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add mocks for loading datasets in cli train tests (#2497) [skip ci]

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* add mocks for loading datasets in cli train tests

* Apply suggestions from code review to fix patched module for preprocess

Co-authored-by: NanoCode012 <nano@axolotl.ai>

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>
This commit is contained in:
Wing Lian
2025-04-11 09:51:59 -04:00
committed by GitHub
parent 22c562533d
commit 7e7180fa10
2 changed files with 60 additions and 50 deletions
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62
tests/cli/test_cli_train.py
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@@ -29,19 +29,21 @@ class TestTrainCommand(BaseCliTest):
with patch("axolotl.cli.train.train") as mock_train:
mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
with patch("axolotl.cli.train.load_datasets") as mock_load_datasets:
mock_load_datasets.return_value = MagicMock()
result = cli_runner.invoke(
cli,
[
"train",
str(config_path),
"--no-accelerate",
],
catch_exceptions=False,
)
result = cli_runner.invoke(
cli,
[
"train",
str(config_path),
"--no-accelerate",
],
catch_exceptions=False,
)
assert result.exit_code == 0
mock_train.assert_called_once()
assert result.exit_code == 0
mock_train.assert_called_once()
def test_train_cli_overrides(self, cli_runner, tmp_path, valid_test_config):
"""Test CLI arguments properly override config values"""
@@ -49,23 +51,25 @@ class TestTrainCommand(BaseCliTest):
with patch("axolotl.cli.train.train") as mock_train:
mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
with patch("axolotl.cli.train.load_datasets") as mock_load_datasets:
mock_load_datasets.return_value = MagicMock()
result = cli_runner.invoke(
cli,
[
"train",
str(config_path),
"--learning-rate",
"1e-4",
"--micro-batch-size",
"2",
"--no-accelerate",
],
catch_exceptions=False,
)
result = cli_runner.invoke(
cli,
[
"train",
str(config_path),
"--learning-rate",
"1e-4",
"--micro-batch-size",
"2",
"--no-accelerate",
],
catch_exceptions=False,
)
assert result.exit_code == 0
mock_train.assert_called_once()
cfg = mock_train.call_args[1]["cfg"]
assert cfg["learning_rate"] == 1e-4
assert cfg["micro_batch_size"] == 2
assert result.exit_code == 0
mock_train.assert_called_once()
cfg = mock_train.call_args[1]["cfg"]
assert cfg["learning_rate"] == 1e-4
assert cfg["micro_batch_size"] == 2