[PT FE]: support aten:Bool, add tests for aten::add_ (#15590)

src/frontends/pytorch/src/op/bool.cpp

@@ -0,0 +1,22 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+OutputVector translate_bool(NodeContext& context) {
+    num_inputs_check(context, 1, 1);
+    return {context.mark_node(std::make_shared<ov::op::v0::Convert>(context.get_input(0), element::boolean))};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -22,6 +22,7 @@ OP_CONVERTER(translate_addmm);
 OP_CONVERTER(translate_arange);
 OP_CONVERTER(translate_as_tensor);
 OP_CONVERTER(translate_avg_poolnd);
+OP_CONVERTER(translate_bool);
 OP_CONVERTER(translate_batch_norm);
 OP_CONVERTER(translate_clamp);
 OP_CONVERTER(translate_constant);
@@ -149,6 +150,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::avg_pool3d", op::translate_avg_poolnd},
         {"aten::batch_norm", op::translate_batch_norm},
         {"aten::bmm", op::translate_1to1_match_2_inputs<opset10::MatMul>},
+        {"aten::Bool", op::translate_bool},
         // {"aten::cat", done as transformation},
         {"aten::ceil", op::translate_1to1_match_1_inputs<opset10::Ceiling>},
         {"aten::ceil_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Ceiling>>},

tests/layer_tests/pytorch_tests/test_add.py

@@ -18,25 +18,30 @@ class TestAdd(PytorchLayerTest):
     def _prepare_input(self):
         return (np.random.randn(2, 5, 3, 4).astype(np.float32), self.input_rhs)
 
-    def create_model(self, alpha):
+    def create_model(self, alpha, op_type):
         class aten_add(torch.nn.Module):
 
-            def __init__(self, alpha) -> None:
+            def __init__(self, alpha, op) -> None:
                 super().__init__()
                 self.alpha = alpha
+                self.forward = self.forward1 if op == "add" else self.forward2
 
-            def forward(self, lhs, rhs):
+            def forward1(self, lhs, rhs):
                 return torch.add(lhs, rhs, alpha=self.alpha)
 
+            def forward2(self, lhs, rhs):
+                return lhs.add_(rhs, alpha=self.alpha)
+
         ref_net = None
 
-        return aten_add(alpha), ref_net, "aten::add"
+        return aten_add(alpha, op_type), ref_net, f"aten::{op_type}"
 
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_add(self, ie_device, precision, ir_version, alpha, input_rhs):
+    @pytest.mark.parametrize("op_type", ["add", "add_"])
+    def test_add(self, ie_device, precision, ir_version, alpha, input_rhs, op_type):
         self.input_rhs = input_rhs
-        self._test(*self.create_model(alpha), ie_device, precision, ir_version)
+        self._test(*self.create_model(alpha, op_type), ie_device, precision, ir_version)
 
 
 class TestAddTypes(PytorchLayerTest):

tests/layer_tests/pytorch_tests/test_bool.py

@@ -0,0 +1,36 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestBool(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1).astype(np.int32),)
+
+    def create_model(self, input_type):
+        import torch
+
+        class prim_bool(torch.nn.Module):
+            def __init__(self, input_type):
+                super(prim_bool, self).__init__()
+                self.forward = self.forward_tensor if input_type != "scalar" else self.forward_scalar
+
+            def forward_tensor(self, x):
+                return bool(x)
+
+            def forward_scalar(self, x:int):
+                return bool(x)
+
+        ref_net = None
+
+        return prim_bool(input_type), ref_net, "aten::Bool"
+
+    @pytest.mark.parametrize("input_type", ["tensor", "scalar"])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_ceil(self, ie_device, precision, ir_version, input_type):
+        self._test(*self.create_model(input_type), ie_device, precision, ir_version)