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TrainerClock: assert dataset_length >= batch_size
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@ -29,6 +29,10 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
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lr_scheduler_interval: TimeValue,
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lr_scheduler_interval: TimeValue,
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verbose: bool = True,
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verbose: bool = True,
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) -> None:
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) -> None:
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assert batch_size > 0, "Batch size must be greater than 0."
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assert (
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dataset_length >= batch_size
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), f"Dataset length ({dataset_length}) must be greater than batch_size ({batch_size})."
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self.dataset_length = dataset_length
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self.dataset_length = dataset_length
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self.batch_size = batch_size
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self.batch_size = batch_size
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self.training_duration = training_duration
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self.training_duration = training_duration
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@ -137,6 +137,30 @@ def training_clock() -> TrainingClock:
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)
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)
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def test_small_dataset_error():
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with pytest.raises(AssertionError):
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TrainingClock(
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dataset_length=3,
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batch_size=10,
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training_duration=TimeValue(number=5, unit=TimeUnit.EPOCH),
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gradient_accumulation=TimeValue(number=1, unit=TimeUnit.EPOCH),
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evaluation_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
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lr_scheduler_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
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)
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def test_zero_batch_size_error():
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with pytest.raises(AssertionError):
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TrainingClock(
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dataset_length=3,
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batch_size=0,
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training_duration=TimeValue(number=5, unit=TimeUnit.EPOCH),
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gradient_accumulation=TimeValue(number=1, unit=TimeUnit.EPOCH),
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evaluation_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
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lr_scheduler_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
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)
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def test_time_unit_to_steps_conversion(training_clock: TrainingClock) -> None:
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def test_time_unit_to_steps_conversion(training_clock: TrainingClock) -> None:
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assert training_clock.convert_time_unit_to_steps(1, TimeUnit.EPOCH) == 10
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assert training_clock.convert_time_unit_to_steps(1, TimeUnit.EPOCH) == 10
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assert training_clock.convert_time_unit_to_steps(2, TimeUnit.EPOCH) == 20
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assert training_clock.convert_time_unit_to_steps(2, TimeUnit.EPOCH) == 20
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