* fix bugs while cond receives dict as input

* add unittest

* change flatten -> _is_sequence_except_dict

* [CodeStyle][black] format dy2static unittests

* format some missing files

* update lineno in test_origin_info

* update lineno in test_error

* update lineno

* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* adjust timeout of test_trt_activation_pass

* test=infer-coverage

* Fix W605 in tools folder by adding escape symbols

* Fix W605 in incubate and some other folders

* Fix W605 in /fluid/test folders

* Update tools/analysisPyXml.py
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>

* Add some changes to manual and auto escape symbols

* revert changes in transformer.py

* Fix new code with W605 error: add escape symbols

* revert changes in transformer.py

* revert changes in transformer.py
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>

* add test for stage2 + dp

* update test for stage2 + dp.

* update.

* update.

* rename op in yaml

* fix test_layout_autotune

* fix layout autotune of transpose

* Add TypeHint Transformer

* add unittest for typehint transformer

* support uniform api in new ad

* add unit test for uniform_random_p

* resolve conflict

* fix uniform_random orig2prim

* fix primrules

* remove ShapeTensor and ShapeTensorList input in uniform_random_p op and add sigmoid orig2prim rules

* Fix recurrent op eager deletion pass error in dy2st

* Polish code

* Refine error message

* Add missing fp32 config and reduce the testing combination

* Reduce trt matmul pass test max examples

* [CodeStyle][py2] fix an decode error caused by 47036

* add a comment

* add an unittest for Block._rename_var

* add test_block_rename_var to static_mode_white_list

* Rewrite strided_slice converter  using  shape tensor 
* clean code

* add parallel tuner

* add unittest

* fix unittest

* set timeout of unittest

* set unittest timeout

* fix auto_mode setting

* update unittest

* sync from develop and update unittest

* remove unused import

* update unittest

* update cmakelist

* add unittests

* first version, accuracy corrected

* disable debug print

* use blockReduceSum in phi

* add UT

* add opCompat

* code style

* code refine

* bug fix

* code refine

* test fix

* bugfix

* codesytle fix

* code style

* code-style

* code-style

* code-style

* git

* style

* leave default relu in kernel

* style

* cleanup FCMKLDNN pattern

* merge conflicts

* update develop

* update develop

* add const

* rename to oneDNN and adjust attributes

* whitespace

* add cost interface

* update inferface and add unittest

* update unittest

* update inferface

* [CodeStyle][py2] remove `compat` module (to_text)

* remove some unnecessary decode

* remove to_text definition and unittest

* Revert "remove to_text definition and unittest"

This reverts commit a6b69cb8dca8b9b031ce10ea32d1040e7e0dd267.

* remove an assertion

* empty commit

* [AutoParallel] add callbacks

* fix unittest

* fix dist_context

* fix engine

* fix cmakelist

* fix unittest's returns

* fix cmakelist

* Update test_sparse_transpose_op.py

* Update test_sparse_transpose_op.py

* init gumbel api

* commit: update test file

* fix：bug

* update Gumbel API

* upgrade distribution/gumbel.py

* add tests/test_distribution_gumbel.py

* fix：code style

* fix：code style

* fix：code style

* fix：code style

* fix bug

* fix：code style

* fix：code style

* fix：rollback uniform

* fix：delete invalid code

* fix：bug and add static test

* fix：code style

* fix：code style

* fix：delete init transforms

* fix：bug

* fix：bug

* fix：code style

* fix：code style

* fix：add transforms

* fix：code style

* fix：code style

* fix：bug

* fix：bug

* fix：code style

* fix：code style

* fix：bug

* fix：code style

* fix：code style

* fix：bug for gumbel.py / add：judge transforms'len for transformed_distribution.py

* update gumbel.py

* fix：bug for test_distribution_gumbel.py

* fix：bug for test_distribution_gumbel_static.py

* fix：code style

* fix：code style

* fix：coverage

* fix：bug

* fix：bug

* fix：code style

* fix：bug

* delete：no use package for gumbel.py

* add：coverage transforms'len judge for test_distribution_gumbel.py

* fix：code style for test_distribution_gumbel.py

* fix：coverage

* fix：code style

* fix：code style

* fix：code style

* fix：code style

* fix：code style

* fix：en doc

* fix：param

* fix：copyright

* fixSample; test=document_fix
Co-authored-by: Ndasen <sen15530876201@163.com>

* add sparse reshape

* change the dtype in all test cases to int64

* just one test case

* modify comments

* Update test_sparse_reshape_op.py

* chang the type of "shape"  from  vector<int64_t>  to  IntArray

* check whether sp_out.to_dense() is the cause  of error

* print sp_out

* Update reshape_kernel.cc

* use numpy to generate the equal paddle tensor

* just check dense_tensor.numpy()

* check cpu and cuda versions

* Update test_sparse_reshape_op.py

* supply all test cases for cpu forward coo kernel

* test forward coo cuda kernel

* change configuration of cuda kernel

* keep only one test case

* test coo cpu kernel (forward and backward)

* row major or column major ???

* test cuda coo forward kernel

* complete declaration and registration

* Update __init__.py

* rebuild

* retrigger CI

* add cudaMalloc and cudaMemcpy  in  ReshapeCooKernel  and change back to row major order in a cuda dense tensor

* midify minor error

* test only cpu coo forward kernel

* add all test cases for coo forward kernel  (both cpu and gpu)

* test all forward kernels (coo, csr; cpu, gpu)

* add all test cases for all kinds of kernels

* just retrigger CI

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* Update sparse_ops.yaml

* resolve conflicts

* Update sparse_ops.yaml

* don't specify tensor place

* new shape has -1 or 0 in it

* Update unary_grad_kernel.h

* correct lvalue error

* code style

* Update sparse_backward.yaml

* Update sparse_ops.yaml

* Update unary_kernel.h

* Update unary.py

* Update sparse_backward.yaml

* Update unary.py

* code style

* code style

* code style

* Update unary.py

* specify tensor place explicitly

* do not use numpy array

* use numpy array in unit test again

* modify example code in docstring