* 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
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* add the missing files.
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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

* Int8: Fix Pooling output scale

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* Update scales quantization for certain operators

These include: concat, transpose, pool and reshape. test=develop

* Move concat minimum scale finding to quantizer

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fix data feed ptr runtime error, pipeline trainer will core in some cases, so set it nullptr as default value.

* update pe reduce config
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*  drop the local_exe_scopes of the previous parallel_executor
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* add is_runnning in communicator, test=develop

* fix prepare context redundant code problem, optimize executor by caching create_varaiables
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* supports collective training in executor

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

add prior_box quantization code

add scale algo rules for prior box

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* clean build_strategy
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* DataBalanceOpHandle has been removed
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* debug

* update build_strategy.
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(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
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* fix doc error
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* remove nccl dep when the number of GPU is 1
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* fix code bug
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* update CPU_NUM config
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* update anakin-engine interfaces for content-dnn

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* support only-gpu mode of Anakin

modify eltwise parse

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* modification for thread-safe

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* Integrated template instance

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* increase template parameters

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* support MLU predictor

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* update anakin cmake files

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* update TargetWrapper::set_device

* update the initialization of anakin subgraph

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* use the default constructor of base class

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Add Pipeline Concurrency Train Mode:
- Cpp: pipeline_trainer & section_worker
- Python: PipelineOptimizer
- Add a new data_feed type: PrivateInstantDataFeed
- Add a test demo of pipeline trainer and the test model is gnn
- Do not support win32 now

* remove attribute in Allocator::Allocate, test=develop

* fix travis ci error, test=develop

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

* delay infershape test=develop

* fall back subblock to paddle test=develop

* fix edge cases test=develop

* remove output duplicates test=develop

* handle reshape2_grad infershape test=develop

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

* add event for fast executor and add threads for scopebuffer executor
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* Enhance fused_elementwise_activation op.
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* Move the api fused_elementwise_activation to contrib.
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* Add including files.
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* Add the support of sigmoid in fused_elementwise_activetion op.

* Update API.spec.
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