Remove const_cast in optimizers

* Enable INT8 transpose kernel for MobileNet-SSD improvement.
test=develop

* Refine the license year.
test=develop

* Delete redundant code.
test=develop

* Add axis check.
test=develop

add more imperative layer tests.

refine fc_infershape

* update load persistables for increment, test=develop

* update load persistables for increment, test=develop

* update API Spec, test=develop

* update API Spec, test=develop

* add doc, test=develop

* add doc, test=develop

* Update lookup_table_utils.py

* Update API.spec

* Update lookup_table_utils.py

test=develop

* Update API.spec

test=develop

* fix api spec

* Update lookup_table_utils.py

test=develop

Improve imperative mode training speed

test=develop

test=develop

* Add cpu_quantize_pass for C-API quantization

test=develop

* add cpu_quantize_pass test

* fix lint: add include memory unorderd_map and unordered_set

test=develop

* fuse_relu 1

test=develop

* tuned 2 without squash

* fixes

test=develop

* remove unused vars

test=develop

* refactored

test=develop

* fix lint c-style cast -> C++ style cast

test=develop

* remove QuantMax and c style casts

test=develop

* last usage of QuantMax removed

test=develop

* Fix Analysis Predictor UT

Check if memory_optimize_pass has already been added
to the analysis config before adding a new one, so
that it is not added multiple times.
test=develop

* change map to unordered_map

fix the forgotten part of cpu_quantize_pass_tester.cc

test=develop

* removed quantized attribute

* fixed cpu_quantize_pass_tester and op attr comments

test=develop

* removed redundant line

test=debug

* removed gmock

test=develop

* fix after merge

graph neural network for imperative mode

test=develop

test=develop

test=develop

improve layers.fc api doc

add yolo_box for detection box calc in YOLOv3

test=develop

test=develop

test=develop

test=develop

Skip compile infer shape in box_coder_op

* Add moving average absmax op in quantilize-aware training.

test=develop

Yolov3 loss: add mixup score and label smooth

* Support Sync Batch Norm.
* Note, do not enable it in one device.

Usage:

build_strategy = fluid.BuildStrategy()
build_strategy.sync_batch_norm = True
binary = fluid.compiler.CompiledProgram(tp).with_data_parallel(
        loss_name=loss_mean.name,
        build_strategy=build_strategy)

* Impl fp16 compute kernel for slice_op

test=develop

* Use data() to replace mutable_data()

test=develop

test=develop

Fix cross_entropy2_op numeric error

test=develop

test=develop

Revert "PaddingRNN model memory optimize"

test=develop

test=develop