【PaddlePaddle Hackathon 4】add paddle softshrink op

src/frontends/paddle/src/op/softshrink.cpp

@@ -0,0 +1,54 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "default_opset.hpp"
+#include "openvino/frontend/paddle/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace paddle {
+namespace op {
+NamedOutputs softshrink(const NodeContext& node) {
+    auto data = node.get_input("X");
+    const float lambda = node.get_attribute<float>("lambda", 0.5f);
+
+    const auto input_element_type = data.get_element_type();
+    std::shared_ptr<ngraph::Node> output;
+    const auto positive_lambda = default_opset::Constant::create(input_element_type, Shape{}, {lambda});
+    std::shared_ptr<ngraph::Node> negative_node = std::make_shared<default_opset::Subtract>(data, positive_lambda);
+    std::shared_ptr<ngraph::Node> zero_node = default_opset::Constant::create(input_element_type, Shape{}, {0});
+    if (input_element_type.is_signed()) {
+        std::shared_ptr<default_opset::Constant> negative_lambda =
+            default_opset::Constant::create(input_element_type, Shape{}, {-lambda});
+
+        // Create masks for values below negative lambda and above positive lambda
+        std::shared_ptr<ngraph::Node> values_below_neg_lambda =
+            std::make_shared<default_opset::Less>(data, negative_lambda);
+        std::shared_ptr<ngraph::Node> values_above_pos_lambda =
+            std::make_shared<default_opset::Greater>(data, positive_lambda);
+
+        std::shared_ptr<ngraph::Node> positive_node = std::make_shared<default_opset::Add>(data, positive_lambda);
+
+        output = std::make_shared<default_opset::Select>(values_above_pos_lambda, negative_node, data);
+        output = std::make_shared<default_opset::Select>(values_below_neg_lambda, positive_node, output);
+
+        std::shared_ptr<ngraph::Node> zero_mask =
+            std::make_shared<default_opset::LogicalOr>(values_below_neg_lambda, values_above_pos_lambda);
+
+        output = std::make_shared<default_opset::Select>(zero_mask, output, zero_node);
+    } else {
+        // Passing -lambd to unsigned type constant will cause an overflow.
+        // For unsigned types the lowest possible value is 0. So we just need to compare with lambda.
+        std::shared_ptr<ngraph::Node> values_above_pos_lambda =
+            std::make_shared<default_opset::Greater>(data, positive_lambda);
+
+        output = std::make_shared<default_opset::Select>(values_above_pos_lambda, negative_node, zero_node);
+    }
+
+    return node.default_single_output_mapping({output}, {"Out"});
+}
+}  // namespace op
+}  // namespace paddle
+}  // namespace frontend
+}  // namespace ov

src/frontends/paddle/src/op_table.cpp

@@ -93,6 +93,7 @@ OP_CONVERTER(shape);
 OP_CONVERTER(slice);
 OP_CONVERTER(softmax);
 OP_CONVERTER(softplus);
+OP_CONVERTER(softshrink);
 OP_CONVERTER(sigmoid);
 OP_CONVERTER(split);
 OP_CONVERTER(sqrt);
@@ -209,6 +210,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"slice", op::slice},
             {"softmax", op::softmax},
             {"softplus", op::softplus},
+            {"softshrink", op::softshrink},
             {"sigmoid", op::sigmoid},
             {"split", op::split},
             {"sqrt", op::sqrt},

src/frontends/paddle/tests/op_fuzzy.cpp

@@ -454,6 +454,8 @@ static const std::vector<std::string> models{
     std::string("softmax"),
     std::string("softmax_minus"),
     std::string("softplus_default_params"),
+    std::string("softshrink_default_params"),
+    std::string("softshrink_threshold_0.6"),
     std::string("split_test1"),
     std::string("split_test2"),
     std::string("split_test3"),

src/frontends/paddle/tests/test_models/gen_scripts/generate_softshrink.py

@@ -0,0 +1,56 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+#
+# softshrink paddle model generator
+#
+import numpy as np
+import sys
+from save_model import saveModel
+
+
+def softshrink(name: str, x, threshold):
+    import paddle
+
+    paddle.enable_static()
+
+    node_x = paddle.static.data(name="x", shape=x.shape, dtype="float32")
+    if threshold == None:
+        out = paddle.nn.functional.softshrink(node_x)
+    else:
+        out = paddle.nn.functional.softshrink(node_x, threshold)
+
+    cpu = paddle.static.cpu_places(1)
+    exe = paddle.static.Executor(cpu[0])
+    # startup program will call initializer to initialize the parameters.
+    exe.run(paddle.static.default_startup_program())
+
+    outs = exe.run(feed={"x": x}, fetch_list=[out])
+
+    saveModel(
+        name,
+        exe,
+        feedkeys=["x"],
+        fetchlist=[out],
+        inputs=[x],
+        outputs=[outs[0]],
+        target_dir=sys.argv[1],
+    )
+
+    return outs[0]
+
+
+def main():
+    data = np.array(
+        [
+            [[2.0, 3.0, 4.0, 5.0], [0.0, 4.0, -5.0, 6.0], [7.0, -8.0, 8.0, 9.0]],
+            [[-1.0, 2.0, 3.0, 4.0], [-5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]],
+        ]
+    ).astype(np.float32)
+
+    softshrink("softshrink_default_params", data, threshold=None)
+    softshrink("softshrink_threshold_0.6", data, threshold=0.6)
+
+
+if __name__ == "__main__":
+    main()