update doc: no need to modify pybind_file

* Create prune.md

* modification based on comment

* remove insertion

* rename id to block_id

* Update prune.md

* formatting

Prune implementation

* Add glog as dependencies of ops

* Use VLOG to logging some information is helpful when we debug Paddle

* Fix Unittests

add sparse update kernel for sgd operator

* Adding Proximal Gradient Descent

* Fixing review comments

* Implement FC layer with helper

* Update LayerHelper

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Layer wrapper for FC

* Fix unittest

* Fix CI

* Add code generator

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* Complete mlp, fit_a_line

* Implementation of simple conv_2d layer

* Fix bugs

* Correct implement BlockDesc destructor

* Fix bugs

* Fix unit test error

* Follow comments

* Add design doc of batch_norm_op

* Move batch_norm_op.png to operator/images

* Refine batch_norm_op design doc

* Add initail design doc for regularization

* Updating image links

* Commiting the images for the equations

* Adding computation graph images

* Adding section on computation graph

* Change ProgramDesc not a global variable

* Polish code style

* Correct implement BlockDesc destructor

* Unify program as parameter name

* Implement FC layer with helper

* Update LayerHelper

* Add debug string for Python ProtoBuf

and Rename `Sync` to `Flush`

* Add check of ProtoBuf initialization

* Layer wrapper for FC

* Fix unittest

* Fix CI

* Add code generator

* AttributeChecker Better error log and speicalize bool

Since lots of types can be cast to bool

* Complete mlp, fit_a_line

* Implementation of simple conv_2d layer

* Fix bugs

* Remove debug code

Update the documentation of how to cross-compile for Android

Simplify executor

* initial matmul operator

Similar to np.matmul, but also has transpose_X and transpose_Y flags,
and only supports tensors from rank 1 to 3 inclusive.

For GPU, uses cublas?gemmStridedBatched. For CPU, uses
cblas_?gemm_batch if available via MKL; otherwise a simple serial
implementation that loops over the batch dimension is employed for now.

Add momentum operator

* init parameter base class

* optimize the Comments of optimizer

* basic implimentation of optimizer

* add test_optimizer

* add no_grad_set to interface

* update optimizer.py

* python code can run

* fix some problem

* add sync_with_cpp to Python Program and Block

* sync vars and ops in block from cpp

* optimize code and add some comment

* add more check for sync

* update optimizer with return value of Backward

* rm unused code

* infer shape when create gradient vairiable

* update test_optimizer

* update test_program.py

* update backward test

* follow comment

enable merge grad unit test

add config helper for dot-product attention.