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# Copyright 2024 MosaicML LLM Foundry authors | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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from typing import Any | ||
from unittest.mock import MagicMock, patch | ||
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import pytest | ||
from omegaconf import OmegaConf | ||
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from llmfoundry.utils.config_utils import (_log_dataset_uri, | ||
_parse_source_dataset) | ||
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mlflow = pytest.importorskip('mlflow') | ||
from mlflow.data.huggingface_dataset_source import HuggingFaceDatasetSource | ||
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def create_config(**kwargs: Any): | ||
"""Helper function to create OmegaConf configurations.""" | ||
return OmegaConf.create(kwargs) | ||
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def test_parse_source_dataset_delta_table(): | ||
cfg = create_config(source_dataset_train='db.schema.train_table', | ||
source_dataset_eval='db.schema.eval_table') | ||
expected = [('delta_table', 'db.schema.train_table', 'train'), | ||
('delta_table', 'db.schema.eval_table', 'eval')] | ||
assert _parse_source_dataset(cfg) == expected | ||
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def test_parse_source_dataset_uc_volume(): | ||
cfg = create_config(source_dataset_train='dbfs:/Volumes/train_data', | ||
source_dataset_eval='dbfs:/Volumes/eval_data') | ||
expected = [('uc_volume', '/Volumes/train_data', 'train'), | ||
('uc_volume', '/Volumes/eval_data', 'eval')] | ||
assert _parse_source_dataset(cfg) == expected | ||
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def test_parse_source_dataset_hf(): | ||
cfg = create_config( | ||
train_loader={'dataset': { | ||
'hf_name': 'huggingface/train_dataset', | ||
}}, | ||
eval_loader={'dataset': { | ||
'hf_name': 'huggingface/eval_dataset', | ||
}}) | ||
expected = [('hf', 'huggingface/train_dataset', 'train'), | ||
('hf', 'huggingface/eval_dataset', 'eval')] | ||
assert _parse_source_dataset(cfg) == expected | ||
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def test_parse_source_dataset_remote(): | ||
cfg = create_config(train_loader={ | ||
'dataset': { | ||
'remote': 'https://remote/train_dataset', | ||
'split': 'train' | ||
} | ||
}, | ||
eval_loader={ | ||
'dataset': { | ||
'remote': 'https://remote/eval_dataset', | ||
'split': 'eval' | ||
} | ||
}) | ||
expected = [('https', 'https://remote/train_dataset/train/', 'train'), | ||
('https', 'https://remote/eval_dataset/eval/', 'eval')] | ||
assert _parse_source_dataset(cfg) == expected | ||
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def test_log_dataset_uri(): | ||
cfg = create_config( | ||
train_loader={'dataset': { | ||
'hf_name': 'huggingface/train_dataset' | ||
}}, | ||
eval_loader={'dataset': { | ||
'hf_name': 'huggingface/eval_dataset' | ||
}}, | ||
source_dataset_train='huggingface/train_dataset', | ||
source_dataset_eval='huggingface/eval_dataset') | ||
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with patch('mlflow.log_input') as mock_log_input: | ||
_log_dataset_uri(cfg) | ||
assert mock_log_input.call_count == 2 | ||
meta_dataset_calls = [ | ||
args[0] for args, _ in mock_log_input.call_args_list | ||
] | ||
assert all( | ||
isinstance(call.source, HuggingFaceDatasetSource) | ||
for call in meta_dataset_calls), 'Source types are incorrect' | ||
# Verify the names | ||
assert meta_dataset_calls[ | ||
0].name == 'train', f"Expected 'train', got {meta_dataset_calls[0].name}" | ||
assert meta_dataset_calls[ | ||
1].name == 'eval', f"Expected 'eval', got {meta_dataset_calls[1].name}" | ||
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def test_multiple_eval_datasets(): | ||
# Setup a configuration with multiple evaluation datasets | ||
cfg = OmegaConf.create({ | ||
'train_loader': { | ||
'dataset': { | ||
'hf_name': 'huggingface/train_dataset', | ||
}, | ||
}, | ||
'eval_loader': [{ | ||
'dataset': { | ||
'hf_name': 'huggingface/eval_dataset1', | ||
}, | ||
}, { | ||
'dataset': { | ||
'hf_name': 'huggingface/eval_dataset2', | ||
}, | ||
}] | ||
}) | ||
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expected_data_paths = [('hf', 'huggingface/train_dataset', 'train'), | ||
('hf', 'huggingface/eval_dataset1', 'eval'), | ||
('hf', 'huggingface/eval_dataset2', 'eval')] | ||
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# Mock mlflow to avoid any actual logging calls | ||
with patch('mlflow.data.meta_dataset.MetaDataset') as mock_meta_dataset: | ||
mock_meta_dataset.side_effect = lambda source, name: MagicMock() | ||
data_paths = _parse_source_dataset(cfg) | ||
assert sorted(data_paths) == sorted( | ||
expected_data_paths), 'Data paths did not match expected' | ||
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@pytest.fixture | ||
def mock_mlflow_classes(): | ||
with patch('mlflow.data.http_dataset_source.HTTPDatasetSource') as http_source, \ | ||
patch('mlflow.data.huggingface_dataset_source.HuggingFaceDatasetSource') as hf_source, \ | ||
patch('mlflow.data.delta_dataset_source.DeltaDatasetSource') as delta_source, \ | ||
patch('mlflow.data.uc_volume_dataset_source.UCVolumeDatasetSource') as uc_source: | ||
yield http_source, hf_source, delta_source, uc_source |