* Remove constraint that last dimension is forced to be 1 by add
lookup_table_v2 test=develop

* modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop

* Revert "modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop"

This reverts commit 8a960bfc61e51aa27c3c529df8fb90b93ebd19f9.

* move api into fluid.embedding test=develop

* fix example code test=develop

* move one_hot into fluid.one_hot

* modify api.spec test=develop

* fix loss shape test=develop

1. Support customize eval function instead of eval program.
2. Fix loading checkpoint in quantization strategy.
3. Support saving eval model when saving a checkpoint.
4. Fix decoder of loading context in PaddleSlim.
5. Fix restoring from the checkpoint of uniform prune strategy.
6. Support saving eval model and infer model during training.
7. Add ‘unitest’ for saving eval model, saving infer model and uniform pruning restoring from the checkpoint.
8. Fix pruning of depthwise_conv_grad op by updating the groups.

* support change shuffle thread num
* support change train thread num
* fix receive shuffle data of each channel
* data norm stop gradient
* add check thread_tensor type and root_tensor type when merge metric
* remove sleep in shuffle, add config
* add config of pslib client to client communication
* fix xbox str
* add data norm op testcase
* add flush in trainer finalize

- First implementation of BWD and FWD of pooling mkl-dnn

- Compilation fix

- Fix

- Fix

 - Fix

- Fix to crash

- Compilation fix

- Combined AcquireBacward with Fwd

test=develop

test=develop

* make OpTest check grad inplace even if forward has no inplace, test=develop

* do not run PE when enable_inplace is False, test=develop

* add conv3d cuda kernel for float16 type, test=develop

* refactor OpTest for inplace, test=develop

* add comments, test=develop

* add recompute based checkpoints methods for large batch training
test=develop

* add append_backward_with_forward_recomputation
test=develop

* refine optimizer
test=develop

* update backward and optimizer
test=develop

* make Variable usable
test=develop

* add recompute code

* refine optimizer
test=develop

* refine addup _append_backward_ops_with_checkpoints_
1) for recompute part, just cache the grad_op_desc without appending to block
2) before appending grad_op_desc to backward part, addup_repetitive_vars, remove unused branch
test=develop

* make method private

* add recompute strategy into DistributedStrategy
test=develop

* checkpoint version3
test=develop

* remove some print information
test=develop

* remove unused sumop
test=develop

* try to fix recompute with graph building modules

* add input names to vars should be held

* add memory debug tool

* backup backward

* Fix bugs

* add backward desc for op not in any segments

* add exception info for sub_block

test=develop

* modify code style

test=develop

* modify code style

test=develop

* remove print functions

test=develop

* add API spec

test=develop
test=document_preview

* make Recompute a child class of Optimizer

test=develop
test=document_preview

* add API spec

test=develop
test=document_preview

* modify API spec

test=develop
test=document_preview

* add document for Recompute

test=develop
test=document_preview

* change API doc of Rcompute

test=develop
test=document_preview

* code cleaning

test=develop
test=document_preview

* modify API spec

* fix bugs when segments hold no element

* add testcase for Recompute Optimizer

test=develop
test=document_preview

* add test for apply_gradient, and code cleaning

test=develop
test=document_preview

* add test case for load function

* enable CI

test=develop
test=document

* add test case

test=develop
test=document_preview

* add sample code for 4 function of recompute optimizer

test=develop
test=document_preview

* Add RecordHistoryLocalExecScopes
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* remove the useless warning for user to avoid confuse test=develop

* add mse_loss op

* add op compatible infomation; test=develop

* add enum type

* add enum type; test=develop

* fix softmax ce time limit check failed. test=develop

* refine softmax calc. test=develop

* move tree_conv to fluid.contrib.layers

test=develop

* update API.spec for tree_conv

test=develop

* update tree_conv api to increase unit coverage

test=develop

* unify DataLoader APIs, test=develop

* integrate iterable CPU Dataset, test=develop
add GPU dataset supporting, test=develop

* add unittests for dataset, test=develop

* add more docs to dataloader apis, test=develop, test=document_preview

* refine doc, test=develop

* refine doc again, test=develop

* increase coverage, test=develop

* add instance norm op

* refine reallocate of workspace size, test=develop

* add lock to cudnn handle calls, test=develop

test=develop

* Initial, functional commit

* Clean commit related files
test=develop

* refactor dygraph,test=develop

* fix failed unittest,test=develop

* polish code,test=develop

* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop

* polish vlog and profiler, test=develop

* try to fix preceding ops order,test=develop

* test transformer in windows ci, test=develop

* use python c-api to speed up tracer.trace,test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, add ut for debug string and gradient_accumulator

* test=develop, add tests for layer/gradient_accumulator/prepared_op

* test=develop, fix complie error for test_prepared_op

* test=develop, add more ut for dygraph

* test=develop, create API.spec for dygraph api change

* test=develop, refoctor name to make it easier to understand

* test=develop, refoctor name to make it easier to understand

* test=develop, fix multi-gpu failed problem , add Tracer tests, change PADDLEENFORCE to PADDLEENFORCE_EQ

* test=develop, fix ut failed on parallel se-resnext

* test=develop, change one more PADDLE_ENFORCE

* support auto prune in dygraph mode

* test=develop, support auto prune

* test=develop, merge develop conflict

* test=develop, fix test_layer and test_tracer ut

* test=develop, fix bug which may cause stop_gradient disabled with a list of backward inputs

* add TRT shape check, test=develop

* model_input_shape == runtime_input_shape, refine message, test=develop

* fix trt bugs when sharing params, test=develop

* add unittest for cascade_rcnn

* refine err msg of out of gpu memory, test=develop

* refine err msg again, test=develop

* refine errog message again, test=develop

* follow reviewer's comments, test=develop

* support 2-level lod of input in sequence_pool test=develop

* fix lod level bug in .cu test=develop

1. group_norm support data_layout=NHWC
2. modified doc of group_norm

* Set states of recurrent op as dependent vars in prune of save inference model

This PR will fix the save/load inference model problem of RNN models.

The reason of the bug is that save_inferenc_model will prune OPs that doesn't contribute to Output. But in recurrent_op, States are not Output, OPs refers States will be pruned. 

This fix adds States of recurrent_op as dependent var so that OPs referring States won't be pruned. 

- LRN mkl-dnn kernel refactor

test=develop

- compilation fix

- Another compilation fix

- Compilation fix

- another compilation fix

- compilation fix

- Crash fix

- optional LRN mkldnn workspace

- Added mid allocation

- Workaround for tests

- Removed gradient from is_test ut

- Removed mid for inference

- Reverted LRN mid removal for is_test

- PADDLE_ENFORCE adjusted

- Rebase to templatization commit

- Compilation fix

- compilation fix

test=develop

- lint

test=develop

- Fix to crash

- Rebase to recent codebase

 - lin

- lint

- compilation fix

modified interpolate_op to support tensor attribute

1. the parameter out_shape of image_resize、resize_nearest/bilinear/trilinear can be a list or a 1-D tensor variable. If a list, each element can be an integer or a tensor variable with shape: [1].

2. the parameter scale of above Ops can be a 1-D tensor variable.
modified document of image_resize, resize_nearest, resize_bilinear, resize_trilinear and add some code example.

add crop_tensor op. The main difference with crop is :

1. If the argument shape is a list, each element is an integer or a tensor variable with shape: [1]. This way is suitable for the case that the shape may be changed each iteration.

2. If the argument shape is a variable. Its rank must be 1. In crop op, the rank of shape must be the same as x

offsets can be a list, in which each element is an integer or a tensor variavle with shape: [1].

* fix conflicts
test=develop

* change mask_bias_reorder
test=develop

* add ComputeMask function to make code clear
test=develop

* change according to reviews
test=develop

* change according to reviews
test=develop