Initial updates for et

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

@@ -192,8 +192,12 @@ def __init__(self, pt_module, fx_gm=None, nodes=None,
                         found_types.append(
                             OVAny(pt_to_ov_type_map[str(value.meta['tensor_meta'].dtype)]))
                     else:
-                        found_shapes.append(None)
-                        found_types.append(None)
+                        if hasattr(value, "meta") and ('val' in value.meta.keys()):
+                            found_shapes.append(value.meta["val"].shape)
+                            found_types.append(None)
+                        else:
+                            found_shapes.append(None)
+                            found_types.append(None)
                 elif value.op == 'output':
                     # Instead of putting output index, refer to its target
                     uargs = self.unpack_containers(value.args)
@@ -453,3 +457,32 @@ def mark_node(self, node):
         node.set_friendly_name(name)
         super().mark_node(node)
         return node
+
+
+class ExecuTorchPythonDecoder (TorchFXPythonDecoder):
+
+    def __init__(self, pt_module, fx_gm=None, nodes=None,
+                 mark_node_callback=None, input_shapes=[], input_types=[], dynamic_shapes=False):
+        TorchFXPythonDecoder.__init__(self, pt_module, fx_gm, nodes, mark_node_callback, input_shapes, input_types, dynamic_shapes)
+
+    def visit_subgraph(self, node_visitor):
+        # make sure topological order is satisfied
+        for node in self._nodes:
+            if node.op == 'placeholder' or node.op == 'output':
+                continue  # skipping non-operational nodes
+            if node.op == 'call_function' and str(node.target) in ["aten._assert_async.msg"]:
+                continue
+            decoder = ExecuTorchPythonDecoder(
+                node, self.fx_gm, self._nodes, mark_node_callback=self.mark_node_callback)
+            self.m_decoders.append(decoder)
+            node_visitor(decoder)
+
+    def get_op_type(self):
+        if "getitem" in str(self.pt_module.target):
+            return str(self.pt_module.target)
+        elif self.pt_module.op == 'call_function':
+            return self.pt_module.target.__name__
+        elif self.pt_module.op == 'get_attr':
+            return 'get_attr'  # FIXME should be aligned with get_attr from TS implementation
+        else:
+            return 'UNKNOWN_TYPE_' + str(self.pt_module.op)

src/bindings/python/src/openvino/frontend/pytorch/torchdynamo/compile.py

@@ -13,7 +13,7 @@
 from torch.fx import GraphModule
 
 from openvino.frontend import FrontEndManager
-from openvino.frontend.pytorch.fx_decoder import TorchFXPythonDecoder
+from openvino.frontend.pytorch.fx_decoder import TorchFXPythonDecoder, ExecuTorchPythonDecoder
 from openvino import Core, Type, PartialShape, serialize
 from openvino.frontend.pytorch.torchdynamo.backend_utils import _get_cache_dir, _get_device, _get_config, _is_cache_dir_in_config
 
@@ -78,7 +78,7 @@ def openvino_compile_cached_model(cached_model_path, options, *example_inputs):
 
     return compiled_model
 
-def openvino_compile(gm: GraphModule, *args, model_hash_str: str = None, options=None):
+def openvino_compile(gm: GraphModule, *args, model_hash_str: str = None, options=None, executorch=False):
     core = Core()
 
     device = _get_device(options)
@@ -101,7 +101,10 @@ def openvino_compile(gm: GraphModule, *args, model_hash_str: str = None, options
                 input_types.append(input_data.type())
                 input_shapes.append(input_data.size())
 
-        decoder = TorchFXPythonDecoder(gm)
+        if executorch:
+            decoder = ExecuTorchPythonDecoder(gm)
+        else:
+            decoder = TorchFXPythonDecoder(gm)
 
         im = fe.load(decoder)