[Bug fixes] Fix partial grad conflicts (#40655)

* [Eager] Support eager grad interface, draft version

* Support eager grad interface with allow_unused and multi startup_op

* Fix code format

* Fix allow_unused case, return PyNone if tensor not initialize

* Support output's stop_gradient related to create_graph

* Support grad exception case in eager mode, fix coverage CI

* Update ToPyObject, return PyNone if not initialize

* AccumulationNode add FLAGS_retain_grad_for_all_tensor

* Fix ci issue

* Fix CI issue

* fix, use core.eager.Tensor

* Add func SetBufferSlotRankZeros for GradTensorHolder

* Support retain_graph by using ClearTensorWrappers

* Support retain_graph by using ClearTensorWrappers

* Update retain_graph and no_grad_vars related test case

* Update code gen logic for ClearTensorWrappers

* Fix by override statement

* fix override func args

* Support retain_graph, update unit tests

* Updated ClearTensorWrappers logic

* fix grad python interface

* Use deep copy and update unit tests

* Polish code

* Polish code

* Fix CI issue, Deep copy only use when user set grad_tensors

* Fix CI, use Backward instead RunBackward

* Fix CI, Declare kernel explicitly in test file

* Polish, remove vector of TensorWrapper

* Refactor the logic of grad/backward, polish codes

* Update code after merge upstream develop

* Polish after merge upstream develop

* Update to adapt new GradNodeBase superclass

* Fix error introduced during conflict resolution

* Update purify potential_startup_nodes logic

* Fix errors

* Polish code

* Remove useless args for ToPyObject

* Remove useless TensorWrappersSet

* Fix code-format, re-install pre-commit

* Fix pre-process logic for potential_startup_ops

* Update unit tests, use eager mode

* Fix conflicts

paddle/fluid/eager/backward.cc

@@ -370,7 +370,7 @@ std::vector<paddle::experimental::Tensor> RunBackward(
       if (grad_tensors[i].is_initialized()) {
         // Deep copy
         paddle::experimental::Tensor tmp_tensor;
-        tmp_tensor.copy_(grad_tensors[i], true);
+        tmp_tensor.copy_(grad_tensors[i], grad_tensors[i].inner_place(), true);
         node_input_buffers_dict[grad_node]->add(input_info.first,
                                                 input_info.second, tmp_tensor);
       } else {