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[WebNN] Always execute decomposed *SimplifiedLayerNormalization in FP32 #24437

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Apr 22, 2025
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[WebNN] Always execute decomposed *SimplifiedLayerNormalization in FP32 #24437

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[WebNN] Always execute decomposed *SimplifiedLayerNormalization in FP32
Honry Apr 16, 2025
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Honry Apr 21, 2025
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47 changes: 44 additions & 3 deletions onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -114,9 +114,33 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
ORT_RETURN_IF_NOT(GetType(*input_defs[0], input_type, logger), "Cannot get input type");
emscripten::val common_options = emscripten::val::object();

if (input_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
// Decomposed *SimplifiedLayerNormalization may lose precision if its data type is float16.
// So cast all inputs to float32 to ensure precision.
common_options.set("label", node.Name() + "_cast_input_to_fp32");
input = model_builder.GetBuilder().call<emscripten::val>("cast", input,
emscripten::val("float32"), common_options);

common_options.set("label", node.Name() + "_cast_scale_to_fp32");
scale = model_builder.GetBuilder().call<emscripten::val>("cast", scale,
emscripten::val("float32"), common_options);

if (!bias.isUndefined()) {
common_options.set("label", node.Name() + "_cast_bias_to_fp32");
bias = model_builder.GetBuilder().call<emscripten::val>("cast", bias,
emscripten::val("float32"), common_options);
}
}

// If it is SkipSimplifiedLayerNormalization, add the skip and bias (if it exists) to the input.
if (op_type == "SkipSimplifiedLayerNormalization") {
emscripten::val skip = model_builder.GetOperand(input_defs[1]->Name());
if (input_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
// Cast skip to float32
common_options.set("label", node.Name() + "_cast_skip_to_fp32");
skip = model_builder.GetBuilder().call<emscripten::val>("cast", skip,
emscripten::val("float32"), common_options);
}
common_options.set("label", node.Name() + "_add_skip");
input = model_builder.GetBuilder().call<emscripten::val>("add", input, skip, common_options);
if (!bias.isUndefined()) {
Expand All @@ -127,12 +151,21 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
// Add SkipSimplifiedLayerNormalization's output input_skip_bias_sum if it exists.
// Now input equals to input_skip_bias_sum.
if (TensorExists(output_defs, 3)) {
model_builder.AddOperand(output_defs[3]->Name(), input);
emscripten::val input_skip_bias_sum = input;
if (input_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
// Cast input_skip_bias_sum back to float16.
common_options.set("label", node.Name() + "_cast_input_skip_bias_sum_to_fp16");
input_skip_bias_sum = model_builder.GetBuilder().call<emscripten::val>("cast", input_skip_bias_sum,
emscripten::val("float16"),
common_options);
}
model_builder.AddOperand(output_defs[3]->Name(), input_skip_bias_sum);
}
}

// Pow
emscripten::val pow_constant = model_builder.CreateOrGetConstant<float>(input_type, 2);
emscripten::val pow_constant =
model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, 2);
common_options.set("label", node.Name() + "_pow");
emscripten::val pow =
model_builder.GetBuilder().call<emscripten::val>("pow", input, pow_constant, common_options);
Expand All @@ -145,7 +178,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
emscripten::val reduce_mean = model_builder.GetBuilder().call<emscripten::val>("reduceMean", pow, reduce_options);

// Add
emscripten::val add_constant = model_builder.CreateOrGetConstant<float>(input_type, epsilon);
emscripten::val add_constant =
model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, epsilon);
common_options.set("label", node.Name() + "_add");
emscripten::val add =
model_builder.GetBuilder().call<emscripten::val>("add", reduce_mean, add_constant, common_options);
Expand All @@ -167,6 +201,13 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
common_options.set("label", node.Name() + "_add_bias");
output = model_builder.GetBuilder().call<emscripten::val>("add", output, bias, common_options);
}

if (input_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
// Cast output back to float16.
common_options.set("label", node.Name() + "_cast_output_to_fp16");
output = model_builder.GetBuilder().call<emscripten::val>("cast", output,
emscripten::val("float16"), common_options);
}
}
} else if (op_type == "InstanceNormalization") {
// WebNN spec only supports 4D input for instanceNormalization.
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