* open fuse optimization ops
test=develop

* first version memory optimize pass, test=develop

* remove move_tensor_sharing_pass, test=develop

* refine code comments, add unittests, test=develop

* turn off memory_optimize by default, test=develop

* follow huihuang's comments, test=develop

* follow chengduoZH's comments, test=develop

* fix grammar error, add const qualifier, fix pass_test exception message, test=develop

* follow chengduoZH's comments 2nd, test=develop

* split test_dist_se_resnext.py into 4 testcases

* extend matmul op to support multiple head multiplication

With the support of multiple head, the multiplication of two big matrixes is
split into multiplication of several (head_number) small matrixes. e.g. if
Mat A is [3, 24] and Mat B is [24, 4], when multiple A and B with head_number
as 4, Mat A will be split as 4 matrix of [3, 6] and Mat B will be 4 matrix of
[6, 4]. The result of final matrix will be 4 matrix of [3, 4], i.e. [3, 16].

* Make lod reset op support for append lod level.

* Fix API.spec
test=develop

* Fix unitest.
test=develop

* Add python api for lod append.
test=develop

* Fix API.spec
test=develop

* Fix format of doc.
test=develop

* Fix unitest.
test=develop

* Fix doc.
test=develop

* prun backward ops
test=develop

* support sparse gradients
test=develop

* supports distributed classification training
* update API.spec
* fix evenly division in python3
* change "index_range" to "index_num" in shard_index operator
test=document_preview
test=develop

The change includes 3 things:

1. Set CPU_NUM to 1 in the tests because the ParallelExecutor will print warning that CPU_NUM is not set and use default 1.

2. Old tests compare two RNNs, hand written simple RNN and same RNN built by Paddle, but initialized RNN weights in numpy random and Paddle random separately. Fixed it by setting weights and bias values.

3. Also set numpy random seed in the tests. Now the two RNNs diff can be smaller (rtol from 0.1, 0.2 to. 0.01) in the tests.

test=develop

test=develop

* split different comm method for mnist distributed training

Test PaddingRNN on V100 GPU device.

Test configuration: large model, padding mode (which is the mode using recurrentOp), one GPU.
                   
GPU memory (MiB):   6414 (this PR)     vs   6837 (without this PR)
Speed (steps/s):         10.28 (this PR)    vs    9.89 (without this PR)
 

test=develop

* add check of executor, test=develop

* feature/auto_growth_allocator, test=develop

* add unittest of AlignedAllocator, test=develop

* try to turn on auto_growth to test on CI, test=develop

* fix segmentation fault in mixed_vector.h, test=develop

* add unittests, test=develop

* hash_op support int64 hash_size
* add corresponding UT

* make auc op compatible with 1 dim

test=develop

* fix int64_t

* update fill constant op unittest

* add empty line

2）add pip install coverage in Dockerfile.tmp
test=develop

* feature/buffer_shared_inplace, test=develop

* refine code, test=develop

* fix elementwise_add op cpu inplace and sum inplace bug, test=develop

* add unittest and debug log, test=develop

* fix parallel_executor scope bug, polish code, test=develop

* fix sum op, activation op, single_in_place_inference bug, test=develop

* remove kLocalExecScopeName, test=develop

* fix unittest,test=develop

* fix out_var first version bug, test=develop

* follow comments,test=develop

remove dygraph.enable from __all__
hidden dygraph. profiler
add doc to dygraph. no_grad

* fix transform matrix bug, test=develop

* modify API.spec

* Fix topk cannot handle 1D vector bug

Add path to handle 1D vector

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

* refine code

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

* test=develop, fix docker with paddle nccl problem

* test=develop, hide no_support api and add ut for it

* add_distributions_of_normal_and_uniform

* paddle/fluid/API.spec

* modify API.spec

* modified paddle/fluid/API.spec, test=develop

* modify paddle/fluid/API.spec, test=develop

* modify paddle/fluid/API.spec, test=develop

* fix some comment, test=develop

* modify API.spec, test=develop

* add comment for init function, modify hard code, test=develop

* modify API.spec, test=develop

* modify API.spec, test=develop

* make unit test function shorter, test=develop

* modify paddle/fluid/API.spec

* make it support float/int learning as input.

* test=develop
support Tensor input for chunk_eval op

* test=develop
fix testcase for chunk_eval op

* test=develop
fix typos in nn.py

move the thread-check code from train_from_dataset to a single function
add UT for the thread check function

1. Since allreduce op has 4 reduce types, We split these four reduce types into four ops
2. We also refined the collective op code, e.g. we separated the collective op kernel into CPUKernel and CUDAKernel, and remove the device specified DeviceContext parameter in template as we already knew the target DeviceContext
3. We remove the newly added Collective op role to reduce the complexity of program and graph analysis