[API] Create stable APIs for PyTorch 2.6

onnxscript/_framework_apis/torch_2_5.py

@@ -17,17 +17,10 @@
 import pathlib
 from typing import Callable
 
-import onnx
-
 from onnxscript import ir, optimizer
 from onnxscript.function_libs.torch_lib import registration
 from onnxscript.ir import _external_data
 
-# Internal flag. Will go away.
-_TORCH_ONNX_SAVE_EXTERNAL_DATA_WITH_IR = (
-    os.getenv("TORCH_ONNX_OFFLOAD_EXTERNAL_DATA_WITH_IR") != "0"
-)
-
 
 @dataclasses.dataclass(frozen=True)
 class _OnnxFunctionMeta:
@@ -83,45 +76,32 @@
     """Save the model with external data. The model is unchanged after saving."""
 
     # TODO(#1835): Decide if we want to externalize large attributes as well
-    if _TORCH_ONNX_SAVE_EXTERNAL_DATA_WITH_IR:
-        initializer_values = tuple(model.graph.initializers.values())
-        tensors = [v.const_value for v in initializer_values]
-        for tensor in tensors:
-            if tensor is None:
-                raise ValueError(
-                    "The model contains uninitialized initializer values. "
-                    "Please make sure all initializer values are initialized."
-                )
-        destination_path = pathlib.Path(model_path)
-        base_dir = destination_path.parent
-        data_path = f"{destination_path.name}.data"
-
-        external_tensors = _external_data.convert_tensors_to_external(
-            tensors,  # type: ignore[arg-type]
-            base_dir,
-            data_path,
-        )
-
-        # Replace the initializer values with external tensors and save the model
-        for initializer, external_tensor in zip(initializer_values, external_tensors):
-            initializer.const_value = external_tensor
-        ir.save(model, model_path)
-
-        # Restore the original initializer values so the model is unchanged
-        for initializer, tensor in zip(initializer_values, tensors):
-            initializer.const_value = tensor
-
-    else:
-        destination_path = pathlib.Path(model_path)
-        # Create the directory if it does not exist
-        data_path = f"{destination_path.name}.data"
-        proto = ir.serde.serialize_model(model)
-        onnx.save_model(
-            proto,
-            model_path,
-            save_as_external_data=True,
-            location=data_path,
-        )
+    initializer_values = tuple(model.graph.initializers.values())
+    tensors = [v.const_value for v in initializer_values]
+    for tensor in tensors:
+        if tensor is None:
+            raise ValueError(
+                "The model contains uninitialized initializer values. "
+                "Please make sure all initializer values are initialized."
+            )
+    destination_path = pathlib.Path(model_path)
+    base_dir = destination_path.parent
+    data_path = f"{destination_path.name}.data"
+
+    external_tensors = _external_data.convert_tensors_to_external(
+        tensors,  # type: ignore[arg-type]
+        base_dir,
+        data_path,
+    )
+
+    # Replace the initializer values with external tensors and save the model
+    for initializer, external_tensor in zip(initializer_values, external_tensors):
+        initializer.const_value = external_tensor
+    ir.save(model, model_path)
+
+    # Restore the original initializer values so the model is unchanged
+    for initializer, tensor in zip(initializer_values, tensors):
+        initializer.const_value = tensor
 
 
 def get_torchlib_ops() -> list[_OnnxFunctionMeta]:

onnxscript/_framework_apis/torch_2_6.py

@@ -0,0 +1,26 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""Stable APIs for PyTorch 2.6."""
+
+from __future__ import annotations
+
+__all__ = [
+    "check_model",
+    "convert_version",
+    "get_torchlib_ops",
+    "optimize",
+    "save_model_with_external_data",
+]
+from onnxscript import ir, optimizer
+from onnxscript._framework_apis.torch_2_5 import (
+    check_model,
+    convert_version,
+    get_torchlib_ops,
+    save_model_with_external_data,
+)
+
+
+def optimize(model: ir.Model) -> ir.Model:
+    """Optimize the model."""
+    optimizer.optimize_ir(model)
+    return model