62 lines
2.1 KiB
Python
62 lines
2.1 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_size in batch_sizes:
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train_config = train_config_factory(
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model=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_size=batch_size,
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)
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assert train_config.batch_size == batch_size
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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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accumulation_steps_list = [1, 2, 4]
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for accumulation_steps in accumulation_steps_list:
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train_config = train_config_factory(
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model=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_size=2,
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accumulation_steps=accumulation_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.accumulation_steps == accumulation_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_size": 1, "accumulation_steps": 8},
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{"batch_size": 2, "accumulation_steps": 4},
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{"batch_size": 4, "accumulation_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=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_size=config["batch_size"],
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accumulation_steps=config["accumulation_steps"],
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)
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assert train_config.accumulation_steps == config["accumulation_steps"]
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