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[ONNX] Added QLinearAveragePool from com.microsoft domain (#28799)
Details: Microsoft Contrib Operator "QLinearAveragePool" for ONNX RT Tickets: N/A --------- Co-authored-by: Georgy Krivoruchko <georgy.krivoruchko@intel.com>
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-12
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3 files changed

+172
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‎src/frontends/onnx/frontend/src/op/com.microsoft/qlinear_activation.cpp

+38-12
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@@ -6,6 +6,7 @@
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#include "exceptions.hpp"
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#include "openvino/frontend/exception.hpp"
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#include "openvino/op/add.hpp"
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#include "openvino/op/avg_pool.hpp"
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#include "openvino/op/constant.hpp"
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#include "openvino/op/convert.hpp"
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#include "openvino/op/divide.hpp"
@@ -57,21 +58,12 @@ ov::OutputVector qlinear_activation(const ov::frontend::onnx::Node& node, const
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}
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ov::OutputVector qlinear_sigmoid(const ov::frontend::onnx::Node& node) {
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// Original documentation:
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// https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#commicrosoftqlinearsigmoid
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// f(x) = quantize(Sigmoid(dequantize(x)))
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return qlinear_activation(node, [](const std::shared_ptr<ov::Node>& input_dequantized) {
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return std::make_shared<v0::Sigmoid>(input_dequantized);
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});
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}
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ov::OutputVector qlinear_leaky_relu(const ov::frontend::onnx::Node& node) {
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// Original documentation:
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// https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#commicrosoftqlinearleakyrelu
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// f(x) = quantize(alpha * dequantize(x)) for x < 0,
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// quantize(dequantize(x)) for x >= 0
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return qlinear_activation(node, [&](const std::shared_ptr<ov::Node>& input_dequantized) {
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auto alpha =
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v0::Constant::create(input_dequantized->get_element_type(), {}, {node.get_attribute_value<float>("alpha")});
@@ -80,10 +72,44 @@ ov::OutputVector qlinear_leaky_relu(const ov::frontend::onnx::Node& node) {
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});
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}
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namespace {
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ONNX_OP("QLinearSigmoid", OPSET_SINCE(1), com_microsoft::opset_1::qlinear_sigmoid, MICROSOFT_DOMAIN);
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ov::OutputVector qlinear_avg_pool(const ov::frontend::onnx::Node& node) {
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return qlinear_activation(node, [&](const std::shared_ptr<ov::Node>& input_dequantized) {
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const auto kernel_shape = node.get_attribute_value<std::vector<int64_t>>("kernel_shape");
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auto strides = node.get_attribute_value<std::vector<int64_t>>("strides", std::vector<int64_t>{1});
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auto pads = node.get_attribute_value<std::vector<int64_t>>("pads", std::vector<int64_t>{0});
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const auto ceil_mode = node.get_attribute_value<int64_t>("ceil_mode", 0);
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const auto count_include_pad = node.get_attribute_value<int64_t>("count_include_pad", 0);
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const auto auto_pad = node.get_attribute_value<std::string>("auto_pad", "NOTSET");
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const auto input_rank = input_dequantized->get_shape().size();
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const size_t num_spatial_dims = input_rank - 2;
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pads.resize(num_spatial_dims * 2, pads.size() == 1 ? pads[0] : 0);
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strides.resize(num_spatial_dims, strides.size() == 1 ? strides[0] : 1);
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auto avg_pool = std::make_shared<v1::AvgPool>(input_dequantized,
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Strides(strides.begin(), strides.end()),
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Shape(pads.begin(), pads.begin() + num_spatial_dims),
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Shape(pads.begin() + num_spatial_dims, pads.end()),
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Shape(kernel_shape.begin(), kernel_shape.end()),
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count_include_pad == 0,
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ceil_mode != 0 ? RoundingType::CEIL : RoundingType::FLOOR,
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auto_pad == "SAME_UPPER" ? PadType::SAME_UPPER
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: auto_pad == "SAME_LOWER" ? PadType::SAME_LOWER
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: PadType::EXPLICIT);
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return avg_pool;
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});
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}
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ONNX_OP("QLinearLeakyRelu", OPSET_SINCE(1), com_microsoft::opset_1::qlinear_leaky_relu, MICROSOFT_DOMAIN);
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bool register_multiple_operators(void) {
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ONNX_OP_M("QLinearSigmoid", OPSET_SINCE(1), com_microsoft::opset_1::qlinear_sigmoid, MICROSOFT_DOMAIN);
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ONNX_OP_M("QLinearLeakyRelu", OPSET_SINCE(1), com_microsoft::opset_1::qlinear_leaky_relu, MICROSOFT_DOMAIN);
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ONNX_OP_M("QLinearAveragePool", OPSET_SINCE(1), com_microsoft::opset_1::qlinear_avg_pool, MICROSOFT_DOMAIN);
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return true;
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}
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static bool registered = register_multiple_operators();
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} // namespace opset_1
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} // namespace com_microsoft

‎src/frontends/onnx/tests/models/com.microsoft/qlinear_avg_pool.prototxt

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ir_version: 3
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producer_name: "OpenVINO ONNX Frontend"
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graph {
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node {
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input: "A"
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input: "A_scale"
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input: "A_zero"
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input: "B_scale"
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input: "B_zero"
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output: "R"
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name: "test_Qlinear_avg_pool"
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domain: "com.microsoft"
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op_type: "QLinearAveragePool"
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attribute {
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name: "auto_pad"
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s: "NOTSET"
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type: STRING
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}
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attribute {
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name: "ceil_mode"
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i: 0
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type: INT
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}
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attribute {
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name: "count_include_pad"
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i: 0
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type: INT
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}
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attribute {
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name: "kernel_shape"
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ints: 7
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ints: 7
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type: INTS
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}
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attribute {
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name: "strides"
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ints: 1
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ints: 1
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type: INTS
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}
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}
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initializer {
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data_type: 1
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float_data: 0.023529412
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name: "A_scale"
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}
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initializer {
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data_type: 2
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int32_data: 0
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name: "A_zero"
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}
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initializer {
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data_type: 1
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float_data: 0.01812745
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name: "B_scale"
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}
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initializer {
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data_type: 2
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int32_data: 0
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name: "B_zero"
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}
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input {
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name: "A"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 7
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}
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dim {
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dim_value: 7
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}
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}
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}
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}
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}
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output {
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name: "R"
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type {
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tensor_type {
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elem_type: 2
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shape {
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 1
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}
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dim {
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dim_value: 1
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}
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}
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}
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}
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}
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}
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opset_import {
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domain: "com.microsoft"
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version: 1
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}

‎src/frontends/onnx/tests/onnx_import_com_microsoft.in.cpp

+17
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@@ -1577,6 +1577,23 @@ OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_matmul_integer_to_float) {
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test_case.run();
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}
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OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_qlinear_avg_pool) {
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const auto model = convert_model("com.microsoft/qlinear_avg_pool.onnx");
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auto test_case = ov::test::TestCase(model, s_device);
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const std::vector<uint8_t> data_A = {140, 138, 137, 136, 135, 134, 133, 132, 131, 130, 129, 128, 127,
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126, 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114,
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113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101,
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100, 99, 98, 97, 96, 95, 94, 93, 92, 91};
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const std::vector<uint8_t> expected_output = {149};
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test_case.add_input<uint8_t>(Shape{1, 1, 7, 7}, data_A);
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test_case.add_expected_output<uint8_t>(Shape{1, 1, 1, 1}, expected_output);
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test_case.run();
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}
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OPENVINO_TEST(${BACKEND_NAME}, onnx_com_microsoft_qlinearsigmoid) {
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const auto model = convert_model("com.microsoft/q_linear_sigmoid.onnx");
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auto test_case = ov::test::TestCase(model, s_device);

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