Don't generate denseResource

Author: sahas3

python/torch_mlir/extras/fx_importer.py

@@ -2127,25 +2127,25 @@ def _make_vtensor_literal_op(
         # One element constants are more optimizable as splat DenseElementsAttr. DenseResourceElementsAttr does not
         # support splats, so don't use it for that case. In addition, at the time of writing, it has bugs with handling
         # 0d tensors.
-        if np_tensor.size == 1:
-            try:
-                dtype = tensor.dtype
-                element_type = TORCH_DTYPE_TO_MLIR_TYPE[dtype]()
-            except KeyError:
-                raise TypeError(f"Could not map Torch dtype {dtype} to an MLIR type")
-            elements_attr = DenseElementsAttr.get(
-                type=element_type, array=np_tensor, shape=np_tensor.shape
-            )
-        else:
-            bytes_view = np_tensor.view(npy_dtype)
-            tensor_type = create_mlir_tensor_type(tensor)
-            shape_desc = "_".join([str(d) for d in tensor.shape])
-            blob_name = f"torch_tensor_{shape_desc}_{str(tensor.dtype)}"
-            elements_attr = DenseResourceElementsAttr.get_from_buffer(
-                bytes_view,
-                blob_name,
-                tensor_type,
-            )
+        # if np_tensor.size == 1:
+        try:
+            dtype = tensor.dtype
+            element_type = TORCH_DTYPE_TO_MLIR_TYPE[dtype]()
+        except KeyError:
+            raise TypeError(f"Could not map Torch dtype {dtype} to an MLIR type")
+        elements_attr = DenseElementsAttr.get(
+            type=element_type, array=np_tensor, shape=np_tensor.shape
+        )
+        # else:
+        #     bytes_view = np_tensor.view(npy_dtype)
+        #     tensor_type = create_mlir_tensor_type(tensor)
+        #     shape_desc = "_".join([str(d) for d in tensor.shape])
+        #     blob_name = f"torch_tensor_{shape_desc}_{str(tensor.dtype)}"
+        #     elements_attr = DenseResourceElementsAttr.get_from_buffer(
+        #         bytes_view,
+        #         blob_name,
+        #         tensor_type,
+        #     )
         mapping.value = elements_attr
     else:
         elements_attr = mapping.value