[Hotfix] Fix the conflicts about ffi-related updated names (#18287)

* Change registration of mock softmax function

* Update check_asf_header.sh

Remove unnecessary blank line in check_asf_header.sh

* Update check_asf_header.sh

* fix

Author: tlopex

python/tvm/relax/relax_to_pyfunc_converter.py

@@ -27,6 +27,7 @@
 
 import tvm
 from tvm import relax
+from tvm.runtime import empty, from_dlpack, Tensor
 from tvm.ir import IRModule, Op
 
 
@@ -608,7 +609,7 @@ def _convert_call_tir(self, call: relax.Call, args: List[Any]) -> Any:
             for arg in converted_args:
                 if isinstance(arg, torch.Tensor):
                     # Convert PyTorch tensor to TVM NDArray via DLPack
-                    tvm_arg = tvm.nd.from_dlpack(torch.to_dlpack(arg))
+                    tvm_arg = from_dlpack(torch.to_dlpack(arg))
                     tvm_args.append(tvm_arg)
                 else:
                     tvm_args.append(arg)
@@ -627,15 +628,15 @@ def _convert_call_tir(self, call: relax.Call, args: List[Any]) -> Any:
                 return f"<call_tir_error: {func_name} - Cannot determine output shape>"
 
             # Allocate output tensor
-            output_tensor = tvm.nd.array(tvm.nd.empty(output_shape, dtype="float32"))
+            output_tensor = empty(output_shape, dtype="float32")
             tvm_args.append(output_tensor)
 
             # Call the TIR function
             tir_function(*tvm_args)
 
             # The result is in the output_tensor we allocated
             # Convert result back to PyTorch tensor via DLPack
-            return torch.from_dlpack(output_tensor.to_dlpack())
+            return torch.from_dlpack(output_tensor)
 
         except (RuntimeError, ValueError, TypeError) as error:
             return f"<call_tir_error: {func_name} - {error}>"
@@ -669,7 +670,7 @@ def _convert_call_dps_packed(self, call: relax.Call, args: List[Any]) -> Any:
             for arg in converted_args:
                 if isinstance(arg, torch.Tensor):
                     # Convert PyTorch tensor to TVM NDArray via DLPack
-                    tvm_arg = tvm.nd.from_dlpack(torch.to_dlpack(arg))
+                    tvm_arg = from_dlpack(torch.to_dlpack(arg))
                     tvm_args.append(tvm_arg)
                 else:
                     tvm_args.append(arg)
@@ -678,8 +679,9 @@ def _convert_call_dps_packed(self, call: relax.Call, args: List[Any]) -> Any:
             result = packed_function(*tvm_args)
 
             # Convert result back to PyTorch tensor via DLPack
-            if isinstance(result, tvm.nd.NDArray):
-                return torch.from_dlpack(result.to_dlpack())
+            if isinstance(result, Tensor):
+                # Convert TVM Tensor to PyTorch tensor
+                return torch.from_dlpack(result)
             else:
                 return result
 

tests/python/relax/test_relax_to_pyfunc_converter.py

@@ -200,7 +200,7 @@ def mock_softmax(x, axis):
         return x
 
     # Register the function globally
-    tvm.register_func("my_softmax", mock_softmax)
+    tvm.register_global_func("my_softmax", mock_softmax)
 
 
 class TestRelaxToPyFuncConverter: