* add parallel build script to ci test=develop
* 1. classify the test case as single card/two cards/multiple cards type
   2. run test case according to the run type

* Speed affine_channel_op unit testing
* Add check in tensor_py
* Fix ONLY_CPU Compiling

Update the filter generation mechanism that it could generate the negative parameter.
The original calling(np.random.random()) couldn't simulate the conv/relu fusion case.

test=develop

* accelerate test_ir_memory_optimize_nlp, test=develop

* accelerate test_ir_memory_optimize_nlp, test=develop

Rename the testcuda function to has_cuda, it will elimate the unnecessary testing.
test=develop

* speedup gc and inplace softmax_with_cross_entropy_grad
test=develop

* refine models gpu mem
Merge skip vars and warning messages of mem opt
remove relu mem opt
test=develop

* follow comments
test=develop

test=develop

* Support seq len equal to 0 in sequence ops

test=develop

* Add more test cases

* Fix some comments

test=develop

* Fix py3 error

test=develop

* fix sampling id op bug, test=develop

test=develop

test=develop

test = develop
resolve #16764

test=develop

test=develop

test=develop

* enable recurrent op test=develop

* make test_dyn_rnn faster

test = develop
resolve #16764

test=develop

test=develop

test=develop

* slimming test_parallel_executor_seresnet.py

test=develop

* fix too long unittest
recommit
test=develop

* add fake_reader.py
test=develop

list of fixed ops:
lookup_table_op
space_to_depth_op
squared_l2_distance_op
squared_l2_norm_op
teacher_student_sigmoid_loss_op
tree_conv_op
warpctc_op

test=develop

* make less batch of tests to fit ci

* test=develop, invoke ci

add some comments back

* test fuse all_reduce

comment np.array(x.get_tensor()) in imperaitve mode to avoid OOM.

* add unit test for fuse_opt_ops
test=develop

test=develop

test=develop