* fix en docs of apis, test=develop, test=document_fix

* follow chunwei's comments, test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, Add Variable api and refine dygraph related API

* test=develop, Add Variable api and refine dygraph related API

* test=develop, refine test for new api and error info

* test=develop, refine error info and test_layers

* test=develop, add API.spec

* test=devleop, fix to_string python2 and python3 compat error and refien doc

* test=devleop, add API spec

* test=devleop, update API spec

* test=devleop, update API spec

* test=develop, invoke ci

* test=develop, fix example code

* test=develop, update API spec

* test=develop, add compat test and fix inplace campat dict error

* fix pool infershape. test=develop

* fix unittest converage. test=develop

* fix format. test=develop

* make pad and split support fp16
test=develop

* add a base class for the Communicator
* add AsyncCommunicator Impl for async distributed training

Refine document of DGCMomentumOptimizer

Polish English docs of elementwise_add/sub/mul/div

* shape and optimize paddle error message stack, test=develop

* limit exception type & add unittest, test=develop

* fix multi-platform problem, test=develop

* fix related unnitest failed, test=develop

* add doc & fix unittest errors, test=develop

* fix function name error, test=develop

* update tensor test exception msg compare, test=develop

* remove unittest on win32, the dir format is different, test=develop

* remove useless package, test=develop

* add paddle enforce handler unittest, test=develop

* add exception checkout, test=develop

* fix coverage failed, test=develop

* fix op registry test failed, test=develop

* refactor whole pr, test=develop

* remove test in CMakelist, test=develop

* fix coverage, test=develop

test=develop
Signed-off-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

Set output type LoDTensor only

After code experiment, I found data doens't support other type

* fix DistributeTranspilerConfig document, test=develop

* Make PaddleSlim support PyReader.
* Fix unittest of sensitive pruning.
* Add some assert.

* impove error message when passing ndarray with object dtype

* imporve message format

* change assert to raise TypeError

* remind user how to locate the irregular data instead of printing

* add unittest for input array type check

* Fix conv2d+dequantize squash for residual fusion

test=develop

* Correct int8 input

test=develop

* Add if exclude or include padding in pool2d mkldnn

test=develop

* x.dims == y.dims test=develop

* refine comment

* Expose `mutable_data` as python binding

test=develop

* Add test for device pointer binding

test=develop

* Make test compatible with python 2

* fix input shape check test=develop

* move PADDLE_ENFORCE test=develop

Add dtype for coalesce_tensor_op

Polish elementwise max min pow document to add more examples

* fix doc of apply_optimize
test=document_fix
test=document_preview

* modify doc of backward
test=develop
test=document_fix

* modify document hash
test=develop
test=document_preview

The new "fluid.data" changes old "fluid.layers.data":

1. Add shape and dtype check.
2. Remove "append_batch_size" parameter. We won't offer this in the new data layer because other deep learning platforms don't have this kind of data layer pre-processing. It may confuse users.
3. Remove "stop gradient" parameter because the data layer doesn't do back-propagation

TODO：
Now data layer feeded by executor is checked, will we want to check the feed data of readers in the future?

* add AdadeltaOptimizer doc, test=develop

* refine doc,test=develop

* folllow lanxiang's comments, test=develop, test=document_fix

* expose set_gradient_clip, test=develop, test=document_preview, test=preview

* expose gradient clip, test=develop, test=document_fix

* refine doc, test=develop

* follow lanxiang's comments, test=develop, test=document_fix

* refine doc, test=develop, test=document_preview

* add kernel for fill_op, test=develop

* modify PADDLE_ENFORCE to PADDLE_ENFORCE_EQ, test=develop

* add op test for fill_op, test=develop

* REGISTER COP CUDA KERNEL, test=develop

* update test_fill_op.py, test=develop

* change FillConstantOpVarTypeInference to FillOpVarTypeInference, test=develop

* fix op test, test=develop

* add head file, test=develop

* add support tensor and tensorlist for strided_slice OP test=develop

* fix the commnet test=develop

* fix test=develop

* fix the bug test=develop

* delete log test=develop

* fix API.spec test=develop

* fix test=develop

* update API.spec

* fix bn

* treat broadcast as non-initial, test=develop

* rename the class name

* rename the class name, test=develop

* add support of matmul with multiple head even different width and height

Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.

One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]

test=develop

* add support of matmul with multiple head even different width and height

Original matmul with multiple head supports only the mat_a.width == mat_b.height,
in that case, mat_b will be horizontally split. In this patch, we extend the
support when mat_a.width != mat_b.height but mat_a.width/head_number == mat_b.height,
in this case, mab_b will be vertically split.

One example is A is [3, 8], B is [2, 16], head_number is 4. In this
case, A will be split as [3, 2], B will be (vertically) split as
[2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]

test=develop

* refactor the code of matmul with multiple head even different width and height

test=develop

* refine ctc align op with padding 
* refine api sample code

* add input type and dtype check for softmax_op

test=develop

* refine error message

test=develop

* Add support for new QAT models

test=develop
Co-Authored-By: NMichał Gallus <michal.gallus@intel.com>
Co-Authored-By: NWojciech Uss <wojciech.uss@intel.com>

* fixed fps results

test=develop

* fix top5 accuracy drop problem

* updated for new QAT models

* skip quantizing average pooling - dirty but working

* add missing pass

* added missing conv+brelu fuse pass

* removed a call to non-existent pass

test=develop

* renamed pass

test=develop

* Adjust finding pooling scale to newest QAT models

* Remove unnecessary code from quantization_mkldnn_pass

* Copy Pooling input scale to output scale in QAT

* Refactor & remove unused code in QAT

* Incorporate fp32 FC into QAT

test=develop

* Enable graph drawing with debug flag

test=develop

* Add tests for QATv2

* Fix paths for QATv2 models

test=develop

* Add option to save transformed int8 qat model

test=develop

* Remove redundant lines from qat mkldnn pass

test=develop

* Delegate disablement of avg pooling to qat

test=develop

* fix CI bug, test=develop

* Follow Wangzhen's Review, test=develop

* Update API.spec

test=develop

* Name False in (is_unsigned, TensorScale) tuple

test=develop

* Removing last dims constraints of seq_pad and seq_unpad test=develop

* fix test_layer api code test=develop

* fix sequence_pad_op.cc conflict test=develop

* remove test_analyzer_mm_dnn test=develop

* fix vectorize bug test=develop

* fix vectorize<int> test=develop

test=develop
test=document_fix