Extend LoRA adapter tests with transpose scenarios and skip unsupported cases

Author: Shehrozkashif

tests/openvino/native/quantization/test_weights_compression.py

@@ -1612,12 +1612,27 @@ def test_call_max_var_criterion_with_dataset_gptq_neg_group_size(mode):
 
 
 @pytest.mark.parametrize(
-    "params, transpose_b",
-    ((None, True), (LoraParams(adapter_rank=4, use_int8_adapters=False), False)),
+    "params, transpose_a, transpose_b",
+    (
+        (None, False, True),  # original
+        (LoraParams(adapter_rank=4, use_int8_adapters=False), False, False),  # original
+        pytest.param(
+            LoraParams(adapter_rank=4, use_int8_adapters=False),
+            True,
+            False,
+            marks=pytest.mark.skip(reason="LoRA correction does not support transpose_a=True yet"),
+        ),
+        pytest.param(
+            LoraParams(adapter_rank=8, use_int8_adapters=True),
+            True,
+            True,
+            marks=pytest.mark.skip(reason="LoRA correction does not support transpose_a=True or transpose_b=True yet"),
+        ),
+    ),
 )
-def test_lora_adapters_in_the_graph(params, transpose_b):
+def test_lora_adapters_in_the_graph(params, transpose_a, transpose_b):
     advanced_parameters = CompressionParams() if params is None else CompressionParams(lora_correction_params=params)
-    model = LMLinearModel(transpose_b=transpose_b)
+    model = LMLinearModel(transpose_a=transpose_a, transpose_b=transpose_b)
     ov_model = model.ov_model
     dataset = Dataset(np.ones(inp.shape) for inp in ov_model.inputs)