* move DataType from paddle::experimental to phi

* convert namespace

* convert namespace

* convert namespace

* clarify namespace

* convert more datatype

* Revert "convert more datatype"

This reverts commit 083b462959e6a22d4d8767707b628b95b396642e.

* convert more in auto_code_generator

* fix conflicts for XPU

* fix namespace conflicts

* fix errors

* Revert "fix errors"

This reverts commit f9d9958b54ee32141112274c8a5c3c381ab0f876.

* fix errors

* fix formatting

* remove_engine_info

* remove_engine_info

* remove_engine_info

* change trtlayerinformation line to json

---------
Co-authored-by: Ngaoziyuan <gaoziyuan@baidu.com>

* add outvar name for nvtx mark

* nly network created with kEXPLICIT_BATCH can setsetMaxBatchSize

* update trt engine to set in/out data type

* update

* Update engine.cc

* Update engine.cc

* update

* set engine output type before freeze the network

* update

* update trt autoscan ut

* update

* update ut

* fix equal bug, update ut

* fix cast and equal ut

* update cast ut using TRT < 8.4

* set datatype from scope

* check output var is nullptr

* Update op_converter.h

* update tensorrt_engine_op_test ut

* update

* add_range

* add_range

* remove using lodtensor part1

* polish history code format

* optimize Paddle-TRT performance

* remove needless using tensor

* remove needless using tensor

* resolve conflict

* replace tensor using

* fix format error

* revert needless changing

* fix rocm and npu compile error

* fix cinn compile error

* fix format error

* fix mkldnn format error

* fix mkldnn format error

* fix cinn compile error

* fix cinn compile error

* fix cinn compile error

* resolve conflict

* Move ITensor construction for Weight (persistable variable) from OpConvert to TensorRTEngine.

* remove int8 fallback

* add varlen_token_prune plugin, pass, convert

* template GetWeightCPUData

* add fc, multihead_mul, shape tensor infer, slice

* paddle inference support new quant_model

* Merged LoDTensor with Tensor,test=allcases

* Patched python level LoDTensor

* Patched python level LoDTensor

* Merge Tensor into DenseTensor

* Fixed namespace issues,test=allcases

* Fixed merge issues

* Fixed inference issues

* Fixed NPU test issues

* Fixed merge issues

* dynamic shape clone supported

* Inspect the information inside a TRT engine.

* Follow up the google code style.

* Fix code error.

* refine structure for cuda and rocm

* update

* update

* update

* update

* disable int8

* size_t to int

* update

* fix ut error

* update ut

* add pool2d convert test

* modify error

* modify error

* modify error

* modify error

* modify error

* modify error

* sparsity support

* trt buildEngineWithConfig is deprecated

* add trt version control

* add trt LT version helper

* upgrade PluginTensorRT to IPluginV2Ext

* trt plugin factory is not usable in IPluginV2

* upgrade add plugin api to use IPluginV2

* remove IPlugin register and adapt getSerializeSize(), serialize()

* adapt IPluginV2Layer

* downgrade to IPluginV2

* implement elementwise clone

* add gelu plugin creator and fix gelu serialization bug

* add swish plugin creator and fix swish serialization bug

* format

* fix typo

* add elementwise plugin creator and fix serialization

* add base creator class

* add gelu plugin creator

* add hard swish creator and fix serialization

* add instance norm creator and fix serialization

* add layer norm creator and fix serialization

* add pool creator and fix serialization

* add prelu creator and fix serialization

* add slice creator and fix serialization

* add swish creator and fix serialization

* add instance norm op unittest

* remove redundent api

* fix wrong graph size to enable trt

* instance norm function move to cc

* add trt elementwise ut to trigger coverage

* remove opt cahce to hit serialization coverage

* remove opt cahce to hit serialization coverage

* remove unused code

* remove unused inputs_

* add dbg info

* remove dbg info

* add instance norm serialization

* roll back

* remove comment code

* remove trt plugin registery

* fix prelu dynamic serialization

* add prelu ut and reduce the input size to reduce memory usage

* fix pool dynamic plugin serialization and add ut

* refine pool ut with subtest

* add env for avoiding oom

* reduce test input size & increase pool op ut to 45s

* add the contributor

* remove copyright (will add in contributor)

* remove copyright (will add in contributor)