Support backward of backward for Relu and add a new gradient checker by comparing theoretical and numerical Jacobian. (#16862)

* Support backward of backward and a new gradient checker
* Rename decorators.py to decorator_helper.py, since Python on Windows CI has decorators package.

1. Add ReluDoubleGradMaker when register relu_grad.
2. Add a new gradient checker by comparing theoretical and numerical Jacobian.  Check double gradients by double_grad_check.

* move gc test to op_test
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* Revert "move gc test to op_test"

This reverts commit cf15da65c38f57c91f53b3d8b3c2365d4aa86016.

* enable gc test in some ops
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* fix ramdom fail
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* add parallel build script to ci test=develop
* 1. classify the test case as single card/two cards/multiple cards type
   2. run test case according to the run type

* Speed affine_channel_op unit testing
* Add check in tensor_py
* Fix ONLY_CPU Compiling

Update the filter generation mechanism that it could generate the negative parameter.
The original calling(np.random.random()) couldn't simulate the conv/relu fusion case.

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* accelerate test_ir_memory_optimize_nlp, test=develop

* accelerate test_ir_memory_optimize_nlp, test=develop

Rename the testcuda function to has_cuda, it will elimate the unnecessary testing.
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* speedup gc and inplace softmax_with_cross_entropy_grad
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* refine models gpu mem
Merge skip vars and warning messages of mem opt
remove relu mem opt
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* follow comments
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* Support seq len equal to 0 in sequence ops

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* Add more test cases

* Fix some comments

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* Fix py3 error

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* fix sampling id op bug, test=develop

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test = develop
resolve #16764

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* enable recurrent op test=develop

* make test_dyn_rnn faster

test = develop
resolve #16764

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* slimming test_parallel_executor_seresnet.py

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* fix too long unittest
recommit
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* add fake_reader.py
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