Improve imperative mode training speed

* 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

improve layers.fc api doc

add yolo_box for detection box calc in YOLOv3

test=develop

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Skip compile infer shape in box_coder_op

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

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

Fix cross_entropy2_op numeric error

test=develop

test=develop

Revert "PaddingRNN model memory optimize"

test=develop

test=develop

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2. remove lod check in reshape op
test=develop

* Add cpu_quantize_squash_pass for C-API quantization

test=develop

* add cpu_quantize_squash_pass teste

* fix lint: add include memory unorderd_map and unordered_set

test=develop

* lint fix 2

* fixes

test=develop

* refactored

test=develop

* fix windows ci

test=develop

test=develop

2. Add binutils 2.27 in manylinux support

test=develop

PaddingRNN model memory optimize

* make timeline.py compatible
test=develop

* Follow comment
test=develop

add all_kernels_must_compute_runtime_shape example for speedup infershape