add unit test

llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py

@@ -152,4 +152,6 @@ class TorchtuneCheckpointer:
                 "Adapter checkpoint not found in state_dict. Please ensure that the state_dict contains adapter weights."
             )
 
-        return model_file_path
+        print("model_file_path", str(model_file_path))
+
+        return str(model_file_path)

llama_stack/providers/inline/post_training/torchtune/post_training.py

@@ -35,6 +35,9 @@ class TorchtunePostTrainingImpl:
         checkpoint_dir: Optional[str],
         algorithm_config: Optional[Union[LoraFinetuningConfig, QATFinetuningConfig]],
     ) -> PostTrainingJob:
+        if job_uuid in self.jobs_list:
+            raise ValueError(f"Job {job_uuid} already exists")
+
         post_training_job = PostTrainingJob(job_uuid=job_uuid)
 
         job_status_response = PostTrainingJobStatusResponse(
@@ -48,6 +51,7 @@ class TorchtunePostTrainingImpl:
             try:
                 recipe = LoraFinetuningSingleDevice(
                     self.config,
+                    job_uuid,
                     training_config,
                     hyperparam_search_config,
                     logger_config,

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -68,6 +68,7 @@ class LoraFinetuningSingleDevice:
     def __init__(
         self,
         config: TorchtunePostTrainingConfig,
+        job_uuid: str,
         training_config: TrainingConfig,
         hyperparam_search_config: Dict[str, Any],
         logger_config: Dict[str, Any],
@@ -76,6 +77,7 @@ class LoraFinetuningSingleDevice:
         algorithm_config: Optional[Union[LoraFinetuningConfig, QATFinetuningConfig]],
         datasetio_api: DatasetIO,
     ) -> None:
+        self.job_uuid = job_uuid
         self.training_config = training_config
         self.algorithm_config = algorithm_config
         self._device = torchtune_utils.get_device(device="cuda")
@@ -366,7 +368,7 @@ class LoraFinetuningSingleDevice:
         )
         return lr_scheduler
 
-    async def save_checkpoint(self, epoch: int) -> None:
+    async def save_checkpoint(self, epoch: int) -> str:
         ckpt_dict = {}
 
         adapter_state_dict = get_adapter_state_dict(self._model.state_dict())
@@ -396,7 +398,7 @@ class LoraFinetuningSingleDevice:
         }
         ckpt_dict.update({training.ADAPTER_CONFIG: adapter_config})
 
-        self._checkpointer.save_checkpoint(
+        return self._checkpointer.save_checkpoint(
             ckpt_dict,
             epoch=epoch,
         )
@@ -514,6 +516,7 @@ class LoraFinetuningSingleDevice:
                 identifier=f"{self.model_id}-sft-{curr_epoch}",
                 created_at=datetime.now(),
                 epoch=curr_epoch,
+                post_training_job_id=self.job_uuid,
                 path=checkpoint_path,
             )
             checkpoints.append(checkpoint)

llama_stack/providers/tests/post_training/test_post_training.py

@@ -59,3 +59,33 @@ class TestPostTraining:
         )
         assert isinstance(response, PostTrainingJob)
         assert response.job_uuid == "1234"
+
+    @pytest.mark.asyncio
+    async def test_get_training_jobs(self, post_training_stack):
+        post_training_impl = post_training_stack
+        jobs_list = await post_training_impl.get_training_jobs()
+        assert isinstance(jobs_list, List)
+        assert jobs_list[0].job_uuid == "1234"
+
+    @pytest.mark.asyncio
+    async def test_get_training_job_status(self, post_training_stack):
+        post_training_impl = post_training_stack
+        job_status = await post_training_impl.get_training_job_status("1234")
+        assert isinstance(job_status, PostTrainingJobStatusResponse)
+        assert job_status.job_uuid == "1234"
+        assert job_status.status == JobStatus.completed
+        assert isinstance(job_status.checkpoints[0], Checkpoint)
+
+    @pytest.mark.asyncio
+    async def test_get_training_job_artifacts(self, post_training_stack):
+        post_training_impl = post_training_stack
+        job_artifacts = await post_training_impl.get_training_job_artifacts("1234")
+        assert isinstance(job_artifacts, PostTrainingJobArtifactsResponse)
+        assert job_artifacts.job_uuid == "1234"
+        assert isinstance(job_artifacts.checkpoints[0], Checkpoint)
+        assert job_artifacts.checkpoints[0].identifier == "Llama3.2-3B-Instruct-sft-0"
+        assert job_artifacts.checkpoints[0].epoch == 0
+        assert (
+            "/.llama/checkpoints/Llama3.2-3B-Instruct-sft-0"
+            in job_artifacts.checkpoints[0].path
+        )