* Fix grid sampler

* Fix code format

cherry-pick prs

#36568
fix fc fuse compat problem

#36610
support lite xpu choose device id

#36010
update lite branch

#36628
add file exists check

Add paddle.nn.functional.sparse_attention API

    本个PR主要将sparse_attention功能在python层进行了一层封装，OP的主体代码见：#PR35676

    此外，对于封装的python 接口，增加了相应的单测。

Add fp16 kernel for clip_op.

Refine comments for GradScaler state_dict.

* Add new API tensordot
cherry-pick #36273

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；

In fused_attention op and fused_ffn op, the fused bias_add+dropout+residual+layernorm kernel or bias_add+dropout+residual kernel is used. To ease the use of this kernel, we provide a wrapper in this PR.
1.To reuse the increment computing code, we exact the corresponding code to "GetSeedDataAndIncrement" routine in dropout_impl_util.h.
2.The fused_dropout_helper.h provides the fused dropout kernel wrapper.

Note: the test of this warper will be provided in the following fused_attention_op and fused_ffn PRs.

* 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

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

* Add functor_primitives.h for kernel primtive api

* fix replicate pad when input size is 0

* add unit test

* remove no_value using var.name

* catch the generatorfunction and intercept it.

* add test generator

* add test case

* refine the testcase

    The code of this PR can only support CUDA 11.2. Currently, CI does not have GPU with CUDA 11.2 , and all tests will be skipped automatically.

    The new OP is paddle._C_ops.sparse_attention. Regarding the work of the python API, it will be resolved in a follow-up PR.

    The code of this PR lacks tests on dynamic graphs and static graphs, and will be added in subsequent PRs.

* quant support matmul_v2

* fix format

* fix async_read bug

* change index place to cpu

* add tensor size judge

* add async_read & async_write test

* fix bug in async_write

* fix mac py3 ci

* fix bug for cpu version paddle

* fix windows ci bug

* change input argument error type

* change const_cast to mutable_data

* add async_write out-of-bound check and consumate error hint

* fix a small bug for dst_tensor

* add docs and refine codes

* refine docs

* notest,test=windows_ci

* fix windows ci

* fix require

* fix code-block

* add core.is_compiled_with_cuda()

[Cherry-pick][Dy2stat]fix no_grad context error in train mode when using save/load (#36434) (#36463)

修复使用jit.save/load接口加载模型后，在train模式和no_grad上下文中，显存会一直增长的问题

* [WIP]Verify the correctness of graph rewrited by GeneratePass, test=develop

* add delete subgraph and unittest, test=develop

* check simple pass, test=develop

* fix coverage, test=develop

* limit with input_spec via Paddle API, test=develop

* add sparse_embedding doc

* modify sample code

* fix sample code error

ATT，cherry-pick #36227

* change time to remove static tempfile

* delete remove_static_file() function

* change the paddle.mm to matmul_v2

* update the code for the mm

* update the document for the mm

* Fix stop_gradient in RunProgramOp

* fix reference

(cherry picked from PR #36095)

PR主要功能：支持C++开发注册GeneratePass，简化针对fusion等子图优化场景开发方式。

对于__getattr__重载后不满足条件的参数，全部抛出AttributeError异常，达到与未重载版本一致。

(cherry picked from PR #36229)