def cast_slice_nodes_inputs_to_int32()

in optimum/onnx/graph_transformations.py [0:0]

• 20 lines of code
• 10 McCabe index (conditional complexity)

def cast_slice_nodes_inputs_to_int32(model: ModelProto) -> ModelProto:
    """
    Convert node inputs of `Slice` nodes from int64 to int32, casting the out of range values.

    The constant node inputs are stored in `model.graph.node`, and the sole way to check which node
    they are consumed by is to iterate over nodes and check `node.input` for a match.

    Note that constant inputs to nodes as `Squeeze`, `Unsqueeze` can not be converted to int32, as the
    these operators explicitely expect int64 inputs according to ONNX specifications:
    https://github.com/onnx/onnx/blob/main/docs/Operators.md
    """
    map_input_node = {}
    map_node_inputs = {}

    for node in model.graph.node:
        for input_name in node.input:
            map_input_node[input_name] = {"op_type": node.op_type, "node_name": node.name}
        map_node_inputs[node.name] = node.input

    for node in model.graph.node:
        if (
            node.op_type == "Constant"
            and node.attribute[0].t.data_type == 7  # int64
            and f"{node.name}_output_0" in map_input_node
            and map_input_node[node.name + "_output_0"]["op_type"] == "Slice"
        ):
            logger.debug(f"Converting {node.name} to int32")

            # `Slice` node is homogeneous (requires parameters of same type), hence cast to int32 only if all of its inputs are constants
            # refer to onnx/defs/schema.h
            cast = all(
                "Constant" in inp for inp in map_node_inputs[map_input_node[node.name + "_output_0"]["node_name"]][1:]
            )
            cast_int64_tensorproto_to_int32(node.attribute[0].t, cast=cast)

    return model