* remove addcmul

* remove unittest and other related code of addcmul

* fix bug

* fix merge conflict

fix abs double grad unittest & define the data range for the abs double grad

* add tensor_indices in AssignGroupBySize

* add rebuild group in reducer

* fix the bug of reducer in embedding

* add comment

* change hard_swish from plugin to layer

* add ut when threshold != scale

* update, test=develop

Co-authored-by: Nroot <root@bjhw-sys-rpm0223.bjhw.baidu.com>

* fix the ut of matmulv2 for broadcast

* update reduce_sum op on xpu

* update reduce_sum op on xpu

* support running on xpu

* fix tensorrt unittest precision error

* fix unittest precision error. test_trt_subgraph_pass && test_trt_dynamic_shape_transformer_prune

* add deform_conv2d,DeformConv2D

* rm complexvariable

* modify test_var_base unittest

* remove duplicated codes

* fix DLTP-15151, paddle.ParamAttr API

* fix DLTP-15083/DLTP-15274, paddle.nn.functionl.assign paddle.cast API

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* fix DLTP-15083, paddle.nn.functionl.assign API

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* support in_dygraph_mode for cast op, test=develop

* fix bug,test=develop

* fix doc

* fix DLTP-15431/DLTP-15432, paddle.static.nn.conv2d paddle.static.nn.conv2d_transpose API

* Fix api docs in RNN, Transformer, layer_norm, WeightNormParamAttr.
test=develop

* Fix api doc for print in label_smooth.
test=develop

* Update api docs according to review comments.
Add name argument in RNN back.
test=develop

* add complex64 and complex128 type; add +-*/@ and slice opreator for complex types

* add test cases for complex elementwise, matmul and getitem unittest

* add test cases for complex types

* add test cases for complex matmul unittest

* kron, reshape, transpose support complex types

* sum and trace op support complex types

* add test case of sum and trace op

* fix the bug of imag part of complex not initialized

* format file

* format code style

* kron support type promotion; modify test cases

* fix expand && concat/transpose to new api

* update uniform_random_op

* update xpu_header

Reduce exception type so that if covert_to_static failed, it reports right error message.

[Dy2Stat] Fix bug: Do not use gast.Subscript to replace gast.Name in when transforming for_enumerate_loop (#29310)

[inplace] Add ShareHolderWith for class Variable and SharePlaceholderWith in VarBase.detach() to share the same Tensor/SelectedRows (#29267)

* basic impl of type promote

* add comment & another testcase

* fix complex bugs & support python op promote type

* fix failed unittests & polish code

* add unittest for coverage

* change to only promote complex type

* polish code details

* polish several comments

* fix gpu emb out of range

Change-Id: I5794ac73bd634d5ea069a6fbbd914274b6d6b7bf

* fix doc

Change-Id: I5a3350b2930a9ab2f52116c192b087307faf8fdf

* use has_grad instead of train_mode

* add vlog for debug

* fix ut

* fix ut

[Dy2stat] Add a decorator paddle.jit.not_to_static to support that not to convert a function in Dynamic-to-Static. (#29253)

Usage scenarios：A function could have run successfully in static mode,  you can use it to decorate a function in the following cases:
  1. An unknown error occurs in the dynamic-to-static conversion process of the function;
  2. In the internal implementation of the function, it has two branches: dynamic branch and static branch;
  3. Users don't want to convert the function in the process of dynamic to static.

* move temporal_shift to functional

* add the weight decay func for the momentum op

* Add the multi_precision function in Momentum Optimizer.

* Make sure that the initial value of master weights are same with the fp16 weights.

* add static loss scaling.

* add the rescale_grad function in the pure fp16 training.

* use the original momentum updating method.

* Polish some codes, such as variable names.

* add docstring for apis.

* update the var creation details of _create_master_weight.

* not modify codes about imperative momentum updating.

* Fix the error of test_dist_sparse_tensor_load_momentum UT.

* add unit test for multi precision fp16 training.

* add more unit tests for CI.

* Use lower threshold values for allclose comparing in test_multi_precision_fp16_train UT.

* For CI Coverage Checking.