[ONNX] Update typing and error messages in symbolic_helper

### Description

- Clearer error messages with more context
-   Created `SymbolicValueError` which adds context of the value to the error message
- Type annotation

example error message:

```
torch.onnx.errors.SymbolicValueError: ONNX symbolic does not understand the Constant node '%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3  3 [ CPULongType{2} ]]()
' specified with descriptor 'is'.  [Caused by the value '1 defined in (%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3  3 [ CPULongType{2} ]]()
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx::Constant'.]

    Inputs:
        Empty
    Outputs:
        #0: 1 defined in (%1 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 3  3 [ CPULongType{2} ]]()
    )  (type 'Tensor')
```

### Issue

- pytorch#77316 (Runtime error during symbolic conversion)

### Testing

Unit tested

ghstack-source-id: 5ffb9ca
Pull Request resolved: pytorch#83007

Author: justinchuby

test/onnx/test_pytorch_onnx_no_runtime.py

@@ -167,7 +167,7 @@ def forward(self, x):
         tm = TraceMe()
         tm = torch.jit.trace(tm, torch.rand(3, 4))
         f = io.BytesIO()
-        torch.onnx._export(tm, (torch.rand(3, 4),), f)
+        torch.onnx.export(tm, (torch.rand(3, 4),), f)
 
     def test_export_tensoroption_to(self):
         def foo(x):

torch/_C/__init__.pyi.in

@@ -1321,8 +1321,10 @@ class TensorType(JitType):
     def getInferred(cls) -> TensorType: ...
     def with_sizes(self, other: Optional[List[Optional[_int]]]) -> TensorType: ...
     def sizes(self) -> Optional[List[_int]]: ...
+    def varyingSizes(self) -> Optional[List[Optional[_int]]]: ...
     def strides(self) -> Optional[List[_int]]: ...
     def device(self) -> Optional[_device]: ...
+    def dim(self) -> _int: ...
     def dtype(self) -> Optional[_dtype]: ...
     @staticmethod
     def create_from_tensor(t: Tensor) -> TensorType: ...

torch/onnx/errors.py

@@ -1,10 +1,18 @@
 """ONNX exporter exceptions."""
+from __future__ import annotations
 
+import textwrap
 from typing import Optional
 
+from torch import _C
 from torch.onnx import _constants
 
-__all__ = ["OnnxExporterError", "CheckerError", "UnsupportedOperatorError"]
+__all__ = [
+    "OnnxExporterError",
+    "CheckerError",
+    "UnsupportedOperatorError",
+    "SymbolicValueError",
+]
 
 
 class OnnxExporterError(RuntimeError):
@@ -14,7 +22,7 @@ class OnnxExporterError(RuntimeError):
 
 
 class CheckerError(OnnxExporterError):
-    r"""Raised when ONNX checker detects an invalid model."""
+    """Raised when ONNX checker detects an invalid model."""
 
     pass
 
@@ -42,3 +50,50 @@ def __init__(
                 "it with the right domain and version."
             )
         super().__init__(msg)
+
+
+class SymbolicValueError(OnnxExporterError):
+    """Errors around TorchScript values and nodes."""
+
+    def __init__(self, msg: str, value: _C.Value):
+        message = (
+            f"{msg}  [Caused by the value '{value}' (type '{value.type()}') in the "
+            f"TorchScript graph. The containing node has kind '{value.node().kind()}'.] "
+        )
+
+        code_location = value.node().sourceRange()
+        if code_location:
+            message += f"\n    (node defined in {code_location})"
+
+        try:
+            # Add its input and output to the message.
+            message += "\n\n"
+            message += textwrap.indent(
+                (
+                    "Inputs:\n"
+                    + (
+                        "\n".join(
+                            f"    #{i}: {input_}  (type '{input_.type()}')"
+                            for i, input_ in enumerate(value.node().inputs())
+                        )
+                        or "    Empty"
+                    )
+                    + "\n"
+                    + "Outputs:\n"
+                    + (
+                        "\n".join(
+                            f"    #{i}: {output}  (type '{output.type()}')"
+                            for i, output in enumerate(value.node().outputs())
+                        )
+                        or "    Empty"
+                    )
+                ),
+                "    ",
+            )
+        except AttributeError:
+            message += (
+                " Failed to obtain its input and output for debugging. "
+                "Please refer to the TorchScript graph for debugging information."
+            )
+
+        super().__init__(message)