* refine strategy compiler and meta optimizers
make async as a_sync

* remove ProgramTranslator.save_inference_model

* adapt save_quantized_model

* revert buffer check implemention

* remove useless import function

* simplify buffered reader to improve DataLoader performance

* fix 22 failed unittests

* fix cuda pinned context condition

* fix test_reader_reset failed

* fix two failed unittests

* change unittest place

* polish error messaage

* polish cast op GetExpecctedKernelType

* remove debug info in unittest

Enhance TracedLayer Error Message

Note: this PR uses assert to check type somewhere and check_type somewhere, the reason is that the check_type skips checking when it is under dygraph mode.

* add more settings for distributed strategy
Basically, DistributedStrategy has several parts of configurations:
- BuildStrategy: the same as paddle.fluid.BuildStrategy, but the distributed arguments are moved out of BuildStrategy
- ExecutionStrategy: the same as paddle.fluid.ExecutionStrategy
- collective communication configs: nccl_comm_num, hierarchical allreduce and so on
- distributed algorithms: async_update(mainly used in PS), lars, lamb and so on

Based on the comment here https://github.com/PaddlePaddle/Paddle/blob/b5f8784cab94eae785659787fc529870c87b254c/paddle/fluid/framework/details/build_strategy.h#L49

The unit test which compares Reduce and AllReduce must have diff. The PR_CI_Night runs on P40 machine and it has 8GB GPU, which is smaller than the 16GB normal CI machines. So we decrease the batch size in the past to make it runnable: https://github.com/PaddlePaddle/Paddle/pull/24651/files  . Decreasing the batch size makes the difference occurs often. So this PR replace the absolute delta by relative delta.

Before this PR, the unit test failure happens with probability about 1/100. After this PR it doesn't happen.

* fix the double grad bug for the star gan. test=develop

* update the retain_graph parameter doc. test=develop

* add the unit test for the retain_graph parameter. test=develop

* fix jit.save input type change error

* add unittes

* add fleet distributed metrics
* test=develop

* refine squeeze, test=develop

* update squeeze, test=develop

* refine compile-time infershape, test=develop

* add more unittest, test=develop

* follow comments, test=develop

* add update_api, test=develop

* follow comments, test=develop

* New features, add sinh and cosh op, test=develop

* remove duplicate test function and remove out paramters, test=develop

* Add out paramters temporary, remove later. test=develop

* remove out args, PR 25570, test=develop

* remove TestParameter, test=developx

* add test api for static dygraph, test=develop

* add backword unittests for sinh and cosh, test=develop

* fix best_fit_allocator_test on windows, test=develop

* enable best_fit_allocator_test and test_math_op_patch_var_base, test=develop

* add op multiply, delete op elementwise_mul from math.py. test=develop,test=document_fix

* bug fix. test=develop,test=document_fix

* bug fix. test=develop,test=document_fix

* bug fix. test=develop,test=document_fix

* bug fix. test=develop,test=document_fix

* add unittest for multiply op. test=develop.

* fix code style. test=develop

* use random input. test=develop

* add test error case for static computation graph. test=develop

* add np.random.seed(7)

* increase input ndarray size. test=develop

* change float32 to float64. test=develop

* add reinforcement learning model test=develop

* align backward test=develop

* add gym in paddle_build.sh test=develop

* rm pip install in script test=develop

* refine paddle_build.sh test=develop

* fix sed error in macOS test=develop

* polish code test=develop

* fix scope problem

* refine code by reviewer comment

just skip case of distribute on windows

* update cross op parameters

We found that the reason of multiple return error in SimNet is that I wrote wrong task_mode. If we set task_mode as "pairwise" correctly, which is a format of the model input data, the multiple return won't have problem in the unit test. In this PR we corrected the task_mode and enable multiple return in SimNet unit test.

refactor fleet api under paddle.fleet
update DistributedStrategy

* add reinforcement learning model test=develop

* align backward test=develop

* add gym in paddle_build.sh test=develop

* rm pip install in script test=develop

* refine paddle_build.sh test=develop

* fix sed error in macOS test=develop

* polish code test=develop

* refine infer ir pass tests, test=develop

* commit to change copy_from_cpu input to np array, test=develop

Enable multiple "return" statement in MNIST as Unit test in dy2stat.

* fix test_activation_op, test=develop

* remove paddle.mul related unittest, test=develop

Add Support for SelectedRows for Transpose OP and Fix a Bug That SelectedRows Cannot be Supported in SimNet (#25536)

This PR fixes a bug that SelectedRows cannot be supported in SimNet. The reason of this bug is that dygraph basic_engine didn't copy var's type when the var needs to be accumulated during backward. So when a var is SelectedRows and needs to be accumulated, like SimNet which calls net for two times, the var's type will be changed to default LoDTensor thus bug happens. To fix it, we just also copy the type.

Without this PR, the accumulated SelectedRows parameters in dygraph will be changed into LoDTensor. So when we fixed the bug of supporting SelectedRows in SimNet, we found `test_imperative_lod_tensor_to_selected_rows` failed and threw the error that SelectedRows was not supported for Transpose OP. To fix it, too, this PR also added support for SelectedRows for Transpose OP.

* add bilateral slice op

* randint API: remove out, devive, stop_gradient; add name; test=develop

* test=develop

* test=develop

* test=develop