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[WebNN EP] Make sure optional input is provided (#19686)

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Some optional input is presented as empty string, we should not only
check if the input size is correct, but also check if the optional input
is not empty.

e.g. Pad node has empty optional input in sam-b-encoder.onnx model:
<img width="514" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/3271201/cc3b06fe-46b9-4ee7-aca5-157bdf112856">
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Wanming Lin 2024-03-07 08:19:59 +08:00 committed by GitHub
parent f2dc725b33
commit 1ce5bfb0ec
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4 changed files with 6 additions and 6 deletions
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6
onnxruntime/core/providers/webnn/builders/impl/pad_op_builder.cc
View file

@ -88,15 +88,15 @@ Status PadOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
const auto& pads_tensor = *initializers.at(input_defs[1]->Name());
ORT_RETURN_IF_NOT(ReadIntArrayFrom1DTensor(pads_tensor, pads, logger), "Error while read pads tensor");
// Constant value and axes are optional.
if (input_defs.size() >= 3) {
// Constant value and axes are optional. Make sure they are not empty.
if (!GetTensorName(input_defs, 2).empty()) {
const auto value_tensor = *initializers.at(input_defs[2]->Name());
emscripten::val value = emscripten::val::object();
ORT_RETURN_IF_NOT(ReadScalarTensorData(value_tensor, value, logger), "Cannot read constant value");
options.set("value", value);
}
if (input_defs.size() == 4) {
if (!GetTensorName(input_defs, 3).empty()) {
const auto input_rank = input_shape.size();
std::vector<int64_t> axes;
const auto& axes_tensor = *initializers.at(input_defs[3]->Name());

2
onnxruntime/core/providers/webnn/builders/impl/reduction_op_builder.cc
View file

@ -65,7 +65,7 @@ Status ReductionOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
if (opset >= 18 || (op_type == "ReduceSum" && opset >= 13)) {
// 'axes' is an optional input.
const auto noop_with_empty_axes = helper.Get("noop_with_empty_axes", 0);
if (input_defs.size() > 1) {
if (!GetTensorName(input_defs, 1).empty()) {
// Optional input axes is provided, use axes initializer data.
const auto& initializers(model_builder.GetInitializerTensors());
const auto& axes_tensor = *initializers.at(input_defs[1]->Name());

2
onnxruntime/core/providers/webnn/builders/impl/split_op_builder.cc
View file

@ -57,7 +57,7 @@ Status SplitOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
axis = SafeInt<int32_t>(HandleNegativeAxis(axis, rank));
options.set("axis", axis);
if (input_defs.size() == 2) {
if (!GetTensorName(input_defs, 1).empty()) {
// Inputs contains optional 'split' input
std::vector<int32_t> splits;
const auto& initializers(model_builder.GetInitializerTensors());

2
onnxruntime/core/providers/webnn/builders/impl/squeeze_unsqueeze_op_builder.cc
View file

@ -58,7 +58,7 @@ Status SqueezeUnsqueezeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_buil
std::vector<int32_t> axes_data;
auto rank = input_rank;
if (node.SinceVersion() >= 13 && input_defs.size() > 1) {
if (node.SinceVersion() >= 13 && !GetTensorName(input_defs, 1).empty()) {
// Input axes is provided, use axes initializer data.
const auto& initializers = model_builder.GetInitializerTensors();
const auto& axes_tensor = *initializers.at(input_defs[1]->Name());