This commit is contained in:
Xi Yan 2024-10-22 20:00:43 -07:00
parent 821810657f
commit aefa84e70a
5 changed files with 117 additions and 54 deletions

View file

@ -20,7 +20,7 @@ class DatasetDef(BaseModel):
identifier: str = Field(
description="A unique name for the dataset",
)
columns_schema: Dict[str, ParamType] = Field(
dataset_schema: Dict[str, ParamType] = Field(
description="The schema definition for this dataset",
)
url: URL

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@ -3,10 +3,10 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import io
from typing import List, Optional
import pandas
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.datasetio import * # noqa: F403
@ -14,6 +14,7 @@ from abc import ABC, abstractmethod
from dataclasses import dataclass
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
from llama_stack.providers.utils.memory.vector_store import parse_data_url
from .config import MetaReferenceDatasetIOConfig
@ -57,6 +58,15 @@ class PandasDataframeDataset(BaseDataset):
else:
return self.df.iloc[idx].to_dict()
def validate_dataset_schema(self) -> None:
if self.df is None:
self.load()
print(self.dataset_def.dataset_schema)
# get columns names
# columns = self.df[self.dataset_def.dataset_schema.keys()]
print(self.df.columns)
def load(self) -> None:
if self.df is not None:
return
@ -105,6 +115,7 @@ class MetaReferenceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
dataset_def: DatasetDef,
) -> None:
dataset_impl = PandasDataframeDataset(dataset_def)
dataset_impl.validate_dataset_schema()
self.dataset_infos[dataset_def.identifier] = DatasetInfo(
dataset_def=dataset_def,
dataset_impl=dataset_impl,

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@ -36,7 +36,7 @@ def available_providers() -> List[ProviderSpec]:
pip_packages=(
META_REFERENCE_DEPS
+ [
"fbgemm-gpu==0.8.0",
"fbgemm-gpu==1.0.0",
]
),
module="llama_stack.providers.impls.meta_reference.inference",

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@ -0,0 +1,5 @@
{"input_query": "What is the capital of France?", "generated_answer": "London", "expected_answer": "Paris"}
{"input_query": "Who is the CEO of Meta?", "generated_answer": "Mark Zuckerberg", "expected_answer": "Mark Zuckerberg"}
{"input_query": "What is the largest planet in our solar system?", "generated_answer": "Jupiter", "expected_answer": "Jupiter"}
{"input_query": "What is the smallest country in the world?", "generated_answer": "China", "expected_answer": "Vatican City"}
{"input_query": "What is the currency of Japan?", "generated_answer": "Yen", "expected_answer": "Yen"}

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@ -8,8 +8,13 @@ import os
import pytest
import pytest_asyncio
from llama_stack.apis.common.type_system import * # noqa: F403
from llama_stack.apis.datasetio import * # noqa: F403
from llama_stack.distribution.datatypes import * # noqa: F403
import base64
import mimetypes
from pathlib import Path
from llama_stack.providers.tests.resolver import resolve_impls_for_test
# How to run this test:
@ -41,6 +46,22 @@ async def datasetio_settings():
}
def data_url_from_file(file_path: str) -> str:
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
with open(file_path, "rb") as file:
file_content = file.read()
base64_content = base64.b64encode(file_content).decode("utf-8")
mime_type, _ = mimetypes.guess_type(file_path)
print(mime_type)
data_url = f"data:{mime_type};base64,{base64_content}"
return data_url
async def register_dataset(datasets_impl: Datasets):
dataset = DatasetDefWithProvider(
identifier="test_dataset",
@ -48,62 +69,88 @@ async def register_dataset(datasets_impl: Datasets):
url=URL(
uri="https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv",
),
columns_schema={},
dataset_schema={},
)
await datasets_impl.register_dataset(dataset)
async def register_local_dataset(datasets_impl: Datasets):
test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.jsonl"
test_jsonl_url = data_url_from_file(str(test_file))
dataset = DatasetDefWithProvider(
identifier="test_dataset",
provider_id=os.environ["PROVIDER_ID"],
url=URL(
uri=test_jsonl_url,
),
dataset_schema={
"generated_answer": StringType(),
"expected_answer": StringType(),
"input_query": StringType(),
},
)
await datasets_impl.register_dataset(dataset)
# @pytest.mark.asyncio
# async def test_datasets_list(datasetio_settings):
# # NOTE: this needs you to ensure that you are starting from a clean state
# # but so far we don't have an unregister API unfortunately, so be careful
# datasets_impl = datasetio_settings["datasets_impl"]
# response = await datasets_impl.list_datasets()
# assert isinstance(response, list)
# assert len(response) == 0
# @pytest.mark.asyncio
# async def test_datasets_register(datasetio_settings):
# # NOTE: this needs you to ensure that you are starting from a clean state
# # but so far we don't have an unregister API unfortunately, so be careful
# datasets_impl = datasetio_settings["datasets_impl"]
# await register_dataset(datasets_impl)
# response = await datasets_impl.list_datasets()
# assert isinstance(response, list)
# assert len(response) == 1
# # register same dataset with same id again will fail
# await register_dataset(datasets_impl)
# response = await datasets_impl.list_datasets()
# assert isinstance(response, list)
# assert len(response) == 1
# assert response[0].identifier == "test_dataset"
# @pytest.mark.asyncio
# async def test_get_rows_paginated(datasetio_settings):
# datasetio_impl = datasetio_settings["datasetio_impl"]
# datasets_impl = datasetio_settings["datasets_impl"]
# await register_dataset(datasets_impl)
# response = await datasetio_impl.get_rows_paginated(
# dataset_id="test_dataset",
# rows_in_page=3,
# )
# assert isinstance(response.rows, list)
# assert len(response.rows) == 3
# assert response.next_page_token == "3"
# # iterate over all rows
# response = await datasetio_impl.get_rows_paginated(
# dataset_id="test_dataset",
# rows_in_page=10,
# page_token=response.next_page_token,
# )
# assert isinstance(response.rows, list)
# assert len(response.rows) == 10
# assert response.next_page_token == "13"
@pytest.mark.asyncio
async def test_datasets_list(datasetio_settings):
async def test_datasets_validation(datasetio_settings):
# NOTE: this needs you to ensure that you are starting from a clean state
# but so far we don't have an unregister API unfortunately, so be careful
datasets_impl = datasetio_settings["datasets_impl"]
response = await datasets_impl.list_datasets()
assert isinstance(response, list)
assert len(response) == 0
@pytest.mark.asyncio
async def test_datasets_register(datasetio_settings):
# NOTE: this needs you to ensure that you are starting from a clean state
# but so far we don't have an unregister API unfortunately, so be careful
datasets_impl = datasetio_settings["datasets_impl"]
await register_dataset(datasets_impl)
response = await datasets_impl.list_datasets()
assert isinstance(response, list)
assert len(response) == 1
# register same dataset with same id again will fail
await register_dataset(datasets_impl)
response = await datasets_impl.list_datasets()
assert isinstance(response, list)
assert len(response) == 1
assert response[0].identifier == "test_dataset"
@pytest.mark.asyncio
async def test_get_rows_paginated(datasetio_settings):
datasetio_impl = datasetio_settings["datasetio_impl"]
datasets_impl = datasetio_settings["datasets_impl"]
await register_dataset(datasets_impl)
response = await datasetio_impl.get_rows_paginated(
dataset_id="test_dataset",
rows_in_page=3,
)
assert isinstance(response.rows, list)
assert len(response.rows) == 3
assert response.next_page_token == "3"
# iterate over all rows
response = await datasetio_impl.get_rows_paginated(
dataset_id="test_dataset",
rows_in_page=10,
page_token=response.next_page_token,
)
assert isinstance(response.rows, list)
assert len(response.rows) == 10
assert response.next_page_token == "13"
await register_local_dataset(datasets_impl)