fix misc

llama_stack/apis/post_training/post_training.py

@@ -66,6 +66,7 @@ class TrainingConfig(BaseModel):
 
 @json_schema_type
 class LoraFinetuningConfig(BaseModel):
+    type: Literal["LoRA"] = "LoRA"
     lora_attn_modules: List[str]
     apply_lora_to_mlp: bool
     apply_lora_to_output: bool
@@ -77,12 +78,13 @@ class LoraFinetuningConfig(BaseModel):
 
 @json_schema_type
 class QATFinetuningConfig(BaseModel):
+    type: Literal["QAT"] = "QAT"
     quantizer_name: str
     group_size: int
 
 
 AlgorithmConfig = Annotated[
-    Union[LoraFinetuningConfig, LoraFinetuningConfig], Field(discriminator="type")
+    Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")
 ]
 
 

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

@@ -39,8 +39,9 @@ class TorchtunePostTrainingImpl:
         checkpoint_dir: Optional[str],
         algorithm_config: Optional[AlgorithmConfig],
     ) -> PostTrainingJob:
-        if job_uuid in self.jobs_list:
-            raise ValueError(f"Job {job_uuid} already exists")
+        for job in self.jobs_list:
+            if job_uuid == job.job_uuid:
+                raise ValueError(f"Job {job_uuid} already exists")
 
         post_training_job = PostTrainingJob(job_uuid=job_uuid)
 

llama_stack/providers/tests/post_training/test_post_training.py

@@ -19,6 +19,7 @@ class TestPostTraining:
     @pytest.mark.asyncio
     async def test_supervised_fine_tune(self, post_training_stack):
         algorithm_config = LoraFinetuningConfig(
+            type="LoRA",
             lora_attn_modules=["q_proj", "v_proj", "output_proj"],
             apply_lora_to_mlp=True,
             apply_lora_to_output=False,