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ColossalAI/tests/test_optimizer/test_nvme.py

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1.9 KiB

import pytest
import torch
from colossalai.nn.optimizer import CPUAdam, HybridAdam
from colossalai.testing import clear_cache_before_run, parameterize
from tests.kit.model_zoo import model_zoo
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)}"
# TODO Something wrong with ci when running this test.
@pytest.mark.skip(reason="skip because of something wrong with CI")
@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):
model_builder, data_gen_fn, *_ = next(iter(model_zoo.get_sub_registry("custom_simple_net").values()))
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)