* rmsprop optimizer support v1 mode

* typo

* optimize code

* refine code

* optimize unit test

* update test_rmsprop_op.py

* update formula of rmsprop

* optimize document

* update API.spec for RMSPropOptimizer

* add default value to check_output_with_place equal_nan

* reimplement auc in pyton

* reimplement auc in fluid

* add auc unittest


* replace new auc in layers


* add batch Auc in Fluid

* name formated

* Refine fusion lstm op code for better readability

* Enable peephole in fusion lstm op (seq_mode part) and add unit test

* Enable peephole in fused lstop op (batch_mode part)

Set batch_mode as default as well

* Use pre-commit to clean format

* Follow up review comments as well as adding more unit tests for seq mode

* Fix bug in generate_proposals_op.
* Fix data type for RoIs.
* Refine and fix rpn_target_assign_op.
* Add the missing file bbox_util.h
* Rename BoxEncoder to BoxToDelta

* Improve and fix fake_quantize_op.

* Add pad2d op.

* Add unitest and python api.

* Fix cuda op kernel.

* Fix python api.

* Fix python api.

* Update API.spec.

* Fix python api

* fix dist train reduce mode

* fix previous fix

* Add generate_proposal_labels for Faster-RCNN.

* Enhance the function of fused_elementwise_activation_op

* enhance unit test

* Clean Code And Add Doc

* Add compound functors

* Fix doc and enhance unit test

* define Dx and Dy for d_binary_func

* add mul_scale

* add mul_scale

* add elementwise_mul

* code refine

* code refine

* add doc

* add  AsIntermediate

* add test for dist and memopt

* update transformer too

* Add proposals generation operator for Faster-RCNN.

* Polish PrintOp

* Polish PrintOp

* Polish PrintOp

* Refine test_print_op

* Improve fake_dequantize_op.
* Follow comments.

* add ScaleOpVarTypeInference for scale op

* scale op support scale selected rows

* optimize code

* use FindVar

* use FindVarRecursive in ScaleOpVarTypeInference

* refine dist train RPC deps

* clean up

* clean up

* fix ut

* remove input for fetch_barrier

* follow comments

* Support int8 type.

* Add pad_constant_batch_size_like

* refine pad_op

* optimize memory