[TF FE] Remove export of API for helper transformations (#13484)
* [TF FE] Remove export API for helper transformation Also, it skips layer tests for SegmentSum and DynamicPartition in case legacy frontend Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> * Split test suites for DynamicPartition * Update CMake to link additional sources for the tests * Update Cmake to fix unresolved symbol get_message_prefix_tf Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
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@ -24,7 +24,10 @@ endif()
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add_library(openvino_tensorflow_frontend_static_tests STATIC EXCLUDE_FROM_ALL
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helper_transforms/block_lstm_replacer.cpp
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helper_transforms/embedding_segments_feature_fusing.cpp
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helper_transforms/gru_block_cell_replacer.cpp
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helper_transforms/unique_replacer.cpp
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pass/transpose_sinking.cpp
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exception.cpp
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openvino_conversions.cpp
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utils.cpp)
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@ -18,7 +18,7 @@ namespace pass {
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// This transformation replaces BlockLSTM with such outputs as concatenated hidden states
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// and cell state from the last time step.
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class TENSORFLOW_API BlockLSTMReplacer : public ov::pass::MatcherPass {
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class BlockLSTMReplacer : public ov::pass::MatcherPass {
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public:
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OPENVINO_RTTI("ov::frontend::tensorflow::pass::BlockLSTMReplacer");
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BlockLSTMReplacer();
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@ -17,7 +17,7 @@ namespace tensorflow {
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namespace pass {
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// This transformation handles GRUBlockCell with just one output - hidden state
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class TENSORFLOW_API GRUBlockCellReplacer : public ov::pass::MatcherPass {
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class GRUBlockCellReplacer : public ov::pass::MatcherPass {
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public:
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OPENVINO_RTTI("ov::frontend::tensorflow::pass::GRUBlockCellReplacer");
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GRUBlockCellReplacer();
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@ -17,7 +17,7 @@ namespace tensorflow {
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namespace pass {
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// This transformation expresses Unique with a sub-graph of OpenVINO operations
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class TENSORFLOW_API UniqueReplacer : public ov::pass::MatcherPass {
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class UniqueReplacer : public ov::pass::MatcherPass {
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public:
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OPENVINO_RTTI("ov::frontend::tensorflow::pass::UniqueReplacer");
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UniqueReplacer();
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@ -45,6 +45,8 @@ class TestDynamicPartition(CommonTFLayerTest):
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@pytest.mark.nightly
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def test_dynamic_partition_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if not use_new_frontend:
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pytest.skip("DynamicPartition operation is not supported via legacy frontend.")
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self._test(*self.create_dynamic_partition_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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@ -58,6 +60,8 @@ class TestDynamicPartition(CommonTFLayerTest):
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@pytest.mark.nightly
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def test_dynamic_partition_other_types(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if not use_new_frontend:
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pytest.skip("DynamicPartition operation is not supported via legacy frontend.")
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self._test(*self.create_dynamic_partition_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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@ -42,6 +42,8 @@ class TestSegmentSum(CommonTFLayerTest):
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@pytest.mark.nightly
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def test_segment_sum_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if not use_new_frontend:
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pytest.skip("SegmentSum operation is not supported via legacy frontend.")
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self._test(*self.create_segment_sum_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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@ -57,6 +59,8 @@ class TestSegmentSum(CommonTFLayerTest):
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@pytest.mark.nightly
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def test_segment_sum_different_types(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if not use_new_frontend:
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pytest.skip("SegmentSum operation is not supported via legacy frontend.")
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self._test(*self.create_segment_sum_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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@ -30,6 +30,7 @@ class TestUnique(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(x_shape=[1], data_type=tf.float32, out_idx=tf.int32),
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dict(x_shape=[50], data_type=tf.float32, out_idx=tf.int32),
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dict(x_shape=[100], data_type=tf.float32, out_idx=tf.int64),
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]
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