Add a verbose mode to torch api for external data save (#2643)

Show progress bar with tqdm when verbose is True.

It will be enabled in PyTorch 2.10

<img width="2261" height="171" alt="image"
src="https://github.com/user-attachments/assets/3184a813-d6d3-4bbc-93ef-78fec481a36c"
/>

---------

Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: justinchuby

onnxscript/_framework_apis/torch_2_5.py

@@ -13,6 +13,7 @@
 ]
 
 import dataclasses
+import importlib.util
 import os
 import pathlib
 from typing import Callable
@@ -63,7 +64,9 @@ def check_model(model: ir.Model) -> None:
     del model  # Unused yet
 
 
-def save_model_with_external_data(model: ir.Model, model_path: str | os.PathLike) -> None:
+def save_model_with_external_data(
+    model: ir.Model, model_path: str | os.PathLike, verbose: bool = False
+) -> None:
     """Save the model with external data. The model is unchanged after saving."""
 
     # TODO(#1835): Decide if we want to externalize large attributes as well
@@ -78,7 +81,31 @@ def save_model_with_external_data(model: ir.Model, model_path: str | os.PathLike
     destination_path = pathlib.Path(model_path)
     data_path = f"{destination_path.name}.data"
 
-    ir.save(model, model_path, external_data=data_path)
+    # Show a progress bar if verbose is True and tqdm is installed
+    use_tqdm = verbose and importlib.util.find_spec("tqdm") is not None
+
+    if use_tqdm:
+        import tqdm  # pylint: disable=import-outside-toplevel
+
+        with tqdm.tqdm() as pbar:
+            total_set = False
+
+            def callback(
+                tensor: ir.TensorProtocol, metadata: ir.external_data.CallbackInfo
+            ) -> None:
+                nonlocal total_set
+                if not total_set:
+                    pbar.total = metadata.total
+                    total_set = True
+
+                pbar.update()
+                pbar.set_description(
+                    f"Saving {tensor.name} ({tensor.dtype.short_name()}, {tensor.shape}) at offset {metadata.offset}"
+                )
+
+            ir.save(model, model_path, external_data=data_path, callback=callback)
+    else:
+        ir.save(model, model_path, external_data=data_path)
 
 
 def get_torchlib_ops() -> list[_OnnxFunctionMeta]: