Merge branch 'github_actions/revert_pr_caches' of https://github.com/…

…mryzhov/openvino into github_actions/revert_pr_caches

src/frontends/pytorch/src/frontend.cpp

@@ -21,7 +21,6 @@
 #include "transformations/op_conversions/convert_convertlike.hpp"
 #include "transformations/op_conversions/convert_convertpromotetypes.hpp"
 #include "transformations/resolve_names_collisions.hpp"
-#include "transforms.hpp"
 #include "transforms/append_list_unpack_replacer.hpp"
 #include "transforms/aten_cat_replacer.hpp"
 #include "transforms/aten_getitem_replacer.hpp"
@@ -221,8 +220,6 @@ void FrontEnd::normalize(const std::shared_ptr<ov::Model>& model) const {
     manager.register_pass<ov::pass::ResolveNameCollisions>(true);
     manager.run_passes(model);
 
-    apply_pytorch_conversion_transforms(model);
-
     // Usually if nn.Module.forward is given as a source model for conversion, there is the first Parameter
     // that represents original `self` argument in forward(self, ...). `self` shouldn't play any role in model
     // inference if model is completely frozen and all methods are inlined. So we check if it doesn't have any

src/frontends/pytorch/src/input_model.cpp

@@ -93,8 +93,8 @@ void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
                             "Provided place is invalid, only inputs are supported for setting tensor value.");
     auto pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(place);
     FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported");
-    const auto el_type = pytorch_place->m_type;
-    const auto p_shape = pytorch_place->m_pshape;
+    const auto& el_type = pytorch_place->m_type;
+    const auto& p_shape = pytorch_place->m_pshape;
     FRONT_END_GENERAL_CHECK(el_type.is_static() && p_shape.is_static(),
                             "Shape and type must be statically defined before calling set_tensor_value");
     auto const_node = ov::op::v0::Constant::create(el_type, p_shape.to_shape(), value);

src/frontends/pytorch/src/node_context.cpp

@@ -23,7 +23,7 @@ OutputVector NodeContext::as_constant() const {
     auto dtype = m_decoder->get_output_type(0);
     if (dtype.is<type::Str>()) {
         // Cannot represent string as Constant, creating FrameworkNode
-        auto str = m_decoder->as_string();
+        const auto& str = m_decoder->as_string();
         auto fw_node = std::make_shared<PtFrameworkNode>(m_decoder, OutputVector{});
         auto attrs = fw_node->get_attrs();
         attrs["string_value"] = str;

src/frontends/pytorch/src/op/full.cpp

@@ -94,7 +94,7 @@ OutputVector translate_full_like(const NodeContext& context) {
     if (context.get_input_size() == 7 && !context.input_is_none(2)) {
         return {base_translate_full_with_convert(context, sizes, value, 2)};
     }
-    auto out = context.input_is_none(3) ? input : context.get_input(3);
+    const auto& out = context.input_is_none(3) ? input : context.get_input(3);
     return {base_translate_full_with_convertlike(context, sizes, value, out)};
 };
 
@@ -118,7 +118,7 @@ OutputVector translate_fill(const NodeContext& context) {
     auto input = context.get_input(0);
     auto value = context.get_input(1);
     auto sizes = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
-    auto out = context.input_is_none(2) ? input : context.get_input(2);
+    const auto& out = context.input_is_none(2) ? input : context.get_input(2);
     auto result = base_translate_full_with_convertlike(context, sizes, value, out);
     if (!context.input_is_none(2)) {
         context.mutate_input(2, result);
@@ -189,7 +189,7 @@ OutputVector translate_zeros_like(const NodeContext& context) {
     if (context.get_input_size() == 6 && !context.input_is_none(1)) {
         return {base_translate_full_with_convert(context, sizes, value, 1)};
     }
-    auto out = context.input_is_none(2) ? input : context.get_input(2);
+    const auto& out = context.input_is_none(2) ? input : context.get_input(2);
     return {base_translate_full_with_convertlike(context, sizes, value, out)};
 };
 
@@ -271,7 +271,7 @@ OutputVector translate_ones_like(const NodeContext& context) {
     if (context.get_input_size() == 6 && !context.input_is_none(1)) {
         return {base_translate_full_with_convert(context, sizes, value, 1)};
     }
-    auto out = context.input_is_none(2) ? input : context.get_input(2);
+    const auto& out = context.input_is_none(2) ? input : context.get_input(2);
     return {base_translate_full_with_convertlike(context, sizes, value, out)};
 };
 
@@ -352,7 +352,7 @@ OutputVector translate_empty_like(const NodeContext& context) {
             empty = base_translate_full_with_convertlike(context, sizes, value, input);
         }
     } else if (context.get_input_size() == 4) {
-        auto out = context.input_is_none(3) ? input : context.get_input(3);
+        const auto& out = context.input_is_none(3) ? input : context.get_input(3);
         empty = base_translate_full_with_convertlike(context, sizes, value, out);
         if (!context.input_is_none(3)) {
             context.mutate_input(3, empty);

src/frontends/pytorch/src/op/if.cpp

@@ -91,8 +91,8 @@ OutputVector translate_if(const NodeContext& context) {
     }
     OutputVector res;
     const auto num_outs = context.get_output_size();
-    const auto then_results = then_body->get_results();
-    const auto else_results = else_body->get_results();
+    const auto& then_results = then_body->get_results();
+    const auto& else_results = else_body->get_results();
     PYTORCH_OP_CONVERSION_CHECK(then_results.size() >= num_outs && else_results.size() >= num_outs,
                                 "Else or then body have less outputs than prim::If requires.");
     for (size_t i = 0; i < num_outs; i++) {

src/frontends/pytorch/src/op/lstm.cpp

@@ -99,7 +99,7 @@ OutputVector generic_rnn(ov::pass::NodeRegistry& rg,
                                 "Unexpected length of list with weights for rnn operation.");
 
     const auto w_hh = all_weights[1];
-    const auto w_hh_pshape = w_hh.get_partial_shape();
+    const auto& w_hh_pshape = w_hh.get_partial_shape();
     PYTORCH_OP_CONVERSION_CHECK(w_hh_pshape.rank().is_static() && w_hh_pshape[1].is_static(), "");
     const auto hidden_size = w_hh_pshape[1].get_length();
 
@@ -151,8 +151,8 @@ OutputVector generic_rnn(ov::pass::NodeRegistry& rg,
             weight_ih = convert_data_format(rg, variant, all_weights[idx]);
             weight_hh = convert_data_format(rg, variant, all_weights[idx + 1]);
             if (has_biases) {
-                const auto bias_ih = all_weights[idx + 2];
-                const auto bias_hh = all_weights[idx + 3];
+                const auto& bias_ih = all_weights[idx + 2];
+                const auto& bias_hh = all_weights[idx + 3];
                 bias_concat = format_bias(rg, variant, bias_ih, bias_hh);
             }
         } else {
@@ -163,12 +163,12 @@ OutputVector generic_rnn(ov::pass::NodeRegistry& rg,
             if (has_biases) {
                 weight_ih_f = all_weights[2 * idx];
                 weight_hh_f = all_weights[2 * idx + 1];
-                const auto bias_ih_f = all_weights[2 * idx + 2];
-                const auto bias_hh_f = all_weights[2 * idx + 3];
+                const auto& bias_ih_f = all_weights[2 * idx + 2];
+                const auto& bias_hh_f = all_weights[2 * idx + 3];
                 weight_ih_b = all_weights[2 * idx + 4];
                 weight_hh_b = all_weights[2 * idx + 5];
-                const auto bias_ih_b = all_weights[2 * idx + 6];
-                const auto bias_hh_b = all_weights[2 * idx + 7];
+                const auto& bias_ih_b = all_weights[2 * idx + 6];
+                const auto& bias_hh_b = all_weights[2 * idx + 7];
                 const auto bias_f = format_bias(rg, variant, bias_ih_f, bias_hh_f);
                 const auto bias_b = format_bias(rg, variant, bias_ih_b, bias_hh_b);
                 bias_concat = rg.make<v0::Concat>(OutputVector{bias_f, bias_b}, 0);

src/frontends/pytorch/src/op/max_poolnd.cpp

@@ -63,7 +63,7 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
         const auto one = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
         const auto two = context.mark_node(v0::Constant::create(element::i32, Shape{}, {2}));
 
-        const auto padding =
+        const auto& padding =
             context.input_is_none(3)
                 ? context.mark_node(std::make_shared<v0::Constant>(element::i32, Shape{pads.size()}, 0))->output(0)
                 : get_input_as_i32(context, 3);
@@ -84,7 +84,7 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
         const auto shape_of_mp = context.mark_node(std::make_shared<v3::ShapeOf>(mp, element::i32));
         const auto gth_out_dims = context.mark_node(std::make_shared<v8::Gather>(shape_of_mp, dim_idxs, zero));
         const auto out_sub_one = context.mark_node(std::make_shared<v1::Subtract>(gth_out_dims, one));
-        const auto stride_node = use_kernel ? context.get_input(1) : context.get_input(2);
+        const auto& stride_node = use_kernel ? context.get_input(1) : context.get_input(2);
         const auto stride_node_i32 = context.mark_node(std::make_shared<v0::Convert>(stride_node, element::i32));
         const auto out_mul_stride = context.mark_node(std::make_shared<v1::Multiply>(out_sub_one, stride_node_i32));
 
@@ -94,7 +94,8 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
 
         // apply padding on input clear pads attribute
         const auto pb = context.mark_node(std::make_shared<v0::Concat>(OutputVector{pads_remaining, padding}, 0));
-        const auto pe = context.mark_node(std::make_shared<v0::Concat>(OutputVector{pads_remaining, selected_pads}, 0));
+        const auto& pe =
+            context.mark_node(std::make_shared<v0::Concat>(OutputVector{pads_remaining, selected_pads}, 0));
         auto minus_inf =
             context.mark_node(v0::Constant::create(element::f32, Shape{}, {-std::numeric_limits<float>::infinity()}));
         minus_inf = context.mark_node(std::make_shared<v1::ConvertLike>(minus_inf, input));

src/frontends/pytorch/src/op/roll.cpp

@@ -25,8 +25,8 @@ OutputVector translate_roll(const NodeContext& context) {
     bool on_flattened = context.input_is_none(2);
     if (!on_flattened) {
         axes = context.get_input(2);
-        const auto shifts_pshape = shifts.get_partial_shape();
-        const auto axes_pshape = axes.get_partial_shape();
+        const auto& shifts_pshape = shifts.get_partial_shape();
+        const auto& axes_pshape = axes.get_partial_shape();
         on_flattened = !axes_pshape.compatible(shifts_pshape);
     }
     if (on_flattened) {

src/frontends/pytorch/src/op/scatter.cpp

@@ -22,7 +22,7 @@ Output<Node> prepare_source(const NodeContext& context,
                             const Output<Node>& src,
                             const Output<Node>& index,
                             const Output<Node>& input) {
-    auto src_partial_shape = src.get_partial_shape();
+    const auto& src_partial_shape = src.get_partial_shape();
     auto index_shape_rank = get_shape_rank(context, index);
     auto index_shape = std::get<0>(index_shape_rank);
     auto index_rank = std::get<1>(index_shape_rank);