mirror of https://github.com/hpcaitech/ColossalAI
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
import copy
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import pytest
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import torch
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import colossalai
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from colossalai.legacy.amp import convert_to_apex_amp, convert_to_torch_amp
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from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn
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from tests.kit.model_zoo import model_zoo
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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 = ["torchvision_resnet18", "custom_simple_net"]
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for test_name in test_models:
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model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry(test_name).values()))
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# create model
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torch_amp_model = model_builder().cuda()
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apex_amp_model = copy.deepcopy(torch_amp_model)
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# create optimizer
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# we use SGD here, since the correctness of gradient clipping can't be tested with Adam
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torch_amp_optimizer = torch.optim.SGD(torch_amp_model.parameters(), lr=1e-3)
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apex_amp_optimizer = torch.optim.SGD(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=128, enabled=True)
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torch_amp_model, torch_amp_optimizer, _ = convert_to_torch_amp(
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torch_amp_model, torch_amp_optimizer, amp_config=torch_amp_config
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)
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apex_amp_config = dict(opt_level="O1", loss_scale=128)
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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 = data_gen_fn()
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data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
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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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# use sum() to get big gradient
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torch_amp_optimizer.backward(torch_amp_output.sum())
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apex_amp_optimizer.backward(apex_amp_output.sum())
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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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# clip gradient
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apex_amp_optimizer.clip_grad_norm(model=apex_amp_model, max_norm=1.0)
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torch_amp_optimizer.clip_grad_norm(model=torch_amp_model, max_norm=1.0)
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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.legacy.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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@clear_cache_before_run()
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def test_torch_amp():
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spawn(run_dist, 1)
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if __name__ == "__main__":
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test_torch_amp()
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