The change includes 3 things:

1. Set CPU_NUM to 1 in the tests because the ParallelExecutor will print warning that CPU_NUM is not set and use default 1.

2. Old tests compare two RNNs, hand written simple RNN and same RNN built by Paddle, but initialized RNN weights in numpy random and Paddle random separately. Fixed it by setting weights and bias values.

3. Also set numpy random seed in the tests. Now the two RNNs diff can be smaller (rtol from 0.1, 0.2 to. 0.01) in the tests.

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test=develop

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do some odd jobs, test=develop

* split different comm method for mnist distributed training

add license

* fix clip_by_norm doc, test=develop

Test PaddingRNN on V100 GPU device.

Test configuration: large model, padding mode (which is the mode using recurrentOp), one GPU.
                   
GPU memory (MiB):   6414 (this PR)     vs   6837 (without this PR)
Speed (steps/s):         10.28 (this PR)    vs    9.89 (without this PR)
 

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* add check of executor, test=develop

Optimize the content of error reporting information, print error code and official document web sites (#18671)

 optimize the error reporting information of cuda related API
 index on develop: 130ac177 Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into 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

* hash_op support int64 hash_size
* add corresponding UT

* remove ctr reader, all functions are satisfied in dataset

* test=develop, fix docker with paddle nccl problem

* test=develop, downgrade gcc to 4.8 for latest-dev

* test=develop, downgrade gcc to 4.8 for latest-dev

* test=develop, modify cmake to renew all third_party

* test=develop, invoke ci

* test=develop, invoke ci

* test=develop, complie python with wide-unicode

* test=deveop, refine env settings

* test=deveop, refine env settings

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

* Add GPU implementation for `prelu` backward pass

test=develop

* Fix logic error in `prelu` GPU backward and simplify a bit

test=develop

* Fix `prelu` backward CUDA implementation

test=develop

CPU version was not used actually, so test passed

* update anakin-engine interfaces for content-dnn

test=develop

* support only-gpu mode of Anakin

modify eltwise parse

test=develop

* 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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* load model from buffer with length

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* modify the access level of class

test=develop

* support anakin for bitmain arch

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* remove files

* checkout cmakelists

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* modify interfaces

test=develop

* add cmake dependments

test=develop

* enforce the outputs of net

test=develop

test=develop

* - Added partial draft of pooling acquire

- Workspace support

- compilation fix

- Added draft of pooling backward reimplementation

- Segfault fix

- reverted 'any' for diff_dst crewation in pooling

- Lint fixes

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- lint fixes

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- Further lint fixes

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* - Fixes after review

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* - Lint fixes

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* - Even more lint fixes

test=develop

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* make auc op compatible with 1 dim

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* not use transferscope cache in cpu case

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* adjust variable name and add comments

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* use correct format for class member in operator.h

* use correct format for class member in operator.cc

test=develop

* fix int64_t

* update fill constant op unittest

* add empty line

* delete m, test=develop

2）add pip install coverage in Dockerfile.tmp
test=develop