[ONNX] Fix the incorrect conversion of the Gemm into a MatMulNBits

src/nncf/quantization/algorithms/weight_compression/onnx_backend.py

@@ -231,16 +231,27 @@ def transform_model(
             node = wc_params.node_with_weight
             weight = self.get_weight(node, wc_params.weight_port_id, model, graph)
             precomputed_compressed_weights = precomputed_compressed_weights or {}
+
+            dequantize_block_size = max(compression_config.group_size, 0)  # 0 - is no block wise quantization
+            dequantize_axis = (
+                get_weight_quantization_axis(node, wc_params.weight_port_id) if dequantize_block_size <= 0 else 0
+            )  # axis = 0 when blockwise
+
+            reduction_axes = wc_params.reduction_axes
+            if node.metatype == onnx_metatypes.ONNXGemmMetatype and opset_version < 21 and dequantize_block_size > 0:
+                attr_name = "transB" if wc_params.weight_port_id == 1 else "transA"
+                transpose = node.layer_attributes.node_attrs[attr_name]
+                weight = fns.transpose(weight) if transpose else weight
+                (axis,) = reduction_axes
+                axis = (axis + 1) % 2 if transpose else axis
+                reduction_axes = (axis,)
+
             compressed_weight = compress_weight(
                 Tensor(weight),
-                wc_params.reduction_axes,
+                reduction_axes,
                 compression_config,
                 precomputed_compressed_weights.get(wc_params.weight_name),
             )
-            dequantize_block_size = max(compression_config.group_size, 0)  # 0 - is no block wise quantization
-            dequantize_axis = (
-                get_weight_quantization_axis(node, wc_params.weight_port_id) if dequantize_block_size <= 0 else 0
-            )  # axis = 0 when blockwise
 
             # NOTE: The `DequantizeLinear` operation supports the `block_size` attribute only starting from opset 21.
             # For opsets earlier than 21, we use the `MatMulNBits` operation from ONNX Runtime contrib operators.
@@ -428,16 +439,21 @@ def _replace_matmul_with_matmulnbits(
 
         original_matmul = self.name_to_node_map[weight_compression_parameters.node_with_weight.node_name]
 
-        activation_input_name = None
-        for input_name in original_matmul.input:
-            if input_name != weight_name:
-                activation_input_name = input_name
-        assert activation_input_name is not None, "Activation input name not found in original matmul node"
+        # Composing operation inputs: A, B, scales, zero_points[optional], g_idx[optional, deprecated], bias
+        bias_name = None
+        if weight_compression_parameters.node_with_weight.layer_attributes.has_bias():
+            bias_name = weight_compression_parameters.node_with_weight.layer_attributes.bias_attrs["name"]
+
+        activation_input_name = next(name for name in original_matmul.input if name not in [weight_name, bias_name])
 
-        # Create MatMulNBits
         inputs = [activation_input_name, quantized_weight_name, scale_name]
         if zero_point is not None:
             inputs.append(zero_point_name)
+        if bias_name:
+            if zero_point is None:
+                inputs.append("")
+            inputs.append("")  # g_idx
+            inputs.append(bias_name)
 
         K, N = orig_weight.shape[0], orig_weight.shape[1]
         matmul_n_bits = helper.make_node(