[PT FE]: support aten::glu and aten::sigmoid_ (#15185)

* [PT FE]: support aten::glu and aten::sigmoid_

* upd headers

* Update src/frontends/pytorch/src/op/glu.cpp

Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>

* return back opset

* Update op_table.cpp

Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>

src/frontends/pytorch/src/op/glu.cpp

@@ -0,0 +1,30 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/sigmoid.hpp"
+#include "openvino/op/split.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_glu(NodeContext& context) {
+    auto x = context.get_input(0);
+    auto dim = context.input_is_none(1) ? context.mark_node(ov::op::v0::Constant::create(element::i64, Shape{}, {-1}))
+                                        : context.get_input(1);
+    auto split = context.mark_node(std::make_shared<ov::op::v1::Split>(x, dim, 2));
+    auto first = split->output(0);
+    auto second = split->output(1);
+    auto sigmoid = context.mark_node(std::make_shared<ov::op::v0::Sigmoid>(second));
+    return {context.mark_node(std::make_shared<ov::op::v1::Multiply>(first, sigmoid))};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -41,6 +41,7 @@ OP_CONVERTER(translate_full);
 OP_CONVERTER(translate_full_like);
 OP_CONVERTER(translate_gelu);
 OP_CONVERTER(translate_get_attr);
+OP_CONVERTER(translate_glu);
 OP_CONVERTER(translate_group_norm);
 OP_CONVERTER(translate_hardtanh);
 OP_CONVERTER(translate_if);
@@ -168,6 +169,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::full", op::translate_full},
         {"aten::full_like", op::translate_full_like},
         {"aten::gelu", op::translate_gelu},
+        {"aten::glu", op::translate_glu},
         {"aten::group_norm", op::translate_group_norm},
         {"aten::ge", op::translate_1to1_match_2_inputs<opset10::GreaterEqual>},
         {"aten::gt", op::translate_1to1_match_2_inputs<opset10::Greater>},
@@ -231,6 +233,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::selu", op::translate_selu},
         {"aten::selu_", op::inplace_op<op::translate_selu>},
         {"aten::sigmoid", op::translate_1to1_match_1_inputs<opset10::Sigmoid>},
+        {"aten::sigmoid_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Sigmoid>>},
         {"aten::silu", op::translate_1to1_match_1_inputs<opset10::Swish>},
         {"aten::silu_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Swish>>},
         {"aten::sin", op::translate_1to1_match_1_inputs<opset10::Sin>},

tests/layer_tests/pytorch_tests/test_glu.py

@@ -0,0 +1,34 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestGlu(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(2, 4, 224, 224).astype(np.float32),)
+
+    def create_model(self, dim):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_glu(torch.nn.Module):
+            def __init__(self, dim):
+                super(aten_glu, self).__init__()
+                self.dim = dim
+
+            def forward(self, x):
+                return F.glu(x, self.dim)
+
+        ref_net = None
+
+        return aten_glu(dim), ref_net, "aten::glu"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("dim", [0, 1, 2, 3, -1, -2])
+    def test_glu(self, dim, ie_device, precision, ir_version):
+        self._test(*self.create_model(dim), ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_sigmoid.py

@@ -0,0 +1,34 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestSigmoid(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 224, 224).astype(np.float32),)
+
+    def create_model(self, inplace=False):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_sigmoid(torch.nn.Module):
+            def __init__(self, inplace):
+                super(aten_sigmoid, self).__init__()
+                self.op = torch.sigmoid if not inplace else torch.sigmoid_
+
+            def forward(self, x):
+                return x, self.op(x)
+
+        ref_net = None
+
+        return aten_sigmoid(inplace), ref_net, "aten::sigmoid" if not inplace else "aten::sigmoid_"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("inplace", [True, False])
+    def test_sigmoid(self, inplace, ie_device, precision, ir_version):
+        self._test(*self.create_model(inplace), ie_device, precision, ir_version)