As the title

* skip quantizing ops in cpu inference, test=develop

As the title

Set expected place in child thread for dataloader to avoid costing cuda memory on other card (#30338)

* set expected place in child thread for dataloader

* set device id when set tensor from numpy

* revert tensor_py change

* add compile guard

* fix ci

* fix bug

* optimize memcpy perf for kunlun

* remove useless unitest for kunlun mean

* minor

* Implemented AddQuantDequantPass in imperative quantization.

* Supported LeakyReLU Quantization

* For meeting coverage rate.

* Changed the file name of test of AddQuantDequant

* Implemented more Quantized NoWeightLayers.

* Fix the loss cannot align problem between static and dynamic model quantization, add swish as supported quantized layer in imperative quantization.

* remove noweight_list

* support 2.0 API such as Pool2D and ReLu

* upgrade oneDNN version to 2.0 master branch

* - Added workarounds for new lib onednn change

* fix regex
Co-authored-by: NJacek Czaja <jacek.czaja@intel.com>

* add sparse embedding & load vars for 2.0

Change-Id: I36b59ed5f015189dc9d9d2e34a9357722d369f1b

* fix hdfs gloo

Change-Id: Ia84d579053720ad804183e54c9a04b4f031c79c6

* fix gloo hdfs

Change-Id: I5ab982fd483cddc10adcdef0b8aa83aca976cb9e

* move loadvar/sparse embedding from incubute to static

Change-Id: I57081d3545ad2efab78c72420d2162c0eacaf3a0

* disable test_pipeline

* fix error

* fix bug of using ignore_index and reduction,test=develop

* fix bug of celoss when using ignore_index and reduction, test=develop

* improve performance when ignore_index=-100, test=develop

* add test in test_cross_entropy_loss.py for coverage rate, test=develop

* rm comment in test_cross_entropy_loss.py, test=develop

* del  hard code of "float64" in python/paddle/nn/functional/loss.py, test=develop

* change mask to a more simplified implementation, test=develop

* del comment in python/paddle/nn/functional/loss.py, test=develop

* del hard code and change mask to a more simplified implementation, test=develop

* change mask to a more simplified implementation, test=develop

* change mask to a more simplified implementation, test=develop

* fix elugradgrad test fail and error message opt

* fix unitest,test=develop

* Update prroi_pool_op.h

fix error message

* opt message,test=develop

* fix ci fail,test=develop

* Fix the accuracy problem of allclose op when using float64 data type in static mode.

* Format the code style.

Support vector<double> as type of op attribute and op set_value suppport vector<double> as value (#30126)

* type promotion for grad

* add type promotion for div op

* disable ut test_tsm on windows

* fix error

* add ut execuate time

* Fix bug for 'save mutiple method'

* To pass coverage.

* edit code to pass coverage.

* edit code to pass coverage.

* add unittest for coverage.

* change for coverage.

* edit for coverage.

Add clone method for static Variable so that this interface will be same as dygraph. It fixed some bugs in dy2stat

* delete paddle.nn.functional.assign

* fix dynamic to static error

reduce the  occupied size  of memory for the fused pattern of elementwise_add Op and activation Op(relu Op for example) (#29885)

* add view strategy on squeeze,unsqueeze,reshape,flatten

* add squeeze unittest

* add unittests

* use View strategy as name rather than Reuse Allacation

* fix view api doc

* fix format

* use core.ops when input of reshape2 is Tensor

* fix test_cross_entropy_loss error because of reshape2

* delete selected_rows

* change op_function

* little change

* solve HandleViewBetweenInputAndOutput

* add cast ops before and after unsupported fp16 ops.

* Keep partial net in FP32 pattern.

* Support check_finite_and_unscale and update_loss_scaling for FP16 calculation mode.

* Add fp16 support for adam op.

* add multi precision attr for adam.

* Fix the bug of test_multi_precision_fp16_train UT.

* Code format for CI.

* Fix the redefine error about MPTypeTrait on windows.

* fix bugs of the _create_accumulators func in Momentum.

* fix bug when inserting post cast op.

* Add the update_loss_scaling op in allow_set of UnusedVarCheck.

* Update for ci coverage.

* Add some doc for OptimizerWithMixedPrecision.

* Fix the code style.

* Imporve the doc of `amp_init`.

* Change for fp16 testing if users have the infer program defined in separate way.

* fix dtype of ungenerated grad var

* update ut

* refine code

* set default dtype

* fix could_use_cudnn bug

* remove debug code

* re-implement

* fix bug

* fix tensor shape bug

* fix op_num

* clean code

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