From 7f722029be15e346c0521cbe57581d2752e82388 Mon Sep 17 00:00:00 2001 From: limiteinductive Date: Sun, 14 Jan 2024 15:06:48 +0100 Subject: [PATCH] add basic unit test for training_utils --- tests/training_utils/mock_config.toml | 32 +++++ tests/training_utils/test_trainer.py | 185 ++++++++++++++++++++++++++ 2 files changed, 217 insertions(+) create mode 100644 tests/training_utils/mock_config.toml create mode 100644 tests/training_utils/test_trainer.py diff --git a/tests/training_utils/mock_config.toml b/tests/training_utils/mock_config.toml new file mode 100644 index 0000000..6064f49 --- /dev/null +++ b/tests/training_utils/mock_config.toml @@ -0,0 +1,32 @@ +[models.mock_model] +train = true + +[training] +duration = "100:epoch" +seed = 0 +gpu_index = 0 +batch_size = 4 +gradient_accumulation = "4:step" +clip_grad_norm = 1.0 +evaluation_interval = "5:epoch" +evaluation_seed = 1 + +[optimizer] +optimizer = "SGD" +learning_rate = 1 +momentum = 0.9 + +[scheduler] +scheduler_type = "ConstantLR" +update_interval = "1:step" +warmup = "20:step" + +[dropout] +dropout = 0.0 + +[checkpointing] +save_interval = "10:epoch" + +[wandb] +mode = "disabled" +project = "mock_project" \ No newline at end of file diff --git a/tests/training_utils/test_trainer.py b/tests/training_utils/test_trainer.py new file mode 100644 index 0000000..38e45d1 --- /dev/null +++ b/tests/training_utils/test_trainer.py @@ -0,0 +1,185 @@ +from dataclasses import dataclass +from functools import cached_property +from pathlib import Path +from warnings import warn + +import pytest +import torch +from torch import Tensor, nn +from torch.utils.data import Dataset + +from refiners.fluxion import layers as fl +from refiners.fluxion.utils import norm +from refiners.training_utils.config import BaseConfig, TimeUnit +from refiners.training_utils.trainer import ( + Trainer, + TrainingClock, + count_learnable_parameters, + human_readable_number, +) + + +@dataclass +class MockBatch: + inputs: torch.Tensor + targets: torch.Tensor + + +class MockDataset(Dataset[MockBatch]): + def __len__(self): + return 20 + + def __getitem__(self, _: 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]), + ) + + +class MockConfig(BaseConfig): + pass + + +class MockModel(fl.Chain): + def __init__(self): + super().__init__( + fl.Linear(10, 10), + fl.Linear(10, 10), + fl.Linear(10, 10), + ) + + +class MockTrainer(Trainer[MockConfig, MockBatch]): + step_counter: int = 0 + + @cached_property + def mock_model(self) -> MockModel: + return MockModel() + + def load_dataset(self) -> Dataset[MockBatch]: + return MockDataset() + + def load_models(self) -> dict[str, fl.Module]: + return {"mock_model": self.mock_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(test_device: torch.device) -> MockConfig: + if not test_device.type == "cuda": + warn("only running on CUDA, skipping") + pytest.skip("Skipping test because test_device is not CUDA") + config = MockConfig.load_from_toml(Path(__file__).parent / "mock_config.toml") + config.training.gpu_index = test_device.index + return config + + +@pytest.fixture +def mock_trainer(mock_config: MockConfig) -> MockTrainer: + return MockTrainer(config=mock_config) + + +@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), + ] + assert count_learnable_parameters(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( + 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}, + checkpointing_save_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 + assert not training_clock.is_checkpointing_step + training_clock.step = 10 # End of the first epoch + assert training_clock.is_evaluation_step + assert training_clock.is_checkpointing_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 + + +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