1. When x is Variable, call nn.shape(x) only in following cases:
 1）The shape of x is used in control flow condition.
 2）The dim to be used is negetive
2. When x is Variable, but x.shape or x.shape[idx] doesn't contain negetive value, don't convert to paddle.shape()

* change to tensor copy sync

* change to tensor copy sync

* make copy_to safe when use TensorCopy

* refine code

* add ut

* add cudapinned garbagecollector

* add testcase: cpu place -> cuda pinned place

In control flow, don't copy TensorArray from subblock to parent block when TensorArray is created in parent block.

* add tensor table

* Quantization supports 2.0 APIs

* Fix the error of save_quantized_model

* Add a necessary condition

* Remove test for white list and add header

* enhance error message of nll_loss op test=develop

Improve some error messages in parallel_executor.cc, conditional_block_op.cc, recurrent_op.cc

* simplify prepared op impl to improve performance

* fix kunlun compile error

* continue fix kunlun compile error

* only transform diff place when dtype diff

* fix failed unittests

* remove useless file

* polish impl by review comment

* test=develop, add index_select_cuda kernel

* enhance error info for py_func

* update

[XPU] Remove lite_xpu ut lite_resnet50_test since fusion pass changes introduced precision diff. test=develop (#30122)

* Simplify the options of spawn based on fleetrun

* polish details

* polish doc details

Change-Id: Ia5279b0cbb6a5b3970aff66e9510e0d85efa70ce

* test=develop, add model_average and lookahead

* fix syncbn convet

* add unittest

* add dispenable input 'shape' for core.ops.reshape2

* add dispenable inputs for core.ops.reshape2/expand/slice

* add ut

* Polish and Optimize the print/repr message of all layer

* fix some code format

* add aarch64 and sunway kunlun lib

* minor

* optimize elementwise_add for kunlun

* update kunlun dependence

* minor

* minor

* snap

* add inference api: DisableTensorRtOPs

* fix code style

* update api to experimental

* update variable name

1. when slice_item is a slice: 
 1) the start of __getitem__ should be std::max(start, 0) if slice
 2) the start of __getitem__ should be std::min(end, dim) 
2. when slice_item is an integer, it should be in [-dim_len, dim_len) 
3. Fix error message to use accurate data