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# Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
# imports
from onnx import TensorProto
from onnx.helper import (
make_model, make_node, make_graph,
make_tensor_value_info, make_value_info)
from onnx.checker import check_model
# inputs
# TensorProto.DOUBLE is the element type, [128] the shape
A = make_tensor_value_info('input_ids', TensorProto.DOUBLE, [128])
B = make_tensor_value_info('attention_mask', TensorProto.DOUBLE, [128])
C = make_tensor_value_info('token_type_ids', TensorProto.DOUBLE, [128])
# outputs, the shape is defined
Y = make_tensor_value_info('vector_Y', TensorProto.DOUBLE, [128])
S = make_tensor_value_info('score', TensorProto.DOUBLE, [1])
# Creates node defined by the operator type, inputs, outputs, and possibly options
node1 = make_node('Mul', ['input_ids', 'attention_mask'], ['masked'])
node2 = make_node('Add', ['masked', 'token_type_ids'], ['vector_Y'])
node3 = make_node('ReduceSum', inputs=['vector_Y'], outputs=['score'], keepdims=1)
# from nodes to graph
# the graph is built from the list of nodes, the list of inputs,
# the list of outputs and a name.
graph = make_graph([node1, node2, node3], # nodes
'ranking_model', # a name
[A, B, C], # inputs
[S]) # outputs
# onnx graph to model
onnx_model = make_model(graph)
# ensure we do not get too new opset version:
del onnx_model.opset_import[:]
opset = onnx_model.opset_import.add()
opset.version = 17
# Let's check the model is consistent, this function is described in
# section Checker and Shape Inference.
check_model(onnx_model)
# The serialization
with open("ranking_model.onnx", "wb") as f:
f.write(onnx_model.SerializeToString())
# the work is done, let's display it...
print(onnx_model)
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