* autofix
Co-authored-by: NLiyulingyue <83450930+Liyulingyue@users.noreply.github.com>

* revert changes in python/paddle/distributed/fleet/utils/hybrid_parallel_util.py

* empty commit, trigger ci

* fix test_slice

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>

* remove init

* delete fluid in context pool

* fix custom op bugs

* fix profiler bugs

* fix ci bugs

* fix window compile bugs

* fix windows bugs

* fix window bugs

* optimizing setup.py develop command

* add libpaddle.so

* modify setup.py

* add python/paddle/distributed/fleet/.gitignore

* add libpaddle.so to .gitignore

* add *.so to python/paddle/libs/.gitignore

* add new gitignore

* support run haokanctr model in heterps-models

* polish setup.py

* polish JVM_LIB in evn_dict

only when -DWITH_XDNN_API=ON, will those files be packed into whl
package

* flash attn

* seed

* almost

* softmax

* fix workspace

* add unitest; linux only

* fix setup

* fix datatype include

* fix setup typo

* fix def scope

* new error api

* use paddle fork

* fix attr bug; complete ut

* update flash hash

* fix rng reset

* fix offset

* fix comments

* remove transpiler

* Revert "remove transpiler"

This reverts commit 46044ccd52011d45d7026786d331f264a6a8f645.

* Revert "Revert "remove transpiler""

This reverts commit 80ad0945401b5b5efebac4baee0ec50a793d4405.

* codestyle

* fix setup

* fix

* fix

* [RM FLUID] rm parameter_server

* [rm ps] for ci

* [rm ps] for ci

* [rm ps] for ci

* [rm ps] for ci

* [mv fleet] mv fleet to distributed

* [mv fleet] for ci

* [mv fleet] for ci

* [mv fleet] solve ci of version

* [RM FLUID] trainer_pass&heter_trainer_pass
* [RM FLUID] rm distributed_strategy

* update include directory

* fully support C++ extension, pass unittest

* fix include directory

* support both extension and operator in one file

* polish testcase

* add jit unittest

* update third_party.cmake, pass CI test

* fix cmake

* fix setup

* fix inference, fix unittest precision

* fix unittest precision

* fix inference_lib cmake

* try fix setup, try fix inference_lib

* try fix inference_lib pybind

* fix mix_op_extension, fix inference_lib

* fix mix_op_extension, fix inference_lib

* change cmake

* change cmake

* add compile flags

* add Python.h headerfile

* add test_custom_plugin_creater cmake

* comment compile flag

* pass all CI

* pass all CI

* comment compile flag

* try solve test_custom_plugin_creater link error

* try solve test_custom_plugin_creater link error

* polish codes

* remove windows compile flag

* remove python_include_path

* update pybind11, 2.4.3->2.6.0

* update pybind11, 2.6.0->2.10.0

* update pybind11, 2.10.0->2.6.0b1

* update pybind11, 2.6.0b1->2.6.0, start fix unittest

* fix pybind11 2.6.0 VarBase print error

* fix pybind11 2.6.0 VarBase print error

* handle PADDLE_ON_INFERENCE

* modify according to reviewer

* fix cmake

* cmake decouple pybind_util when not ON_INFER

* cmake decouple pybind_util when not ON_INFER

* remove copy of inference_lib.cmake

* change pybind.cc headerfile fluid->phi

* move fluid.transpiler.details

* fix setup

* fix

* fix setup

* add setup

* [RM FLUID] rm fluid_pslib_init

* [RM FLUID] for ci

* [RM FLUID] for ci

* add cinn_git_tag param

* fix format error

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

* remove incubate.data_generator

* modify the setup.py

* modifyt the setup.py.in

* optimize setup.py for conda envir

* check python dependency

* optimize code after reviewed

* test

* find paddlepaddle-0.0.0-cp37-cp37m-macosx_10_14_x86_64.whl

* test

* debug

* debug

* debug

* debug

* debug

* debug

* test

* test

* modify inference_problem

* modify gen_fluid_lib_by_Setup

* debug

* debug

* debug

* debug

* debug

* debug

* modify env_dict.py.in

* modify setup.py

* modify env_dict.py

* fix bug

* Add tools for quantization-aware training
1. Expose an API named paddle.quantization.QAT
2. Define a wrapper class to insert quanters into model for QAT
3. Add some functions in QuantConfig for QAT
4. Add unittest for QAT

* Add QuantedConv2D and QuantedLinear for QAT

* Add paddle.nn.quant.qat to setup.py

* modify setup.py and paddle_build.sh

* modify setup.py and paddle_build.sh

* modify setup.py and paddle_build.sh

* modify setup.py

* modify run_setup

* modify setup.py

* fix make_clean

* modify setup.py

* modify setup.py

* delete setting python_libary

* debug

* debug

* debug

* debug

* update requirements.txt and setup.py.in

* update requirements.txt setup.py.in setup.py

* update pypi doc

* update pypi doc

* update pypi doc

* empty commit, re-trigger all ci
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>

* fix CINN should add float16.h may install bug

* reupdate setuppy support float16

* add only if float16.h file exists