* move fluid.transpiler.details

* fix setup

* fix

* fix setup

* add setup

* change error

* fix

* Add unittest for zero-dim kron.

* Fix zero dim kron.

* rename multi_tensor_adam to fused_adam

* fix some bugs

* fix CI coverage

* rename test_fused_adam.py

* fix some bug

* add test_fused_adam_op.py

* fix some bugs

* fix fused_adam_op.cc

* fix CI bugs

* fix CI bug

* fix CI bug

* change SHA

* update to oneDNN 2.7

* update to 2.7.1

* update to 2.7.2

* add supported hardsigmoid

* update to 2.7.3

* limit cpu threads for int8 test

* group activations

* [XPU] add fp16 support for cumsum and log.

* [XPU] add fp16 support for cumsum and log.

[XPU] add multi_encoder_xpu_slice_fuse_pass, generate_sequence_xpu_fuse_pass, generate_sequence_xpu kernel (#50570)

* 修改英文注释synchronize

* 修改英文注释synchronize;test=document_fix

* test=document_fix

* test=document_fix

* test=document_fix

* 修改hfftn的注释错误;test=document_fix

* test=document_fix

* [RM FLUID] rm fluid_pslib_init

* [RM FLUID] for ci

* [RM FLUID] for ci

* [Dy2St]Remove PE logic in @to_static

* fix typo

* fix infer_program

* fix typo

* fix op_size

* [dy2static-bugfix] fix backward gradient aggregation bugs
1. Yolov3 and Yolov5 all face the same problem.

* remove set_device

* code review fix

* New onednn test

* checkopoint

* added new test, fixed issue with onednn bias

* fix bias check

* remove prints, refactor code

* delete old test

* update python tests cmake

* Delete depracated conv bias

* Delete outdated bias from convolution test

* add logspace yaml

* update by comments

* resolve test framework conflicct

Add PTQ and exporting function
1. Add Post-Training Quantization
1.1 Abstract some functions from QAT to Quantization class
1.2 Add Post-Training Quantization by extending Quantization class
1.3 Add observers for PTQ
1.4 Add unittest for PTQ
2. Add exporting function for QAT and PTQ

* support xpu multi-card infer

* add ut

* clean code

* clean code

* fix

* fix

* fix

* fix

* polish namespace

* change static_tensor_operants

* polish namespace

* support add, subtract, divide

* add unit test

* polish unittest

* fix cmake error

* polish unittest

* [XPU] add group_norm kernel

* update

* add xpu sin, cos, randint, linspace kernels

* update

* update

* Use StandaloneExecutor in FleetExecutor

* Update FLAGS

* Fix CI errors

* Update code

* Add force_root_scope_vars config

* Update code

* Fix CI errors

* Fix test_layer_new errors

* disable unewanted dygraph tests

* mine_hard_exa

* beta

* small commit

* add batch_norm composite rule

move composite test case

remove unuseful var

add composite op blacklist

* small change v2

* finish the test_composite_mean and test_composite_mean_grad

* add ops assertion to the tests

* add cinn test

* fix the error and inappropriate usage in func: mean_composite

* remove the ref of outer lib in primtives.py

* modify sample code of reduce_sum

* fix composite mean op map

* modify testcases to test more float type

* remove cpu float16 test

* cinn test fix

* remove reduce_max

* change the name sum to sum_x

* change the use of reduce_sum to sum

---------
Co-authored-by: Ncyber-pioneer <chenzhuo@tju.edu.cn>

* Done

* Deleted old python test, fixed new python test, changed names in parallel_UT

* Revert parallel UT changes

* Revert parallel UT changes v2

* Review fixes and simplification of conv output shape calculation, disabled sqrt from conv_act_duse_pass

* delete sqrt from possible activations from conv_concat_relu test

* review refactor

* merge main

* delete sqrt from list of compatible activations

* Test with no outdated inputs

* auto parallel align tool

* modify function get_var's return

* add save and load in align_tool

* modify load function and save function

* add finding different ops in align tool

* full auto parallel align tool

add test file for auto parallel align tool

set timeout for test

modify get_backward_tmp_var function

add annotation for align tool

modify test file

modify code to restart CI

remove timeout

* set timeout

* map output from composite rule to origin op

add mean layer_norm dropout op map

add input map check

composite softmax support input shape []

* composite softmax support shape []

* polish log

* solve conflict

* polish code

* polish op map output

* add check dtype