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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
import os
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import pytest
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from ludwig.constants import COMBINER, EPOCHS, INPUT_FEATURES, OUTPUT_FEATURES, TRAINER, TYPE
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from tests.integration_tests.utils import binary_feature, generate_data, run_test_suite, text_feature
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@pytest.mark.integration_tests_i
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@pytest.mark.parametrize(
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"backend",
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[
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pytest.param("local", id="local"),
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pytest.param("ray", id="ray", marks=[pytest.mark.distributed, pytest.mark.distributed_f]),
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],
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)
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def test_text_adapter_lora(tmpdir, backend, ray_cluster_2cpu):
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input_features = [
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text_feature(
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encoder={
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"type": "auto_transformer",
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"pretrained_model_name_or_path": "hf-internal-testing/tiny-bert-for-token-classification",
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"trainable": True,
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"adapter": {"type": "lora"},
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},
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),
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]
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output_features = [binary_feature()]
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data_csv_path = os.path.join(tmpdir, "dataset.csv")
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dataset = generate_data(input_features, output_features, data_csv_path)
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config = {
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INPUT_FEATURES: input_features,
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OUTPUT_FEATURES: output_features,
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COMBINER: {TYPE: "concat", "output_size": 14},
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TRAINER: {EPOCHS: 1},
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}
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model = run_test_suite(config, dataset, backend)
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state_dict = model.model.state_dict()
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# check that at least one of the keys contains the word "lora_" denoting a lora parameter
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assert any("lora_" in key for key in state_dict.keys())
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