63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from astrai.trainer import Trainer
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# train_config_factory is injected via fixture
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def test_different_batch_sizes(base_test_env, random_dataset, train_config_factory):
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"""Test training with different batch sizes"""
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batch_sizes = [1, 2, 4, 8]
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for batch_per_device in batch_sizes:
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train_config = train_config_factory(
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model_fn=lambda: base_test_env["model"],
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dataset=random_dataset,
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test_dir=base_test_env["test_dir"],
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device=base_test_env["device"],
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batch_per_device=batch_per_device,
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)
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assert train_config.batch_per_device == batch_per_device
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def test_gradient_accumulation(base_test_env, random_dataset, train_config_factory):
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"""Test training with different gradient accumulation steps"""
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grad_accum_steps_list = [1, 2, 4]
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for grad_accum_steps in grad_accum_steps_list:
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train_config = train_config_factory(
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model_fn=lambda: base_test_env["model"],
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dataset=random_dataset,
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test_dir=base_test_env["test_dir"],
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device=base_test_env["device"],
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batch_per_device=2,
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grad_accum_steps=grad_accum_steps,
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)
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trainer = Trainer(train_config)
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trainer.train()
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assert train_config.grad_accum_steps == grad_accum_steps
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def test_memory_efficient_training(base_test_env, random_dataset, train_config_factory):
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"""Test training with memory-efficient configurations"""
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# Test with smaller batch sizes and gradient checkpointing
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small_batch_configs = [
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{"batch_per_device": 1, "grad_accum_steps": 8},
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{"batch_per_device": 2, "grad_accum_steps": 4},
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{"batch_per_device": 4, "grad_accum_steps": 2},
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]
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for config in small_batch_configs:
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train_config = train_config_factory(
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model_fn=lambda: base_test_env["model"],
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dataset=random_dataset,
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test_dir=base_test_env["test_dir"],
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device=base_test_env["device"],
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batch_per_device=config["batch_per_device"],
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grad_accum_steps=config["grad_accum_steps"],
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)
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assert train_config.grad_accum_steps == config["grad_accum_steps"]
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assert train_config.batch_per_device == config["batch_per_device"]
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