ColossalAI/tests/test_trainer/test_trainer_with_non_pipe_...

60 lines
2.0 KiB
Python

import pytest
import torch
import colossalai
from colossalai.amp.amp_type import AMP_TYPE
from colossalai.logging import get_dist_logger
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from colossalai.trainer import Trainer
from colossalai.utils import MultiTimer
from tests.components_to_test.registry import non_distributed_component_funcs
BATCH_SIZE = 4
IMG_SIZE = 32
NUM_EPOCHS = 200
CONFIG = dict(fp16=dict(mode=AMP_TYPE.TORCH))
@parameterize('model_name', ['repeated_computed_layers', 'resnet18', 'nested_model'])
def run_trainer(model_name):
get_components_func = non_distributed_component_funcs.get_callable(model_name)
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
model = model_builder()
optimizer = optimizer_class(model.parameters(), lr=1e-3)
engine, train_dataloader, *_ = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader)
logger = get_dist_logger()
logger.info("engine is built", ranks=[0])
timer = MultiTimer()
trainer = Trainer(engine=engine, logger=logger, timer=timer)
logger.info("trainer is built", ranks=[0])
logger.info("start training", ranks=[0])
trainer.fit(train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
epochs=NUM_EPOCHS,
max_steps=3,
display_progress=True,
test_interval=5)
torch.cuda.empty_cache()
def run_dist(rank, world_size, port):
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_trainer_no_pipeline():
world_size = 4
spawn(run_dist, world_size)
if __name__ == '__main__':
test_trainer_no_pipeline()