Use posix_memalign to allocate aligned memory, since some SIMD instructions require the alignment of memory accesses.

* refine evaluator op types

* update

* follow comments

* update

* fix v2 mnist case

* fix v2 mnist case

* update

* update

* Complete accuray layer

* Fix error

* Fix error

* Add 'accuracy' to __all__

* update

* Fix Type error

* Fix error

* Refine unit tests

* Fix an unit test error

* reimplement pow operator

* add pow_grad operator

* fix code style

* fix build error

* fix op_test bug

* revert pow operator

* add FIXME comment

* Fix Type error

* Fix error

* Fix top_k_op GPU code data type

* Fix Type error

* Fix error

2. Fix comments and paddle enforce check

* add batch_norm_layer

* add img_conv_group layer and test

* add check to Tensor.type()

* forward can run

* with backward

* change label data time from int32 to int64

* refine code

* follow comment

* Cast Operator

Cast input variable to other data type

* Fix compile error

* Add cast op

* Follow comments

* Accuracy does not support float/double, only support integers
* Polish error message when an operator does not support some device.

* add sparse support for sum op

* typo fix

* fix gpu build error

* fix unittest error

* typo fix

* infer var type and shape in op_test

* follow comments

* fix build error

* bypass some unittests depend on NetOp

* support sparse output for lookup table grad op

* refine codes

* fix gpu build error

* fix lookup table grad gpu kernel

* fix ci

* fix ci

* fix ci

* fix bug in lookup_table_grad op

* fix bug in test_word2vec

* register double kernel for some operators

* set is_sparse=True in test_word2vec

* fix lookup table grad op CUDA kernel bug

* disable test_modified_huber_loss_op temporarily

* disable test_lstm_unit_op temporarily

* fix compile error

* remove unittest

* disable huber loss unittest

* Adding L1 norm op for L1 regularization

* Addressing code review feedback

* Address code review feedback

* Change variable names to match google style guide

* Op developer can add `InferShape` to any operator

* add sparse support for sum op

* typo fix

* fix gpu build error

* fix unittest error

* typo fix

* infer var type and shape in op_test

* follow comments

* fix build error

* bypass some unittests depend on NetOp

* Simplize Gradient Check

* Stash

* Extract apply_backward_pass to backward.py

Rename apply_backward_pass to append_backward_ops

* Use graph API to check gradient

* Fix ci

* Fix CI

* Fix backward for double precision

* Stash

* Fix CI

* Fix ci

* Ignore GRU test

* Ignore xe op

* Fix CI

* Fix softmax with xe gradient

The correct equation should be IG = OG * (d_softmax_with_xe())

* Fix typo

* Fix merge error

* Disable LRN

Add local response normalize