* test=kunlun

[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

* improve performance of elementwise_sum_grad

* fix tensorrt output shape error

* fix unittest tensorrt_engine_op_test

* fix code style for unitest

[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.

* fix doc, test=document_fix

* fix DATA_HOME path in win

* fix nll_loss doc;test=document_fix;

* remove numpy and set_device;test=document_fix;

* remove numpy;test=document_fix;

* 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.

* add fp16 for layer_norm op

* revert layernorm api

* fix forward

* fix forward

* fix backward for layernorm with fp16

* fix unit test for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16

* 1. revert to PADDLE_ENFORCE_NOT_NULL, 2. change static_cast<float> to static_cast<U>

* fix with_mkldnn compile error for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16
Co-authored-by: Nzhiqiu <chenqiuliang@baidu.com>

* Update IterableDataset API

* Update TensorDataset API

* Update APIs in paddle/text/datasets

* Update dataset.py

* Update Cifar Codes

* Update VOC2012 Codes

* Update voc2012.py

* Update voc2012.py

* Update cifar.py

* Update cifar.py

* Update voc2012.py

* Update get_worker_info API

* Update dataloader_iter.py

* Update dataloader_iter.py

* Update dataloader_iter.py

* Update conv3d API

* Update nn.py

* Update nn.py

* Update nn.py

* Update nn.py

* Update nn.py

* Update nn.py

* Update nn.py

* Update nn.py

This PR fixes several problems in dy2stat for Deoldify model in PaddleGan.

In model, software engineer wrote if x.shape == y.shape, the Tenser shape is a tuple in dygraph so the == returns True/False, but in static graph the == becomes element-wise comparison, which is a different behavior. In this PR we reduce the element-wise comparison result.

If software engineer write computations which uses parameters in hooks, the static graph can loss the parameter variable because we put param_guard at forward of a Layer. In this PR we made param_guard cover pre-hook and post-hook.

In PaddleGan, software engineer calculated some parameter values in __init__ by running some dygraph code. Those code also run during dy2stat. So some variables may be assign as a VarBase (Tensor) first and then Variable, which raised an error. We fixed the bug in this PR by handling the case.

TODO: We just added testcase for the 1. shape comparison. Should add test case for 2. and 3. But since we are chasing 2.0RC, I will do it in the near future PR

* add compile option WITH_TENSORRT

* add WITH_TENSORRT to ci paddle_buils.sh

* add WITH_TENSORRT to paddle_build.sh

* change FATAL to WARNING when TensorRT is not found and WITN_TENSORRT=ON, just to pass ci-py3 temporarily