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chore(misc): make tests and starter faster

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This commit is contained in:
Ashwin Bharambe 2025-08-05 13:57:15 -07:00
parent ea46f74092
commit 2b4e88a3de
19 changed files with 2860 additions and 1660 deletions
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5
llama_stack/providers/inline/post_training/huggingface/config.py
View file

@ -6,7 +6,8 @@
from typing import Any, Literal
from pydantic import BaseModel
from pydantic import BaseModel, Field
import tempfile
class HuggingFacePostTrainingConfig(BaseModel):
@ -71,7 +72,7 @@ class HuggingFacePostTrainingConfig(BaseModel):
dpo_beta: float = 0.1
use_reference_model: bool = True
dpo_loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"] = "sigmoid"
dpo_output_dir: str = "./checkpoints/dpo"
dpo_output_dir: str = Field(default_factory=lambda: tempfile.mkdtemp(prefix="dpo_output_"))
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:

19
llama_stack/providers/inline/post_training/huggingface/post_training.py
View file

@ -22,15 +22,8 @@ from llama_stack.apis.post_training import (
from llama_stack.providers.inline.post_training.huggingface.config import (
HuggingFacePostTrainingConfig,
)
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
HFFinetuningSingleDevice,
)
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
HFDPOAlignmentSingleDevice,
)
from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
from llama_stack.schema_utils import webmethod
class TrainingArtifactType(Enum):
@ -85,6 +78,10 @@ class HuggingFacePostTrainingImpl:
algorithm_config: AlgorithmConfig | None = None,
) -> PostTrainingJob:
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
HFFinetuningSingleDevice,
)
on_log_message_cb("Starting HF finetuning")
recipe = HFFinetuningSingleDevice(
@ -124,6 +121,10 @@ class HuggingFacePostTrainingImpl:
logger_config: dict[str, Any],
) -> PostTrainingJob:
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
HFDPOAlignmentSingleDevice,
)
on_log_message_cb("Starting HF DPO alignment")
recipe = HFDPOAlignmentSingleDevice(
@ -168,7 +169,6 @@ class HuggingFacePostTrainingImpl:
data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
return data[0] if data else None
@webmethod(route="/post-training/job/status")
async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
job = self._scheduler.get_job(job_uuid)
@ -195,16 +195,13 @@ class HuggingFacePostTrainingImpl:
resources_allocated=self._get_resources_allocated(job),
)
@webmethod(route="/post-training/job/cancel")
async def cancel_training_job(self, job_uuid: str) -> None:
self._scheduler.cancel(job_uuid)
@webmethod(route="/post-training/job/artifacts")
async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
job = self._scheduler.get_job(job_uuid)
return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))
@webmethod(route="/post-training/jobs", method="GET")
async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
return ListPostTrainingJobsResponse(
data=[PostTrainingJob(job_uuid=job.id) for job in self._scheduler.get_jobs()]

7
llama_stack/providers/inline/post_training/torchtune/post_training.py
View file

@ -23,9 +23,6 @@ from llama_stack.apis.post_training import (
from llama_stack.providers.inline.post_training.torchtune.config import (
TorchtunePostTrainingConfig,
)
from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
LoraFinetuningSingleDevice,
)
from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
from llama_stack.schema_utils import webmethod
@ -84,6 +81,10 @@ class TorchtunePostTrainingImpl:
if isinstance(algorithm_config, LoraFinetuningConfig):
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
LoraFinetuningSingleDevice,
)
on_log_message_cb("Starting Lora finetuning")
recipe = LoraFinetuningSingleDevice(