import random import warnings from dataclasses import dataclass from pathlib import Path from typing import cast import pytest import torch from torch import Tensor, nn from torch.optim import SGD from refiners.fluxion import layers as fl from refiners.fluxion.utils import norm from refiners.training_utils.callback import Callback, CallbackConfig from refiners.training_utils.clock import ClockConfig from refiners.training_utils.common import ( Epoch, Step, count_learnable_parameters, human_readable_number, scoped_seed, ) from refiners.training_utils.config import BaseConfig, IterationOrEpochField, ModelConfig, TimeValueField from refiners.training_utils.data_loader import DataLoaderConfig, create_data_loader from refiners.training_utils.trainer import ( Trainer, TrainingClock, WarmupScheduler, count_learnable_parameters, human_readable_number, register_callback, register_model, ) @dataclass class MockBatch: inputs: torch.Tensor targets: torch.Tensor class MockModelConfig(ModelConfig): use_activation: bool class MockCallbackConfig(CallbackConfig): on_batch_end_interval: TimeValueField on_batch_end_seed: int on_optimizer_step_interval: IterationOrEpochField class MockConfig(BaseConfig): # we register the `early_callback` before the `clock` callback to test the callback ordering early_callback: CallbackConfig = CallbackConfig() clock: ClockConfig = ClockConfig() mock_model: MockModelConfig mock_callback: MockCallbackConfig data_loader: DataLoaderConfig 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()) class MockCallback(Callback["MockTrainer"]): def __init__(self, config: MockCallbackConfig) -> None: self.config = config self.optimizer_step_count = 0 self.step_end_count = 0 self.optimizer_step_random_int: int | None = None self.step_end_random_int: int | None = None def on_init_begin(self, trainer: "MockTrainer") -> None: pass def on_optimizer_step_begin(self, trainer: "MockTrainer") -> None: if not trainer.clock.is_due(self.config.on_optimizer_step_interval): return self.optimizer_step_count += 1 self.optimizer_step_random_int = random.randint(0, 100) def on_step_end(self, trainer: "MockTrainer") -> None: if not trainer.clock.is_due(self.config.on_batch_end_interval): return # We verify that this callback is always called before the clock is updated (see `_call_callbacks` in trainer.py) assert trainer.clock.step // 3 <= self.step_end_count self.step_end_count += 1 with scoped_seed(self.config.on_batch_end_seed): self.step_end_random_int = random.randint(0, 100) class EarlyMockCallback(Callback["MockTrainer"]): """ A callback that will be registered before the Clock callback to test the callback ordering. """ def on_train_begin(self, trainer: "MockTrainer") -> None: assert trainer.clock.start_time is not None, "Clock callback should have been called before this callback." class MockTrainer(Trainer[MockConfig, MockBatch]): step_counter: int = 0 model_registration_counter: int = 0 @property def dataset_length(self) -> int: return 20 def get_item(self, index: int) -> MockBatch: 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]), ) def create_data_iterable(self): return create_data_loader( get_item=self.get_item, length=self.dataset_length, config=self.config.data_loader, collate_fn=self.collate_fn, ) @register_callback() def early_callback(self, config: CallbackConfig) -> EarlyMockCallback: return EarlyMockCallback() @register_callback() def mock_callback(self, config: MockCallbackConfig) -> MockCallback: return MockCallback(config) @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 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: 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) @pytest.fixture def mock_trainer_short(mock_config: MockConfig) -> MockTrainer: mock_config_short = mock_config.model_copy(deep=True) mock_config_short.training.duration = Step(3) return MockTrainer(config=mock_config_short) @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 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( training_duration=Epoch(5), gradient_accumulation=Step(1), lr_scheduler_interval=Epoch(1), ) 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_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 def test_training_cycle(mock_trainer: MockTrainer) -> None: clock = mock_trainer.clock config = mock_trainer.config assert mock_trainer.step_counter == 0 assert clock.epoch == 0 mock_trainer.train() assert clock.epoch == config.training.duration.number assert mock_trainer.step_counter == mock_trainer.clock.step def test_callback_registration(mock_trainer: MockTrainer) -> None: mock_trainer.train() # Check that the callback skips every other iteration assert mock_trainer.mock_callback.optimizer_step_count == mock_trainer.clock.iteration // 2 assert mock_trainer.mock_callback.step_end_count == mock_trainer.clock.step // 3 + 1 # Check that the random seed was set assert mock_trainer.mock_callback.optimizer_step_random_int == 93 assert mock_trainer.mock_callback.step_end_random_int == 81 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) class MockConfig_2_Models(BaseConfig): mock_model1: ModelConfig mock_model2: ModelConfig @pytest.fixture 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 class MockTrainerNoDataLoader(MockTrainer): def create_data_iterable(self) -> list[MockBatch]: # type: ignore return [MockBatch(inputs=torch.randn(4, 10), targets=torch.randn(4, 10)) for _ in range(5)] def test_trainer_no_data_loader(mock_config: MockConfig) -> None: trainer = MockTrainerNoDataLoader(config=mock_config) trainer.train() assert trainer.step_counter == 500 assert trainer.clock.epoch == 100