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@ -545,6 +545,7 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
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.to(torch.float16)
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.to("cuda")
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)
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w1, w2 = mlp.fc1.weight.clone(), mlp.fc2.weight.clone()
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for i in range(100):
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b1 = torch.randn(16, 8, 32, device="cuda").half()
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@ -567,8 +568,15 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
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assert o1.requires_grad
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grad_proj = torch.randn_like(o1)
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mlp.zero_grad()
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(o1 * grad_proj).sum().backward()
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grad_ref = grad_proj.flatten(2) @ w2 @ w1
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assert torch.allclose(b1.grad, grad_ref)
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