Fixed bug where beta2 was not passed into Lion 32-bit.
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792af5c883
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2eb3108356
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@ -665,7 +665,7 @@ class Optimizer1State(Optimizer8bit):
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step,
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config["lr"],
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None,
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0.0,
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config['betas'][1],
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config["weight_decay"],
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gnorm_scale,
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state["unorm_vec"] if config["max_unorm"] > 0.0 else None,
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@ -22,7 +22,7 @@ def assert_most_approx_close(a, b, rtol=1e-3, atol=1e-3, max_error_count=0):
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idx = torch.isclose(a, b, rtol, atol)
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error_count = (idx == 0).sum().item()
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if error_count > max_error_count:
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print(f"Too many values not close: assert {sumval} < {count}")
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print(f"Too many values not close: assert {error_count} < {max_error_count}")
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torch.testing.assert_allclose(a, b, rtol, atol)
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@ -170,6 +170,7 @@ def test_optimizer32bit(dim1, dim2, gtype, optim_name):
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bnb_optimizer.step()
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torch_optimizer.step()
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for name1, name2 in str2statenames[optim_name]:
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torch.testing.assert_allclose(
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torch_optimizer.state[p1][name1],
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@ -178,7 +179,9 @@ def test_optimizer32bit(dim1, dim2, gtype, optim_name):
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rtol=rtol,
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)
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torch.testing.assert_allclose(p1, p2.float(), atol=atol, rtol=rtol)
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# since Lion can have pretty noisy updates where things lie at the boundary
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# allow up to 10 errors for Lion
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assert_most_approx_close(p1, p2.float(), atol, rtol, max_error_count=10)
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if i % (k // 5) == 0 and i > 0:
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path = get_temp_dir()
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@ -188,14 +191,15 @@ def test_optimizer32bit(dim1, dim2, gtype, optim_name):
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bnb_optimizer = str2optimizers[optim_name][1]([p2])
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bnb_optimizer.load_state_dict(torch.load(join(path, "opt.pt")))
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rm_path(path)
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torch.testing.assert_allclose(p1, p2.float(), atol=atol, rtol=rtol)
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# since Lion can have pretty noisy updates where things lie at the boundary
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# allow up to 10 errors for Lion
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assert_most_approx_close(p1, p2.float(), atol, rtol, max_error_count=10)
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for name1, name2 in str2statenames[optim_name]:
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torch.testing.assert_allclose(
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torch_optimizer.state[p1][name1],
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bnb_optimizer.state[p2][name2],
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atol=atol,
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rtol=rtol,
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)
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# since Lion can have pretty noisy updates where things lie at the boundary
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# allow up to 10 errors for Lion
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assert_most_approx_close(torch_optimizer.state[p1][name1], bnb_optimizer.state[p2][name2],
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atol=atol, rtol=rtol,
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max_error_count=10)
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if gtype == torch.float16:
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# the adam buffers should also be close because they are 32-bit
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@ -343,7 +347,7 @@ def test_optimizer8bit(dim1, dim2, gtype, optim_name):
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dequant_states.append(s1.clone())
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err = torch.abs(p1 - p2)
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relerr = err / torch.abs(p1)
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relerr = err / (torch.abs(p1)+1e-9)
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assert err.mean() < 0.0001
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assert relerr.mean() < 0.001
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