mirror of https://github.com/hpcaitech/ColossalAI
50 lines
1.9 KiB
Python
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)
|