functions=scoring_functions,
|
|
provider_id="meta-reference",
|
|
)
|
|
await eval_tasks_impl.register_eval_task(task_def)
|
|
response = await eval_impl.evaluate_rows(
|
|
task_id=task_id,
|
|
input_rows=rows.rows,
|
|
scoring_functions=scoring_functions,
|
|
task_config=AppEvalTaskConfig(
|
|
eval_candidate=ModelCandidate(
|
|
model="Llama3.2-3B-Instruct",
|
|
sampling_params=SamplingParams(),
|
|
),
|
|
),
|
|
)
|
|
assert len(response.generations) == 3
|
|
assert "meta-reference::llm_as_judge_8b_correctness" in response.scores
|
|
assert "meta-reference::equality" in response.scores
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_eval_run_eval(self, eval_stack):
|
|
eval_impl, eval_tasks_impl, datasets_impl, models_impl = (
|
|
eval_stack[Api.eval],
|
|
eval_stack[Api.eval_tasks],
|
|
eval_stack[Api.datasets],
|
|
eval_stack[Api.models],
|
|
)
|
|
for model_id in ["Llama3.2-3B-Instruct", "Llama3.1-8B-Instruct"]:
|
|
await models_impl.register_model(
|
|
model_id=model_id,
|
|
provider_id="",
|
|
)
|
|
await register_dataset(
|
|
datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval"
|
|
)
|
|
|
|
scoring_functions = [
|
|
"meta-reference::llm_as_judge_8b_correctness",
|
|
"meta-reference::subset_of",
|
|
]
|
|
|
|
task_id = "meta-reference::app_eval-2"
|
|
task_def = EvalTaskDefWithProvider(
|
|
identifier=task_id,
|
|
dataset_id="test_dataset_for_eval",
|
|
scoring_functions=scoring_functions,
|
|
provider_id="meta-reference",
|
|
)
|
|
await eval_tasks_impl.register_eval_task(task_def)
|
|
response = await eval_impl.run_eval(
|
|
task_id=task_id,
|
|
task_config=AppEvalTaskConfig(
|
|
eval_candidate=ModelCandidate(
|
|
model="Llama3.2-3B-Instruct",
|
|
sampling_params=SamplingParams(),
|
|
),
|
|
),
|
|
)
|
|
assert response.job_id == "0"
|
|
job_status = await eval_impl.job_status(task_id, response.job_id)
|
|
assert job_status and job_status.value == "completed"
|
|
eval_response = await eval_impl.job_result(task_id, response.job_id)
|
|
|
|
assert eval_response is not None
|
|
assert len(eval_response.generations) == 5
|
|
assert "meta-reference::subset_of" in eval_response.scores
|
|
assert "meta-reference::llm_as_judge_8b_correctness" in eval_response.scores
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_eval_run_benchmark_eval(self, eval_stack):
|
|
eval_impl, eval_tasks_impl, datasets_impl, models_impl = (
|
|
eval_stack[Api.eval],
|
|
eval_stack[Api.eval_tasks],
|
|
eval_stack[Api.datasets],
|
|
eval_stack[Api.models],
|
|
)
|
|
for model_id in ["Llama3.2-3B-Instruct", "Llama3.1-8B-Instruct"]:
|
|
await models_impl.register_model(
|
|
model_id=model_id,
|
|
provider_id="",
|
|
)
|
|
response = await datasets_impl.list_datasets()
|
|
assert len(response) > 0
|
|
if response[0].provider_id != "huggingface":
|
|
pytest.skip(
|
|
"Only huggingface provider supports pre-registered remote datasets"
|
|
)
|
|
# register dataset
|
|
mmlu = DatasetDefWithProvider(
|
|
identifier="mmlu",
|
|
url=URL(uri="https://huggingface.co/datasets/llamastack/evals"),
|
|
dataset_schema={
|
|
"input_query": StringType(),
|
|
"expected_answer": StringType(),
|
|
"chat_completion_input": ChatCompletionInputType(),
|
|
},
|
|
metadata={
|
|
"path": "llamastack/evals",
|
|
"name": "evals__mmlu__details",
|
|
"split": "train",
|
|
},
|
|
provider_id="",
|
|
)
|
|
|
|
await datasets_impl.register_dataset(mmlu)
|
|
|
|
# register eval task
|
|
meta_reference_mmlu = EvalTaskDefWithProvider(
|
|
identifier="meta-reference-mmlu",
|
|
dataset_id="mmlu",
|
|
scoring_functions=["meta-reference::regex_parser_multiple_choice_answer"],
|
|
provider_id="",
|
|
)
|
|
|
|
await eval_tasks_impl.register_eval_task(meta_reference_mmlu)
|
|
|
|
# list benchmarks
|
|
response = await eval_tasks_impl.list_eval_tasks()
|
|
assert len(response) > 0
|
|
|
|
benchmark_id = "meta-reference-mmlu"
|
|
response = await eval_impl.run_eval(
|
|
task_id=benchmark_id,
|
|
task_config=BenchmarkEvalTaskConfig(
|
|
eval_candidate=ModelCandidate(
|
|
model="Llama3.2-3B-Instruct",
|
|
sampling_params=SamplingParams(),
|
|
),
|
|
num_examples=3,
|
|
),
|
|
)
|
|
job_status = await eval_impl.job_status(benchmark_id, response.job_id)
|
|
assert job_status and job_status.value == "completed"
|
|
eval_response = await eval_impl.job_result(benchmark_id, response.job_id)
|
|
assert eval_response is not None
|
|
assert len(eval_response.generations) == 3
|