Merge branch 'main' into eval_api_final

tests/client-sdk/post_training/__init__.py

@@ -0,0 +1,5 @@
+# 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.

tests/client-sdk/post_training/test_supervied_fine_tuning.py

@@ -0,0 +1,60 @@
+# 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
+
+POST_TRAINING_PROVIDER_TYPES = ["remote::nvidia"]
+
+
+@pytest.mark.integration
+@pytest.fixture(scope="session")
+def post_training_provider_available(llama_stack_client):
+    providers = llama_stack_client.providers.list()
+    post_training_providers = [p for p in providers if p.provider_type in POST_TRAINING_PROVIDER_TYPES]
+    return len(post_training_providers) > 0
+
+
+@pytest.mark.integration
+def test_post_training_provider_registration(llama_stack_client, post_training_provider_available):
+    """Check if post_training is in the api list.
+    This is a sanity check to ensure the provider is registered."""
+    if not post_training_provider_available:
+        pytest.skip("post training provider not available")
+
+    providers = llama_stack_client.providers.list()
+    post_training_providers = [p for p in providers if p.provider_type in POST_TRAINING_PROVIDER_TYPES]
+    assert len(post_training_providers) > 0
+
+
+@pytest.mark.integration
+def test_get_training_jobs(llama_stack_client, post_training_provider_available):
+    """Test listing all training jobs."""
+    if not post_training_provider_available:
+        pytest.skip("post training provider not available")
+
+    jobs = llama_stack_client.post_training.get_training_jobs()
+    assert isinstance(jobs, dict)
+    assert "data" in jobs
+    assert isinstance(jobs["data"], list)
+
+
+@pytest.mark.integration
+def test_get_training_job_status(llama_stack_client, post_training_provider_available):
+    """Test getting status of a specific training job."""
+    if not post_training_provider_available:
+        pytest.skip("post training provider not available")
+
+    jobs = llama_stack_client.post_training.get_training_jobs()
+    if not jobs["data"]:
+        pytest.skip("No training jobs available to check status")
+
+    job_uuid = jobs["data"][0]["job_uuid"]
+    job_status = llama_stack_client.post_training.get_training_job_status(job_uuid=job_uuid)
+
+    assert job_status is not None
+    assert "job_uuid" in job_status
+    assert "status" in job_status
+    assert job_status["job_uuid"] == job_uuid

tests/integration/inference/test_text_inference.py

@@ -99,6 +99,33 @@ def test_text_completion_streaming(client_with_models, text_model_id, test_case)
     assert len(content_str) > 10
 
 
+@pytest.mark.parametrize(
+    "test_case",
+    [
+        "inference:completion:stop_sequence",
+    ],
+)
+def test_text_completion_stop_sequence(client_with_models, text_model_id, inference_provider_type, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
+    # This is only supported/tested for remote vLLM: https://github.com/meta-llama/llama-stack/issues/1771
+    if inference_provider_type != "remote::vllm":
+        pytest.xfail(f"{inference_provider_type} doesn't support 'stop' parameter yet")
+    tc = TestCase(test_case)
+
+    response = client_with_models.inference.completion(
+        content=tc["content"],
+        stream=True,
+        model_id=text_model_id,
+        sampling_params={
+            "max_tokens": 50,
+            "stop": ["1963"],
+        },
+    )
+    streamed_content = [chunk.delta for chunk in response]
+    content_str = "".join(streamed_content).lower().strip()
+    assert "1963" not in content_str
+
+
 @pytest.mark.parametrize(
     "test_case",
     [

tests/integration/test_cases/inference/completion.json

@@ -10,6 +10,11 @@
             "expected": "1963"
         }
     },
+    "stop_sequence": {
+        "data": {
+            "content": "Return the exact same sentence and don't add additional words): Michael Jordan was born in the year of 1963"
+        }
+    },
     "streaming": {
         "data": {
             "content": "Roses are red,"

tests/unit/providers/nvidia/__init__.py

@@ -0,0 +1,5 @@
+# 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.

tests/unit/providers/nvidia/conftest.py

@@ -0,0 +1,45 @@
+# 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 asyncio
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+mock_session = MagicMock()
+mock_session.closed = False
+mock_session.close = AsyncMock()
+mock_session.__aenter__ = AsyncMock(return_value=mock_session)
+mock_session.__aexit__ = AsyncMock()
+
+
+@pytest.fixture(scope="session", autouse=True)
+def patch_aiohttp_session():
+    with patch("aiohttp.ClientSession", return_value=mock_session):
+        yield
+
+
+@pytest.fixture
+def event_loop():
+    """Create and provide a new event loop for each test."""
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    yield loop
+    loop.close()
+
+
+@pytest.fixture
+def run_async():
+    """Fixture to run async functions in tests."""
+
+    def _run_async(coro):
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        try:
+            return loop.run_until_complete(coro)
+        finally:
+            loop.close()
+
+    return _run_async

tests/unit/providers/nvidia/test_parameters.py

@@ -0,0 +1,271 @@
+# 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 os
+import unittest
+import warnings
+from unittest.mock import patch
+import pytest
+
+from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig
+from llama_stack_client.types.post_training_supervised_fine_tune_params import (
+    TrainingConfig,
+    TrainingConfigDataConfig,
+    TrainingConfigOptimizerConfig,
+    TrainingConfigEfficiencyConfig,
+)
+from llama_stack.providers.remote.post_training.nvidia.post_training import (
+    NvidiaPostTrainingAdapter,
+    NvidiaPostTrainingConfig,
+)
+
+
+class TestNvidiaParameters(unittest.TestCase):
+    def setUp(self):
+        os.environ["NVIDIA_BASE_URL"] = "http://nemo.test"
+        os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
+
+        config = NvidiaPostTrainingConfig(
+            base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None
+        )
+        self.adapter = NvidiaPostTrainingAdapter(config)
+
+        self.make_request_patcher = patch(
+            "llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request"
+        )
+        self.mock_make_request = self.make_request_patcher.start()
+        self.mock_make_request.return_value = {
+            "id": "job-123",
+            "status": "created",
+            "created_at": "2025-03-04T13:07:47.543605",
+            "updated_at": "2025-03-04T13:07:47.543605",
+        }
+
+    def tearDown(self):
+        self.make_request_patcher.stop()
+
+    def _assert_request_params(self, expected_json):
+        """Helper method to verify parameters in the request JSON."""
+        call_args = self.mock_make_request.call_args
+        actual_json = call_args[1]["json"]
+
+        for key, value in expected_json.items():
+            if isinstance(value, dict):
+                for nested_key, nested_value in value.items():
+                    assert actual_json[key][nested_key] == nested_value
+            else:
+                assert actual_json[key] == value
+
+    @pytest.fixture(autouse=True)
+    def inject_fixtures(self, run_async):
+        self.run_async = run_async
+
+    def test_customizer_parameters_passed(self):
+        """Test scenario 1: When an optional parameter is passed and value is correctly set."""
+        custom_adapter_dim = 32  # Different from default of 8
+        algorithm_config = LoraFinetuningConfig(
+            type="LoRA",
+            adapter_dim=custom_adapter_dim,
+            adapter_dropout=0.2,
+            apply_lora_to_mlp=True,
+            apply_lora_to_output=True,
+            alpha=16,
+            rank=16,
+            lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+        )
+
+        data_config = TrainingConfigDataConfig(dataset_id="test-dataset", batch_size=16)
+        optimizer_config = TrainingConfigOptimizerConfig(lr=0.0002)
+        training_config = TrainingConfig(
+            n_epochs=3,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+        )
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+
+            self.run_async(
+                self.adapter.supervised_fine_tune(
+                    job_uuid="test-job",
+                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    checkpoint_dir="",
+                    algorithm_config=algorithm_config,
+                    training_config=training_config,
+                    logger_config={},
+                    hyperparam_search_config={},
+                )
+            )
+
+            warning_texts = [str(warning.message) for warning in w]
+
+            fields = [
+                "apply_lora_to_output",
+                "lora_attn_modules",
+                "apply_lora_to_mlp",
+            ]
+            for field in fields:
+                assert any(field in text for text in warning_texts)
+
+        self._assert_request_params(
+            {
+                "hyperparameters": {
+                    "lora": {"adapter_dim": custom_adapter_dim, "adapter_dropout": 0.2, "alpha": 16},
+                    "epochs": 3,
+                    "learning_rate": 0.0002,
+                    "batch_size": 16,
+                }
+            }
+        )
+
+    def test_required_parameters_passed(self):
+        """Test scenario 2: When required parameters are passed."""
+        required_model = "meta-llama/Llama-3.1-8B-Instruct"
+        required_dataset_id = "required-dataset"
+        required_job_uuid = "required-job"
+
+        algorithm_config = LoraFinetuningConfig(
+            type="LoRA",
+            adapter_dim=16,
+            adapter_dropout=0.1,
+            apply_lora_to_mlp=True,
+            apply_lora_to_output=True,
+            alpha=16,
+            rank=16,
+            lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+        )
+
+        data_config = TrainingConfigDataConfig(
+            dataset_id=required_dataset_id,  # Required parameter
+            batch_size=8,
+        )
+
+        optimizer_config = TrainingConfigOptimizerConfig(lr=0.0001)
+
+        training_config = TrainingConfig(
+            n_epochs=1,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+        )
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+
+            self.run_async(
+                self.adapter.supervised_fine_tune(
+                    job_uuid=required_job_uuid,  # Required parameter
+                    model=required_model,  # Required parameter
+                    checkpoint_dir="",
+                    algorithm_config=algorithm_config,
+                    training_config=training_config,
+                    logger_config={},
+                    hyperparam_search_config={},
+                )
+            )
+
+            warning_texts = [str(warning.message) for warning in w]
+
+            fields = [
+                "rank",
+                "apply_lora_to_output",
+                "lora_attn_modules",
+                "apply_lora_to_mlp",
+            ]
+            for field in fields:
+                assert any(field in text for text in warning_texts)
+
+        self.mock_make_request.assert_called_once()
+        call_args = self.mock_make_request.call_args
+
+        assert call_args[1]["json"]["config"] == "meta/llama-3.1-8b-instruct"
+        assert call_args[1]["json"]["dataset"]["name"] == required_dataset_id
+
+    def test_unsupported_parameters_warning(self):
+        """Test that warnings are raised for unsupported parameters."""
+        data_config = TrainingConfigDataConfig(
+            dataset_id="test-dataset",
+            batch_size=8,
+            # Unsupported parameters
+            shuffle=True,
+            data_format="instruct",
+            validation_dataset_id="val-dataset",
+        )
+
+        optimizer_config = TrainingConfigOptimizerConfig(
+            lr=0.0001,
+            weight_decay=0.01,
+            # Unsupported parameters
+            optimizer_type="adam",
+            num_warmup_steps=100,
+        )
+
+        efficiency_config = TrainingConfigEfficiencyConfig(
+            enable_activation_checkpointing=True  # Unsupported parameter
+        )
+
+        training_config = TrainingConfig(
+            n_epochs=1,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+            # Unsupported parameters
+            efficiency_config=efficiency_config,
+            max_steps_per_epoch=1000,
+            gradient_accumulation_steps=4,
+            max_validation_steps=100,
+            dtype="bf16",
+        )
+
+        # Capture warnings
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+
+            self.run_async(
+                self.adapter.supervised_fine_tune(
+                    job_uuid="test-job",
+                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    checkpoint_dir="test-dir",  # Unsupported parameter
+                    algorithm_config=LoraFinetuningConfig(
+                        type="LoRA",
+                        adapter_dim=16,
+                        adapter_dropout=0.1,
+                        apply_lora_to_mlp=True,
+                        apply_lora_to_output=True,
+                        alpha=16,
+                        rank=16,
+                        lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+                    ),
+                    training_config=training_config,
+                    logger_config={"test": "value"},  # Unsupported parameter
+                    hyperparam_search_config={"test": "value"},  # Unsupported parameter
+                )
+            )
+
+            assert len(w) >= 4
+            warning_texts = [str(warning.message) for warning in w]
+
+            fields = [
+                "checkpoint_dir",
+                "hyperparam_search_config",
+                "logger_config",
+                "TrainingConfig",
+                "DataConfig",
+                "OptimizerConfig",
+                "max_steps_per_epoch",
+                "gradient_accumulation_steps",
+                "max_validation_steps",
+                "dtype",
+                # required unsupported parameters
+                "rank",
+                "apply_lora_to_output",
+                "lora_attn_modules",
+                "apply_lora_to_mlp",
+            ]
+            for field in fields:
+                assert any(field in text for text in warning_texts)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/unit/providers/nvidia/test_supervised_fine_tuning.py

@@ -0,0 +1,295 @@
+# 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 os
+import unittest
+from unittest.mock import patch
+import warnings
+import pytest
+
+from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig, QatFinetuningConfig
+from llama_stack_client.types.post_training_supervised_fine_tune_params import (
+    TrainingConfig,
+    TrainingConfigDataConfig,
+    TrainingConfigOptimizerConfig,
+)
+
+from llama_stack.providers.remote.post_training.nvidia.post_training import (
+    NvidiaPostTrainingAdapter,
+    NvidiaPostTrainingConfig,
+    NvidiaPostTrainingJobStatusResponse,
+    ListNvidiaPostTrainingJobs,
+    NvidiaPostTrainingJob,
+)
+
+
+class TestNvidiaPostTraining(unittest.TestCase):
+    def setUp(self):
+        os.environ["NVIDIA_BASE_URL"] = "http://nemo.test"  # needed for llm inference
+        os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"  # needed for nemo customizer
+
+        config = NvidiaPostTrainingConfig(
+            base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None
+        )
+        self.adapter = NvidiaPostTrainingAdapter(config)
+        self.make_request_patcher = patch(
+            "llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request"
+        )
+        self.mock_make_request = self.make_request_patcher.start()
+
+    def tearDown(self):
+        self.make_request_patcher.stop()
+
+    @pytest.fixture(autouse=True)
+    def inject_fixtures(self, run_async):
+        self.run_async = run_async
+
+    def _assert_request(self, mock_call, expected_method, expected_path, expected_params=None, expected_json=None):
+        """Helper method to verify request details in mock calls."""
+        call_args = mock_call.call_args
+
+        if expected_method and expected_path:
+            if isinstance(call_args[0], tuple) and len(call_args[0]) == 2:
+                assert call_args[0] == (expected_method, expected_path)
+            else:
+                assert call_args[1]["method"] == expected_method
+                assert call_args[1]["path"] == expected_path
+
+        if expected_params:
+            assert call_args[1]["params"] == expected_params
+
+        if expected_json:
+            for key, value in expected_json.items():
+                assert call_args[1]["json"][key] == value
+
+    def test_supervised_fine_tune(self):
+        """Test the supervised fine-tuning API call."""
+        self.mock_make_request.return_value = {
+            "id": "cust-JGTaMbJMdqjJU8WbQdN9Q2",
+            "created_at": "2024-12-09T04:06:28.542884",
+            "updated_at": "2024-12-09T04:06:28.542884",
+            "config": {
+                "schema_version": "1.0",
+                "id": "af783f5b-d985-4e5b-bbb7-f9eec39cc0b1",
+                "created_at": "2024-12-09T04:06:28.542657",
+                "updated_at": "2024-12-09T04:06:28.569837",
+                "custom_fields": {},
+                "name": "meta-llama/Llama-3.1-8B-Instruct",
+                "base_model": "meta-llama/Llama-3.1-8B-Instruct",
+                "model_path": "llama-3_1-8b-instruct",
+                "training_types": [],
+                "finetuning_types": ["lora"],
+                "precision": "bf16",
+                "num_gpus": 4,
+                "num_nodes": 1,
+                "micro_batch_size": 1,
+                "tensor_parallel_size": 1,
+                "max_seq_length": 4096,
+            },
+            "dataset": {
+                "schema_version": "1.0",
+                "id": "dataset-XU4pvGzr5tvawnbVxeJMTb",
+                "created_at": "2024-12-09T04:06:28.542657",
+                "updated_at": "2024-12-09T04:06:28.542660",
+                "custom_fields": {},
+                "name": "sample-basic-test",
+                "version_id": "main",
+                "version_tags": [],
+            },
+            "hyperparameters": {
+                "finetuning_type": "lora",
+                "training_type": "sft",
+                "batch_size": 16,
+                "epochs": 2,
+                "learning_rate": 0.0001,
+                "lora": {"adapter_dim": 16, "adapter_dropout": 0.1},
+            },
+            "output_model": "default/job-1234",
+            "status": "created",
+            "project": "default",
+            "custom_fields": {},
+            "ownership": {"created_by": "me", "access_policies": {}},
+        }
+
+        algorithm_config = LoraFinetuningConfig(
+            type="LoRA",
+            adapter_dim=16,
+            adapter_dropout=0.1,
+            apply_lora_to_mlp=True,
+            apply_lora_to_output=True,
+            alpha=16,
+            rank=16,
+            lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+        )
+
+        data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
+
+        optimizer_config = TrainingConfigOptimizerConfig(
+            lr=0.0001,
+        )
+
+        training_config = TrainingConfig(
+            n_epochs=2,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+        )
+
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter("always")
+            training_job = self.run_async(
+                self.adapter.supervised_fine_tune(
+                    job_uuid="1234",
+                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    checkpoint_dir="",
+                    algorithm_config=algorithm_config,
+                    training_config=training_config,
+                    logger_config={},
+                    hyperparam_search_config={},
+                )
+            )
+
+        # check the output is a PostTrainingJob
+        assert isinstance(training_job, NvidiaPostTrainingJob)
+        assert training_job.job_uuid == "cust-JGTaMbJMdqjJU8WbQdN9Q2"
+
+        self.mock_make_request.assert_called_once()
+        self._assert_request(
+            self.mock_make_request,
+            "POST",
+            "/v1/customization/jobs",
+            expected_json={
+                "config": "meta/llama-3.1-8b-instruct",
+                "dataset": {"name": "sample-basic-test", "namespace": "default"},
+                "hyperparameters": {
+                    "training_type": "sft",
+                    "finetuning_type": "lora",
+                    "epochs": 2,
+                    "batch_size": 16,
+                    "learning_rate": 0.0001,
+                    "lora": {"alpha": 16, "adapter_dim": 16, "adapter_dropout": 0.1},
+                },
+            },
+        )
+
+    def test_supervised_fine_tune_with_qat(self):
+        algorithm_config = QatFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
+        data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
+        optimizer_config = TrainingConfigOptimizerConfig(
+            lr=0.0001,
+        )
+        training_config = TrainingConfig(
+            n_epochs=2,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+        )
+        # This will raise NotImplementedError since QAT is not supported
+        with self.assertRaises(NotImplementedError):
+            self.run_async(
+                self.adapter.supervised_fine_tune(
+                    job_uuid="1234",
+                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    checkpoint_dir="",
+                    algorithm_config=algorithm_config,
+                    training_config=training_config,
+                    logger_config={},
+                    hyperparam_search_config={},
+                )
+            )
+
+    def test_get_training_job_status(self):
+        self.mock_make_request.return_value = {
+            "created_at": "2024-12-09T04:06:28.580220",
+            "updated_at": "2024-12-09T04:21:19.852832",
+            "status": "completed",
+            "steps_completed": 1210,
+            "epochs_completed": 2,
+            "percentage_done": 100.0,
+            "best_epoch": 2,
+            "train_loss": 1.718016266822815,
+            "val_loss": 1.8661999702453613,
+        }
+
+        job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
+
+        status = self.run_async(self.adapter.get_training_job_status(job_uuid=job_id))
+
+        assert isinstance(status, NvidiaPostTrainingJobStatusResponse)
+        assert status.status.value == "completed"
+        assert status.steps_completed == 1210
+        assert status.epochs_completed == 2
+        assert status.percentage_done == 100.0
+        assert status.best_epoch == 2
+        assert status.train_loss == 1.718016266822815
+        assert status.val_loss == 1.8661999702453613
+
+        self.mock_make_request.assert_called_once()
+        self._assert_request(
+            self.mock_make_request, "GET", f"/v1/customization/jobs/{job_id}/status", expected_params={"job_id": job_id}
+        )
+
+    def test_get_training_jobs(self):
+        job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
+        self.mock_make_request.return_value = {
+            "data": [
+                {
+                    "id": job_id,
+                    "created_at": "2024-12-09T04:06:28.542884",
+                    "updated_at": "2024-12-09T04:21:19.852832",
+                    "config": {
+                        "name": "meta-llama/Llama-3.1-8B-Instruct",
+                        "base_model": "meta-llama/Llama-3.1-8B-Instruct",
+                    },
+                    "dataset": {"name": "default/sample-basic-test"},
+                    "hyperparameters": {
+                        "finetuning_type": "lora",
+                        "training_type": "sft",
+                        "batch_size": 16,
+                        "epochs": 2,
+                        "learning_rate": 0.0001,
+                        "lora": {"adapter_dim": 16, "adapter_dropout": 0.1},
+                    },
+                    "output_model": "default/job-1234",
+                    "status": "completed",
+                    "project": "default",
+                }
+            ]
+        }
+
+        jobs = self.run_async(self.adapter.get_training_jobs())
+
+        assert isinstance(jobs, ListNvidiaPostTrainingJobs)
+        assert len(jobs.data) == 1
+        job = jobs.data[0]
+        assert job.job_uuid == job_id
+        assert job.status.value == "completed"
+
+        self.mock_make_request.assert_called_once()
+        self._assert_request(
+            self.mock_make_request,
+            "GET",
+            "/v1/customization/jobs",
+            expected_params={"page": 1, "page_size": 10, "sort": "created_at"},
+        )
+
+    def test_cancel_training_job(self):
+        self.mock_make_request.return_value = {}  # Empty response for successful cancellation
+        job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
+
+        result = self.run_async(self.adapter.cancel_training_job(job_uuid=job_id))
+
+        assert result is None
+
+        self.mock_make_request.assert_called_once()
+        self._assert_request(
+            self.mock_make_request,
+            "POST",
+            f"/v1/customization/jobs/{job_id}/cancel",
+            expected_params={"job_id": job_id},
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/unit/providers/test_configs.py

@@ -14,17 +14,10 @@ from llama_stack.distribution.utils.dynamic import instantiate_class_type
 class TestProviderConfigurations:
     """Test suite for testing provider configurations across all API types."""
 
-    def test_all_api_providers_exist(self):
-        provider_registry = get_provider_registry()
-        for api in providable_apis():
-            providers = provider_registry.get(api, {})
-            assert providers, f"No providers found for API type: {api}"
-
     @pytest.mark.parametrize("api", providable_apis())
     def test_api_providers(self, api):
         provider_registry = get_provider_registry()
         providers = provider_registry.get(api, {})
-        assert providers, f"No providers found for API type: {api}"
 
         failures = []
         for provider_type, provider_spec in providers.items():

tests/unit/providers/vector_io/test_sqlite_vec.py

@@ -5,17 +5,16 @@
 # the root directory of this source tree.
 
 import asyncio
-import sqlite3
 
 import numpy as np
 import pytest
 import pytest_asyncio
-import sqlite_vec
 
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
 from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
     SQLiteVecIndex,
     SQLiteVecVectorIOAdapter,
+    _create_sqlite_connection,
     generate_chunk_id,
 )
 
@@ -36,29 +35,25 @@ def loop():
     return asyncio.new_event_loop()
 
 
-@pytest.fixture(scope="session", autouse=True)
-def sqlite_connection(loop):
-    conn = sqlite3.connect(":memory:")
-    try:
-        conn.enable_load_extension(True)
-        sqlite_vec.load(conn)
-        yield conn
-    finally:
-        conn.close()
-
-
 @pytest_asyncio.fixture(scope="session", autouse=True)
-async def sqlite_vec_index(sqlite_connection, embedding_dimension):
-    return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")
+async def sqlite_vec_index(embedding_dimension, tmp_path_factory):
+    temp_dir = tmp_path_factory.getbasetemp()
+    db_path = str(temp_dir / "test_sqlite.db")
+    index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank")
+    yield index
+    await index.delete()
 
 
 @pytest.mark.asyncio
 async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=2)
-    cur = sqlite_vec_index.connection.cursor()
+    connection = _create_sqlite_connection(sqlite_vec_index.db_path)
+    cur = connection.cursor()
     cur.execute(f"SELECT COUNT(*) FROM {sqlite_vec_index.metadata_table}")
     count = cur.fetchone()[0]
     assert count == len(sample_chunks)
+    cur.close()
+    connection.close()
 
 
 @pytest.mark.asyncio
@@ -79,13 +74,14 @@ async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dime
     sample_embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32)
 
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
-
-    cur = sqlite_vec_index.connection.cursor()
+    connection = _create_sqlite_connection(sqlite_vec_index.db_path)
+    cur = connection.cursor()
 
     # Retrieve all chunk IDs to check for duplicates
     cur.execute(f"SELECT id FROM {sqlite_vec_index.metadata_table}")
     chunk_ids = [row[0] for row in cur.fetchall()]
     cur.close()
+    connection.close()
 
     # Ensure all chunk IDs are unique
     assert len(chunk_ids) == len(set(chunk_ids)), "Duplicate chunk IDs detected across batches!"