chore: miscellaneous fixes for handling graph breaks

py/torch_tensorrt/_compile.py

@@ -584,6 +584,7 @@ def save(
     arg_inputs: Optional[Sequence[torch.Tensor]] = None,
     kwarg_inputs: Optional[dict[str, Any]] = None,
     retrace: bool = False,
+    pickle_protocol: int = 2,
 ) -> None:
     """
     Save the model to disk in the specified output format.
@@ -596,6 +597,7 @@ def save(
         output_format (str): Format to save the model. Options include exported_program | torchscript.
         retrace (bool): When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.
                 This flag is experimental for now.
+        pickle_protocol (int): The pickle protocol to use to save the model. Default is 2. Increase this to 4 or higher for large models (eg: SAM2)
     """
     if isinstance(module, CudaGraphsTorchTensorRTModule):
         module = module.compiled_module
@@ -668,7 +670,9 @@ def save(
                         "Provided model is a torch.fx.GraphModule and retrace is False, inputs or arg_inputs is not necessary during save."
                     )
                 exp_program = export(module)
-                torch.export.save(exp_program, file_path)
+                torch.export.save(
+                    exp_program, file_path, pickle_protocol=pickle_protocol
+                )
             else:
                 if arg_inputs is None:
                     raise ValueError(
@@ -680,4 +684,6 @@ def save(
                     kwargs=kwarg_inputs,
                     strict=False,
                 )
-                torch.export.save(exp_program, file_path)
+                torch.export.save(
+                    exp_program, file_path, pickle_protocol=pickle_protocol
+                )

py/torch_tensorrt/dynamo/conversion/truncate_double.py

@@ -195,7 +195,7 @@ def repair_double_inputs(
 
         # If the data type of the input is long/double, insert necessary
         # casts to replace the operation
-        if param.dtype == torch.float64:
+        if isinstance(param, torch.Tensor) and param.dtype == torch.float64:
             # Ensure outputs are only repaired once per submodule to avoid
             # unnecessary ops showing up in the graph
             if not repaired_outputs_once:

py/torch_tensorrt/dynamo/utils.py

@@ -419,7 +419,9 @@ def unwrap_tensor_dtype(tensor: Union[torch.Tensor, FakeTensor, torch.SymInt]) -
     """
     Returns the dtype of torch.tensor or FakeTensor. For symbolic integers, we return int64
     """
-    if isinstance(tensor, (torch.Tensor, FakeTensor, int, float, bool)):
+    if isinstance(tensor, (torch.Tensor, FakeTensor)):
+        return tensor.dtype
+    elif isinstance(tensor, (int, float, bool)):
         return torch.tensor(tensor).dtype
     elif isinstance(tensor, torch.SymInt):
         return torch.int64
@@ -791,6 +793,8 @@ def get_output_dtypes(output: Any, truncate_doulbe: bool = False) -> List[dtype]
                     output_dtypes.append(dtype.float32)
                 else:
                     output_dtypes.append(dtype._from(output_meta.dtype))
+            elif isinstance(output_meta, torch.SymInt):
+                output_dtypes.append(dtype.int64)
         elif "tensor_meta" in output.meta:
             output_meta = output.meta["tensor_meta"]
             output_dtypes.append(dtype._from(output_meta.dtype))