Inference for model with function and optional inputs does not work

[Johansmm]

In ONNX models with function that uses operations with optional inputs, the inference fails when optional input does not take any value. The expected behavior would be that if a model does not define such an input, this input should not be passed to the operation.

Any idea how can I resolve this problem ? A sample code would be

import numpy as np

from onnx import TensorProto as TP
from onnx.helper import (make_tensor_value_info, make_function, make_opsetid,
                         make_node, make_graph, make_model)
from onnx.checker import check_model
from onnx.defs import onnx_opset_version
from onnx.reference.reference_evaluator import ReferenceEvaluator

from onnxruntime import InferenceSession

# Define function
DOMAIN = "custom"
nodes_fn = [make_node("Conv", ["X", "W", "B"], ["Y"])]
func = make_function(domain=DOMAIN,
                     fname="custom_conv",
                     inputs=["X", "W", "B"],
                     outputs=["Y"],
                     nodes=nodes_fn,
                     opset_imports=[make_opsetid("", onnx_opset_version())])

# Define ValueInfoProto
X = make_tensor_value_info("X", TP.FLOAT, ["N", 3, 64, 64])
W = make_tensor_value_info("W", TP.FLOAT, [10, 3, 3, 3])
B = make_tensor_value_info("B", TP.FLOAT, [10])
Y = make_tensor_value_info("Y", TP.FLOAT, ["N", 10, 62, 62])

# First model : with bias
nodes = [make_node("custom_conv", ["X", "W", "B"], ["Y"], domain=DOMAIN)]
graph = make_graph(nodes, "custom-biased-conv", [X, W, B], [Y])
model_b = make_model(graph, opset_imports=[make_opsetid(DOMAIN, 1)], functions=[func])
check_model(model_b)

# Second model : without bias
nodes = [make_node("custom_conv", ["X", "W", ""], ["Y"], domain=DOMAIN)]
graph = make_graph(nodes, "custom-conv", [X, W], [Y])
model = make_model(graph, opset_imports=[make_opsetid(DOMAIN, 1)], functions=[func])
check_model(model)

# Create test samples
feed_b = {"X": np.random.rand(1, 3, 64, 64).astype("float32"),
          "W": np.random.rand(10, 3, 3, 3).astype("float32"),
          "B": np.random.rand(10).astype("float32")}
feed = {"X": feed_b["X"], "W": feed_b["W"]}

# Run samples in ReferenceEvaluator (this works)
sess = ReferenceEvaluator(model)
sess_b = ReferenceEvaluator(model_b)

out = sess.run(None, feed)[0]
out_b = sess_b.run(None, feed_b)[0]
np.testing.assert_equal(out + feed_b["B"].reshape(1, -1, 1, 1), out_b)

# Run samples in onnxruntime (fails in InferenceSession, when no bias)
sess = InferenceSession(model.SerializeToString(), providers=['CPUExecutionProvider'],
                        disabled_optimizers=False)
sess_b = InferenceSession(model_b.SerializeToString(), providers=['CPUExecutionProvider'])

out = sess.run(None, feed)[0]
out_b = sess_b.run(None, feed_b)[0]
np.testing.assert_equal(out + feed_b["B"].reshape(1, -1, 1, 1), out_b)

It raises the error:

    sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Node () Op (custom_conv) [TypeInferenceError] Input 2 type is missing.

[xadupre]

Which version of onnxruntime are you using?

[Johansmm]

Hello @xadupre . I tried in onnxruntime 1.14.0 and 1.15.0.