fix: miscellaneous job management improvements in torchtune (#1136)

- **refactor: simplify job status extraction a bit**
- **torchtune: save job status on schedule**
- **refactor: get rid of job_list in torchtune job management code**

# What does this PR do?

A failed job is now registered in API, and one can consult its status.

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan

```
$ llama-stack-client post_training status --job-uuid test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73                                                      
JobStatusResponse(checkpoints=[], job_uuid='test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73', status='failed', completed_at=None, resources_allocated=None, scheduled_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 3252), started_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 10688))
```

[//]: # (## Documentation)

---------

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>

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

@@ -40,8 +40,7 @@ class TorchtunePostTrainingImpl:
         self.datasets_api = datasets
 
         # TODO: assume sync job, will need jobs API for async scheduling
-        self.jobs_status = {}
-        self.jobs_list = []
+        self.jobs = {}
         self.checkpoints_dict = {}
 
     async def supervised_fine_tune(
@@ -54,8 +53,7 @@ class TorchtunePostTrainingImpl:
         checkpoint_dir: Optional[str],
         algorithm_config: Optional[AlgorithmConfig],
     ) -> PostTrainingJob:
-        for job in self.jobs_list:
-            if job_uuid == job.job_uuid:
+        if job_uuid in self.jobs:
             raise ValueError(f"Job {job_uuid} already exists")
 
         post_training_job = PostTrainingJob(job_uuid=job_uuid)
@@ -65,8 +63,8 @@ class TorchtunePostTrainingImpl:
             status=JobStatus.scheduled,
             scheduled_at=datetime.now(),
         )
+        self.jobs[job_uuid] = job_status_response
 
-        self.jobs_list.append(post_training_job)
         if isinstance(algorithm_config, LoraFinetuningConfig):
             try:
                 recipe = LoraFinetuningSingleDevice(
@@ -100,8 +98,6 @@ class TorchtunePostTrainingImpl:
         else:
             raise NotImplementedError()
 
-        self.jobs_status[job_uuid] = job_status_response
-
         return post_training_job
 
     async def preference_optimize(
@@ -115,13 +111,11 @@ class TorchtunePostTrainingImpl:
     ) -> PostTrainingJob: ...
 
     async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
-        return ListPostTrainingJobsResponse(data=self.jobs_list)
+        return ListPostTrainingJobsResponse(data=[PostTrainingJob(job_uuid=uuid_) for uuid_ in self.jobs])
 
     @webmethod(route="/post-training/job/status")
     async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]:
-        if job_uuid in self.jobs_status:
-            return self.jobs_status[job_uuid]
-        return None
+        return self.jobs.get(job_uuid, None)
 
     @webmethod(route="/post-training/job/cancel")
     async def cancel_training_job(self, job_uuid: str) -> None: