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import torch
import numpy as np
from optimizer import AdamW
seed = 0
def test_optimizer(opt_class) -> torch.Tensor:
rng = np.random.default_rng(seed)
torch.manual_seed(seed)
model = torch.nn.Linear(3, 2, bias=False)
opt = opt_class(
model.parameters(),
lr=1e-3,
weight_decay=1e-4,
correct_bias=True,
)
for i in range(1000):
opt.zero_grad()
x = torch.FloatTensor(rng.uniform(size=[model.in_features]))
y_hat = model(x)
y = torch.Tensor([x[0] + x[1], -x[2]])
loss = ((y - y_hat) ** 2).sum()
loss.backward()
opt.step()
return model.weight.detach()
ref = torch.tensor(np.load("optimizer_test.npy"))
actual = test_optimizer(AdamW)
print(ref)
print(actual)
assert torch.allclose(ref, actual, atol=1e-6, rtol=1e-4)
print("Optimizer test passed!")
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