[PT FE] Support dynamic shapes torch.export

src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py

@@ -515,12 +515,12 @@ def may_produce_alias(self, in_index: int, out_index: int) -> bool:
             # Sometimes pytorch fails to get result with IndexError exception while these indexes exist in node
             return False
 
-    def inlined_input(self, index):
-        return []
-
     def is_input_inlined(self, index):
         return False
 
+    def get_inlined_input_decoder(self, index):
+        return None
+
     def get_attribute(self, name):
         return OVAny(None)
 

src/bindings/python/src/pyopenvino/frontend/pytorch/decoder.hpp

@@ -114,14 +114,14 @@ class PyDecoder : public ov::frontend::pytorch::TorchDecoder {
         PYBIND11_OVERRIDE_PURE(bool, TorchDecoder, may_produce_alias, in_index, out_index);
     }
 
-    ov::OutputVector inlined_input(size_t index) const override {
-        PYBIND11_OVERRIDE_PURE(ov::OutputVector, TorchDecoder, inlined_input, index);
-    }
-
     bool is_input_inlined(size_t index) const override {
         PYBIND11_OVERRIDE_PURE(bool, TorchDecoder, is_input_inlined, index);
     }
 
+    std::shared_ptr<TorchDecoder> get_inlined_input_decoder(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(std::shared_ptr<TorchDecoder>, TorchDecoder, get_inlined_input_decoder, index);
+    }
+
     ov::Any get_attribute(const std::string &name) const override{
         PYBIND11_OVERRIDE_PURE(ov::Any, TorchDecoder, get_attribute, name);
     }

src/frontends/pytorch/include/openvino/frontend/pytorch/decoder.hpp

@@ -113,14 +113,13 @@ class TorchDecoder : public IDecoder {
     /// \brief Returns if output may contain alias of input in AliasDB
     virtual bool may_produce_alias(size_t in_index, size_t out_index) const = 0;
 
-    /// Returns new nodes for inputs inlined in the op itself
-    // Used in Torch.FX decoder
-    virtual OutputVector inlined_input(size_t index) const = 0;
-
     /// Returns if input is inlined
     // Used in Torch.FX decoder
     virtual bool is_input_inlined(size_t index) const = 0;
 
+    /// Return decoder for inlined input
+    virtual std::shared_ptr<TorchDecoder> get_inlined_input_decoder(size_t index) const = 0;
+
     /// Returns named attribute as Any. For example kwargs input for FX graph
     virtual ov::Any get_attribute(const std::string& name) const = 0;
 

src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp

@@ -51,44 +51,9 @@ class NodeContext : public frontend::NodeContext {
 
     // Search for input in tensor map and return an output port for already converted op
     // TODO: int due to base class uses it, but naturally it should be size_t for PT
-    Output<Node> get_input(int index) const override {
-        size_t index_ = static_cast<size_t>(index);
-        FRONT_END_GENERAL_CHECK(!m_decoder->input_is_none(index_),
-                                "Input doesn't exist with index: ",
-                                index,
-                                " for operation ",
-                                get_op_type());
-        auto input = m_decoder_inputs.at(index);
-        if (input == 0) {
-            // Case when input can be inlined (possible only for fx decoder)
-            if (m_decoder->is_input_inlined(index_)) {
-                auto inlined_input = m_decoder->inlined_input(index_);
-                FRONT_END_GENERAL_CHECK(inlined_input.size() == 1,
-                                        "Incorrect inlined input with index: ",
-                                        index,
-                                        " for operation ",
-                                        get_op_type());
-                return inlined_input[0];
-            }
-        }
-        FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist.");
-        return m_tensor_map->at(input);
-    }
+    Output<Node> get_input(int index) const override;
 
-    Output<Node> get_input(const std::string& name) const override {
-        FRONT_END_GENERAL_CHECK(has_attribute(name), "Input with name ", name, " doesn't exist");
-        auto attr = get_attribute_as_any(name);
-        if (attr.is<Output<Node>>()) {
-            // Case when input is constant value
-            return attr.as<Output<Node>>();
-        } else if (attr.is<type::PyNone>()) {
-            // None means input is unknown type, most likely a Node
-            auto input = m_decoder->get_named_input(name);
-            FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist.");
-            return m_tensor_map->at(input);
-        }
-        FRONT_END_GENERAL_CHECK(false, "Input has type which can't be converted to ov::Node.");
-    }
+    Output<Node> get_input(const std::string& name) const override;
 
     Any get_values_from_const_input(int index) const override;
 

src/frontends/pytorch/src/node_context.cpp

@@ -145,39 +145,65 @@ std::shared_ptr<ov::Model> NodeContext::convert_subgraph(size_t index) const {
     return model;
 }
 
+Output<Node> NodeContext::get_input(int index) const {
+    size_t index_ = static_cast<size_t>(index);
+    auto input = m_decoder_inputs.at(index);
+    if (input == 0) {
+        // Case when input can be inlined (possible only for fx decoder)
+        if (m_decoder->is_input_inlined(index_)) {
+            if (m_decoder->input_is_none(index_)) {
+                // some operations like aten.index.Tensor can have None inputs
+                auto dummy_decoder = std::make_shared<InternalOpDecoder>("torch::None", 1);
+                auto fw_node = std::make_shared<PtFrameworkNode>(dummy_decoder, OutputVector{});
+                auto attrs = fw_node->get_attrs();
+                attrs["none_value"] = "";
+                attrs[PtFrameworkNode::failed_conversion_key] =
+                    "None constant cannot be converted to OpenVINO opset and should be removed by consuming "
+                    "operation.";
+                fw_node->set_attrs(attrs);
+                return fw_node->output(0);
+            } else {
+                auto inlined_decoder = m_decoder->get_inlined_input_decoder(index_);
+                auto inlined_ctx = NodeContext(inlined_decoder,
+                                               m_ext_tensor_map,
+                                               m_tensor_map,
+                                               m_external_parameters,
+                                               m_mutated_tensors,
+                                               m_translate_session);
+                auto inlined_input = m_translate_session->convert_node(inlined_ctx);
+                FRONT_END_GENERAL_CHECK(inlined_input.size() == 1,
+                                        "Incorrect inlined input with index: ",
+                                        index,
+                                        " for operation ",
+                                        get_op_type());
+                return inlined_input[0];
+            }
+        }
+    }
+    auto tensor_it = m_tensor_map->find(input);
+    FRONT_END_GENERAL_CHECK(tensor_it != m_tensor_map->end(), "No tensor corresponding input: ", input, " exist.");
+    return tensor_it->second;
+}
+
+Output<Node> NodeContext::get_input(const std::string& name) const {
+    FRONT_END_GENERAL_CHECK(has_attribute(name), "Input with name ", name, " doesn't exist");
+    auto attr = get_attribute_as_any(name);
+    if (attr.is<Output<Node>>()) {
+        // Case when input is constant value
+        return attr.as<Output<Node>>();
+    } else if (attr.is<type::PyNone>()) {
+        // None means input is unknown type, most likely a Node
+        auto input = m_decoder->get_named_input(name);
+        FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist.");
+        return m_tensor_map->at(input);
+    }
+    FRONT_END_GENERAL_CHECK(false, "Input has type which can't be converted to ov::Node.");
+}
+
 OutputVector NodeContext::inputs() const {
     OutputVector res;
     for (size_t i = 0; i < m_decoder_inputs.size(); i++) {
-        auto input = m_decoder_inputs.at(i);
-        if (input == 0) {
-            // Case when input can be inlined (possible only for fx decoder)
-            if (m_decoder->is_input_inlined(i)) {
-                if (input_is_none(i)) {
-                    // some operations like aten.index.Tensor can have None inputs
-                    auto dummy_decoder = std::make_shared<InternalOpDecoder>("torch::None", 1);
-                    auto fw_node = std::make_shared<PtFrameworkNode>(dummy_decoder, OutputVector{});
-                    auto attrs = fw_node->get_attrs();
-                    attrs["none_value"] = "";
-                    attrs[PtFrameworkNode::failed_conversion_key] =
-                        "None constant cannot be converted to OpenVINO opset and should be removed by consuming "
-                        "operation.";
-                    fw_node->set_attrs(attrs);
-                    res.push_back(fw_node->output(0));
-                } else {
-                    auto inlined_input = m_decoder->inlined_input(i);
-                    FRONT_END_GENERAL_CHECK(inlined_input.size() == 1,
-                                            "Incorrect inlined input with index: ",
-                                            i,
-                                            " for operation ",
-                                            get_op_type());
-                    res.push_back(inlined_input[0]);
-                }
-                continue;
-            }
-        }
-        auto tensor_it = m_tensor_map->find(input);
-        FRONT_END_GENERAL_CHECK(tensor_it != m_tensor_map->end(), "No tensor corresponding input: ", input, " exist.");
-        res.push_back(tensor_it->second);
+        res.push_back(get_input(static_cast<int>(i)));
     }
     return res;
 }

src/frontends/pytorch/src/op/as_strided.cpp

@@ -20,21 +20,20 @@ namespace pytorch {
 namespace op {
 
 using namespace ov::op;
+
+namespace {
 bool compare_strides(const std::tuple<size_t, size_t>& a, const std::tuple<size_t, size_t>& b) {
     return std::get<0>(a) > std::get<0>(b);
 }
-OutputVector translate_as_strided(const NodeContext& context) {
-    // "aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)"
-    num_inputs_check(context, 3, 4);
-    auto decoder = context.get_decoder();
-    auto input = context.get_input(0);
+
+OutputVector translate_as_strided_common(const NodeContext& context,
+                                         const Output<Node>& input,
+                                         const std::vector<size_t>& input_strides,
+                                         const std::deque<Output<Node>>& sizes,
+                                         const std::deque<Output<Node>>& strides) {
     auto const_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
     auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
     auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
-    auto input_strides = decoder->get_input_strides(0);
-    PYTORCH_OP_CONVERSION_CHECK(input_strides.size() != 0,
-                                "aten::as_strided: Couldn't retrieve input stride information from torchscript.");
-
     std::vector<size_t> idxs(input_strides.size());
     iota(idxs.begin(), idxs.end(), 0);
     std::vector<std::tuple<size_t, size_t>> stride_idxs(idxs.size());
@@ -53,26 +52,6 @@ OutputVector translate_as_strided(const NodeContext& context) {
         context.mark_node(v0::Constant::create(element::i32, Shape{transpose_idx.size()}, transpose_idx));
     auto transposed_input = context.mark_node(std::make_shared<v1::Transpose>(input, transpose_idx_const));
     auto flat_input = context.mark_node(std::make_shared<v1::Reshape>(transposed_input, const_neg_1, false));
-    std::deque<Output<Node>> sizes;
-    std::deque<Output<Node>> strides;
-    if (std::dynamic_pointer_cast<v0::Constant>(context.get_input_from_visible_context(1).get_node_shared_ptr())) {
-        auto input_vector = context.const_input<std::vector<int64_t>>(1);
-        std::for_each(input_vector.rbegin(), input_vector.rend(), [&](int64_t input_val) {
-            auto const_input = context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_val}));
-            sizes.push_front(const_input);
-        });
-    } else {
-        sizes = get_list_as_outputs(context.get_input(1));
-    }
-    if (std::dynamic_pointer_cast<v0::Constant>(context.get_input_from_visible_context(2).get_node_shared_ptr())) {
-        auto input_vector = context.const_input<std::vector<int64_t>>(2);
-        std::for_each(input_vector.rbegin(), input_vector.rend(), [&](int64_t input_val) {
-            auto const_input = context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_val}));
-            strides.push_front(const_input);
-        });
-    } else {
-        strides = get_list_as_outputs(context.get_input(2));
-    }
     auto offset = const_0->output(0);
     if (!context.input_is_none(3)) {
         offset = get_input_as_i32(context, 3);
@@ -84,12 +63,12 @@ OutputVector translate_as_strided(const NodeContext& context) {
     auto strides_length_const = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {strides.size()}));
     auto ones_strides_len = context.mark_node(std::make_shared<v0::Tile>(const_1, strides_length_const));
     auto indices = const_0;
-    std::for_each(strides.rbegin(), strides.rend(), [&](Output<Node>& stride) {
+    std::for_each(strides.rbegin(), strides.rend(), [&](const Output<Node>& stride) {
         auto const_num_iter = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {strides_size - i}));
-        stride = context.mark_node(std::make_shared<v0::Convert>(stride, element::i32));
+        auto stride_conv = context.mark_node(std::make_shared<v0::Convert>(stride, element::i32));
         auto size = sizes.at(strides_size - i);
         auto range = context.mark_node(std::make_shared<v4::Range>(const_0, size, const_1, element::i32));
-        range = context.mark_node(std::make_shared<v1::Multiply>(range, stride));
+        range = context.mark_node(std::make_shared<v1::Multiply>(range, stride_conv));
         auto iteration_shape = context.mark_node(
             std::make_shared<v3::ScatterUpdate>(ones_strides_len, const_num_iter, const_neg_1, const_0));
         range = context.mark_node(std::make_shared<v1::Reshape>(range, iteration_shape, false));
@@ -99,7 +78,41 @@ OutputVector translate_as_strided(const NodeContext& context) {
     indices = context.mark_node(std::make_shared<v1::Add>(indices, offset));
     auto gather = context.mark_node(std::make_shared<v8::Gather>(flat_input, indices, const_0));
     return {gather};
+}
+}  // namespace
+
+OutputVector translate_as_strided(const NodeContext& context) {
+    // "aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)"
+    num_inputs_check(context, 3, 4);
+    auto decoder = context.get_decoder();
+    auto input = context.get_input(0);
+    auto input_strides = decoder->get_input_strides(0);
+    PYTORCH_OP_CONVERSION_CHECK(input_strides.size() != 0,
+                                "aten::as_strided: Couldn't retrieve input stride information from torchscript.");
+
+    std::deque<Output<Node>> sizes;
+    std::deque<Output<Node>> strides;
+    if (std::dynamic_pointer_cast<v0::Constant>(context.get_input_from_visible_context(1).get_node_shared_ptr())) {
+        auto input_vector = context.const_input<std::vector<int64_t>>(1);
+        std::for_each(input_vector.rbegin(), input_vector.rend(), [&](int64_t input_val) {
+            auto const_input = context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_val}));
+            sizes.push_front(const_input);
+        });
+    } else {
+        sizes = get_list_as_outputs(context.get_input(1));
+    }
+    if (std::dynamic_pointer_cast<v0::Constant>(context.get_input_from_visible_context(2).get_node_shared_ptr())) {
+        auto input_vector = context.const_input<std::vector<int64_t>>(2);
+        std::for_each(input_vector.rbegin(), input_vector.rend(), [&](int64_t input_val) {
+            auto const_input = context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_val}));
+            strides.push_front(const_input);
+        });
+    } else {
+        strides = get_list_as_outputs(context.get_input(2));
+    }
+    return translate_as_strided_common(context, input, input_strides, sizes, strides);
 };
+
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend

src/frontends/pytorch/src/op/cat.cpp

@@ -104,18 +104,11 @@ OutputVector translate_cat(const NodeContext& context) {
 };
 
 OutputVector translate_cat_fx(const NodeContext& context) {
-    // This translator is only needed to get axis as constant from external scope
-    num_inputs_check(context, 1, context.get_input_size());
-    std::deque<Output<Node>> list_elems;
-    for (size_t i = 0; i < context.get_input_size() - 1; i++) {
-        list_elems.push_back(context.get_input(static_cast<int>(i)));
-    }
+    num_inputs_check(context, 1, 2);
+    const auto&& list_elems = get_list_as_outputs(context.get_input(0));
     int64_t axis = 0;
-    if (!context.get_input_type(context.get_input_size() - 1).is<type::List>()) {
-        // axis can be not present and that means that last input will have List type
-        axis = context.const_input<int64_t>(context.get_input_size() - 1);
-    } else {
-        list_elems.push_back(context.get_input(static_cast<int>(context.get_input_size() - 1)));
+    if (!context.input_is_none(1)) {
+        axis = context.const_input<int64_t>(1);
     }
     return translate_cat_common(context, list_elems, axis, true);
 };

src/frontends/pytorch/src/op/expand.cpp

@@ -43,24 +43,6 @@ OutputVector translate_expand_as(const NodeContext& context) {
     return {context.mark_node(std::make_shared<v3::Broadcast>(x, shape, BroadcastType::BIDIRECTIONAL))};
 };
 
-OutputVector translate_expand_fx(const NodeContext& context) {
-    auto num_inputs = context.get_input_size();
-    num_inputs_check(context, 2, num_inputs);
-    auto x = context.get_input(0);
-    std::vector<int32_t> shape_vec;
-    if (context.get_input_type(1).is<type::List>()) {
-        auto concat = concat_list_from_inputs(context, 1, num_inputs);
-        return base_expand(context, x, concat);
-    } else {
-        auto x = context.get_input(0);
-        auto sizes = context.get_input(1);
-        // TODO: figure out what implicit means
-        PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<bool>(2) == false,
-                                    "Unexpected value of implicit for expand operation");
-        return base_expand(context, x, sizes);
-    }
-};
-
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend

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

@@ -75,15 +75,9 @@ OutputVector translate_full(const NodeContext& context) {
 OutputVector translate_full_fx(const NodeContext& context) {
     // aten.full.default([16, 16], 0, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'),
     // pin_memory = False)
-    auto num_inputs = context.get_input_size();
-    num_inputs_check(context, 2, num_inputs);
-    ov::Output<ov::Node> sizes;
-    if (context.get_input_type(0).is<type::List>()) {
-        sizes = concat_list_from_inputs(context, 0, num_inputs - 1);
-    } else {
-        sizes = context.get_input(0);
-    }
-    auto value = context.get_input(static_cast<int>(num_inputs - 1));
+    num_inputs_check(context, 2, 2);
+    auto sizes = get_input_concat_if_list(context, 0);
+    auto value = context.get_input(1);
 
     auto filled_tensor = base_translate_full(context, sizes, value);
     if (context.has_attribute("dtype")) {