* print user-friendly error message

* adjust error sumary

* add nll loss API, update demo code of the comment

* adaptive expand op to 2.0 (align to torch.expand) , test=develop

* expose and unify the Tensor concepts to the user

* expose tensor to user

* add copy place for Tensor

* add copy place for Tensor

* add note

* add macro PADDLE_WITH_CUDA

* remove RUN_TYPE=DIST

* fix some error

* add tile_op, test=develop

test=document_fix

remove activation wording

test=develop,test=document_fix

remove activation wording

test=develop,test=document_fix

remove activation wording

test=develop,test=document_fix

remove activation wording
tanh -> tan

test=develop,test=document_fix

explain input/out put range and out of boundary behavior

Update the code for the min,max, test=develop

* move paddle.fleet to paddle.distributed.fleet

* add unchaged infershape function

* add broadcast infershape function

* fix bug

* rename infershape functions

* add UnaryOpUnchangedInferShapeCheckAxis

* add error message

* add test for common infer shape functions

* dont update existed ops

* dont update op_desc.h

* add more test

* add error check, refine error message

* add auto_cast, test=develop

* add loss scaler, test=develop

* add comments, test=develop

* refine code, test=develop

* refine code, test=develop

* do not set flags automatically, test=develop

* fix custom op bug, test=develop

* add more test, test=develop

* refine enable logic, test=develop

* enable amp test with GPU, test=develop

* add unittest

* add test for found_inf

* follow comments

* follow comments

* remove global variable, use singleton

* add some notes

* update comments

* update comments

* update comments

* add use_dynamic_loss_scaling argument

* refine found_inf

* refine found_inf

* fix loaded var suffix repeat error

* use new dygraph name for loaded param

* add nll loss API, update demo code of the comment

* Add the max, min, maximum, minimum api for the API 2.0, test=develop

add lamb to fleet meta optimizer

* Add laxical GRU unit test

performance works

* Get model accuracy

* model and data name to be confirmed
test=develop

* update model name and output format
test=develop

* update according to reviews
test=develop

* add accuracy check

* accuracy check between native and analysis
test=develop

* fix a reading bug, fix gru passes sequence
test=develop

* fix passes sequence
test=develop

* add encrypt for inference lib

* fix code;test=develop

* fix test; test=develop

* bug fix; test=develop

* add MakeCipher;test=develop

* fix bug;test=develop

* move MakeCipher to paddle space; test=develop

* fix include dir ;test=develop

* add include dir; test=develop

* move include; test=develop

* move include; test=develop

* fix for windows ci

* fix cmake; test=develop

* fix bug

bug fix

* add the support for multi-node training

update cumsum api and fix up the cumsum op

* Add oneDNN relu6 op

* Lint fixes

test=develop

* add paddle.fleet.AsyncOptimizer
Co-authored-by: Ndongdaxiang <dongdaxiang@baidu.com>

* add pin memory control

* fix buffered reader init problem

* fix unittest error

* add unittest for coverage

* add cuda pinned place branch

* add unittest

* add skip when not gpu

1. update document template for unary elementwise layers(a.k.a. activation layer);
2. remove generate_op_noattr and use generate_activation instead; remove redundant function copies;
3. minor update for docstring to fix rst format errors.
4. fix doc for Rsqrt OP
5. add sample code for each activation separately;
6. remove the unused deprecated decorator.

* Change use_quantizer attribute name and data type

* Fix problem with setting attribute

* Add changes due to review

* Small change in function

* Restore use_quantizer attr for compatibility

* refine unsqueeze, test=develop

* update unsqueeze, test=develop

* refine unsqueeze, test=develop

* refine unsqueeze, test=develop

* update

* remove None, test=develop

* follow comments

* support bool

* update doc

* follow comments

* merge develop

* fix large scale KV 
* fix single training using async ssa graph

* add flags_call_stack_level

* update

* refine code

test=develop