ColossalAI/tests/test_optimizer/test_nvme.py

50 lines
1.9 KiB
Python

import pytest
import torch
from colossalai.nn.optimizer import CPUAdam, HybridAdam
from colossalai.testing import clear_cache_before_run, parameterize
from tests.components_to_test.registry import non_distributed_component_funcs
def move_some_params_to_cuda(model, torch_model):
model.embed.weight.data = model.embed.weight.cuda()
torch_model.embed.weight.data = model.embed.weight.cuda()
model.ln1.weight.data = model.ln1.weight.cuda()
torch_model.ln1.weight.data = model.ln1.weight.cuda()
def check_params_equal(model, torch_model):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
assert torch.allclose(p, torch_p, atol=1e-3), f'diff: {torch.abs(p - torch_p)}'
@clear_cache_before_run()
@parameterize('nvme_offload_fraction', [0.0, 0.5, 1.0])
@parameterize('nvme_offload_dir', ['./offload', None])
@parameterize('adam_cls', [CPUAdam, HybridAdam])
def test_nvme_adam(nvme_offload_fraction, nvme_offload_dir, adam_cls):
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
model = model_builder()
torch_model = model_builder()
move_some_params_to_cuda(model, torch_model)
optimizer = adam_cls(model.parameters(),
lr=0.1,
nvme_offload_fraction=nvme_offload_fraction,
nvme_offload_dir=nvme_offload_dir)
torch_optimizer = torch.optim.Adam(torch_model.parameters(), lr=0.1)
with torch.no_grad():
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
torch_p.copy_(p)
p.grad = torch.rand_like(p)
torch_p.grad = p.grad
for _ in range(3):
optimizer.step()
torch_optimizer.step()
check_params_equal(model, torch_model)
if __name__ == '__main__':
test_nvme_adam(0.5, './offload', CPUAdam)