Add force_no_igemmlt to test params

tests/test_linear8bitlt.py

@@ -69,9 +69,9 @@ def test_linear_no_igemmlt():
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
-@pytest.mark.parametrize("has_fp16_weights, serialize_before_forward, deserialize_before_cuda",
-                         list(product([False, True], [False, True], [False, True])))
-def test_linear_serialization(has_fp16_weights, serialize_before_forward, deserialize_before_cuda):
+@pytest.mark.parametrize("has_fp16_weights, serialize_before_forward, deserialize_before_cuda, force_no_igemmlt",
+                         list(product([False, True], [False, True], [False, True], [False, True])))
+def test_linear_serialization(has_fp16_weights, serialize_before_forward, deserialize_before_cuda, force_no_igemmlt):
     linear = torch.nn.Linear(32, 96)
     x = torch.randn(3, 32, dtype=torch.half)
 
@@ -82,6 +82,9 @@ def test_linear_serialization(has_fp16_weights, serialize_before_forward, deseri
         has_fp16_weights=has_fp16_weights,
         threshold=6.0,
     )
+    if force_no_igemmlt:
+        linear_custom.state.force_no_igemmlt = True
+
     linear_custom.weight = bnb.nn.Int8Params(
         linear.weight.data.clone(), requires_grad=has_fp16_weights, has_fp16_weights=has_fp16_weights
     )
@@ -118,6 +121,8 @@ def test_linear_serialization(has_fp16_weights, serialize_before_forward, deseri
         has_fp16_weights=has_fp16_weights,
         threshold=6.0,
     )
+    if force_no_igemmlt:
+        new_linear_custom.state.force_no_igemmlt = True
 
     if deserialize_before_cuda:
         with nullcontext() if has_fp16_weights else pytest.raises(RuntimeError):