mirror of
https://github.com/meta-llama/llama-stack.git
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Merge 352d990176 into b6cb817897
This commit is contained in:
Mustafa Elbehery
GitHub
commit
040ff976a3
1 changed files with 178 additions and 153 deletions
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@ -5,7 +5,6 @@
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# the root directory of this source tree.
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import os
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import unittest
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from unittest.mock import MagicMock, patch
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import pytest
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@ -13,6 +12,8 @@ import pytest
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from llama_stack.apis.benchmarks import Benchmark
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from llama_stack.apis.common.job_types import Job, JobStatus
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from llama_stack.apis.eval.eval import BenchmarkConfig, EvaluateResponse, ModelCandidate, SamplingParams
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from llama_stack.apis.inference.inference import TopPSamplingStrategy
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from llama_stack.apis.resource import ResourceType
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from llama_stack.models.llama.sku_types import CoreModelId
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from llama_stack.providers.remote.eval.nvidia.config import NVIDIAEvalConfig
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from llama_stack.providers.remote.eval.nvidia.eval import NVIDIAEvalImpl
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@ -21,181 +22,205 @@ MOCK_DATASET_ID = "default/test-dataset"
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MOCK_BENCHMARK_ID = "test-benchmark"
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class TestNVIDIAEvalImpl(unittest.TestCase):
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def setUp(self):
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os.environ["NVIDIA_EVALUATOR_URL"] = "http://nemo.test"
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@pytest.fixture
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def nvidia_eval_setup():
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"""Set up the NVIDIA eval implementation with mocked dependencies."""
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os.environ["NVIDIA_EVALUATOR_URL"] = "http://nemo.test"
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# Create mock APIs
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self.datasetio_api = MagicMock()
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self.datasets_api = MagicMock()
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self.scoring_api = MagicMock()
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self.inference_api = MagicMock()
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self.agents_api = MagicMock()
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# Create mock APIs
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datasetio_api = MagicMock()
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datasets_api = MagicMock()
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scoring_api = MagicMock()
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inference_api = MagicMock()
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agents_api = MagicMock()
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self.config = NVIDIAEvalConfig(
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evaluator_url=os.environ["NVIDIA_EVALUATOR_URL"],
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)
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config = NVIDIAEvalConfig(
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evaluator_url=os.environ["NVIDIA_EVALUATOR_URL"],
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)
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self.eval_impl = NVIDIAEvalImpl(
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config=self.config,
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datasetio_api=self.datasetio_api,
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datasets_api=self.datasets_api,
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scoring_api=self.scoring_api,
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inference_api=self.inference_api,
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agents_api=self.agents_api,
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)
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eval_impl = NVIDIAEvalImpl(
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config=config,
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datasetio_api=datasetio_api,
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datasets_api=datasets_api,
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scoring_api=scoring_api,
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inference_api=inference_api,
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agents_api=agents_api,
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)
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# Mock the HTTP request methods
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self.evaluator_get_patcher = patch(
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"llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_get"
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)
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self.evaluator_post_patcher = patch(
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"llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post"
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)
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self.mock_evaluator_get = self.evaluator_get_patcher.start()
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self.mock_evaluator_post = self.evaluator_post_patcher.start()
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def tearDown(self):
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"""Clean up after each test."""
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self.evaluator_get_patcher.stop()
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self.evaluator_post_patcher.stop()
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def _assert_request_body(self, expected_json):
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"""Helper method to verify request body in Evaluator POST request is correct"""
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call_args = self.mock_evaluator_post.call_args
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actual_json = call_args[0][1]
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# Check that all expected keys contain the expected values in the actual JSON
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for key, value in expected_json.items():
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assert key in actual_json, f"Key '{key}' missing in actual JSON"
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if isinstance(value, dict):
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for nested_key, nested_value in value.items():
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assert nested_key in actual_json[key], f"Nested key '{nested_key}' missing in actual JSON['{key}']"
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assert actual_json[key][nested_key] == nested_value, f"Value mismatch for '{key}.{nested_key}'"
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else:
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assert actual_json[key] == value, f"Value mismatch for '{key}'"
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@pytest.fixture(autouse=True)
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def inject_fixtures(self, run_async):
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self.run_async = run_async
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def test_register_benchmark(self):
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eval_config = {
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"type": "custom",
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"params": {"parallelism": 8},
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"tasks": {
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"qa": {
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"type": "completion",
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"params": {"template": {"prompt": "{{prompt}}", "max_tokens": 200}},
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"dataset": {"files_url": f"hf://datasets/{MOCK_DATASET_ID}/testing/testing.jsonl"},
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"metrics": {"bleu": {"type": "bleu", "params": {"references": ["{{ideal_response}}"]}}},
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}
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},
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# Mock the HTTP request methods
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with (
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patch("llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_get") as mock_evaluator_get,
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patch("llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post") as mock_evaluator_post,
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):
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yield {
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"eval_impl": eval_impl,
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"mock_evaluator_get": mock_evaluator_get,
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"mock_evaluator_post": mock_evaluator_post,
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"datasetio_api": datasetio_api,
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"datasets_api": datasets_api,
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"scoring_api": scoring_api,
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"inference_api": inference_api,
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"agents_api": agents_api,
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}
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benchmark = Benchmark(
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provider_id="nvidia",
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type="benchmark",
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identifier=MOCK_BENCHMARK_ID,
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dataset_id=MOCK_DATASET_ID,
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scoring_functions=["basic::equality"],
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metadata=eval_config,
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)
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# Mock Evaluator API response
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mock_evaluator_response = {"id": MOCK_BENCHMARK_ID, "status": "created"}
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self.mock_evaluator_post.return_value = mock_evaluator_response
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def _assert_request_body(mock_evaluator_post, expected_json):
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"""Helper method to verify request body in Evaluator POST request is correct"""
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call_args = mock_evaluator_post.call_args
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actual_json = call_args[0][1]
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# Register the benchmark
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self.run_async(self.eval_impl.register_benchmark(benchmark))
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# Check that all expected keys contain the expected values in the actual JSON
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for key, value in expected_json.items():
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assert key in actual_json, f"Key '{key}' missing in actual JSON"
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# Verify the Evaluator API was called correctly
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self.mock_evaluator_post.assert_called_once()
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self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config})
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if isinstance(value, dict):
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for nested_key, nested_value in value.items():
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assert nested_key in actual_json[key], f"Nested key '{nested_key}' missing in actual JSON['{key}']"
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assert actual_json[key][nested_key] == nested_value, f"Value mismatch for '{key}.{nested_key}'"
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else:
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assert actual_json[key] == value, f"Value mismatch for '{key}'"
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def test_run_eval(self):
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benchmark_config = BenchmarkConfig(
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eval_candidate=ModelCandidate(
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type="model",
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model=CoreModelId.llama3_1_8b_instruct.value,
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sampling_params=SamplingParams(max_tokens=100, temperature=0.7),
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)
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)
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "created"}
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self.mock_evaluator_post.return_value = mock_evaluator_response
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@pytest.mark.asyncio
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async def test_register_benchmark(nvidia_eval_setup):
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eval_impl = nvidia_eval_setup["eval_impl"]
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mock_evaluator_post = nvidia_eval_setup["mock_evaluator_post"]
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# Run the Evaluation job
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result = self.run_async(
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self.eval_impl.run_eval(benchmark_id=MOCK_BENCHMARK_ID, benchmark_config=benchmark_config)
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)
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# Verify the Evaluator API was called correctly
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self.mock_evaluator_post.assert_called_once()
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self._assert_request_body(
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{
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"config": f"nvidia/{MOCK_BENCHMARK_ID}",
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"target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"},
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eval_config = {
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"type": "custom",
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"params": {"parallelism": 8},
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"tasks": {
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"qa": {
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"type": "completion",
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"params": {"template": {"prompt": "{{prompt}}", "max_tokens": 200}},
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"dataset": {"files_url": f"hf://datasets/{MOCK_DATASET_ID}/testing/testing.jsonl"},
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"metrics": {"bleu": {"type": "bleu", "params": {"references": ["{{ideal_response}}"]}}},
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}
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},
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}
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benchmark = Benchmark(
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provider_id="nvidia",
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type=ResourceType.benchmark,
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identifier=MOCK_BENCHMARK_ID,
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dataset_id=MOCK_DATASET_ID,
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scoring_functions=["basic::equality"],
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metadata=eval_config,
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)
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# Mock Evaluator API response
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mock_evaluator_response = {"id": MOCK_BENCHMARK_ID, "status": "created"}
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mock_evaluator_post.return_value = mock_evaluator_response
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# Register the benchmark
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await eval_impl.register_benchmark(benchmark)
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# Verify the Evaluator API was called correctly
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mock_evaluator_post.assert_called_once()
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_assert_request_body(
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mock_evaluator_post, {"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config}
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)
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@pytest.mark.asyncio
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async def test_run_eval(nvidia_eval_setup):
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eval_impl = nvidia_eval_setup["eval_impl"]
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mock_evaluator_post = nvidia_eval_setup["mock_evaluator_post"]
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benchmark_config = BenchmarkConfig(
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eval_candidate=ModelCandidate(
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type="model",
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model=CoreModelId.llama3_1_8b_instruct.value,
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sampling_params=SamplingParams(max_tokens=100, strategy=TopPSamplingStrategy(temperature=0.7)),
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)
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)
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# Verify the result
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assert isinstance(result, Job)
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assert result.job_id == "job-123"
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assert result.status == JobStatus.in_progress
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "created"}
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mock_evaluator_post.return_value = mock_evaluator_response
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def test_job_status(self):
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "completed"}
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self.mock_evaluator_get.return_value = mock_evaluator_response
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# Run the Evaluation job
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result = await eval_impl.run_eval(benchmark_id=MOCK_BENCHMARK_ID, benchmark_config=benchmark_config)
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# Get the Evaluation job
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result = self.run_async(self.eval_impl.job_status(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
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# Verify the Evaluator API was called correctly
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mock_evaluator_post.assert_called_once()
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_assert_request_body(
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mock_evaluator_post,
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{
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"config": f"nvidia/{MOCK_BENCHMARK_ID}",
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"target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"},
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},
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)
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# Verify the result
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assert isinstance(result, Job)
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assert result.job_id == "job-123"
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assert result.status == JobStatus.completed
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# Verify the result
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assert isinstance(result, Job)
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assert result.job_id == "job-123"
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assert result.status == JobStatus.in_progress
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# Verify the API was called correctly
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self.mock_evaluator_get.assert_called_once_with(f"/v1/evaluation/jobs/{result.job_id}")
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def test_job_cancel(self):
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "cancelled"}
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self.mock_evaluator_post.return_value = mock_evaluator_response
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@pytest.mark.asyncio
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async def test_job_status(nvidia_eval_setup):
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eval_impl = nvidia_eval_setup["eval_impl"]
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mock_evaluator_get = nvidia_eval_setup["mock_evaluator_get"]
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# Cancel the Evaluation job
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self.run_async(self.eval_impl.job_cancel(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "completed"}
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mock_evaluator_get.return_value = mock_evaluator_response
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# Verify the API was called correctly
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self.mock_evaluator_post.assert_called_once_with("/v1/evaluation/jobs/job-123/cancel", {})
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# Get the Evaluation job
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result = await eval_impl.job_status(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")
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def test_job_result(self):
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# Mock Evaluator API responses
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mock_job_status_response = {"id": "job-123", "status": "completed"}
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mock_job_results_response = {
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"id": "job-123",
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"status": "completed",
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"results": {MOCK_BENCHMARK_ID: {"score": 0.85, "details": {"accuracy": 0.85, "f1": 0.84}}},
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}
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self.mock_evaluator_get.side_effect = [
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mock_job_status_response, # First call to retrieve job
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mock_job_results_response, # Second call to retrieve job results
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]
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# Verify the result
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assert isinstance(result, Job)
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assert result.job_id == "job-123"
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assert result.status == JobStatus.completed
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# Get the Evaluation job results
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result = self.run_async(self.eval_impl.job_result(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
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# Verify the API was called correctly
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mock_evaluator_get.assert_called_once_with(f"/v1/evaluation/jobs/{result.job_id}")
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# Verify the result
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assert isinstance(result, EvaluateResponse)
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assert MOCK_BENCHMARK_ID in result.scores
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assert result.scores[MOCK_BENCHMARK_ID].aggregated_results["results"][MOCK_BENCHMARK_ID]["score"] == 0.85
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# Verify the API was called correctly
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assert self.mock_evaluator_get.call_count == 2
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self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123")
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self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123/results")
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@pytest.mark.asyncio
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async def test_job_cancel(nvidia_eval_setup):
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eval_impl = nvidia_eval_setup["eval_impl"]
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mock_evaluator_post = nvidia_eval_setup["mock_evaluator_post"]
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# Mock Evaluator API response
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mock_evaluator_response = {"id": "job-123", "status": "cancelled"}
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mock_evaluator_post.return_value = mock_evaluator_response
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# Cancel the Evaluation job
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await eval_impl.job_cancel(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")
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# Verify the API was called correctly
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mock_evaluator_post.assert_called_once_with("/v1/evaluation/jobs/job-123/cancel", {})
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@pytest.mark.asyncio
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async def test_job_result(nvidia_eval_setup):
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eval_impl = nvidia_eval_setup["eval_impl"]
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mock_evaluator_get = nvidia_eval_setup["mock_evaluator_get"]
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# Mock Evaluator API responses
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mock_job_status_response = {"id": "job-123", "status": "completed"}
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mock_job_results_response = {
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"id": "job-123",
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"status": "completed",
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"results": {MOCK_BENCHMARK_ID: {"score": 0.85, "details": {"accuracy": 0.85, "f1": 0.84}}},
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}
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mock_evaluator_get.side_effect = [
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mock_job_status_response, # First call to retrieve job
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mock_job_results_response, # Second call to retrieve job results
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]
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# Get the Evaluation job results
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result = await eval_impl.job_result(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123")
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# Verify the result
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assert isinstance(result, EvaluateResponse)
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assert MOCK_BENCHMARK_ID in result.scores
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assert result.scores[MOCK_BENCHMARK_ID].aggregated_results["results"][MOCK_BENCHMARK_ID]["score"] == 0.85
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# Verify the API was called correctly
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assert mock_evaluator_get.call_count == 2
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mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123")
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mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123/results")
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|
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