fix code style after rebase (#7)

tests/openvino/utils_tests.py

@@ -295,58 +295,3 @@ def compare_num_quantized_nodes_per_model(
         expected_num_weight_nodes.update({k: 0 for k in set(num_weight_nodes) - set(expected_num_weight_nodes)})
         actual_num_weights_per_model.append(num_weight_nodes)
     test_case.assertEqual(expected_num_weight_nodes_per_model, actual_num_weights_per_model)
-
-
-@contextmanager
-def mock_torch_cuda_is_available(to_patch):
-    original_is_available = torch.cuda.is_available
-    if to_patch:
-        torch.cuda.is_available = lambda: True
-    try:
-        yield
-    finally:
-        if to_patch:
-            torch.cuda.is_available = original_is_available
-
-
-@contextmanager
-def patch_awq_for_inference(to_patch):
-    orig_gemm_forward = None
-    if to_patch:
-        # patch GEMM module to allow inference without CUDA GPU
-        from awq.modules.linear.gemm import WQLinearMMFunction
-        from awq.utils.packing_utils import dequantize_gemm
-
-        def new_forward(
-            ctx,
-            x,
-            qweight,
-            qzeros,
-            scales,
-            w_bit=4,
-            group_size=128,
-            bias=None,
-            out_features=0,
-        ):
-            ctx.out_features = out_features
-
-            out_shape = x.shape[:-1] + (out_features,)
-            x = x.to(torch.float16)
-
-            out = dequantize_gemm(qweight, qzeros, scales, w_bit, group_size)
-            out = torch.matmul(x, out)
-
-            out = out + bias if bias is not None else out
-            out = out.reshape(out_shape)
-
-            if len(out.shape) == 2:
-                out = out.unsqueeze(0)
-            return out
-
-        orig_gemm_forward = WQLinearMMFunction.forward
-        WQLinearMMFunction.forward = new_forward
-    try:
-        yield
-    finally:
-        if orig_gemm_forward is not None:
-            WQLinearMMFunction.forward = orig_gemm_forward