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[WebNN EP] Support QuantizeLinear and DequantizeLinear ops (#22097)
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81 changes: 0 additions & 81 deletions
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onnxruntime/core/providers/webnn/builders/impl/dequantizeLinear_op_builder.cc
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152 changes: 152 additions & 0 deletions
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onnxruntime/core/providers/webnn/builders/impl/qdq_op_builder.cc
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| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Copyright (c) Intel Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
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| #include "core/common/safeint.h" | ||
| #include "core/optimizer/initializer.h" | ||
| #include "core/providers/common.h" | ||
| #include "core/providers/shared/utils/utils.h" | ||
| #include "core/providers/webnn/builders/helper.h" | ||
| #include "core/providers/webnn/builders/model_builder.h" | ||
| #include "core/providers/webnn/builders/op_builder_factory.h" | ||
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| #include "core/providers/webnn/builders/impl/base_op_builder.h" | ||
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| namespace onnxruntime { | ||
| namespace webnn { | ||
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| class QDQOpBuilder : public BaseOpBuilder { | ||
| // Add operator related. | ||
| private: | ||
| Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node, | ||
| const logging::Logger& logger) const override ORT_MUST_USE_RESULT; | ||
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| // Operator support related. | ||
| bool HasSupportedInputsImpl(const Node& node, const emscripten::val& wnn_limits, | ||
| const logging::Logger& logger) const override; | ||
| }; | ||
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| Status QDQOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, | ||
| const Node& node, | ||
| const logging::Logger& logger) const { | ||
| const auto& op_type = node.OpType(); | ||
| const auto& input_defs = node.InputDefs(); | ||
| const auto& output_defs = node.OutputDefs(); | ||
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| std::vector<int64_t> input_shape; | ||
| std::vector<int64_t> scale_shape; | ||
| ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get input shape"); | ||
| ORT_RETURN_IF_NOT(GetShape(*input_defs[1], scale_shape, logger), "Cannot get scale shape"); | ||
| int32_t input_type = 0; | ||
| int32_t output_type = 0; | ||
| int32_t zero_point_type = 0; | ||
| ORT_RETURN_IF_NOT(GetType(*input_defs[0], input_type, logger), "Cannot get input data type"); | ||
| ORT_RETURN_IF_NOT(GetType(*output_defs[0], output_type, logger), "Cannot get output data type"); | ||
| emscripten::val input = model_builder.GetOperand(input_defs[0]->Name()); | ||
| emscripten::val scale = model_builder.GetOperand(input_defs[1]->Name()); | ||
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| emscripten::val zero_point = emscripten::val::null(); | ||
| if (input_defs.size() == 3 && input_defs[2]->Exists()) { | ||
| zero_point = model_builder.GetOperand(node.InputDefs()[2]->Name()); | ||
| } else { | ||
| // DequantizeLinear: x_zero_point's data type equals to input data type | ||
| // QuantizeLinear: x_zero_point's data type equals to output data type | ||
| zero_point_type = op_type == "DequantizeLinear" ? input_type : output_type; | ||
| zero_point = model_builder.GetZeroConstant(zero_point_type); | ||
| } | ||
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| emscripten::val output; | ||
| NodeAttrHelper helper(node); | ||
| int32_t axis = helper.Get("axis", 1); | ||
| int32_t block_size = helper.Get("block_size", 0); | ||
| // axis is valid for input shape greater than 1D. | ||
| if (input_shape.size() > 1) { | ||
| axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_shape.size())); | ||
| } | ||
| // Insert ones before and after the axis dimension for broadcasting of 1D scale tensor. | ||
| if (1 == scale_shape.size() && 1 < input_shape.size()) { | ||
| std::vector<int32_t> target_shape{static_cast<int>(input_shape[axis])}; | ||
| target_shape.insert(target_shape.begin(), axis, 1); | ||
| target_shape.insert(target_shape.end(), input_shape.size() - axis - 1, 1); | ||
| emscripten::val reshape_scale_options = emscripten::val::object(); | ||
| reshape_scale_options.set("label", node.Name() + "_reshape_scale"); | ||
| scale = model_builder.GetBuilder().call<emscripten::val>("reshape", | ||
| scale, | ||
| emscripten::val::array(target_shape), | ||
| reshape_scale_options); | ||
| emscripten::val reshape_zero_point_options = emscripten::val::object(); | ||
| reshape_zero_point_options.set("label", node.Name() + "_reshape_zero_point"); | ||
| zero_point = model_builder.GetBuilder().call<emscripten::val>("reshape", | ||
| zero_point, | ||
| emscripten::val::array(target_shape), | ||
| reshape_zero_point_options); | ||
| } | ||
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| // If block_size is specified, we need to expand the scale and zero_point tensors. | ||
| if (block_size > 1) { | ||
| emscripten::val concat_scale_inputs = emscripten::val::array(); | ||
| emscripten::val concat_zero_point_inputs = emscripten::val::array(); | ||
| for (int i = 0; i < block_size; i++) { | ||
| concat_scale_inputs.call<void>("push", scale); | ||
| concat_zero_point_inputs.call<void>("push", zero_point); | ||
| } | ||
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| emscripten::val concat_scale_options = emscripten::val::object(); | ||
| concat_scale_options.set("label", node.Name() + "_concat_scale"); | ||
| scale = model_builder.GetBuilder().call<emscripten::val>("concat", concat_scale_inputs, axis, concat_scale_options); | ||
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| emscripten::val concat_zero_point_options = emscripten::val::object(); | ||
| concat_zero_point_options.set("label", node.Name() + "_concat_zero_point"); | ||
| zero_point = model_builder.GetBuilder().call<emscripten::val>( | ||
| "concat", concat_zero_point_inputs, axis, concat_zero_point_options); | ||
| } | ||
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| emscripten::val options = emscripten::val::object(); | ||
| options.set("label", node.Name()); | ||
| std::string webnn_op_type; | ||
| ORT_RETURN_IF_NOT(GetWebNNOpType(op_type, webnn_op_type), "Cannot get WebNN op type"); | ||
| output = model_builder.GetBuilder().call<emscripten::val>(webnn_op_type.c_str(), input, scale, zero_point, options); | ||
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| model_builder.AddOperand(output_defs[0]->Name(), std::move(output)); | ||
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| return Status::OK(); | ||
| } | ||
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| bool QDQOpBuilder::HasSupportedInputsImpl(const Node& node, const emscripten::val& wnn_limits, | ||
| const logging::Logger& logger) const { | ||
| const auto& input_defs = node.InputDefs(); | ||
| const auto& op_type = node.OpType(); | ||
| int32_t input0_type = 0; // input data type | ||
| int32_t input1_type = 0; // x_scale data type | ||
| int32_t input2_type = 0; // x_zero_point data type | ||
| bool has_input2 = input_defs.size() > 2 && input_defs[2]->Exists(); | ||
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| if (!GetType(*input_defs[0], input0_type, logger) || | ||
| !GetType(*input_defs[1], input1_type, logger) || | ||
| (has_input2 && !GetType(*input_defs[2], input2_type, logger))) { | ||
| return false; | ||
| } | ||
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| return IsDataTypeSupportedByOp(op_type, input0_type, wnn_limits, "input", "x", logger) && | ||
| IsDataTypeSupportedByOp(op_type, input1_type, wnn_limits, "scale", "x_scale", logger) && | ||
| (!has_input2 || IsDataTypeSupportedByOp(op_type, input2_type, wnn_limits, "zeroPoint", "x_zero_point", logger)); | ||
| } | ||
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| void CreateQDQOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) { | ||
| if (op_registrations.op_builder_map.count(op_type) > 0) | ||
| return; | ||
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| static std::vector<std::string> op_types = | ||
| { | ||
| "DequantizeLinear", | ||
| "QuantizeLinear", | ||
| }; | ||
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| op_registrations.builders.push_back(std::make_unique<QDQOpBuilder>()); | ||
| for (const auto& type : op_types) { | ||
| op_registrations.op_builder_map.emplace(type, op_registrations.builders.back().get()); | ||
| } | ||
| } | ||
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| } // namespace webnn | ||
| } // namespace onnxruntime |
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