* Fix a critical bug in softmax_with_cross_entropy_op, which will lead to the wrong gradients.

* Enhance unit testing.

* "add softmax cudnn functor support"

* "add testing"

* "refine cmakelist"

* "sequence softmax forward speed up"

* "add softmax grad"

* "fix sequence softmax test"

* "add double precision'

* "fix softmax test"

* "add softmax cudnn support"

* "fix softmax cudnn test"

* "add softmax to nn.py"

* "fix compile bug"

* "refine cmakelist"

* "fix ci"

* "fix based on comment"

* "fix based on comments"

* "fix ci"

* add fp16 mul op support

* small fix

* fix bug

* small fix

* fix PADDLE_WITH_CUDA compiling issue

* reorg code

* test for pybind

* treate as float16 as uint16_t in pybind

* bind np.float16 to paddle float16

* small fix

* clean code

* remove redundancy

* fix mul_op test

* address comments

* small fix

* add is_float16_supported func

* "exported scatter to python"

* Revert ""exported scatter to python""

This reverts commit 38745a62.

* "polish scatter and export to python"

* add memopt level

* add opt level for image classification demo

* clean code

* add delete op

* clean code

* test machine translation demo

* clean code

* clean code

* skip fill constant with force cpu

* clean code

* clean code

* refine code

* clean code

* fix bug

* Clipping bbox in the mAP evaluator calculation.

* Fix bug in detection_output and mAP calculation in SSD.

* Fix bug in detection.py.

* Fix bug in test_detection_map_op.py.

* MKLDNN pool2d OP kernel added

* conv2d and pool2d MKLDNN kernels renamed

* MKLDNN conv2d kernel refactoring

* fix mac build error

* override GetExpectedKernelType for cast op

* fix typo

* add cuda unittest

Fix sparse update memory error for distributed training

* MKLDNN conv2 OP kernel added

* TODOs added

* mkldnn conv2d OP refactor

* CanCUDNNBeUsed and CanMKLDNNBeUsed moved

* Fix mAP evaluator bug.

* Fix bug in detection mAP evaluator.

* Fix unit testing.

* Support to set background label index in detection mAP op.

* change learning_rate_decay to learning_rate_scheduler
* optimize code
* change nn.cast to tensor.cast