* add shape description in accuracy api doc test=develop

* fix details test=develop

* fix details test=develop

* Fix example in log_loss doc, test=develop

* Use none for unknown dim, test=develop, test=document_fix

* convert dtype of vars for range op, test=develop

* update error information about batch_norm_grad

* update bn,test=develop

 fix the doc of scatter

* update layers used in mnist dygraph model, test=develop

* fix import issue, test=develop

* add dygraph utils, test=develop

* add unittest, test=develop

* fix stat shape back in global auc scenario
* add UT to cover global auc

* update layers, test=develop

* fix input numpy, test=develop

* fix bugs, test=develop

* follow commments, test=develop

* update getitem, test=develop

Update iou_similarity op to support non-normalized bbox

* add unittests, test=develop

* set dtype of compare op to bool, test=develop

adds unary operator __neg__ for VarBase in dygraph mode, and for Variable in static graph mode.

* increase example code of py_func, fix some wrong description of English API doc

* yolo_box OP add Attr(clip_bbox). test=develop

* patch math method for varbase using auto-generated op functions, test=develop

* clean code that handles batch_size, test=develop

* follow comments, test=develop

* follow comments, test=develop

* code clean, test=develop

As the title

Add basic while_loop

Add tests to use dy/dx to make sure the gradient values calculated by the control flow backward is correct. Also fixed bugs detected by those tests.

Fix bugs:

1. Unlike sum_op, optimizer ops don't allow uninitialized input tensor. But in conditional_block_grad_op, since the conditional_block may not run, the output gradient tensor may be uninitialized, which will cause the optimizer op error. To fix it, we should let optimizer ops support uninitialized input like sum_op or assign the uninitialized gradient to 0 when the conditional_block_grad_op doesn't run. I found there are about 10+ optimizer ops. **To be simpler, I just assign output gradient of the conditional_block_grad_op to 0 in this PR**. But it can be further explored whether we can make optimizer ops like sum_op to support uninitialized input tensor because theoretically we can speed up without the assigning in conditional_block_grad_op.

2. Infer parameter shapes during append_backward. I didn't know that all our parameters are in global block. When op_desc is inferring shapes at the sub-block, it may not know the shape of gradients of parameters whose shape information is at global block. I fixed it by inferring shapes of gradients from forward var.

This PR also did some code clean up:
1. Print the var name when sgd_op catches shape error so that it is easier to debug
2. Fix a typo: dicta -> dict

    Add a python interface for Gelu.
    Add documentation for fluid.layers.gelu.

* dygraph mode support linear lr warm up; test=develop

* add unitest for linear warmup; test=develop

* add input type check; test=develop

* fix type check assert error; test=develop

* change type error; test=develop

* test=develop, fix docker with paddle nccl problem

* don't expose numerous Tensor.set(), test=develop

* fix condition, test=develop

* fix float16 bug, test=develop

* feed should be Tensor or np.array, not Variable or number, test=develop

* use forcecast to copy numpy slice to new array, test=develop

* remove float16-uint16 hacking, test=develop

* add variable method to varbase and refactor to_variable to support return varbase

* support kwargs in varbase constructor

* add VarBase constructor to support default python args

* refine varbase initial method

* reset branch

* fix ut for change VarBase error info to PaddleEnforce

* cherry is parameter change before

* overload isinstance to replace too many change of is_variable

* rm useless files

* rm useless code merged by git

* test=develop, fix some ut failed error

* test=develop, fix test_graph_wrapper

* add some tests, test=develop

* refine __getitem__, test=develop

* add tests, test=develop

* fix err_msg, test=develop

* fix fill_constant_batch_size_like_op precious problem  test=develop

* set dim[0] to -1 if dim[0] < 0 and remove assertion to runtime, test=develop

* modify ENFORCE message, test=develop

* add validation for x.shape[0] > 0, test=develop

* add ut, test=develop

* fix adam sample code bug test=document_fix

* fix sample code bug in scale test=document_fix

This reverts commit 007c9975.

* refactor AUC OP and add its CUDA Kernel
* the layout of global auc doesn't change

* fix the device supported of the op unique and unique_with_counts.
test=develop
test=document_fix

* Fix the precision of test in the op of unique and unique_with_counts.
test=develop
test=document_fix

Add English doc for cond

* Commit before merging develop

test=develop

* Backup after working with Huihuang logs

* Commit before deleting Huihuang debug loggings

* Commit before debug

test=develop

* Fix bug commit

test=develop

* Backup of fixing bugs

test=develop

* Clean up code

test=develop

* Fix a bug in sum_op

test=develop