* [NPU] modifications for model ernie-1.0

* rollback 503003 and change cast to dtype

这个PR是fused_feedforward反向的代码

相关kernel实现：fused_dropout_act_bias, fused_residual_dropout_bias, fused_layernorm_residual_dropout_bias

fused_feedforward是一个融合算子，该算子对transformer模型的feed forward层的算子进行融合和封装，使得前端只呈现一个接口，通过融合减少部分访存和kernel launch的时间，以此提升性能。

* Add bincount op

* upload cpu version

* fix unitest

* fix unittest

* fix unittest

* fix en doc

* add more test

* fix en doc

* add more test case

* fix test

* fix input vailidation

* fix input check

* fix unittest

* fix test

* fix en doc

* Init the functions of CinnCompiler.

* Add the unit test for CinnCompiler.

* Fix some compilation errors.

* Update the UT of cinn_compiler.

* Use Decomposer&OpFusion passes in CinnCompiler::CompileGraph.

* Update some comments.

* Uncomment some includes in build_cinn_pass.cc.

* Use refs instead of ptrs as returned types of FindGraph & Compile in
CinnCompiler.

* Use the merged CinnGraphSymbolization functions in CinnCompiler.

* add some ops to train ssd on kunlun

* add some ops to train ssd on kunlun

* add some ops to train ssd on kunlun

* update cast op unittest

* update cast op unittest

* update cast op unittest

* update xpu cmake

* update cast unittest

* add align for WorkQueue

* add spinlock

* merge develop

* merge

* Add EventsWaiter

* update

* update

* update Error MSG

* update EventsWaiter

这个PR只包含fused_feedforward前向的代码。

相关kernel实现：fused_dropout_act_bias, fused_residual_dropout_bias, fused_layernorm_residual_dropout_bias

fused_feedforward是一个融合算子，该算子对transformer模型的feed forward层的算子进行融合和封装，使得前端只呈现一个接口，通过融合减少部分访存和kernel launch的时间，以此提升性能。

* add cinn graph symbolization

* fix some bug

* add paddle scope to cinn scope

* add paddle scope to CINN scope in Symbolization, and add feed op when build cinn pass

* fix some bug

* fix some bug by review advices

* optimize code problem

* revert build_cinn_pass and move the change to https://github.com/PaddlePaddle/Paddle/pull/36503

* fix some bug after co-compilation

* perfect single test script

* remove scope and rename feed_target to input_tensor

* using std::unordered_map instead of absl::flat_hash_map

* fix single test bug

* revert to preverion for WITH_CINN has add in later PR

* full error information for CI

* full enfore information for CI pass

* disable padding if dynamic shape

* add parentheses

* correct

* add file check

* add ut

* add transformer of paddle desc and cinn desc

* change LOG(FATAL) to PADDLE_THROW for ci

* full error imformation for ci

* fix some problem as review advice

* fix some bug

* move vat type utils to tansform_desc header file

* add if NOT WITH_CINN control whether compile

* build_strategy check whether open WITH_CINN

* add control WITH_CINN in cmake

This PR added some changes to match the CINN change for compilation. It also tried to fix JiangCheng's Problem in PR: https://github.com/PaddlePaddle/Paddle/pull/36100

These changes include:
1. Set `CINN_GIT_TAG` to a newer tag
2. CINN now just `make cinnapi -j`
3. We have to add `-DPY_VERSION=${PY_VERSION} -DWITH_TESTING=ON` to CINN cmake args
4. For CINN's third party dependencies, we could just include headers without target_link_libraries
5. Moved `cinn.cmake` from `paddle/cmake` to `paddle/cmake/external` to match old style. External folder contains `lite`, which is the same level of `cinn`
6. CINN added `-DNAMESPACE=cinn_gflags` in `gflags.cmake` to have different gflag namespaces between CINN and Paddle. It solved re-define problem.
7. Change namespace of `::google::` in gflags to `::GFLAGS_NAMESPACE`

* slice

* add UT

功能：本PR的目标是提高attention模块的计算性能。
为了减少框架层对op的调度开销，本PR通过在C++层手动实现attention模块，对外提供attention 大op；
为了减少防存开销，本PR采取了两种优化方法：
（1）在q,k,v计算时通过共享输入X，将该处的gemm，transpose和bias add从三次调用减少为一次；
（2）使用kernel融合优化技术，在不同cuda kernel之间通过寄存器传输数据；

* [hapi] support dygrapg amp O2

* fix problem of static pure fp16 in hapi

* fix bug

* fix format

* fix ut

* follow comments

* update ut

* update amp save/load

* fix ut

* refine code format

* Support elementwise_add triple grad Kernel

* Change code-format to follow CI std

* Removed unreasonable code, and fixed an input uninitialized issue

* Support elementwise_add triple grad Kernel

* Change code-format to follow CI std

* Removed unreasonable code, and fixed an input uninitialized issue

* added base changes for matmul_v2+trans+resh fuse pass

* added full matmul_v2+transpose+reshape pass

* removed a file added by mistake

* added reviewers suggestions

* Changed ops type in checking capatibility version

* Deteled one statement

* add sync_batch_norm (support train, infer, and fp32, fp16, and NCHW, NHWC)

* [NPU] Delete debug codes

* [NPU] Remove FP16

* add viterbi decode cpu kernel

* add viterbi decoder api in paddle.text

* add a data buffer once to avoid create many small pieces of data buffer frequently

* fix viterbi max_seq_length bug

* fix seq_len=1 bug

* fix device context

* move split out of for loop

* remove INVERSE_SUB

* remove 2 GET_CAST_MASK

* remove 1 loop

* remove Functor

* add to_static deploy code

* use MAX_FUNC instead of ELE_MAX

* add MaxFunctor

* impl max_func

* remove MaxFunctor

* remove cast op

* use REGISTER_OP_WITHOUT_GRADIENT

* add viterbi cuda kernel

* add FIX_BLOCKDIM_CASE macro

* add MKL add, mul; add get data mask

* add arange mkl impl

* add CPU Argmax

* add cpu gather

* use EXECUTE_MKL_ELEMENT_BINARY_OP instead of some ADD, MUL

* use SameDimsBinaryOP instead of EXECUTE_MKL_ELEMENT_BINARY_OP

* use SAME_DIMS_ELEMENT_BINARY_OP

* add SimpleBroadcastBinaryOP

* use int instead of int64_t to accelerate

* optimize SimpleBroadcastBinaryOP

* optimize SimpleBroadcastBinaryOP

* optimize performance in both single thread and multithread situation

* remove useless line

* remove useless code

* add CREATE_TENSOR_BUFFER macro

* add INIT_REQUIRED_TENSOR macro

* add comment

* fix windows ci

* add viterbi unittest

* remove cuda add functor

* remove cuda equal

* remove a template function

* fix windows ci

* fix windows dtype

* remove some template instance

* remove useless header file

* remove some blockdim

* remove transpose impl

* accelerate cpu performance on single thread situation

* viterbi_decode->crf_decode

* rename crf params name

* add viterbi api test

* remove useless import

* add enable_static

* use viterbi decoder

* fix viterbi len=1

* fix  viterbi unittest

* remove useless comments

* reconstruct viterbi decode

* remove ADD,SUB,MUL structure

* fix coverage

* remove CREATE_TENSOR

* add name args

* crf.py->ops.py; with_start_stop_tag->include_start_end_tag

* update crf_decode en docs

* fix viterbi decode en docs

* fix some review comments

* add FIXED_BLOCK_DIM_CASE in cuda

* push_back->emplace_back

* crf_decode->viterbi_decode; include_start_end_tag->include_bos_eos_tag

* paddle.text.ops.viterbi_decode->paddle.text.viterbi_decode

* fix viterbi_decode en docs

* add some ops to train ssd on kunlun

* update test_fill_any_like_op_xpu.py

* Update the implement of reduceAnyKernel according to kernel primitive api
* Fix a bug in ReadData, ReadDataBc and ReadDataReduce when NX != 1

* add ctr table depends

* code style

* fix

* fix

* fix naming

* rename

* rename

* add align for WorkQueue

* add spinlock

* merge develop

* merge

* Add EventsWaiter

* Revert "Add EventsWaiter"

This reverts commit e206173aa9be7401b83a53581627bfaf557c8fb2.

* adjust multithread using, fix flame graph

* update

* Add kQueueSync.synchronize_run_ logic

* Support No DataTransform From GetKernelTypeForVar

* split into PreBuildTask and BuildPull; slove endpass bug;test=develop

* change buildcpu into prebuild and buildcpu into build;test=develop

* fix global gather and global scatter operators

Add Tokenizer related functionalities for Transformer model in order that the process of training and predicting is consistent.

* support the text string as an input Tensor
* support the "VOCAB"unordered_map<wstring, int> as an input Tensor to lookup tokens
* Tokenizer used for BERT. This tokenizer applies an end-to-end, text string to wordpiece tokenization.
* It first applies basic tokenization, followed by wordpiece tokenization.