[PT FE]: revert usage mo.convert_model in pt layer tests (#16573)
* [PT FE]: revert usage mo.convert_model in tests * fix failed test
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@ -104,7 +104,7 @@ class TorchScriptPythonDecoder (Decoder):
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else:
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else:
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self.graph_element = graph_element
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self.graph_element = graph_element
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self.pt_module = pt_module
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self.pt_module = pt_module
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self.raw_inputs = [inp for inp in self.graph_element.inputs()]
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self.raw_inputs = list(self.graph_element.inputs())
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self.raw_outputs = list(self.graph_element.outputs())
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self.raw_outputs = list(self.graph_element.outputs())
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if self._input_signature is not None and self.raw_inputs[0].debugName() == "self":
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if self._input_signature is not None and self.raw_inputs[0].debugName() == "self":
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self._input_signature.insert(0, "self")
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self._input_signature.insert(0, "self")
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@ -171,7 +171,7 @@ class TorchScriptPythonDecoder (Decoder):
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return self._raw_input(index).debugName()
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return self._raw_input(index).debugName()
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def get_input_signature_name(self, index: int) -> str:
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def get_input_signature_name(self, index: int) -> str:
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if self._input_signature is not None:
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if self._input_signature is not None and index < len(self._input_signature):
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return self._input_signature[index]
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return self._input_signature[index]
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return self.get_input_debug_name(index)
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return self.get_input_debug_name(index)
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@ -26,7 +26,7 @@ OutputVector translate_add(const NodeContext& context) {
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// Case when two lists gets concatenated
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// Case when two lists gets concatenated
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FRONT_END_OP_CONVERSION_CHECK(false, "aten::add is used for concatenation of lists, not possible to convert");
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FRONT_END_OP_CONVERSION_CHECK(false, "aten::add is used for concatenation of lists, not possible to convert");
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}
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}
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align_eltwise_input_types(context, lhs, rhs);
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align_eltwise_input_types(context, lhs, rhs, true);
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if (!context.input_is_none(2)) {
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if (!context.input_is_none(2)) {
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auto converted_alpha = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(context.get_input(2), rhs));
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auto converted_alpha = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(context.get_input(2), rhs));
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rhs = context.mark_node(std::make_shared<ov::op::v1::Multiply>(converted_alpha, rhs));
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rhs = context.mark_node(std::make_shared<ov::op::v1::Multiply>(converted_alpha, rhs));
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@ -92,7 +92,7 @@ OutputVector translate_1to1_match_2_inputs_align_types(const NodeContext& contex
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FRONT_END_OP_CONVERSION_CHECK(!context.input_is_none(0) && !context.input_is_none(1), "Inputs should not be None.");
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FRONT_END_OP_CONVERSION_CHECK(!context.input_is_none(0) && !context.input_is_none(1), "Inputs should not be None.");
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auto lhs = context.get_input(0);
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auto lhs = context.get_input(0);
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auto rhs = context.get_input(1);
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auto rhs = context.get_input(1);
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align_eltwise_input_types(context, lhs, rhs);
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align_eltwise_input_types(context, lhs, rhs, true);
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return {context.mark_node(std::make_shared<T>(lhs, rhs))};
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return {context.mark_node(std::make_shared<T>(lhs, rhs))};
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}
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}
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@ -52,15 +52,8 @@ class PytorchLayerTest:
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inp, np.ndarray) else inp for inp in inputs]
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inp, np.ndarray) else inp for inp in inputs]
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trace_model = kwargs.get('trace_model', False)
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trace_model = kwargs.get('trace_model', False)
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freeze_model = kwargs.get('freeze_model', True)
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freeze_model = kwargs.get('freeze_model', True)
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use_mo_convert = kwargs.get("use_mo_convert", True)
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model, converted_model = self.convert_directly_via_frontend(model, torch_inputs, trace_model, dynamic_shapes, inputs, freeze_model)
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if not freeze_model or not use_mo_convert:
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model, converted_model = self.convert_directly_via_frontend(
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model, torch_inputs, trace_model, dynamic_shapes, inputs, freeze_model)
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else:
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model, converted_model = self.convert_via_mo(
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model, torch_inputs, trace_model, dynamic_shapes, inputs)
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graph = model.inlined_graph
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graph = model.inlined_graph
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print(graph)
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if kind is not None and not isinstance(kind, (tuple, list)):
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if kind is not None and not isinstance(kind, (tuple, list)):
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kind = [kind]
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kind = [kind]
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@ -181,6 +174,7 @@ class PytorchLayerTest:
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model = torch.jit.trace(model, example_input)
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model = torch.jit.trace(model, example_input)
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else:
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else:
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model = torch.jit.script(model)
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model = torch.jit.script(model)
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print(model.inlined_graph)
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decoder = TorchScriptPythonDecoder(model, freeze=freeze_model)
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decoder = TorchScriptPythonDecoder(model, freeze=freeze_model)
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im = fe.load(decoder)
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im = fe.load(decoder)
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om = fe.convert(im)
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om = fe.convert(im)
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