* Add cpu_quantize_squash_pass for C-API quantization

test=develop

* add cpu_quantize_squash_pass teste

* fix lint: add include memory unorderd_map and unordered_set

test=develop

* lint fix 2

* fixes

test=develop

* refactored

test=develop

* fix windows ci

test=develop

test=develop

* MKL-DNN: Add test for conv bias fuse pass

test=develop

* Remove const cast from Conv Bias Pass Test

* Add conv with bias test case for conv+bias fuse ut

test=develop

test=develop

* mem opt

* test=develop

* test=develop

* test=develop

* test=develop

* test=develop

* test=develop

* test=develop

* refine code  test=develop

* refine code  test=develop

* refine code  test=develop

* refine code  test=develop

* refine with cub test=develop

* fix mkldnn test && remove comments && test=develop

* polish code && test=develop

* add only_forward test && test=develop

test=develop

test=develop

test=develop

fix conv+elemenwise fuse bug.

test=develop

test=develop

(test=develop)

* add is_test to pooling and activations

add prop_kind support for layers activation. conv and pooling

add a pass that sets is_test to true

add transpiler version of is_test pass

test=develop

* patch test and pass

test=develop

* add pass to analyzer.h

test=develop

* add is_test attr description & pass only on mkldnn

in:
activation_op.cc
batch_norm_op.cc
conv_op.cc
dropout_op.cc
lrn_op.cc
pool_op.cc
sequence_pool_op.cc
softmax_op.cc

* fix is_test handling for activation pool and conv

* change description of is_test for all layers again

* remove GetAttr(use_mkldnn) from pass

* rename correct_mkldnn_test_phase to is_test

and remove dependency on MKLDNN
test=develop

* review fix magic number

* two if(..)s into one

* Check is_test once and pass mkldnn forward prop kind

* dereference shared_ptr with * (without get())

test=develop

* add is_test_pass back

test=develop

test=develop

added depthwise conv mkldnn pass which for MKLDNN changes depthwise_conv operator to conv operator because for mkldnn this is the same api
test=develop

* fix nccl2 lars dist support

* put lars in momentum op

* add tests lars

* fix ci

* fix cpu kernel

* soft warning

* remove lars in test_recognize_digits.py

* move to another op

* add file

* update api.spec test=develop

* update test=develop

* fix api.spec test=develop

* wip

* wip, finish grad merge ops

* wip, finish graph build

* wip test running

* work on 1 gpu

* workable version

* update

* fix tests

* fuse broadcast op

* fix compile failed

* refine

* add batch merge test mnist

* fix CI test=develop

* fix build

* use independent bn params for batch merge test=develop

* update api.spec

* follow comments and for test

* wip

* refine tests test=develop

* follow comments test=develop

* remove startup bn modify test=develop

* follow comments test=develop

* fix merge test=develop

test=develop

MKLDNN conv + elementwise_add fusion: implementation of patterns refarctored, applied to graph. UTs added

test=develop

* add MKL-DNN placement pass

This patch also refactors conv+bn (includes changes from PR
https://github.com/PaddlePaddle/Paddle/pull/13926)
updated to use the mkldnn-placement-pass.

test=develop

* remove redundant pass list

* add comment on the default first pass

* fix test for conv+relu mkldnn fuse

test=develop

review fix

review from hshen14 fix

test=develop

fix error in broadcast and code cleanup

rename bias -> eltwise and added macro to shorten code

formatting

- add naive executor
- fix concurrency performance issue

- Added draft of new operator

- Added fused embedding fc lstm files

- First time embedding_fc_lstm_fuse_pass was invoked in
  test_text_classification

- Added Embedding pattern

- Not crashing

- Enabled draft of embedding_fc_lstm pass (does it job)

- First working (Seqcompute only) version

- Removed diagnostic comment

- First enabling of BatchCompute

- Disabling pass for embedding with is_sparse and is_distributed

- Cosmetics

- Style

- Style

* Add Preface

* Add demo code

* Save file

* Refine code

* seems can work

* use elementwise strategy

* Use ElementwiseComputeEx

* Add comments

* extract functions from operator

* Refine code

* Follow comment

* code refine

* add op_fuse  pass

* add backward

* code refine

* use TopologySortOperations

* follow comments

* refine IsFusible

* code enhance

* fix op_fusion_pass

* refine code

* refine fuse_elemwise_act_op

* adjust the input and output

* refine logic

* add intermediate_edge

* disable inplace

* follow comments

* refine logic

* follow comments

* Remove the removable IntermediateOut

* change strategy

* code refine

* enable fuse backward

* code refine

* code refine

* rename unit test

* follow comments