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)
 

* 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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* Add conv2d_grad_grad_op
* Extracte the cuDNN conv algo searching code in conv_cudnn_helper.h.
    - Now use it in conv2d_grad_grad.
    - Will simply the searching code in conv2d and conv2d_grad in next PR.
* Enhance and fix bug in unit testing of gradient_checker.
* Support to fetch empty variables，return None in Python.

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* fix warnings

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* fix enforce test

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* Correct the comment in control_flow.py.

* Correct the argument list of ops.
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* Update API.spec.
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* Skip op_callstack attr for all op apis.
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* Remove use_mkldnn and is_test from python api.
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* Remove use_mkldnn and is_test from op_proto_maker and hard-coding them in python when generating doc string.
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* Revert the modification of while_op in #14764.
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* Remove the dependency of GRPC_DEPS.
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* Simplify the compare op for CPU.

* Use asynchronous tensor copy in reshape_op's kernel.

* Optimize while_op for test, avoiding creating variables every time.
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* Enable the cache of kernel type and kernel function.
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* Enable profiling with gperftools.

* Remove flags for testing, and fix the linking error.
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* Delete the codes of ChooseKernel.
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* Fix bug when preparing ExecutorPrepareContext for while_op.

* Fix missing depending on grpc libraries.

* Remove the redundant print.
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* Follow comments.

* Remove the codes related to prepare the ExecutorPrepareContext for while_op.
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add unittest
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add unittest
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