Update base for rebase with regression fix on "[Migration][DO NOT MER…

…GE] Fix and enable all new tests"


- Somewhat unrelated: moved "tests" folder out of "onnxscript".
- Updated import paths in copied code.
- Updated testdata lookup path due to relative path changes.
- Discovered an issue with installing via "pip install -e .". #1327
- Fixed a few issues of code under "ir" that doesn't work under python 3.8, or onnx w/ ir_version < 10.

All CI tests except for linter are expected to pass.

[ghstack-poisoned]

onnxscript/function_libs/torch_lib/graph_building.py

@@ -822,7 +822,9 @@ def _override_with_symbolic_value_info_proto(self, onnx_model: onnx.ModelProto):
         # nn.Modules exported by dynamo exporter have unique call sites, their function
         # op_type name can serve to form the unique identifier for value info.
         # Store inside top level GraphProto.
-        new_value_info = self.generate_maingraph_value_info_proto()
+        new_value_info = self.generate_subgraphs_value_info_proto()
+        # Insert value info for nodes in top level graph.
+        new_value_info.update(self.generate_maingraph_value_info_proto())
         # Do not store input, output or initializer into value_info
         for input in onnx_model.graph.input:
             new_value_info.pop(input.name, None)
@@ -908,7 +910,7 @@ def generate_function_value_info_proto(
         return named_value_info
 
     @runtime_typing.checked
-    def generate_subgraphs_value_info_proto(self) -> Mapping[str, onnx.ValueInfoProto]:
+    def generate_subgraphs_value_info_proto(self) -> Dict[str, onnx.ValueInfoProto]:
         """Unique naming strategies for values inside subgraphs, i.e. local functions.
 
             {function_domain::function_op_type}/{value_name}

onnxscript/function_libs/torch_lib/graph_building_test.py

@@ -142,53 +142,41 @@ def test_add_initializer_allows_adding_the_same_tensor_twice_using_same_name(sel
         graph.add_initializer("x", x_tensor)
 
 
+class _MLP(torch.nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super().__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.fc2 = torch.nn.Linear(hidden_size, output_size)
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+
 @unittest.skipIf(
     IS_WINDOWS and version_utils.torch_older_than("2.3"),
     "dynamo_export not supported on Windows in PyTorch<2.3",
 )
 class TestModelSaving(unittest.TestCase):
     def test_save_initializer_to_files_for_large_model(self):
-        class MLP(torch.nn.Module):
-            def __init__(self, input_size, hidden_size, output_size):
-                super().__init__()
-                self.fc1 = torch.nn.Linear(input_size, hidden_size)
-                self.fc2 = torch.nn.Linear(hidden_size, output_size)
-                self.relu = torch.nn.ReLU()
-
-            def forward(self, x):
-                out = self.fc1(x)
-                out = self.relu(out)
-                out = self.fc2(out)
-                return out
-
         # # of model parameters:
         #  input_size x hidden_size + hidden_size +
         #  hidden_size x output_size + output_size
         #  ~= 3GB below
         batch_size, input_size, hidden_size, output_size = 1, 4, 50000000, 10
-        model = MLP(input_size, hidden_size, output_size)
+        model = _MLP(input_size, hidden_size, output_size)
         x = torch.randn(batch_size, input_size)
 
         model_proto = torch.onnx.dynamo_export(model, x).model_proto
         # Assert model is larger than 2GB (~=3GB)
         self.assertGreater(model_proto.ByteSize(), 2**31)
 
     def test_input_output_and_initializer_are_not_stored_in_value_info(self):
-        class MLP(torch.nn.Module):
-            def __init__(self, input_size, hidden_size, output_size):
-                super().__init__()
-                self.fc1 = torch.nn.Linear(input_size, hidden_size)
-                self.fc2 = torch.nn.Linear(hidden_size, output_size)
-                self.relu = torch.nn.ReLU()
-
-            def forward(self, x):
-                out = self.fc1(x)
-                out = self.relu(out)
-                out = self.fc2(out)
-                return out
-
         batch_size, input_size, hidden_size, output_size = 1, 4, 5, 10
-        model = MLP(input_size, hidden_size, output_size)
+        model = _MLP(input_size, hidden_size, output_size)
         x = torch.randn(batch_size, input_size)
 
         model_proto = torch.onnx.dynamo_export(model, x).model_proto
@@ -201,6 +189,24 @@ def forward(self, x):
         for i in model_proto.graph.initializer:
             self.assertNotIn(i.name, v_names)
 
+    def test_experimental_function_value_info_are_stored_in_graph_value_info(self):
+        batch_size, input_size, hidden_size, output_size = 1, 4, 5, 10
+        model = _MLP(input_size, hidden_size, output_size)
+        x = torch.randn(batch_size, input_size)
+
+        model_proto = torch.onnx.dynamo_export(model, x).model_proto
+        v_names = {v.name for v in model_proto.graph.value_info}
+        torch_functions = [
+            f for f in model_proto.functions if f.domain.startswith("pkg.torch")
+        ]
+        self.assertNotEqual(len(torch_functions), 0)
+        for f in torch_functions:
+            for n in f.node:
+                for i in n.input:
+                    self.assertIn(f"{f.domain}::{f.name}/{i}", v_names)
+                for o in n.output:
+                    self.assertIn(f"{f.domain}::{f.name}/{o}", v_names)
+
 
 if __name__ == "__main__":
     unittest.main()