* MKLDNN v1.0 rebase to Paddle 1.6
test=develop

* Add hacky paddle::string::to_string() implementation

* vectorize<int64-t>() -> vectorize() cleanup
test=develop

* PADDLE_ENFORCE and void_cast fixes
test=develop

* Rebase changes
test=develop

* Cosmetics
test=develop

* Delete MKL from mkldnn.cmake
test=develop

* CMake debug commands
test=develop

* Delete MKLDNN_VERBOSE and rebase fixes
test=develop

* Rebase fixes
test=develop

* Temporarily disable int8 resnet101 vgg16 and vgg19 tests
test=develop

* Add libmkldnn.so.1 to python setup
test=develop

* Add libmkldnn.so.1 to inference_lib cmake after rebase
test=develop

* Post rebase fixes + FC int8 changes
test=develop

* Fix LRN NHWC
test=develop

* Fix NHWC conv3d
test=develop

* Windows build fix + next conv3d fix
test=develop

* Fix conv2d on AVX2 machines
test=develop

* ZeroCopyTensor::mutable_data in the right device, test=develop

* add unittest for zerocopy, test=develop

* fix master patch when slot is dense
* test=develop

* fix code style of fleet_wrapper
* test=develop

* accelerate mean op test=develop

* add op function generator, test=develop

* add unittest, test=develop

* follow comments, test=develop

* fix windows compilation problem, test=develop

test=document_fix

change according to reviews
test=develop test=document_fix

improve some descriptions and some models
test=develop test=document_fix

update models benchmark data
test=develop test=document_fix

update int8v2 and qat2 performance
test=develop test=document_fix

* support float64 for GradClipByGlobalNorm in dygraph, test=develop

* fix comment for GradClipByGlobalNorm, test=develop

* update benchmark for int8v2, QAT1, QAT2 accuracy and performance
test=document_fix

* change according to reviews
test=develop test=document_fix

* improve some descriptions and some models
test=develop test=document_fix

* update models benchmark data
test=develop test=document_fix

* update int8v2 and qat2 performance
test=develop test=document_fix

As the title

* fix python API tests that do not need to inherit OpTest, test=develop

* fix fp16 cases that will only be enabled in GPU mode, test=develop

* remove TestSoftmaxFP16Op from test cases of softmax_mkldnn_op, test=develop

* fix tests so that the cases are only created in GPU mode, test=develop

* fix compile error in some python version, test=develop

* remove redudant code, test=develop

* add shell scripts. test=develop

* rename test_pybind_inference to test_pybind_interface and print repeat process in check_op_desc.py. test=develop

* add approval RD. test=develop

Add basic while_loop

* refine init function, test=develop

* add tests, test=develop

* remove extern, which may cause symbol error in gcc-4.8, test=develop

change input data type and decrease max_relative_error value in test_check_grad for grop_nom_op test test=develop (#21608)

* dygraph_grad_maker supports varbase without grad_var, test=develop

* fix compile, test=develop

* fix test_tracer, test=develop

* follow comments, test=develop

* move get_all_ops_desc from check_op_desc.py to print_op_desc.py

* polish error message. test=develop

* remove optimize_for in framework.proto
* test=develop

* polish infer shape registry, test=develop

* modify some operators registry, test=develop

Add tests to use dy/dx to make sure the gradient values calculated by the control flow backward is correct. Also fixed bugs detected by those tests.

Fix bugs:

1. Unlike sum_op, optimizer ops don't allow uninitialized input tensor. But in conditional_block_grad_op, since the conditional_block may not run, the output gradient tensor may be uninitialized, which will cause the optimizer op error. To fix it, we should let optimizer ops support uninitialized input like sum_op or assign the uninitialized gradient to 0 when the conditional_block_grad_op doesn't run. I found there are about 10+ optimizer ops. **To be simpler, I just assign output gradient of the conditional_block_grad_op to 0 in this PR**. But it can be further explored whether we can make optimizer ops like sum_op to support uninitialized input tensor because theoretically we can speed up without the assigning in conditional_block_grad_op.

2. Infer parameter shapes during append_backward. I didn't know that all our parameters are in global block. When op_desc is inferring shapes at the sub-block, it may not know the shape of gradients of parameters whose shape information is at global block. I fixed it by inferring shapes of gradients from forward var.

This PR also did some code clean up:
1. Print the var name when sgd_op catches shape error so that it is easier to debug
2. Fix a typo: dicta -> dict

* Add a single_process_multi_thread transpiler.
* Add some UTs.
* Fix some API description.

* add file check_op_desc.py and add interface to get default value. test=develop

* add test for c++ coverage rate. test=develop

* Correct typo. test=develop

    Add a python interface for Gelu.
    Add documentation for fluid.layers.gelu.

test=develop

* - BAtch norm mkl-dnn NHWC

test=develop

- compilation fix

test=develop

- UT fix

- cosmetics

test=develop

- Fix to Batch Norm MKL-DNN NHWC UT

test=develop

Conflicts:
	paddle/fluid/operators/batch_norm_op.h

* - Lint fixes

test=develop

As the title