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fix: add todo for schema validation (#1991)

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# What does this PR do?
Change validation to TODO same as was done
[here](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/eval/meta_reference/eval.py#L87)
until validation can be implemented
Closes #1849

## Test Plan

Signed-off-by: Kevin <kpostlet@redhat.com>
This commit is contained in:
Kevin Postlethwait 2025-04-29 03:59:35 -04:00 • committed by GitHub
parent fe9b5ef08b
commit 2aca7265b3
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GPG key ID: B5690EEEBB952194
2 changed files with 3 additions and 28 deletions
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20
llama_stack/providers/inline/post_training/common/validator.py
View file

@ -17,10 +17,8 @@ from llama_stack.apis.common.type_system import (
DialogType,
StringType,
)
from llama_stack.apis.datasets import Datasets
from llama_stack.providers.utils.common.data_schema_validator import (
ColumnName,
validate_dataset_schema,
)
EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = {
@ -36,21 +34,3 @@ EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = {
}
],
}
async def validate_input_dataset_schema(
datasets_api: Datasets,
dataset_id: str,
dataset_type: str,
) -> None:
dataset_def = await datasets_api.get_dataset(dataset_id=dataset_id)
if not dataset_def:
raise ValueError(f"Dataset {dataset_id} does not exist.")
if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
if dataset_type not in EXPECTED_DATASET_SCHEMA:
raise ValueError(f"Dataset type {dataset_type} is not supported.")
validate_dataset_schema(dataset_def.dataset_schema, EXPECTED_DATASET_SCHEMA[dataset_type])

11
llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
View file

@ -48,9 +48,6 @@ from llama_stack.apis.post_training import (
from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
from llama_stack.distribution.utils.model_utils import model_local_dir
from llama_stack.models.llama.sku_list import resolve_model
from llama_stack.providers.inline.post_training.common.validator import (
validate_input_dataset_schema,
)
from llama_stack.providers.inline.post_training.torchtune.common import utils
from llama_stack.providers.inline.post_training.torchtune.common.checkpointer import (
TorchtuneCheckpointer,
@ -348,11 +345,9 @@ class LoraFinetuningSingleDevice:
all_rows = await fetch_rows(dataset_id)
rows = all_rows.data
await validate_input_dataset_schema(
datasets_api=self.datasets_api,
dataset_id=dataset_id,
dataset_type=self._data_format.value,
)
# TODO (xiyan): validate dataset schema
# dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
data_transform = await utils.get_data_transform(self._data_format)
ds = SFTDataset(
rows,