diff --git a/js/web/docs/webnn-operators.md b/js/web/docs/webnn-operators.md index af7348dba532f..636767e506a6f 100644 --- a/js/web/docs/webnn-operators.md +++ b/js/web/docs/webnn-operators.md @@ -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 | com.microsoft(1+) | pow, reduceMean, add, sqrt, div, mul | ✓ | ✓ | | | 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 | ✓ | ✓ | | diff --git a/onnxruntime/core/providers/webnn/builders/helper.h b/onnxruntime/core/providers/webnn/builders/helper.h index a06f46f1bdf0a..c68653ae8ebcb 100644 --- a/onnxruntime/core/providers/webnn/builders/helper.h +++ b/onnxruntime/core/providers/webnn/builders/helper.h @@ -278,6 +278,7 @@ static const InlinedHashMap op_map = { {"Softplus", "softplus"}, {"Softsign", "softsign"}, {"Sin", "sin"}, + {"SkipSimplifiedLayerNormalization", "layerNormalization"}, {"Slice", "slice"}, {"Softmax", "softmax"}, {"Split", "split"}, diff --git a/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc index 50e49884bdfa9..b560938be209b 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc @@ -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()); @@ -45,7 +46,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder options.set("label", node.Name()); std::vector 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") { @@ -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 (input_defs.size() > bias_input_index && input_defs[bias_input_index]->Exists()) { // Bias input exists, and bias's shape should be the same as scale's shape. std::vector 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); } @@ -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()); @@ -85,7 +87,9 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder } output = model_builder.GetBuilder().call("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 axes(rank - SafeInt(axis)); @@ -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("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; @@ -137,6 +145,25 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder // Mul common_options.set("label", node.Name() + "_mul"); output = model_builder.GetBuilder().call("mul", scale, div, common_options); + + // Add (if bias exits) + if (!bias.isUndefined()) { + common_options.set("label", node.Name() + "_add_bias"); + output = model_builder.GetBuilder().call("add", output, bias, common_options); + } + + // SkipSimplifiedLayerNormalization's output input_skip_bias_sum is the sum of input, skip, and bias. + if (op_type == "SkipSimplifiedLayerNormalization" && output_defs.size() > 3 && output_defs[3]->Exists()) { + 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("add", input, skip, common_options); + if (!bias.isUndefined()) { + common_options.set("label", node.Name() + "_add_skip_bias"); + input_skip_bias_sum = model_builder.GetBuilder().call( + "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. @@ -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(); } @@ -214,10 +241,20 @@ bool NormalizationOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initi return false; } - 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") { + const auto& output_defs = node.OutputDefs(); + for (size_t i = 1; i < output_defs.size(); i++) { + if (output_defs[i]->Exists() && i < 3) { + // Output mean and inv_std_var are used for training mode, which is not supported. + const auto output_name = i == 1 ? "mean" : "inv_std_var"; + LOGS(logger, VERBOSE) << "SkipSimplifiedLayerNormalization's output: " << output_name << " is not supported."; + } + } + } 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)) { @@ -277,6 +314,7 @@ void CreateNormalizationOpBuilder(const std::string& op_type, OpBuilderRegistrat "InstanceNormalization", "LayerNormalization", "SimplifiedLayerNormalization", + "SkipSimplifiedLayerNormalization", }; op_registrations.builders.push_back(std::make_unique()); diff --git a/onnxruntime/core/providers/webnn/builders/op_builder_factory.cc b/onnxruntime/core/providers/webnn/builders/op_builder_factory.cc index 6d1c572128b93..e0ca50a36dbf9 100644 --- a/onnxruntime/core/providers/webnn/builders/op_builder_factory.cc +++ b/onnxruntime/core/providers/webnn/builders/op_builder_factory.cc @@ -159,6 +159,7 @@ static OpBuilderRegistrations CreateOpBuilderRegistrations() { CreateNormalizationOpBuilder("InstanceNormalization", op_registrations); CreateNormalizationOpBuilder("LayerNormalization", op_registrations); CreateNormalizationOpBuilder("SimplifiedLayerNormalization", op_registrations); + CreateNormalizationOpBuilder("SkipSimplifiedLayerNormalization", op_registrations); } { // Pad