mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-09-19 19:05:28 +00:00
211 lines
6.8 KiB
Python
211 lines
6.8 KiB
Python
from dataclasses import dataclass
|
|
from functools import cached_property
|
|
from pathlib import Path
|
|
from typing import cast
|
|
from warnings import warn
|
|
|
|
import pytest
|
|
import torch
|
|
from torch import Tensor, nn
|
|
from torch.optim import SGD
|
|
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,
|
|
WarmupScheduler,
|
|
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),
|
|
]
|
|
# 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(
|
|
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
|
|
|
|
|
|
@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:
|
|
for _ in range(102):
|
|
warmup_scheduler.step()
|
|
optimizer = warmup_scheduler.optimizer
|
|
for group in optimizer.param_groups:
|
|
assert group["lr"] == 0.1
|