test=release/1.4

cherry-pick 16520, add NN and utest

Fix auto growth bug of optimizer in dygraph mode

* fix the bug of reusing different types of variables in memory_optimiz… (#16547)

* fix the bug of reusing different types of variables in memory_optimize_pass, test=develop

* disable SELECTED_ROWS AND LOD_TENSOR_ARRAY reusage, test=develop

* only use the latest version variable for inplace strategy (#16736)

* bug-fix, test=develop

* tweak code, test=develop

* cherry-pick PR 16547,16736,16739 test=release/1.4

test=release/1.4

* Fix imperative bugs

test=release/1.4

* Polish code

test=release/1.4

* Imperative deep-first backward process (#16605)

* Fix bug of gradient interface

* shrink transformer

* Right transformer

* Change from width-first backward to deep-first backward process

test=develop

* Reverse iterator op's input

test=develop

* Polish code

* Change the iteration direction in ingrads' map slots

test=develop

* Polish code

test=develop

* test=develop, cherry-pick fix for transformer in dygraph

* test=develop, fix transformer in dygraph
/

* test=develop, ptb_rnn fix op

* test=release/1.4, refine code

* test=release/1.4, fix ci failed error

test=release/1.4

test_analyzer_int8 tests use default pass order

Cherry-pick from 16498 : Deal with softmax layer in anakin subgraph

test=release/1.4

cherry-pick:load_op.cc hot fix

test=release/1.4

* fix the bug of all_reduce_deps_pass
test=release/1.4

fix load bug about context.place

(cherry picked from commit bddb2cd3)

Cherry-pick of #16559, #16606 and #16608 

* fix preprocess script with processbar, integrity check and logs

* delete unnecessary empty lines, change function name

test=release/1.4

(cherry picked from commit bddb2cd3)

Train with pipe reader merge develop

Support imperative learning rate scheduler

* Add linear learning warmup method

This warmup lr can be combinated with other learning rate strategies.
For example:
            decayed_lr = fluid.layers.linear_lr_warmup(
                fluid.layers.piecewise_decay(boundaries, lr_steps),
                warmup_steps, start_lr, end_lr)