refactor ResumableSamplePass.sample_handled to a abstract method (#466)

* init 'symbolic_dimension_reifier' field in graph_net.json

* remove unused files

* support --subgraph-ranges-json for typical_sequence_split_points.py

* minor fix for torch/decompose_util.py

* refactor ResumableSamplePass.sample_handled to a abstract method

Author: lixinqi

graph_net/sample_pass/resumable_sample_pass_mixin.py

@@ -18,11 +18,15 @@ def declare_config(
     ):
         pass
 
+    @abc.abstractmethod
     def sample_handled(self, rel_model_path: str) -> bool:
+        raise NotImplementedError()
+
+    def naive_sample_handled(self, rel_model_path: str, search_file_name: str) -> bool:
         dst_model_path = Path(self.config["output_dir"]) / rel_model_path
         if not dst_model_path.exists():
             return False
-        num_model_py_files = len(list(dst_model_path.rglob("model.py")))
+        num_model_py_files = len(list(dst_model_path.rglob(search_file_name)))
         assert num_model_py_files <= 1
         return num_model_py_files == 1
 

graph_net/torch/fx_graph_module_util.py

@@ -5,6 +5,13 @@
 from dataclasses import asdict
 
 
+def get_fx_graph_num_ops(fx_graph_module):
+    def get_num_ops(node):
+        return 0 if node.op in {"placeholder", "output"} else 1
+
+    return sum(map(get_num_ops, fx_graph_module.graph.nodes))
+
+
 def get_torch_module_and_inputs(model_path, use_dummy_inputs=True):
     module = _get_torch_module(model_path)
     tensor_metas = _get_tensor_metas(model_path)

graph_net/torch/sample_passes/device_rewrite_sample_pass.py

@@ -26,6 +26,9 @@ def declare_config(
     def __call__(self, rel_model_path: str):
         self.resumable_handle_sample(rel_model_path)
 
+    def sample_handled(self, rel_model_path: str) -> bool:
+        return self.naive_sample_handled(rel_model_path, search_file_name="model.py")
+
     def resume(self, rel_model_path: str):
         return self.copy_sample_and_handle_model_py_file(rel_model_path)