ColossalAI/tests/test_amp/test_torch_fp16.py

91 lines
3.4 KiB
Python
Raw Normal View History

2022-11-10 08:40:26 +00:00
import torch
import colossalai
import torch.multiprocessing as mp
from tests.components_to_test.registry import non_distributed_component_funcs
from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use
from colossalai.utils import free_port
from colossalai.amp import convert_to_torch_amp, convert_to_apex_amp
import copy
import pytest
from functools import partial
def run_torch_amp():
"""
In this test, we compare the torch amp and apex amp implemented in colossalai
"""
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# create layer
test_models = ['resnet18', 'simple_net']
for test_name in test_models:
get_component_func = non_distributed_component_funcs.get_callable(test_name)
model_builder, train_dataloader, _, optim_class, _ = get_component_func()
# create model
torch_amp_model = model_builder(checkpoint=True).cuda()
apex_amp_model = copy.deepcopy(torch_amp_model)
# create optimizer
torch_amp_optimizer = optim_class(torch_amp_model.parameters(), lr=1e-3)
apex_amp_optimizer = optim_class(apex_amp_model.parameters(), lr=1e-3)
# inject torch and apex amp
torch_amp_config = dict(init_scale=1280, enabled=True)
torch_amp_model, torch_amp_optimizer, _ = convert_to_torch_amp(torch_amp_model,
torch_amp_optimizer,
amp_config=torch_amp_config)
apex_amp_config = dict(opt_level='O1', loss_scale=1280)
apex_amp_model, apex_amp_optimizer = convert_to_apex_amp(apex_amp_model, apex_amp_optimizer, apex_amp_config)
# create data
data_iter = iter(train_dataloader)
data, label = next(data_iter)
data = data.cuda()
# forward pass
torch_amp_output = torch_amp_model(data)
apex_amp_output = apex_amp_model(data)
assert_close_loose(torch_amp_output, apex_amp_output)
for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
assert_close_loose(torch_amp_param, apex_amp_param)
# backward
torch_amp_optimizer.backward(torch_amp_output.mean())
apex_amp_optimizer.backward(apex_amp_output.mean())
# check grad
# In apex amp, grad is not scaled before backward, but torch amp does
for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
assert_close_loose(torch_amp_param.grad, apex_amp_param.grad * apex_amp_config['loss_scale'])
# step
torch_amp_optimizer.step()
apex_amp_optimizer.step()
# check updated param and grad
for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
assert_close_loose(torch_amp_param.grad, apex_amp_param.grad)
assert_close_loose(torch_amp_param, apex_amp_param)
def run_dist(rank, world_size, port):
colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
run_torch_amp()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_torch_amp():
world_size = 1
run_func = partial(run_dist, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_torch_amp()