mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-14 00:58:13 +00:00
206 lines
6.5 KiB
Python
206 lines
6.5 KiB
Python
from dataclasses import dataclass
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from functools import cached_property
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from pathlib import Path
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from typing import cast
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import pytest
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import torch
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from torch import Tensor, nn
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from torch.optim import SGD
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from torch.utils.data import Dataset
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from refiners.fluxion import layers as fl
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from refiners.fluxion.utils import norm
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from refiners.training_utils.config import BaseConfig, TimeUnit
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from refiners.training_utils.trainer import (
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Trainer,
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TrainingClock,
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WarmupScheduler,
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count_learnable_parameters,
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human_readable_number,
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)
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@dataclass
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class MockBatch:
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inputs: torch.Tensor
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targets: torch.Tensor
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class MockDataset(Dataset[MockBatch]):
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def __len__(self):
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return 20
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def __getitem__(self, _: int) -> MockBatch:
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return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
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def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
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return MockBatch(
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inputs=torch.cat([b.inputs for b in batch]),
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targets=torch.cat([b.targets for b in batch]),
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)
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class MockConfig(BaseConfig):
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pass
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class MockModel(fl.Chain):
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def __init__(self):
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super().__init__(
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fl.Linear(10, 10),
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fl.Linear(10, 10),
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fl.Linear(10, 10),
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)
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class MockTrainer(Trainer[MockConfig, MockBatch]):
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step_counter: int = 0
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@cached_property
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def mock_model(self) -> MockModel:
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return MockModel()
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def load_dataset(self) -> Dataset[MockBatch]:
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return MockDataset()
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def load_models(self) -> dict[str, fl.Module]:
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return {"mock_model": self.mock_model}
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def compute_loss(self, batch: MockBatch) -> Tensor:
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self.step_counter += 1
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inputs, targets = batch.inputs.to(self.device), batch.targets.to(self.device)
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outputs = self.mock_model(inputs)
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return norm(outputs - targets)
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@pytest.fixture
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def mock_config() -> MockConfig:
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config = MockConfig.load_from_toml(Path(__file__).parent / "mock_config.toml")
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return config
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@pytest.fixture
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def mock_trainer(mock_config: MockConfig) -> MockTrainer:
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return MockTrainer(config=mock_config)
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@pytest.fixture
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def mock_model() -> fl.Chain:
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return MockModel()
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def test_count_learnable_parameters_with_params() -> None:
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params = [
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nn.Parameter(torch.randn(2, 2), requires_grad=True),
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nn.Parameter(torch.randn(5), requires_grad=False),
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nn.Parameter(torch.randn(3, 3), requires_grad=True),
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]
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# cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
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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:
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assert count_learnable_parameters(mock_model.parameters()) == 330
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def test_human_readable_number() -> None:
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assert human_readable_number(123) == "123.0"
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assert human_readable_number(1234) == "1.2K"
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assert human_readable_number(1234567) == "1.2M"
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@pytest.fixture
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def training_clock() -> TrainingClock:
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return TrainingClock(
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dataset_length=100,
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batch_size=10,
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training_duration={"number": 5, "unit": TimeUnit.EPOCH},
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gradient_accumulation={"number": 1, "unit": TimeUnit.EPOCH},
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evaluation_interval={"number": 1, "unit": TimeUnit.EPOCH},
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lr_scheduler_interval={"number": 1, "unit": TimeUnit.EPOCH},
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checkpointing_save_interval={"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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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(1, TimeUnit.STEP) == 1
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def test_steps_to_time_unit_conversion(training_clock: TrainingClock) -> None:
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assert training_clock.convert_steps_to_time_unit(10, TimeUnit.EPOCH) == 1
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assert training_clock.convert_steps_to_time_unit(20, TimeUnit.EPOCH) == 2
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assert training_clock.convert_steps_to_time_unit(1, TimeUnit.STEP) == 1
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def test_clock_properties(training_clock: TrainingClock) -> None:
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assert training_clock.num_batches_per_epoch == 10
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assert training_clock.num_epochs == 5
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assert training_clock.num_iterations == 5
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assert training_clock.num_steps == 50
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def test_timer_functionality(training_clock: TrainingClock) -> None:
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training_clock.start_timer()
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assert training_clock.start_time is not None
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training_clock.stop_timer()
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assert training_clock.end_time is not None
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assert training_clock.time_elapsed >= 0
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def test_state_based_properties(training_clock: TrainingClock) -> None:
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training_clock.step = 5 # Halfway through the first epoch
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assert not training_clock.is_evaluation_step # Assuming evaluation every epoch
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assert not training_clock.is_checkpointing_step
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training_clock.step = 10 # End of the first epoch
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assert training_clock.is_evaluation_step
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assert training_clock.is_checkpointing_step
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def test_mock_trainer_initialization(mock_config: MockConfig, mock_trainer: MockTrainer) -> None:
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assert mock_trainer.config == mock_config
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assert isinstance(mock_trainer, MockTrainer)
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assert mock_trainer.optimizer is not None
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assert mock_trainer.lr_scheduler is not None
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def test_training_cycle(mock_trainer: MockTrainer) -> None:
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clock = mock_trainer.clock
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config = mock_trainer.config
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assert clock.num_step_per_iteration == config.training.gradient_accumulation["number"]
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assert clock.num_batches_per_epoch == mock_trainer.dataset_length // config.training.batch_size
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assert mock_trainer.step_counter == 0
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assert mock_trainer.clock.epoch == 0
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mock_trainer.train()
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assert clock.epoch == config.training.duration["number"]
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assert clock.step == config.training.duration["number"] * clock.num_batches_per_epoch
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assert mock_trainer.step_counter == mock_trainer.clock.step
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@pytest.fixture
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def warmup_scheduler():
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optimizer = SGD([nn.Parameter(torch.randn(2, 2), requires_grad=True)], lr=0.1)
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scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer, 1)
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return WarmupScheduler(optimizer, scheduler, warmup_scheduler_steps=100)
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def test_initial_lr(warmup_scheduler: WarmupScheduler) -> None:
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optimizer = warmup_scheduler.optimizer
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for group in optimizer.param_groups:
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assert group["lr"] == 1e-3
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def test_warmup_lr(warmup_scheduler: WarmupScheduler) -> None:
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for _ in range(102):
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warmup_scheduler.step()
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optimizer = warmup_scheduler.optimizer
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for group in optimizer.param_groups:
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assert group["lr"] == 0.1
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