Address review comments; add support for integer inputs

py/torch_tensorrt/dynamo/conversion/converter_utils.py

@@ -1,3 +1,4 @@
+import collections
 import functools
 import logging
 import re
@@ -50,20 +51,28 @@ def get_node_io(
 ) -> str:
     """Gets a string representing the node inputs and outputs including tensor shapes and dtypes"""
 
-    def format_tensor_metadata(
-        metadata: Union[TensorMetadata, Sequence[TensorMetadata]]
-    ) -> str:
+    def format_tensor_metadata(metadata: Union[Any, Sequence[Any]]) -> str:
         """Formats the metadata for a single node"""
         # If the provided data is a simple TensorMetadata object, parse it
-        if isinstance(metadata, TensorMetadata):
-            return f"{tuple(metadata.shape)}@{metadata.dtype}"
+        if isinstance(metadata, TensorMetadata) or issubclass(
+            type(metadata), torch.Tensor
+        ):
+            return f"{tuple(metadata.shape)}@{metadata.dtype}"  # type: ignore
+        # If the provided data is a scalar, return it as is
+        elif isinstance(metadata, (int, float, bool)):
+            return f"{metadata}@Python-{type(metadata)}"
         # If the provided data is a sequence, recursively parse it
-        else:
+        elif isinstance(metadata, collections.abc.Sequence):
             formatted_str = "("
             for meta in metadata:
                 formatted_str += format_tensor_metadata(meta) + ", "
 
             return formatted_str[:-2] + ")"
+        else:
+            _LOGGER.warning(
+                f"Detected unparseable type in node formatting: {type(metadata)}"
+            )
+            return ""
 
     # Format input tensors
     metadata_string = "Inputs: ("
@@ -74,8 +83,10 @@ def format_tensor_metadata(
             if arg.op == "get_attr":
                 shape, dtype = constant_mapping[str(arg)]
                 arg_repr = f"{shape}@{dtype}"
-            elif arg.meta.get("tensor_meta", False):
+            elif arg.meta.get("tensor_meta") is not None:
                 arg_repr = format_tensor_metadata(arg.meta["tensor_meta"])
+            elif arg.meta.get("val") is not None:
+                arg_repr = format_tensor_metadata(arg.meta["val"])
             else:
                 arg_repr = ""
 
@@ -92,8 +103,10 @@ def format_tensor_metadata(
     if node.op == "get_attr":
         shape, dtype = constant_mapping[str(node)]
         node_repr = f"{shape}@{dtype}"
-    elif node.meta.get("tensor_meta", False):
+    elif node.meta.get("tensor_meta") is not None:
         node_repr = format_tensor_metadata(node.meta["tensor_meta"])
+    elif node.meta.get("val") is not None:
+        node_repr = format_tensor_metadata(node.meta["val"])
     else:
         node_repr = ""
     metadata_string += f"{node}: {node_repr}, "

py/torch_tensorrt/dynamo/lowering/passes/replace_max_pool_with_indices.py

@@ -43,6 +43,7 @@ def replace_max_pool_with_indices(
                     args=node.args,
                     kwargs=node.kwargs,
                 )
+                maxpool_fused.meta = node.meta
 
             logger.debug(
                 f"Replacing all uses of nodes {node}, {getitem_node} with fused maxpool node {maxpool_fused} "