* support pow_double_grad op

* add unit test for pow double grad

* fix pow double grad

* optimize pow double grad kernel

* fix pow double grad kernel

* generate static graph code for some activation op

* fix bug

* fix infermeta of selected_rows

* Standardise batch norm

* standardize conv3d and depwise_conv2d

* fix ci bugs

* support generating static code of high order grad op by yaml

* polish code

* standard_api

* add hardtanh

* gene static graph code for ceil, expm1 op

* gene static graph code for some activation op

* fix bug

* revert doc of silu and logsigmoid

* add extra attr property set

* add type_info for all context

* add onednn context to all context

* fix context compile error

* simplify conv kernel args

* pass runtime attr into dev_ctx

* fix marco error

* clear conv_grad_kernel extra args

* merge conv_grad_grad into conv_grad

* clear conv2d_grad_grad extra attrs

* clear yaml and eager extra attr

* fix conv1d error

* change to thread local

* fix npu compile failed

* try to fix windows compile failed

* add conv2d onednn phi kernel

* fix ci bugs (#36)

* fix compile bugs (#38)

* fix extra input transform bug (#39)

* support dynamic created attr (#40)

* reset extra info gen code

* rm conv_grad_grad kernel

* reimpl pass attr adapting

* add int attr support

* remove vector inputnames creating

* fix map at error

* Update paddle/phi/kernels/onednn/conv_grad_kernel.cc
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* remove useless extra attrs

* replace mkldnn_engine by onednn_engine
Co-authored-by: NYuanRisheng <yuanrisheng@baidu.com>
Co-authored-by: NSławomir Siwek <slawomir.siwek@intel.com>

* standard api

* fix ci bugs

* fix ci bugs

* fix ce bugs

* generate static graph code for some activation op

* fix example code of cosh

* gene static graph code for exp

* refactor the doc of exp

* fix bug

* fix bug

* update doc of exp

* fix sparse op

* rename op in yaml

* fix test_layout_autotune

* fix layout autotune of transpose

* add yaml entry for rnn and rrnn_grad, move infershape function for rnn_grad to phi infer meta

* WIP: move rnn kernrl to phi

* Change the code generation to avoid converting from intializer list to tuple of heterogeneous types.
This is only triggered when an api has intermediate outputs, and the result of the outputs are of heterogeneous types.

* fix the bug that when none in a vector of tensors requires gradient, the conversion to InferShapeContext to InferMetaContext (a.k.a. BuildInferMetaContext) produces errorous results.

* fix ci bugs

* fix ci bugs

* fix ci bugs

* modify code according comment
Co-authored-by: Nchenfeiyu <chenfeiyu@baidu.com>

* support cast op backward refuse forward

* Fix the bug of high order unit test framework

Adapt tensor output_size for conv2d_transpose and depthwise_conv2d_transpose

* [Eager] fix pool2d interface under eager mode

* add use_gpudnn for pool2d

* fix pool2d yaml conflicts

* fix pool2d yaml

* [Eager, Performance optimization] fix pool3d under eager mode

* polish code

* [OpAttr]ksize of pool2d support Tensor type

* fix unittest

* add unittest

* [OpAttr]output_size of unpool support Tensor type

* fix coverage

* fix contain_var

* fix coverage

* fix split bug

* solve function redefine

* fix fluid.layers.split and add unit test

* delete splitInferMeta register in unary.cc

* modify test_split_op GPU unit test

* modify test_split_op GPU unit test place param

* refactor split op and fix infershape bugs

* add () in && and ||

* fix split C++ unit test

* fix split infershape

* [OpAttr]Adapt tensor axis for reduce_min/max/mean/sum/prod

* [OpAttr]padding_value of Pad support Tensor type

* fix unittest

* fix unittest

* fix coverage

* support refuse forward dygraph

* modify backward api exponential__grad yaml

* remove print code

* 当反向复用前向时进行需不需要更高阶的反向判断，如果不需要调用c++ api，需要的话则调用前向动态图生成反向节点

* fix some backward bugs

* modify the generated dygraph function name

* [OpAttr]axis of Reverse Support Tensor type

* fix coverage

* fix unittest

* sync_batch_norm_grad delete mean and variance