[2/n][torchtune integration] implement job management and return training artifacts (#593)

### Context 
In this PR, we 
- Implement the post training job management and get training artifacts
apis
  - get_training_jobs
  - get_training_job_status
  - get_training_job_artifacts
- get_training_job_logstream is deleted since the trace can be directly
accessed by UI with Jaeger
https://llama-stack.readthedocs.io/en/latest/building_applications/telemetry.html#jaeger-to-visualize-traces
- Refactor the post training and training types definition to make them
more intuitive.
- Rewrite the checkpointer to make it compatible with llama-stack file
system and can be recognized during inference


### Test
Unit test
`pytest llama_stack/providers/tests/post_training/test_post_training.py
-m "torchtune_post_training_huggingface_datasetio" -v -s --tb=short
--disable-warnings`

<img width="1506" alt="Screenshot 2024-12-10 at 4 06 17 PM"
src="https://github.com/user-attachments/assets/16225029-bdb7-48c4-9d13-e580cc769c0a">


e2e test with client side call

<img width="888" alt="Screenshot 2024-12-10 at 4 09 44 PM"
src="https://github.com/user-attachments/assets/de375e4c-ef67-4dcc-a045-4037d9489191">

llama_stack/apis/post_training/post_training.py

@@ -6,6 +6,7 @@
 
 from datetime import datetime
 from enum import Enum
+
 from typing import Any, Dict, List, Optional, Protocol, Union
 
 from llama_models.schema_utils import json_schema_type, webmethod
@@ -14,6 +15,7 @@ from pydantic import BaseModel, Field
 from typing_extensions import Annotated
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.common.job_types import JobStatus
 from llama_stack.apis.datasets import *  # noqa: F403
 from llama_stack.apis.common.training_types import *  # noqa: F403
 
@@ -64,6 +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
@@ -75,12 +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")
 ]
 
 
@@ -92,14 +96,6 @@ class PostTrainingJobLogStream(BaseModel):
     log_lines: List[str]
 
 
-@json_schema_type
-class PostTrainingJobStatus(Enum):
-    running = "running"
-    completed = "completed"
-    failed = "failed"
-    scheduled = "scheduled"
-
-
 @json_schema_type
 class RLHFAlgorithm(Enum):
     dpo = "dpo"
@@ -144,7 +140,7 @@ class PostTrainingJobStatusResponse(BaseModel):
     """Status of a finetuning job."""
 
     job_uuid: str
-    status: PostTrainingJobStatus
+    status: JobStatus
 
     scheduled_at: Optional[datetime] = None
     started_at: Optional[datetime] = None
@@ -166,7 +162,7 @@ class PostTrainingJobArtifactsResponse(BaseModel):
 
 
 class PostTraining(Protocol):
-    @webmethod(route="/post-training/supervised-fine-tune")
+    @webmethod(route="/post-training/supervised-fine-tune", method="POST")
     async def supervised_fine_tune(
         self,
         job_uuid: str,
@@ -181,7 +177,7 @@ class PostTraining(Protocol):
         algorithm_config: Optional[AlgorithmConfig] = None,
     ) -> PostTrainingJob: ...
 
-    @webmethod(route="/post-training/preference-optimize")
+    @webmethod(route="/post-training/preference-optimize", method="POST")
     async def preference_optimize(
         self,
         job_uuid: str,
@@ -192,24 +188,18 @@ class PostTraining(Protocol):
         logger_config: Dict[str, Any],
     ) -> PostTrainingJob: ...
 
-    @webmethod(route="/post-training/jobs")
+    @webmethod(route="/post-training/jobs", method="GET")
     async def get_training_jobs(self) -> List[PostTrainingJob]: ...
 
-    # sends SSE stream of logs
-    @webmethod(route="/post-training/job/logs")
-    async def get_training_job_logstream(
-        self, job_uuid: str
-    ) -> PostTrainingJobLogStream: ...
-
-    @webmethod(route="/post-training/job/status")
+    @webmethod(route="/post-training/job/status", method="GET")
     async def get_training_job_status(
         self, job_uuid: str
-    ) -> PostTrainingJobStatusResponse: ...
+    ) -> Optional[PostTrainingJobStatusResponse]: ...
 
-    @webmethod(route="/post-training/job/cancel")
+    @webmethod(route="/post-training/job/cancel", method="POST")
     async def cancel_training_job(self, job_uuid: str) -> None: ...
 
-    @webmethod(route="/post-training/job/artifacts")
+    @webmethod(route="/post-training/job/artifacts", method="GET")
     async def get_training_job_artifacts(
         self, job_uuid: str
-    ) -> PostTrainingJobArtifactsResponse: ...
+    ) -> Optional[PostTrainingJobArtifactsResponse]: ...