softmax_with_cross_entropy optimization with soft label. This PR includes optimization of
    "SoftmaxWithCrossEntropySoftLabel" : compute log_softmax and then compute loss.
    "CrossEntropySoftLabel" : compute loss with softmax as input.
These optimization includes following technics:
    read data to buffer with vectorization
    compute max and sum in warp
    fixed loop size with macro
Performance (computation time):
    softmax_with_cross_entropy_0 (forward) : -40.1%
    softmax_with_cross_entropy_0 (backward): -41%

* add matmul_scale matmul_v2_scale fuse pass

* add scaletensor judge

* modify var name

* add timeout notest;test=coverag

* fix error commit

* fix use_mkldnn attr

* fix use_mkldnn attr

* test if windows still need numpy <=1.19

* modify acoording to zhouwei's comment

* add multi_tensor for momentum and clear_grads for optimizer

* fix bug for dygraph

* add unittest

* refine comment

* add param_group

* refine regularizaiton logic

* del clear_grads

* add clear_grads

* add dispensable check of None

* refine clear_grad

* fix build bug

* refine code by comment

* refine code

* add multi tensor check

* refine param_group update

* add multi tensor for static mode

* refine comments

* delete useless comma for momentum

* refine comment for momentum

* refine code by commment

* fix bugs when run reshape

* fix ci bug

* add test_conv_act_mkldnn_fuse_pass

* update cmakelist

* fix cmakelist

* fix timeout

* fix timeout

* fix timeout

* fix

* add complex op and `paddle.complex`.

* add planner

* add planner

* add cost model update

* add relaunch updation

* update process_group

* fix error

* add unitest

* update unitest

* update cost model

* avoid api problem

* support more eager tensor api

* support multiple constructor for eager tensor

* add place related code

* polish code

* specific randint with dtype of int64

* Support pure cpu test

* refine test in pure cpu

* refine test in pure cpu

* add compile_dir

* follow comments

* support multi precision update for LAMB

* hide some api

* fix ci uts

* fix lamb output of dygraph

* remove some changes to some PR

* try to fix Py3 CI compile error

* fix test_imperative_optimizer, add lars ut, add layer_norm ut

* fix ut, fix format

* fix ut

* fix windows ci

* modify sum mean args

* add GetExpectedPtenKernelArgs for redcue_op

* modify kernel args number

* modify kernel args number

* Rearranged Eager AutoCodeGen directory structure

* Removed USE_OP in Eager AutoCodeGen

* Enabled generation for Operators without Grad/Inputs/Outputs

* Resolved operators without input

* Fixed merge conflicts

* Enabled Eager AutoCodeGen for 10+ more operators

* Refactored Eager AutoCodeGen with more organized helper objects

* Enabled Eager AutoCodeGen for operators with multiple OpBases

* Adjusted Eager AutoCodeGen to Enable Passing Output Tensor as Input Argument

* Handled Dispensable Inputs/Outputs in Eager AutoCodeGen

* Adjusted function generation/call between Python-C API & Dygraph API

* Synchronized auto-generated Python-C API with Dygraph Forward Functions

* Generated CoreOpsInfos for potential use in append_op API

* Fixed CI problem

* add timeout

* add timeout

* Inspect the information inside a TRT engine.

* Follow up the google code style.

* Fix code error.

* Add RWLock to protect loading module under multi-thread

* refine code

* remove import statement

From --ptxas-options=-v, SegmentOpsKernel uses 66 registers in a block.
There are two ways to resolve this problem:
    Reduce the threads per block launch configuration
    add __launch_bound__ to give information to nvcc compiler for reducing registers usage
this PR chooses __launch_bound__ solution because changing gpu_launch_config may affect other ops.

* add gpt modeling

* update file name

* Get GPU BasePtr from CUDA allocation

* Fix compile error for ROCm

* Add BasePtr function for IPUPlace in naive_best_fit_allocator.cc

* Add alignment for BuddyAllocator

* Set address alignment of BuddyAllocator to 32 bytes

* Fix CI error

* Remove code for naive_best_fit strategy