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