refiners/tests/training_utils/test_trainer.py

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import warnings
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from dataclasses import dataclass
from pathlib import Path
from typing import cast
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import pytest
import torch
from torch import Tensor, nn
from torch.optim import SGD
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from refiners.fluxion import layers as fl
from refiners.fluxion.utils import norm
from refiners.training_utils.common import TimeUnit, count_learnable_parameters, human_readable_number
from refiners.training_utils.config import BaseConfig, ModelConfig
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from refiners.training_utils.trainer import (
Trainer,
TrainingClock,
WarmupScheduler,
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count_learnable_parameters,
human_readable_number,
register_model,
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)
@dataclass
class MockBatch:
inputs: torch.Tensor
targets: torch.Tensor
class MockModelConfig(ModelConfig):
use_activation: bool
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class MockConfig(BaseConfig):
mock_model: MockModelConfig
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class MockModel(fl.Chain):
def __init__(self):
super().__init__(
fl.Linear(10, 10),
fl.Linear(10, 10),
fl.Linear(10, 10),
)
def add_activation(self) -> None:
self.insert(1, fl.SiLU())
self.insert(3, fl.SiLU())
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class MockTrainer(Trainer[MockConfig, MockBatch]):
step_counter: int = 0
model_registration_counter: int = 0
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@property
def dataset_length(self) -> int:
return 20
def get_item(self, index: int) -> MockBatch:
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return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
return MockBatch(
inputs=torch.cat([b.inputs for b in batch]),
targets=torch.cat([b.targets for b in batch]),
)
@register_model()
def mock_model(self, config: MockModelConfig) -> MockModel:
model = MockModel()
if config.use_activation:
model.add_activation()
self.model_registration_counter += 1
return model
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def compute_loss(self, batch: MockBatch) -> Tensor:
self.step_counter += 1
inputs, targets = batch.inputs.to(self.device), batch.targets.to(self.device)
outputs = self.mock_model(inputs)
return norm(outputs - targets)
@pytest.fixture
def mock_config() -> MockConfig:
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config = MockConfig.load_from_toml(Path(__file__).parent / "mock_config.toml")
return config
@pytest.fixture
def mock_trainer(mock_config: MockConfig) -> MockTrainer:
return MockTrainer(config=mock_config)
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@pytest.fixture
def mock_trainer_short(mock_config: MockConfig) -> MockTrainer:
mock_config_short = mock_config.model_copy(deep=True)
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mock_config_short.training.duration = {"number": 3, "unit": TimeUnit.STEP}
return MockTrainer(config=mock_config_short)
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@pytest.fixture
def mock_model() -> fl.Chain:
return MockModel()
def test_count_learnable_parameters_with_params() -> None:
params = [
nn.Parameter(torch.randn(2, 2), requires_grad=True),
nn.Parameter(torch.randn(5), requires_grad=False),
nn.Parameter(torch.randn(3, 3), requires_grad=True),
]
# cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
assert count_learnable_parameters(cast(list[nn.Parameter], params)) == 13
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def test_count_learnable_parameters_with_model(mock_model: fl.Chain) -> None:
assert count_learnable_parameters(mock_model.parameters()) == 330
def test_human_readable_number() -> None:
assert human_readable_number(123) == "123.0"
assert human_readable_number(1234) == "1.2K"
assert human_readable_number(1234567) == "1.2M"
@pytest.fixture
def training_clock() -> TrainingClock:
return TrainingClock(
dataset_length=100,
batch_size=10,
training_duration={"number": 5, "unit": TimeUnit.EPOCH},
gradient_accumulation={"number": 1, "unit": TimeUnit.EPOCH},
evaluation_interval={"number": 1, "unit": TimeUnit.EPOCH},
lr_scheduler_interval={"number": 1, "unit": TimeUnit.EPOCH},
)
def test_time_unit_to_steps_conversion(training_clock: TrainingClock) -> None:
assert training_clock.convert_time_unit_to_steps(1, TimeUnit.EPOCH) == 10
assert training_clock.convert_time_unit_to_steps(2, TimeUnit.EPOCH) == 20
assert training_clock.convert_time_unit_to_steps(1, TimeUnit.STEP) == 1
def test_steps_to_time_unit_conversion(training_clock: TrainingClock) -> None:
assert training_clock.convert_steps_to_time_unit(10, TimeUnit.EPOCH) == 1
assert training_clock.convert_steps_to_time_unit(20, TimeUnit.EPOCH) == 2
assert training_clock.convert_steps_to_time_unit(1, TimeUnit.STEP) == 1
def test_clock_properties(training_clock: TrainingClock) -> None:
assert training_clock.num_batches_per_epoch == 10
assert training_clock.num_epochs == 5
assert training_clock.num_iterations == 5
assert training_clock.num_steps == 50
def test_timer_functionality(training_clock: TrainingClock) -> None:
training_clock.start_timer()
assert training_clock.start_time is not None
training_clock.stop_timer()
assert training_clock.end_time is not None
assert training_clock.time_elapsed >= 0
def test_state_based_properties(training_clock: TrainingClock) -> None:
training_clock.step = 5 # Halfway through the first epoch
assert not training_clock.is_evaluation_step # Assuming evaluation every epoch
training_clock.step = 10 # End of the first epoch
assert training_clock.is_evaluation_step
def test_mock_trainer_initialization(mock_config: MockConfig, mock_trainer: MockTrainer) -> None:
assert mock_trainer.config == mock_config
assert isinstance(mock_trainer, MockTrainer)
assert mock_trainer.optimizer is not None
assert mock_trainer.lr_scheduler is not None
assert mock_trainer.model_registration_counter == 1
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def test_training_cycle(mock_trainer: MockTrainer) -> None:
clock = mock_trainer.clock
config = mock_trainer.config
assert clock.num_step_per_iteration == config.training.gradient_accumulation["number"]
assert clock.num_batches_per_epoch == mock_trainer.dataset_length // config.training.batch_size
assert mock_trainer.step_counter == 0
assert mock_trainer.clock.epoch == 0
mock_trainer.train()
assert clock.epoch == config.training.duration["number"]
assert clock.step == config.training.duration["number"] * clock.num_batches_per_epoch
assert mock_trainer.step_counter == mock_trainer.clock.step
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def test_training_short_cycle(mock_trainer_short: MockTrainer) -> None:
clock = mock_trainer_short.clock
config = mock_trainer_short.config
assert mock_trainer_short.step_counter == 0
assert mock_trainer_short.clock.epoch == 0
mock_trainer_short.train()
assert clock.step == config.training.duration["number"]
@pytest.fixture
def warmup_scheduler():
optimizer = SGD([nn.Parameter(torch.randn(2, 2), requires_grad=True)], lr=0.1)
scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer, 1)
return WarmupScheduler(optimizer, scheduler, warmup_scheduler_steps=100)
def test_initial_lr(warmup_scheduler: WarmupScheduler) -> None:
optimizer = warmup_scheduler.optimizer
for group in optimizer.param_groups:
assert group["lr"] == 1e-3
def test_warmup_lr(warmup_scheduler: WarmupScheduler) -> None:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
category=UserWarning,
message=r"Detected call of `lr_scheduler.step\(\)` before `optimizer.step\(\)`",
)
for _ in range(102):
warmup_scheduler.step()
optimizer = warmup_scheduler.optimizer
for group in optimizer.param_groups:
assert group["lr"] == 0.1
class MockTrainerWith2Models(MockTrainer):
@register_model()
def mock_model1(self, config: ModelConfig) -> MockModel:
return MockModel()
@register_model()
def mock_model2(self, config: ModelConfig) -> MockModel:
return MockModel()
def compute_loss(self, batch: MockBatch) -> Tensor:
self.step_counter += 1
inputs, targets = batch.inputs.to(self.device), batch.targets.to(self.device)
outputs = self.mock_model2(self.mock_model1(inputs))
return norm(outputs - targets)
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class MockConfig_2_Models(BaseConfig):
mock_model1: ModelConfig
mock_model2: ModelConfig
@pytest.fixture
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def mock_config_2_models() -> MockConfig_2_Models:
return MockConfig_2_Models.load_from_toml(Path(__file__).parent / "mock_config_2_models.toml")
@pytest.fixture
def mock_trainer_2_models(mock_config_2_models: MockConfig) -> MockTrainerWith2Models:
return MockTrainerWith2Models(config=mock_config_2_models)
def test_optimizer_parameters(mock_trainer_2_models: MockTrainerWith2Models) -> None:
assert len(mock_trainer_2_models.optimizer.param_groups) == 2
assert mock_trainer_2_models.optimizer.param_groups[0]["lr"] == 1e-5