bb1cae1a20
* add --launcher option; explicit True/False bool args; small cleanup * refactor * add torchrun, accelerate cli args * add rdzv arg default + tests * update _quarto * coderabbit * fix * we can't set rdvz_id independently across nodes * coderabbit * fix tests
70 lines
1.7 KiB
Python
70 lines
1.7 KiB
Python
"""
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unit tests for generating sweep configurations
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"""
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from axolotl.cli.utils import generate_sweep_configs
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def test_generate_sweep_configs_no_pairs():
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base_config = {
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"learning_rate": 0.1,
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"micro_batch_size": 1,
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"sample_packing": True,
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}
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sweeps_config = {"micro_batch_size": [1, 2, 4], "weight_decay": [0.0, 0.1]}
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generate_sweep_configs(base_config, sweeps_config)
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assert len(generate_sweep_configs(base_config, sweeps_config)) == 6
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cfg_1 = {
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"learning_rate": 0.1,
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"micro_batch_size": 2,
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"weight_decay": 0.0,
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"sample_packing": True,
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}
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assert any(
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cfg_1 == cfg for cfg in generate_sweep_configs(base_config, sweeps_config)
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)
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def test_generate_sweep_configs_with_pairs():
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base_config = {
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"learning_rate": 0.1,
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"micro_batch_size": 1,
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"sample_packing": True,
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}
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sweeps_config = {
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"_": [
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{
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 8,
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},
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{
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 4,
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},
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{
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 2,
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},
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{
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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},
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],
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"weight_decay": [0.0, 0.1],
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}
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generate_sweep_configs(base_config, sweeps_config)
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assert len(generate_sweep_configs(base_config, sweeps_config)) == 8
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assert all(
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cfg["gradient_accumulation_steps"] * cfg["micro_batch_size"] == 8
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for cfg in generate_sweep_configs(base_config, sweeps_config)
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)
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