[PT FE]: support aten::floor_divide_ and aten::movedim (openvinotoolkit#23446)

### Details:
 - *aten::floor_divide*
 - *aten::movedim*

### Tickets:
 - *ticket-id*

Author: eaidova

src/frontends/pytorch/src/op/transpose.cpp

@@ -6,12 +6,17 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/add.hpp"
+#include "openvino/op/broadcast.hpp"
 #include "openvino/op/concat.hpp"
 #include "openvino/op/constant.hpp"
 #include "openvino/op/equal.hpp"
 #include "openvino/op/if.hpp"
+#include "openvino/op/non_zero.hpp"
+#include "openvino/op/not_equal.hpp"
 #include "openvino/op/range.hpp"
+#include "openvino/op/reshape.hpp"
 #include "openvino/op/scatter_elements_update.hpp"
+#include "openvino/op/shape_of.hpp"
 #include "openvino/op/unsqueeze.hpp"
 #include "utils.hpp"
 
@@ -81,7 +86,47 @@ OutputVector translate_t(const NodeContext& context) {
         if_node->set_input(input, param_then, param_else);
         return {if_node->set_output(result_then, result_else)};
     }
-}
+};
+
+OutputVector translate_movedim(const NodeContext& context) {
+    // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)
+    // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
+    // based on https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/TensorShape.cpp#L3816
+    num_inputs_check(context, 3, 3);
+    auto x = context.get_input(0);
+    auto src_dims = context.get_input(1);
+    auto dst_dims = context.get_input(2);
+    Output<Node> rank;
+    std::tie(std::ignore, rank) = get_shape_rank(context, context.get_input(0), true);
+    src_dims = normalize_axis(context, src_dims, rank);
+    dst_dims = normalize_axis(context, dst_dims, rank);
+    auto const_0 = context.mark_node(v0::Constant::create(element::i32, {}, {0}));
+    auto const_1 = context.mark_node(v0::Constant::create(element::i32, {}, {1}));
+    auto range = context.mark_node(std::make_shared<v4::Range>(const_0, rank, const_1, element::i32));
+    auto dims_1d_shape = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
+    // operation accepts 0d and 1d source and destination, make them always 1d
+    src_dims = context.mark_node(std::make_shared<v1::Reshape>(src_dims, dims_1d_shape, false));
+    dst_dims = context.mark_node(std::make_shared<v1::Reshape>(dst_dims, dims_1d_shape, false));
+    auto dims_shape = context.mark_node(std::make_shared<v3::ShapeOf>(src_dims, element::i32));
+    auto minus_one_replaces = context.mark_node(std::make_shared<v1::Broadcast>(dims_1d_shape, dims_shape));
+    // update position for the dim provided by user and mark used dims for source and destination as -1
+    auto perm_dims = context.mark_node(std::make_shared<v3::ScatterElementsUpdate>(range, dst_dims, src_dims, const_0));
+    auto src_perm_dims =
+        context.mark_node(std::make_shared<v3::ScatterElementsUpdate>(range, src_dims, minus_one_replaces, const_0));
+    auto dst_perm_dims =
+        context.mark_node(std::make_shared<v3::ScatterElementsUpdate>(range, dst_dims, minus_one_replaces, const_0));
+    // Remove the dims whose position we already know, the ones marked with -1 in previous step
+    auto not_changed_src = context.mark_node(std::make_shared<v1::NotEqual>(src_perm_dims, dims_1d_shape));
+    auto not_changed_dst = context.mark_node(std::make_shared<v1::NotEqual>(dst_perm_dims, dims_1d_shape));
+    auto indices = context.mark_node(std::make_shared<v3::NonZero>(not_changed_dst, element::i32));
+    auto updates = context.mark_node(std::make_shared<v3::NonZero>(not_changed_src, element::i32));
+    // Update the position of the remaining dimensions. indices now contains the original position
+    // updates contains the new position it will shifted to after considering the user inputs.
+    indices = context.mark_node(std::make_shared<v1::Reshape>(indices, dims_1d_shape, false));
+    updates = context.mark_node(std::make_shared<v1::Reshape>(updates, dims_1d_shape, false));
+    auto scatter = std::make_shared<v3::ScatterElementsUpdate>(perm_dims, indices, updates, const_0);
+    return {context.mark_node(std::make_shared<v1::Transpose>(x, scatter))};
+};
 
 }  // namespace op
 }  // namespace pytorch

src/frontends/pytorch/src/op_table.cpp

@@ -135,6 +135,7 @@ OP_CONVERTER(translate_mean);
 OP_CONVERTER(translate_meshgrid);
 OP_CONVERTER(translate_min);
 OP_CONVERTER(translate_minimum);
+OP_CONVERTER(translate_movedim);
 OP_CONVERTER(translate_multinomial);
 OP_CONVERTER(translate_narrow);
 OP_CONVERTER(translate_native_multi_head_attention);
@@ -433,6 +434,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::floor", op::optional_out<op::translate_1to1_match_1_inputs<opset10::Floor>, 1>},
         {"aten::floor_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Floor>>},
         {"aten::floor_divide", op::translate_floor_divide},
+        {"aten::floor_divide_", op::inplace_op<op::translate_floor_divide>},
         {"aten::floordiv", op::translate_floor_divide},
         {"aten::fmod", op::translate_fmod},
         {"aten::frobenius_norm", op::translate_frobenius_norm},
@@ -520,6 +522,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::mish", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Mish>},
         {"aten::mish_", op::inplace_op<op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Mish>>},
         {"aten::mm", op::translate_1to1_match_2_inputs<opset10::MatMul>},
+        {"aten::movedim", op::translate_movedim},
         {"aten::mul", op::translate_mul},
         {"aten::mul_", op::translate_mul_},
         {"aten::multiply", op::translate_mul},

tests/layer_tests/pytorch_tests/test_floor_divide.py

@@ -41,6 +41,21 @@ def forward(self, input_tensor, other_tensor):
 
         return aten_floor_divide(), ref_net, "aten::floor_divide"
 
+    def create_model_inplace(self):
+        import torch
+
+        class aten_floor_divide_(torch.nn.Module):
+            def __init__(self):
+                super(aten_floor_divide_, self).__init__()
+
+            def forward(self, input_tensor, other_tensor):
+                return input_tensor.floor_divide_(other_tensor), input_tensor
+
+        ref_net = None
+
+        return aten_floor_divide_(), ref_net, "aten::floor_divide_"
+
+
     @pytest.mark.parametrize('input_tensor',
     ([
         [5], [5, 5, 1], [1, 1, 5, 5],
@@ -65,6 +80,29 @@ def test_floor_divide(self, input_tensor, other_tensor, ie_device, precision, ir
             self.other_tensor = other_tensor
         self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True, use_convert_model=True)
 
+    @pytest.mark.parametrize('input_tensor',
+    ([
+        [5, 5, 5], [1, 1, 5, 5],
+    ]))
+    @pytest.mark.parametrize('other_tensor',
+    ([
+        np.array([0.5]).astype(np.float32), [5], [5, 1], [1, 5]
+    ]))
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
+                       reason='Ticket - 122715')
+    def test_floor_divide_(self, input_tensor, other_tensor, ie_device, precision, ir_version):
+        if type(input_tensor) is list:
+            self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
+        else:
+            self.input_tensor = input_tensor
+        if type(other_tensor) is list:
+            self.other_tensor = np.random.randn(*other_tensor).astype(np.float32)
+        else:
+            self.other_tensor = other_tensor
+        self._test(*self.create_model_inplace(), ie_device, precision, ir_version, trace_model=True, use_convert_model=True)
+
     @pytest.mark.parametrize('input_data',
     [
         { "tensor": [5], "low": 0, "high": 10 },

tests/layer_tests/pytorch_tests/test_transpose.py

@@ -50,6 +50,30 @@ def test_transpose(self, dim0, dim1, op_type, ie_device, precision, ir_version):
         self._test(*self.create_model(dim0, dim1, op_type), ie_device, precision, ir_version, trace_model=True)
 
 
+class TestMoveDim(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.randn(2, 3, 4, 5).astype(np.float32),)
+
+    def create_model(self, dim0, dim1):
+        class aten_move_dim(torch.nn.Module):
+            def __init__(self, dim0, dim1):
+                super(aten_move_dim, self).__init__()
+                self.dim0 = dim0
+                self.dim1 = dim1
+
+            def forward(self, x):
+                return torch.movedim(x, self.dim0, self.dim1)
+
+        ref_net = None
+
+        return aten_move_dim(dim0, dim1), ref_net, f"aten::movedim"
+
+    @pytest.mark.parametrize(("dim0", "dim1"), [[0, 1], [-1, 0], [2, -2], [3, 1], [3, 3], [[1, 2], [3, 0]], [[-4, 1], [1, -1]], [[1, 3, 2], [0, 1, 2 ]]])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_move_dim(self, dim0, dim1, ie_device, precision, ir_version):
+        self._test(*self.create_model(dim0, dim1), ie_device, precision, ir_version, trace_model=True)
+
 class TestTSmall(PytorchLayerTest):
     def _prepare_input(self, num_dims=2, input_dtype="float32"):
         shape = (2, 3)