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[QNN EP] Enable Expand op (microsoft#18234)
### Description Enable Expand Op. There no directly mapping from Onnx Expand op to QNN. Need to use ElementWiseMultiply to do the data broadcast. Basically create the 2nd input with value 1.0 and use the shape data from Expand op.
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139 changes: 139 additions & 0 deletions
139
onnxruntime/core/providers/qnn/builder/opbuilder/expand_op_builder.cc
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| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
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| #include "core/providers/common.h" | ||
| #include "core/providers/shared/utils/utils.h" | ||
| #include "core/providers/qnn/builder/qnn_model_wrapper.h" | ||
| #include "core/providers/qnn/builder/op_builder_factory.h" | ||
| #include "core/providers/qnn/builder/qnn_utils.h" | ||
| #include "core/common/safeint.h" | ||
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| #include "base_op_builder.h" | ||
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| namespace onnxruntime { | ||
| namespace qnn { | ||
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| class ExpandOpBuilder : public BaseOpBuilder { | ||
| public: | ||
| ExpandOpBuilder() : BaseOpBuilder("ExpandOpBuilder") {} | ||
| ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(ExpandOpBuilder); | ||
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| protected: | ||
| Status ProcessInputs(QnnModelWrapper& qnn_model_wrapper, | ||
| const NodeUnit& node_unit, | ||
| const logging::Logger& logger, | ||
| std::vector<std::string>& input_names, | ||
| bool do_op_validation) const override ORT_MUST_USE_RESULT; | ||
| }; | ||
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| template <typename T> | ||
| void FillShapeInputData(std::vector<uint8_t>& shape_data, int shape_size, T ini_value) { | ||
| shape_data.resize(shape_size * sizeof(T)); | ||
| T* shape_data_float = reinterpret_cast<T*>(shape_data.data()); | ||
| std::fill(shape_data_float, shape_data_float + shape_size, ini_value); | ||
| } | ||
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| // Use ElementWiseMultiply to implement data broadcast | ||
| // Get the shape data, and create a initializer input with value 1 and same shape | ||
| // input[0] * input[1] | ||
| Status ExpandOpBuilder::ProcessInputs(QnnModelWrapper& qnn_model_wrapper, | ||
| const NodeUnit& node_unit, | ||
| const logging::Logger& logger, | ||
| std::vector<std::string>& input_names, | ||
| bool do_op_validation) const { | ||
| ORT_UNUSED_PARAMETER(do_op_validation); | ||
| const auto& inputs = node_unit.Inputs(); | ||
| ORT_RETURN_IF(inputs.size() != 2, "Expand should has 2 inputs!"); | ||
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| ORT_RETURN_IF_ERROR(ProcessInput(qnn_model_wrapper, inputs[0], logger, input_names)); | ||
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| // Process shape input | ||
| const auto& input_name = inputs[1].node_arg.Name(); | ||
| bool is_initializer_input = qnn_model_wrapper.IsInitializerInput(input_name); | ||
| ORT_RETURN_IF_NOT(is_initializer_input, "QNN doesn't support dynamic shape."); | ||
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| std::vector<uint32_t> shape; | ||
| ORT_RETURN_IF_NOT(qnn_model_wrapper.GetOnnxShape(inputs[1].node_arg, shape), "Cannot get shape"); | ||
| uint32_t shape_rank = shape[0]; | ||
| std::vector<uint8_t> unpacked_tensor; | ||
| const auto& input_tensor = qnn_model_wrapper.GetInitializerTensors().at(input_name); | ||
| ORT_RETURN_IF_ERROR(qnn_model_wrapper.UnpackInitializerData(*input_tensor, unpacked_tensor)); | ||
| const int64_t* shape_data_int64 = reinterpret_cast<const int64_t*>(unpacked_tensor.data()); | ||
| std::vector<uint32_t> input_shape(shape_rank, 0); | ||
| std::transform(shape_data_int64, shape_data_int64 + shape_rank, input_shape.begin(), | ||
| [](int64_t item) { return SafeInt<uint32_t>(item); }); | ||
| int shape_size = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<uint32_t>()); | ||
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| std::vector<uint8_t> shape_data; | ||
| bool is_quantized_tensor = inputs[0].quant_param.has_value(); | ||
| Qnn_DataType_t qnn_data_type = QNN_DATATYPE_FLOAT_32; | ||
| const auto* type_proto = inputs[0].node_arg.TypeAsProto(); | ||
| Qnn_QuantizeParams_t quantize_param = QNN_QUANTIZE_PARAMS_INIT; | ||
| if (is_quantized_tensor) { | ||
| ORT_RETURN_IF_ERROR(utils::GetQnnDataType(true, type_proto, qnn_data_type)); | ||
| float scale = 0.0f; | ||
| int zero_point = 0; | ||
| float rmax = 1.0f; | ||
| float rmin = 1.0f; | ||
| ORT_RETURN_IF_ERROR(utils::GetQuantParams(rmin, | ||
| rmax, | ||
| qnn_data_type, | ||
| scale, | ||
| zero_point)); | ||
| utils::InitializeQuantizeParam(quantize_param, true, scale, zero_point); | ||
| int quant_value_int = 0; | ||
| double ini_value = 1.0; | ||
| ORT_RETURN_IF_ERROR(utils::Quantize(ini_value, scale, zero_point, qnn_data_type, quant_value_int)); | ||
| switch (qnn_data_type) { | ||
| case QNN_DATATYPE_SFIXED_POINT_8: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<int8_t>(quant_value_int)); | ||
| break; | ||
| } | ||
| case QNN_DATATYPE_UFIXED_POINT_8: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<uint8_t>(quant_value_int)); | ||
| break; | ||
| } | ||
| case QNN_DATATYPE_UFIXED_POINT_16: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<uint16_t>(quant_value_int)); | ||
| break; | ||
| } | ||
| default: | ||
| return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Type not supported."); | ||
| } // switch | ||
| } else { | ||
| ORT_RETURN_IF_ERROR(utils::GetQnnDataType(false, type_proto, qnn_data_type)); | ||
| switch (qnn_data_type) { | ||
| case QNN_DATATYPE_FLOAT_32: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<float>(1.0)); | ||
| break; | ||
| } | ||
| case QNN_DATATYPE_INT_32: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<int32_t>(1)); | ||
| break; | ||
| } | ||
| case QNN_DATATYPE_UINT_32: { | ||
| FillShapeInputData(shape_data, shape_size, static_cast<uint32_t>(1)); | ||
| break; | ||
| } | ||
| default: | ||
| return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Type not supported."); | ||
| } // switch | ||
| } // if-else | ||
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| const std::string& output_name = node_unit.Outputs()[0].node_arg.Name(); | ||
| std::string shape_input_name(input_name + "_" + output_name); | ||
| QnnTensorWrapper input_tensorwrapper(shape_input_name, QNN_TENSOR_TYPE_STATIC, qnn_data_type, quantize_param, | ||
| std::move(input_shape), std::move(shape_data)); | ||
| ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(input_tensorwrapper)), "Failed to add tensor."); | ||
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| input_names.push_back(shape_input_name); | ||
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| return Status::OK(); | ||
| } | ||
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| void CreateExpandOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) { | ||
| op_registrations.AddOpBuilder(op_type, std::make_unique<ExpandOpBuilder>()); | ||
| } | ||
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| } // namespace qnn | ||
| } // namespace onnxruntime |
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