Isolated CUDASetup logging; all tests green.

1 files changed, 0 insertions, 71 deletions

diff --git a/tests/test_modules.py b/tests/test_modules.py
index 2879846..ccbf670 100644
--- a/tests/test_modules.py
+++ b/tests/test_modules.py
@@ -310,77 +310,6 @@ class Linear8bit(nn.Module):
         return LinearFunction.apply(x, self.weight, self.bias, self.args)
 
 
-def test_linear8bit():
-    l0 = torch.nn.Linear(32, 64).cuda().half()
-    l1 = bnb.nn.Linear8bit(32, 64, args=get_args()).cuda().half()
-    l2 = Linear8bit(32, 64, args=get_args()).cuda().half()
-    l3 = bnb.nn.Linear8bitLt(32, 64).cuda().half()
-
-    l0.weight.data = l2.weight.data.clone()
-    l0.bias.data = l2.bias.data.clone()
-
-    l1.weight.data = l2.weight.data.clone()
-    l1.bias.data = l2.bias.data.clone()
-
-    l3.weight.data = l2.weight.data.clone()
-    l3.bias.data = l2.bias.data.clone()
-
-    for i in range(100):
-        b1 = torch.randn(16, 8, 32, device="cuda").half()
-        t = torch.randn(16, 8, 64, device="cuda").half()
-        b2 = b1.clone()
-        b3 = b1.clone()
-        b0 = b1.clone()
-
-        o0 = l0(b0)
-        o1 = l1(b1)
-        o2 = l2(b2)
-        o3 = l3(b3)
-
-        assert_all_approx_close(o1, o2, atol=0.013, rtol=0.05, count=1)
-        assert_all_approx_close(o3, o2, atol=0.013, rtol=0.05, count=1)
-
-        loss0 = torch.nn.functional.mse_loss(o0, t)
-        loss1 = torch.nn.functional.mse_loss(o1, t)
-        loss2 = torch.nn.functional.mse_loss(o2, t)
-        loss3 = torch.nn.functional.mse_loss(o3, t)
-
-        loss0.backward()
-        loss1.backward()
-        loss2.backward()
-        loss3.backward()
-
-        assert_all_approx_close(
-            l1.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2
-        )
-        assert_all_approx_close(
-            l3.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2
-        )
-        assert_all_approx_close(
-            l1.weight.grad, l2.weight.grad, atol=0.013, rtol=0.05, count=2
-        )
-        assert_all_approx_close(
-            l3.weight.grad, l2.weight.grad, atol=0.013, rtol=0.05, count=2
-        )
-
-        err1 = torch.abs(l0.weight.grad - l1.weight.grad).mean().item()
-        err2 = torch.abs(l0.weight.grad - l2.weight.grad).mean().item()
-        err3 = torch.abs(l0.weight.grad - l3.weight.grad).mean().item()
-
-        assert err1 * 0.8 < err2
-        assert err2 * 0.8 < err3
-        assert err3 * 0.8 < err1
-
-        l0.weight.grad = None
-        l1.weight.grad = None
-        l2.weight.grad = None
-        l3.weight.grad = None
-        l0.bias.grad = None
-        l1.bias.grad = None
-        l2.bias.grad = None
-        l3.bias.grad = None
-
-
 threshold = [0.0, 3.0]
 values = threshold
 names = ["threshold_{0}".format(vals) for vals in values]