python inference API support enable_memory_optim

* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
test=develop

* add the missing files.
test=develop

* 1 add trt fp16 support
test=develop

* update parallel.py
test=develop

* remove legacy memory optimization codes, test=develop

* follow huihuang's comments,test=develop

* follow luotao's comments, test=develop

* dump slot

* test

* proto

* dump slot

* test

* proto

* code style

* code style

* code style

* style

* add delete after unseen days

* add unseen days

* code style

* conflict solve
test=develop

* add clear model

* code style
test=develop

* code style
test=develop

test=develop

（1）support patch data （merge slots of instances of same line id, modify dense layer which
changes its size）
（2）add fleet load_one_table interface, support load from paddle model and load from pslib model
（3）fix push sparse bug which cause push sparse cost more time（about 10% in my testcase）
（4）when some slots are not in one of your network (join/update, etc.)，data feed、collect label info、push/pull sparse will skip these slots， instead of throw error.
（5）add more debug info in TrainFilesWithProfiler

* support sparse gradients
test=develop

* feature/auto_growth_allocator, test=develop

* add unittest of AlignedAllocator, test=develop

* try to turn on auto_growth to test on CI, test=develop

* fix segmentation fault in mixed_vector.h, test=develop

* add unittests, test=develop

* remove async executor and add data_feed.proto to the deps of train demo

* fix int64_t

* update fill constant op unittest

* add empty line

* feature/buffer_shared_inplace, test=develop

* refine code, test=develop

* fix elementwise_add op cpu inplace and sum inplace bug, test=develop

* add unittest and debug log, test=develop

* fix parallel_executor scope bug, polish code, test=develop

* fix sum op, activation op, single_in_place_inference bug, test=develop

* remove kLocalExecScopeName, test=develop

* fix unittest,test=develop

* fix out_var first version bug, test=develop

* follow comments,test=develop

* Fix Mask rcnn predictor
    1. refine memory optim algorithm to support the model with the block op.
    2. output diff : modify the affine channel fuse
    3. add condition_block_infer op
add interface for setting trt calib table dir
test=develop

* add the missing files.
test=develop

1. Since allreduce op has 4 reduce types, We split these four reduce types into four ops
2. We also refined the collective op code, e.g. we separated the collective op kernel into CPUKernel and CUDAKernel, and remove the device specified DeviceContext parameter in template as we already knew the target DeviceContext
3. We remove the newly added Collective op role to reduce the complexity of program and graph analysis

Fix dygraph show style for FluidDoc.

* add is_runnning in communicator, test=develop

* fix prepare context redundant code problem, optimize executor by caching create_varaiables
test=develop

* supports collective training in executor

* make fetch_list runable with variables, add more unittest for use_program_cache
test=develop

* fix comment
test=develop

* use unique name for nccl_id

* supports output to stream in program_to_code

* insert sync_comm_stream before regularization; add skip_op_callstack capability in program_to_code

* set op role in collective training

* add collective op role

* remove orig file

* add build optimizer by strategy

* add collective strategy

* refine collective strategy

* add multi-process role maker

* refine strategy building factory so that we can easily plugin more strategy

* scale loss grad in collective sgd transpiler

* add support for distributed fc

* code format

* revert some features for dist fc

* add support for distributed fc training

* fix prepare context redundant code problem, optimize executor by caching create_varaiables
test=develop

* supports collective training in executor

* make fetch_list runable with variables, add more unittest for use_program_cache
test=develop

* use unique name for nccl_id

* supports output to stream in program_to_code

* insert sync_comm_stream before regularization; add skip_op_callstack capability in program_to_code

* set op role in collective training

* add collective op role

* fix comment
test=develop

* remove orig file

* add build optimizer by strategy

* add collective strategy

* refine collective strategy

* add multi-process role maker

* refine strategy building factory so that we can easily plugin more strategy

* scale loss grad in collective sgd transpiler

* add support for distributed fc

* code format

* revert some features for dist fc

* add support for distributed fc training

* test=develop
add collective op unittest standard

* test=develop
remove the test_collective directory

* test=develop
remove the test_collective directory

* remove slicegather test

* code format for reducescatter

* update attr of shard_index_op

* Modify macro nccl_helper

* remove test without distribute

* macro collective_helper

* marcro update

* test=develop
update support python3.5

* test=develop change gpu memory use to 0.1 when test

* test=develop
update ut equal func

* test=develop
set flags to 1.5

* test=develop fix pickle dumple  py35

* test=develop
fix divide in slice and add sync_comm_stream
update atol and rtol to 1e-05
rm shard_index op and test
modify read input from file to read from memory
remove origin_program in framework and add i/o in c_sync_calc_stream

* test=develop update unittest sync operator I/O

* refine cuda place error msg, test=develop

* use LOG(ERROR)+exit(-1), test=develop

(1) use channel instead of vector/BlockingQueue in Dataset，to keep same with existing implementation, and make code more readable and flexible (dataset single output channel or multi output channel). one previous memory out of limit problem is cause by not release memory after training.
(2) add Record because MultiSlotType costs too much memory (80B)，fix memory out of limit problem.
(3) add Channel, Archive in paddle/fluid/framework
(4) change dataset from shared_ptr to unique_ptr in pybind
(5) move create/destroy readers from trainer to dataset
(6) move shuffle from datafeed to dataset. dataset holds memory, datafeed is only for load data and feed data to network.
(7) fix thread num bug of Dataset when filelist size < thread num
(8) support set_queue_num in InMemoryDataset

* update execution_strategy option default value
test=develop

* fix doc error
test=develop

* fix dygraph mem leak, test=develop

* polish msg, test=develop

* support avx and noavx core

* add catch and give some log

test=develop

* fix build

test=develop

* add missing package

test=develop

* fix pybind name

test=develop

* fix import error

test=develop

* conbime noavx core

test=develop

* add requirements

test=develop

* fix unkown message

test=develop

* fix api spec

test=develop

* refine and clean

test=develop

* update

* pass dist ut

* follow comments

test=develop

* refine scripts

test=develop

* WIP

* WIP

* test=develop, add api doc and example code for dygraph

* fix the ParallelExecutor on Windows
test=develop
* restrict to use one GPU only under windows

* refactor PR 16865

* delete mergetool files

* test=develop

* test=develop

* test=develop

* test=develop

* create dir for int8 model before call SaveOptimModel

* test=develop

* mkldnn int8 only support linux; test=develop

* refine code; test=develop

* remove comment; test=develop

* refine code; test=develop

* fix bug; test=develop

* add exception for mkldnn_post_training_strategy

* reuse int8v2 CAPI dataset; test=develop

* fix accuracy check bug; test=develop

* remove tab

* convert files to unix format

* test=develop

* reduce CI time;test=develop

* reduce CI time and refine code;test=develop

* refine comment; test=develop

* add cmake FLAGS;test=develop

* remove predict_num;test=develop

* use pyreader to read data

* add return_list to PyReader to support return value represented as list

* use python api to reduce python time cost, test=develop

* fix travis ci, test=develop

* fix Py_None error,test=develop

* for debug

* test=develop, memory optimize for dygraph using shared_ptr

* test=develop, fix travis ci showed error

* test=develop, fix bug for recurrent usage of varbase

* test=develop, init varbase when it need to be Add

* fix prepare context redundant code problem, optimize executor by caching create_varaiables
test=develop

* cache sub_scope, program, var when use_program_cache=True is set

* make fetch_list runable with variables, add more unittest for use_program_cache

* fluid int8 train and trt int8 predict align.
trt int8 predict init
op converter

* 2. align fluid int8 train and trt int8 inference.
enhance quant dequant fuse pass
enhance op converter, trt engine, trt engine op, trt subgraph pass.

* 3. add delete_quant_dequant_pass for trt

test=develop

* 4. add the missing file
test=develop

* 5. i modify the c++ interface, but forget to modify the pybind code
fix the IS_TRT_VERSION_GE bug, and fix elementwise op converter
test=develop

* add __str__ method for tensor and lodtensor to support print test=develop

add Run Prepared Ctx, fix pybind problem

Python examples of fluid.layers.io.double_buffer and some BuildStrategy's methods.

* polish_executor_and_add_ctx_cache