* fix linspace error in amp

* fix log

* fix amp error

* saved_tensors_hooks

* make eager log readable

* fix compile error

* recover test

* invoke ci again

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* support bmm and bmm_grad in xpu

* add error removal

* test=kunlun

* refactor code for better structure

* test=kunlun

* add fp16 kernel for bmm

* test=kunlun

* normalize yaml op label

* revert op_compat yaml change

* fix prelu and rnn compat problem

* replace api by op

* add scope cache & reuse

* add gc scope for end of each train step

* del scope reuse for jit

* refine code

* test

* 1. make list transformer into jit form.
2. fix some bugs in tensor_array, such as append.
3. enhance the function analysis visitor to recognize push/pop.
4. add setter/getter helper to deal with 2+ name sets.

* fix ci errors:
1. add to_tensor_array logic in convert_cond
2. fix IfExpr error.
3. fix erros while return_names or push_pop_names is None
4. fix slice error in a[i]=1 where a is tensor_array
5. add pop interface in Variable

* 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

* add interpretercore

* refine backward program id

* add code

* refine program

* refine code

* create forward/backward_program by prog2graph2prog method

* test, do not care

* refine code

* refine code

* refine code

* test, do not care

* add interpretorcore

* add scope

* refine scope create method

* add jit for new_exe

* solve conflict

* delete unused code

* polish code

* polish code

* refine scope in inplace

* refine for datatransfer

* refine _rebuild_from_desc

* refine control eager deletion attr

* refine used_for_jit

* refine jit for infer

* op size0 use ori program

* polish code

* refine jit

* refine run_program_op ut

* refine inplace

* refine control

* refine graph helper

* refine control

* refine inplace

* refine buffer_share_inplace_pass

* polish code

* polish code

* refine usage for compilerProgram

* refine control

* test

* test core cache

* refine code

* refine io.py

* increase test_seq2seq timeout

* refine convert program

* refine interpretercore_cache release

* delete buildinplace

* refine partial_program && io

* refine code for io

* test

* test

* test

* 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

* pylayer set has grad with create_graph

* add legacy_api.yaml

* set merged_momentum inplace only

* support inplace optional<vector<tensor>>

* add dygraph_mode api

* optimize TensorToConstDenseTensorPtr

* 修复生成动态图代码时，如果输出没有配置名字，会导致下标越界的问题。

* decide forward_return[0] is not none

* 修改反向yaml前向输出只有一个时，未配置名字，那么输出自动生成为out

* modify code style

* fix_stop_gradient

* [Eager draft] forward_only interface migrate to autograd_api

* strings api add dygraph forward function

* rm useless comments

* draft version for check CI

* fix ci

* forward-only no need compute_require_grad and pass stop_gradient, rm useless comments

* polish yaml and using CPUPlace = phi::CPUPlace

* rm useless comments

* polish yaml and update some test case

* rm useless funcs

* polish eager_gen code

* polish code

* support optional<vector<Tensor>> in yaml and eager

* delete useless comments in eager_gen.py

* fix api_base.py support optional<vector<TTensor>>

* python_c_gen.py support optional<vector<tensor>>

* transfer linear_interp_v2 yaml from fluid to phi

* fix op_test typo error

* change linear_interp_v2 testcase

* fix args in final_state_linear_interp_v2

* fix zeropad2d typo. test=document_fix

* Polish black_ops_list logic in eager_gen

* update black_ops_list

1. change the codegen code to avoid conversion from heterogeneous 'initializer list' to tuple, which fails on gcc 5.4; (#45036)

2. add a template CheckTensorHasNanOrInf to handle arbitary tuple of supported types.

* [Eager] use final_state_fill_constant_

* fill_constant use str_value

* add fill_constant_ to no_amp_list

* use float(value) as input

* support final state full_ same as fill_constant

* clean tensor.h

* fix gather_nd

* [Eager] just need to run needed nodes

* [Eager] just need to run needed nodes

* manuel_fused_gemm_epilogue

* remove cudaDeviceContext

* remove more template

* fix rocm compile

* remove alias name CUDADeviceContext

* fix compile

* fix tests

* revert changes