Rename intermediate values to _val and _const | feat(torchlib) (#881)

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at
bottom):
* #883
* #882
* __->__ #881

The torchscript ONNX graph generator creates numeric value names by
default (`0`, `1`). These are not legal ONNX tensor names, since ONNX
requires the names to be valid C variable names. This change updates the
names by prepending a prefix `_val_` or `_const_` to make them valid
ONNX names. It also improves readability by making the names less likely
to be confused with shape values.

I decided to use the `_` prefix to reduce the chance of name collision
with FX names.

After:

```
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.1.0"
>
torch_jit (float[5,5,5,5] input_0, int64[2] input_1_3) => (float[5,5,5,2] _val_10) {
   _val_2 = Transpose <perm = [0, 1, 2, 3]> (input_0)
   _val_3 = Max (input_1_3)
   _val_4 = Shape <start = 0> (_val_3)
   _val_5 = Expand (input_1_3, _val_4)
   _const_6 = Constant <value = int64 {-1}> ()
   _val_7 = Unsqueeze (_val_5, _const_6)
   _val_8 = Concat <axis = -1> (_val_7)
   _val_9 = GatherND <batch_dims = 0> (_val_2, _val_8)
   _val_10 = Transpose <perm = [0, 1, 2, 3]> (_val_9)
}
```

Before:

```
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.1.0"
>
torch_jit (float[5,5,5,5] input_0, int64[2] input_1_3) => (float[5,5,5,2] 10) {
   2 = Transpose <perm = [0, 1, 2, 3]> (input_0)
   3 = Max (input_1_3)
   4 = Shape <start = 0> (3)
   5 = Expand (input_1_3, 4)
   6 = Constant <value = int64 {-1}> ()
   7 = Unsqueeze (5, 6)
   8 = Concat <axis = -1> (7)
   9 = GatherND <batch_dims = 0> (2, 8)
   10 = Transpose <perm = [0, 1, 2, 3]> (9)
}
```

Author: justinchuby

onnxscript/function_libs/torch_lib/graph_building.py

@@ -76,6 +76,18 @@
 # TODO(justinchuby): Build a context manager to handle source information.
 
 
+def _rename_intermediate_value(name: str) -> str:
+    if name.isdigit():
+        return f"_val_{name}"
+    return name
+
+
+def _rename_intermediate_constant(name: str) -> str:
+    if name.isdigit():
+        return f"_const_{name}"
+    return name
+
+
 class TorchScriptTensor(onnxscript_tensor.Tensor):
     """A onnxscript tensor that wraps a torchscript Value."""
 
@@ -454,6 +466,7 @@ def _add_constant_to_graph(self, constant) -> torch.Value:
                 self._torch_graph, "prim::Constant", inputs=(), attributes={}
             )[0]
             value.setType(torch.OptionalType.ofTensor())
+            value.setDebugName(_rename_intermediate_constant(value.debugName()))
             return value
 
         if isinstance(constant, bool):
@@ -475,12 +488,14 @@ def _add_constant_to_graph(self, constant) -> torch.Value:
             raise TypeError(
                 f"Constant input '{constant}' of type '{type(constant)}' is not supported"
             )
-        return _create_op_call_in_torch_graph(
+        value = _create_op_call_in_torch_graph(
             self._torch_graph,
             "onnx::Constant",
             inputs=(),
             attributes=dict(value=constant_tensor),
         )[0]
+        value.setDebugName(_rename_intermediate_constant(value.debugName()))
+        return value
 
     @runtime_typing.checked
     def _add_torchscript_op_call(
@@ -524,9 +539,15 @@ def _add_torchscript_op_call(
             attributes=onnx_attributes,
             n_outputs=n_outputs,
         )
-        if len(result) <= 1:
-            return TorchScriptTensor(result[0])
-        return tuple(TorchScriptTensor(v) for v in result)
+        assert result, "Expected at least one output from ONNX op call."
+        if len(result) == 1:
+            tensor = TorchScriptTensor(result[0])
+            tensor.name = _rename_intermediate_value(tensor.name)
+            return tensor
+        tensors = tuple(TorchScriptTensor(v) for v in result)
+        for tensor in tensors:
+            tensor.name = _rename_intermediate_value(tensor.name)
+        return tensors
 
     @runtime_typing.checked
     def fetch_function_proto_dict(