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Make AdEMAMix32bit inherit AdEMAMix so it allocates the double state buffer #2076

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Make AdEMAMix32bit inherit AdEMAMix so it allocates the double state buffer #2076

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12 changes: 5 additions & 7 deletions bitsandbytes/optim/ademamix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -352,7 +352,7 @@ def __init__(
)


class AdEMAMix32bit(Optimizer2State):
class AdEMAMix32bit(AdEMAMix):
def __init__(
self,
params: Iterable[torch.nn.Parameter],
Expand All @@ -367,19 +367,17 @@ def __init__(
is_paged: bool = False,
):
super().__init__(
"ademamix",
params=params,
params,
lr=lr,
betas=betas,
alpha=alpha,
t_alpha=t_alpha,
t_beta3=t_beta3,
eps=eps,
weight_decay=weight_decay,
optim_bits=32,
args=None,
min_8bit_size=min_8bit_size,
is_paged=is_paged,
alpha=alpha,
t_alpha=t_alpha,
t_beta3=t_beta3,
)


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40 changes: 40 additions & 0 deletions tests/test_optim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -575,6 +575,46 @@ def test_benchmark_blockwise(dim1, dim2, gtype, optim_name, device):
# assert s < 3.9


ademamix_32bit_classes = [
("AdEMAMix32bit", bnb.optim.AdEMAMix32bit),
("PagedAdEMAMix32bit", bnb.optim.PagedAdEMAMix32bit),
]


@pytest.mark.parametrize("scheduled", [False, True], ids=["unscheduled", "scheduled"])
@pytest.mark.parametrize(
"optim_name,optim_cls",
ademamix_32bit_classes,
ids=[x[0] for x in ademamix_32bit_classes],
)
@pytest.mark.parametrize("device", get_available_devices())
@pytest.mark.skipif(not get_available_devices(), reason="No device")
def test_ademamix32bit_matches_ademamix(optim_name, optim_cls, scheduled, device):
"""AdEMAMix32bit must allocate the (2, *p.shape) m1/m2 buffer and step exactly like AdEMAMix(optim_bits=32)."""
if device == "cpu" and optim_name.startswith("Paged"):
pytest.skip("Paged optimizers are not meaningful on CPU")

sched = dict(t_alpha=100, t_beta3=100) if scheduled else {}

torch.manual_seed(0)
p_ref = torch.nn.Parameter(torch.randn(4096, device=device))
p_test = torch.nn.Parameter(p_ref.detach().clone())
opt_ref = bnb.optim.AdEMAMix([p_ref], lr=1e-3, optim_bits=32, **sched)
opt_test = optim_cls([p_test], lr=1e-3, **sched)

for _ in range(5):
g = torch.randn(4096, device=device)
p_ref.grad = g.clone()
p_test.grad = g.clone()
opt_ref.step()
opt_test.step()

assert opt_test.state[p_test]["state1"].shape == (2, 4096)
torch.testing.assert_close(opt_test.state[p_test]["state1"], opt_ref.state[p_ref]["state1"])
torch.testing.assert_close(opt_test.state[p_test]["state2"], opt_ref.state[p_ref]["state2"])
torch.testing.assert_close(p_test, p_ref)


ademamix_state_dict_opts = [
("AdEMAMix8bit", lambda p: bnb.optim.AdEMAMix8bit(p, lr=1e-3)),
("AdEMAMix32bit", lambda p: bnb.optim.AdEMAMix(p, lr=1e-3)),
Expand Down