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[WebNN] Support SkipSimplifiedLayerNormalization op #23151

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[WebNN] Support SkipSimplifiedLayerNormalization op #23151

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c8d9efc
[WebNN] Support SkipSimplifiedLayerNormalization op
Honry Dec 19, 2024
49801bd
Addressed comments
Honry Dec 20, 2024
136d094
Fix domain of SimplifiedLayerNormalization
Honry Dec 20, 2024
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3 changes: 2 additions & 1 deletion js/web/docs/webnn-operators.md
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Expand Up @@ -89,9 +89,10 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
| ScatterElements | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterElements | ✗ | ✓ | Only supports 'reduction' == 'none' |
| ScatterND | ai.onnx(11-12, 13-15, 16-17, 18+) | scatterND | ✗ | ✓ | Only supports 'reduction' == 'none' |
| Shape | ai.onnx(7-12, 13-14, 15-18, 19-20, 21+) | slice | ✓ | ✓ | |
| SimplifiedLayerNormalization | ai.onnx(1+) | pow + reduceMean + add + sqrt + div + mul | ✓ | ✓ | |
| SimplifiedLayerNormalization | ai.onnx(1+) | pow, reduceMean, add, sqrt, div, mul | ✓ | ✓ | |
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@fdwr fdwr Dec 21, 2024 •
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Is there an active ONNX CR for this? Otherwise SLN is Microsoft-specific (not an ONNX operator). https://github.com/onnx/onnx/blob/main/docs/Operators.md

| Sigmoid | ai.onnx(7-12, 13+) | sigmoid | ✓ | ✓ | |
| Sign | ai.onnx(9-12, 13+) | sign | ✓ | ✓ | |
| SkipSimplifiedLayerNormalization | com.microsoft(1+) | pow, reduceMean, add, sqrt, div, mul | ✓ | ✓ | |
| Softplus | ai.onnx(7+) | softplus | ✓ | ✓ | |
| Softsign | ai.onnx(7+) | softsign | ✓ | ✓ | |
| Sin | ai.onnx(7+) | sin | ✓ | ✓ | |
Expand Down
5 changes: 5 additions & 0 deletions onnxruntime/core/providers/webnn/builders/helper.h
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Expand Up @@ -181,6 +181,10 @@ inline bool IsEmptyTensor(const InitializedTensorSet& initializers, const std::s
return std::any_of(dims.begin(), dims.end(), [](auto d) { return d == 0; });
}

inline bool TensorExists(const ConstPointerContainer<std::vector<NodeArg*>>& defs, size_t tensor_index) noexcept {
return tensor_index < defs.size() && defs[tensor_index]->Exists();
}

bool IsTensorShapeSupported(const NodeArg& node_arg, const std::string& parent_name,
const logging::Logger& logger, bool allow_empty_input = false);

Expand Down Expand Up @@ -278,6 +282,7 @@ static const InlinedHashMap<std::string, std::string> op_map = {
{"Softplus", "softplus"},
{"Softsign", "softsign"},
{"Sin", "sin"},
{"SkipSimplifiedLayerNormalization", "layerNormalization"},
{"Slice", "slice"},
{"Softmax", "softmax"},
{"Split", "split"},
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc
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Expand Up @@ -405,8 +405,8 @@ bool ConvOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initia
int32_t input1_type; // weight data type
int32_t input2_type; // bias or x_zero_point data type
int32_t input3_type; // w_zero_point data type
bool has_input2 = input_defs.size() > 2 && input_defs[2]->Exists();
bool has_input3 = input_defs.size() > 3 && input_defs[3]->Exists();
bool has_input2 = TensorExists(input_defs, 2);
bool has_input3 = TensorExists(input_defs, 3);

if (!GetType(*input_defs[0], input0_type, logger) ||
!GetType(*input_defs[1], input1_type, logger) ||
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/einsum_op_builder.cc
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Expand Up @@ -742,7 +742,7 @@ bool EinsumOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* init
const auto& op_type = node.OpType();
int32_t input0_type;
int32_t input1_type;
bool has_input1 = input_defs.size() > 1 && input_defs[1]->Exists();
bool has_input1 = TensorExists(input_defs, 1);

if (!GetType(*input_defs[0], input0_type, logger) ||
(has_input1 && !GetType(*input_defs[1], input1_type, logger))) {
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/gemm_op_builder.cc
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Expand Up @@ -223,8 +223,8 @@ bool GemmOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initia
int32_t input1_type; // B data type
int32_t input2_type; // C or a_zero_point data type
int32_t input3_type; // b_zero_point data type
bool has_input2 = input_defs.size() > 2 && input_defs[2]->Exists();
bool has_input3 = input_defs.size() > 3 && input_defs[3]->Exists();
bool has_input2 = TensorExists(input_defs, 2);
bool has_input3 = TensorExists(input_defs, 3);

if (!GetType(*input_defs[0], input0_type, logger) ||
!GetType(*input_defs[1], input1_type, logger) ||
Expand Down
20 changes: 10 additions & 10 deletions onnxruntime/core/providers/webnn/builders/impl/gru_op_builder.cc
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Expand Up @@ -33,7 +33,7 @@ class GruOpBuilder : public BaseOpBuilder {
};

void GruOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
if (node.InputDefs().size() > 4 && node.InputDefs()[4]->Exists()) {
if (TensorExists(node.InputDefs(), 4)) {
model_builder.AddInitializerToSkip(node.InputDefs()[4]->Name()); // sequence_lens
model_builder.AddInputToSkip(node.InputDefs()[4]->Name());
}
Expand All @@ -56,7 +56,7 @@ Status GruOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const No
options.set("label", node.Name());
options.set("layout", emscripten::val("zrn"));

if (input_defs.size() > 3 && input_defs[3]->Exists()) {
if (TensorExists(input_defs, 3)) {
emscripten::val bias = model_builder.GetOperand(input_defs[3]->Name());
emscripten::val split_options = emscripten::val::object();
split_options.set("label", node.Name() + "_split");
Expand All @@ -68,16 +68,16 @@ Status GruOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const No
options.set("recurrentBias", splitted_biases[1]);
}

if (input_defs.size() > 5 && input_defs[5]->Exists()) {
if (TensorExists(input_defs, 5)) {
options.set("initialHiddenState", model_builder.GetOperand(input_defs[5]->Name()));
}

bool linear_before_reset = !!helper.Get("linear_before_reset ", 0);
options.set("resetAfter", linear_before_reset);

const auto& output_defs = node.OutputDefs();
bool has_Y = output_defs.size() > 0 && output_defs[0]->Exists();
bool has_Y_h = output_defs.size() > 1 && output_defs[1]->Exists();
bool has_Y = TensorExists(output_defs, 0);
bool has_Y_h = TensorExists(output_defs, 1);
options.set("returnSequence", has_Y);

std::string direction = helper.Get("direction", "forward");
Expand Down Expand Up @@ -134,7 +134,7 @@ bool GruOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers, c
}
int32_t steps = static_cast<int32_t>(input_shape[0]);

if (input_defs.size() > 4 && input_defs[4]->Exists()) {
if (TensorExists(input_defs, 4)) {
if (!Contains(initializers, input_defs[4]->Name())) {
LOGS(logger, ERROR) << "GRU: sequence_lens must be constant";
return false;
Expand Down Expand Up @@ -196,8 +196,8 @@ bool GruOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initial
int32_t input_R_type = 0; // recurrent weight data type
int32_t input_B_type = 0; // bias data type
int32_t input_initial_h_type = 0; // initial hidden state data type
bool has_input_B = input_defs.size() > 3 && input_defs[3]->Exists();
bool has_input_initial_h = input_defs.size() > 5 && input_defs[5]->Exists();
bool has_input_B = TensorExists(input_defs, 3);
bool has_input_initial_h = TensorExists(input_defs, 5);

if (!GetType(*input_defs[0], input_X_type, logger) ||
!GetType(*input_defs[1], input_W_type, logger) ||
Expand Down Expand Up @@ -229,8 +229,8 @@ bool GruOpBuilder::HasSupportedOutputsImpl(const Node& node,
const auto& op_type = node.OpType();
int32_t Y_type = 0;
int32_t Y_h_type = 0;
bool has_Y = output_defs.size() > 0 && output_defs[0]->Exists();
bool has_Y_h = output_defs.size() > 1 && output_defs[1]->Exists();
bool has_Y = TensorExists(output_defs, 0);
bool has_Y_h = TensorExists(output_defs, 1);

bool Y_supported = has_Y && GetType(*output_defs[0], Y_type, logger);
bool Y_h_supported = has_Y_h && GetType(*output_defs[1], Y_h_type, logger);
Expand Down
32 changes: 16 additions & 16 deletions onnxruntime/core/providers/webnn/builders/impl/lstm_op_builder.cc
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Expand Up @@ -32,7 +32,7 @@ class LstmOpBuilder : public BaseOpBuilder {
};

void LstmOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
if (node.InputDefs().size() > 4 && node.InputDefs()[4]->Exists()) {
if (TensorExists(node.InputDefs(), 4)) {
model_builder.AddInitializerToSkip(node.InputDefs()[4]->Name()); // sequence_lens
model_builder.AddInputToSkip(node.InputDefs()[4]->Name());
}
Expand All @@ -56,7 +56,7 @@ Status LstmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
options.set("label", node.Name());
options.set("layout", emscripten::val("iofg"));

if (input_defs.size() > 3 && input_defs[3]->Exists()) {
if (TensorExists(input_defs, 3)) {
emscripten::val bias = model_builder.GetOperand(input_defs[3]->Name());
emscripten::val split_options = emscripten::val::object();
split_options.set("axis", 1);
Expand All @@ -67,13 +67,13 @@ Status LstmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
options.set("bias", splitted_biases[0]);
options.set("recurrentBias", splitted_biases[1]);
}
if (input_defs.size() > 5 && input_defs[5]->Exists()) {
if (TensorExists(input_defs, 5)) {
options.set("initialHiddenState", model_builder.GetOperand(input_defs[5]->Name()));
}
if (input_defs.size() > 6 && input_defs[6]->Exists()) {
if (TensorExists(input_defs, 6)) {
options.set("initialCellState", model_builder.GetOperand(input_defs[6]->Name()));
}
if (input_defs.size() > 7 && input_defs[7]->Exists()) {
if (TensorExists(input_defs, 7)) {
options.set("peepholeWeight", model_builder.GetOperand(input_defs[7]->Name()));
}

Expand All @@ -87,9 +87,9 @@ Status LstmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
}

const auto& output_defs = node.OutputDefs();
bool has_Y = output_defs.size() > 0 && output_defs[0]->Exists();
bool has_Y_h = output_defs.size() > 1 && output_defs[1]->Exists();
bool has_Y_c = output_defs.size() > 2 && output_defs[2]->Exists();
bool has_Y = TensorExists(output_defs, 0);
bool has_Y_h = TensorExists(output_defs, 1);
bool has_Y_c = TensorExists(output_defs, 2);
options.set("returnSequence", has_Y);

if (helper.HasAttr("activations")) {
Expand Down Expand Up @@ -140,7 +140,7 @@ bool LstmOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers,
}
int32_t steps = static_cast<int32_t>(input_shape[0]);

if (input_defs.size() > 4 && input_defs[4]->Exists()) {
if (TensorExists(input_defs, 4)) {
if (!Contains(initializers, input_defs[4]->Name())) {
LOGS(logger, ERROR) << "LSTM: sequence_lens must be constant";
return false;
Expand Down Expand Up @@ -210,10 +210,10 @@ bool LstmOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet& /* initia
int32_t input5_type = 0; // initialHiddenState data type
int32_t input6_type = 0; // initialCellState data type
int32_t input7_type = 0; // peepholeWeight data type
bool has_input3 = input_defs.size() > 3 && input_defs[3]->Exists();
bool has_input5 = input_defs.size() > 5 && input_defs[5]->Exists();
bool has_input6 = input_defs.size() > 6 && input_defs[6]->Exists();
bool has_input7 = input_defs.size() > 7 && input_defs[7]->Exists();
bool has_input3 = TensorExists(input_defs, 3);
bool has_input5 = TensorExists(input_defs, 5);
bool has_input6 = TensorExists(input_defs, 6);
bool has_input7 = TensorExists(input_defs, 7);

if (!GetType(*input_defs[0], input0_type, logger) ||
!GetType(*input_defs[1], input1_type, logger) ||
Expand Down Expand Up @@ -253,9 +253,9 @@ bool LstmOpBuilder::HasSupportedOutputsImpl(const Node& node,
int32_t Y_type = 0;
int32_t Y_h_type = 0;
int32_t Y_c_type = 0;
bool has_Y = output_defs.size() > 0 && output_defs[0]->Exists();
bool has_Y_h = output_defs.size() > 1 && output_defs[1]->Exists();
bool has_Y_c = output_defs.size() > 2 && output_defs[2]->Exists();
bool has_Y = TensorExists(output_defs, 0);
bool has_Y_h = TensorExists(output_defs, 1);
bool has_Y_c = TensorExists(output_defs, 2);

if (has_Y && GetType(*output_defs[0], Y_type, logger)) {
return IsDataTypeSupportedByOp(op_type, Y_type, wnn_limits, "outputs", "Y", logger);
Expand Down
90 changes: 65 additions & 25 deletions onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
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Expand Up @@ -34,6 +34,7 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
const logging::Logger& logger) const {
const auto& op_type = node.OpType();
const auto& input_defs = node.InputDefs();
const auto& output_defs = node.OutputDefs();
ORT_RETURN_IF_NOT(input_defs.size() >= 2, op_type, " requires at least two inputs.");

emscripten::val input = model_builder.GetOperand(input_defs[0]->Name());
Expand All @@ -45,7 +46,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
options.set("label", node.Name());

std::vector<int64_t> scale_shape;
ORT_RETURN_IF_NOT(GetShape(*input_defs[1], scale_shape, logger), "Cannot get scale shape");
const size_t scale_input_index = op_type == "SkipSimplifiedLayerNormalization" ? 2 : 1;
ORT_RETURN_IF_NOT(GetShape(*input_defs[scale_input_index], scale_shape, logger), "Cannot get scale shape");
const auto scale_size = scale_shape.size();
// Except LayerNormalization, other normalization ops' scale input should be 1-D.
if (op_type == "LayerNormalization") {
Expand All @@ -55,19 +57,17 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
ORT_RETURN_IF_NOT(scale_size == 1, "The scale size should be one.");
}

if (input_defs.size() >= 3 && !input_defs[2]->Name().empty()) {
emscripten::val scale = model_builder.GetOperand(input_defs[scale_input_index]->Name());
options.set("scale", scale);

const size_t bias_input_index = op_type == "SkipSimplifiedLayerNormalization" ? 3 : 2;
emscripten::val bias = emscripten::val::undefined();
if (TensorExists(input_defs, bias_input_index)) {
// Bias input exists, and bias's shape should be the same as scale's shape.
std::vector<int64_t> bias_shape;
ORT_RETURN_IF_NOT(GetShape(*input_defs[2], bias_shape, logger), "Cannot get bias shape");
ORT_RETURN_IF_NOT(GetShape(*input_defs[bias_input_index], bias_shape, logger), "Cannot get bias shape");
ORT_RETURN_IF_NOT(bias_shape == scale_shape, "The bias' shape should be equal to scale's shape.");
}

emscripten::val scale = model_builder.GetOperand(input_defs[1]->Name());
options.set("scale", scale);

if (input_defs.size() >= 3 && !input_defs[2]->Name().empty()) {
// Bias input exists, and bias's shape is the same as scale's shape.
emscripten::val bias = model_builder.GetOperand(input_defs[2]->Name());
bias = model_builder.GetOperand(input_defs[bias_input_index]->Name());
options.set("bias", bias);
}

Expand All @@ -76,6 +76,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
options.set("epsilon", epsilon);

emscripten::val output = emscripten::val::undefined();
// SkipSimplifiedLayerNormalization's output: input_skip_bias_sum.
emscripten::val input_skip_bias_sum = emscripten::val::undefined();
if (op_type == "BatchNormalization") {
ORT_RETURN_IF_NOT(input_defs.size() == 5, "BatchNormalization requires five inputs.");
emscripten::val mean = model_builder.GetOperand(input_defs[3]->Name());
Expand All @@ -85,7 +87,9 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
}

output = model_builder.GetBuilder().call<emscripten::val>("batchNormalization", input, mean, variance, options);
} else if (op_type == "LayerNormalization" || op_type == "SimplifiedLayerNormalization") {
} else if (op_type == "LayerNormalization" ||
op_type == "SimplifiedLayerNormalization" ||
op_type == "SkipSimplifiedLayerNormalization") {
int64_t axis = helper.Get("axis", -1);
axis = HandleNegativeAxis(axis, rank);
std::vector<uint32_t> axes(rank - SafeInt<uint32_t>(axis));
Expand All @@ -94,13 +98,17 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
if (op_type == "LayerNormalization") {
options.set("axes", emscripten::val::array(axes));
output = model_builder.GetBuilder().call<emscripten::val>("layerNormalization", input, options);
} else { // SimplifiedLayerNormalization
} else { // SimplifiedLayerNormalization or SkipSimplifiedLayerNormalization
/**
WebNN doesn't support SimplifiedLayerNormalization. So decompose it into a series of ops:
X --> Pow --> ReduceMean --> Add --> Sqrt --> Div -> Mul
^ ^ ^ ^ ^
| | | | |
Y:2 axis B:epsilon A:X A:scale
WebNN doesn't support SimplifiedLayerNormalization or SkipSimplifiedLayerNormalization.
So decompose it into a series of ops:
X --> Pow --> ReduceMean --> Add --> Sqrt --> Div -> Mul -> Add (optional)
^ ^ ^ ^ ^ ^
| | | | | |
Y:2 axis B:epsilon A:X A:scale B:bias

If it is SkipSimplifiedLayerNormalization and its output input_skip_bias_sum exists,
input_skip_bias_sum = X + skip + bias (if it exists)
*/

int32_t input_type;
Expand Down Expand Up @@ -137,6 +145,25 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
// Mul
common_options.set("label", node.Name() + "_mul");
output = model_builder.GetBuilder().call<emscripten::val>("mul", scale, div, common_options);

// Add (if bias exits)
if (!bias.isUndefined()) {
common_options.set("label", node.Name() + "_add_bias");
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output = model_builder.GetBuilder().call<emscripten::val>("add", output, bias, common_options);
}

// SkipSimplifiedLayerNormalization's output input_skip_bias_sum is the sum of input, skip, and bias.
if (op_type == "SkipSimplifiedLayerNormalization" && TensorExists(output_defs, 3)) {
emscripten::val skip = model_builder.GetOperand(input_defs[1]->Name());
common_options.set("label", node.Name() + "_add_skip");
input_skip_bias_sum = model_builder.GetBuilder().call<emscripten::val>("add", input, skip, common_options);
if (!bias.isUndefined()) {
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common_options.set("label", node.Name() + "_add_skip_bias");
input_skip_bias_sum = model_builder.GetBuilder().call<emscripten::val>(
"add", input_skip_bias_sum, bias, common_options);
}
model_builder.AddOperand(output_defs[3]->Name(), std::move(input_skip_bias_sum));
}
}
} else if (op_type == "InstanceNormalization") {
// WebNN spec only supports 4D input for instanceNormalization.
Expand Down Expand Up @@ -188,7 +215,7 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
} else {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Unsupported normalization op: ", op_type);
}
model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
model_builder.AddOperand(output_defs[0]->Name(), std::move(output));

return Status::OK();
}
Expand All @@ -215,9 +242,21 @@ bool NormalizationOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initi
}

const auto& output_defs = node.OutputDefs();
if (output_defs.size() != 1) {
LOGS(logger, VERBOSE) << op_type << " output count must be one.";
return false;
if (op_type == "SkipSimplifiedLayerNormalization") {
if (output_defs.size() > 4) {
LOGS(logger, VERBOSE) << "SkipSimplifiedLayerNormalization output count must not exceed 4.";
return false;
}
if (TensorExists(output_defs, 1) || TensorExists(output_defs, 2)) {
// Output mean and inv_std_var are used for training mode, which is not supported.
LOGS(logger, VERBOSE) << "SkipSimplifiedLayerNormalization's output mean and inv_std_var are not supported.";
return false;
}
} else {
if (output_defs.size() != 1) {
LOGS(logger, VERBOSE) << op_type << " output count must be one.";
return false;
}
}

if (op_type == "BatchNormalization" && helper.Get("training_mode", 0)) {
Expand All @@ -238,9 +277,9 @@ bool NormalizationOpBuilder::HasSupportedInputsImpl(const InitializedTensorSet&
int32_t input2_type; // B data type
int32_t input3_type; // mean data type
int32_t input4_type; // var data type
bool has_input2 = input_defs.size() > 2 && input_defs[2]->Exists();
bool has_input3 = input_defs.size() > 3 && input_defs[3]->Exists();
bool has_input4 = input_defs.size() > 3 && input_defs[4]->Exists();
bool has_input2 = TensorExists(input_defs, 2);
bool has_input3 = TensorExists(input_defs, 3);
bool has_input4 = TensorExists(input_defs, 4);

if (!GetType(*input_defs[0], input0_type, logger) ||
!GetType(*input_defs[1], input1_type, logger) ||
Expand Down Expand Up @@ -277,6 +316,7 @@ void CreateNormalizationOpBuilder(const std::string& op_type, OpBuilderRegistrat
"InstanceNormalization",
"LayerNormalization",
"SimplifiedLayerNormalization",
"SkipSimplifiedLayerNormalization",
};

op_registrations.builders.push_back(std::make_unique<NormalizationOpBuilder>());
Expand Down
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