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参数初始化的问题? #182

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junranhe opened this issue Sep 29, 2015 · 24 comments
Closed

参数初始化的问题? #182

junranhe opened this issue Sep 29, 2015 · 24 comments

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@junranhe
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我用了以前在cxxnet上跑的数据测试mxnet(网络结构用了alexnet和inception),发现一个奇怪的现象,只要不用batchnorm,网络都不收敛,而且运行的结果看起来学不到任何东西,简单地在卷积层后加上bn,网络就收敛正常了,一开始怀疑是inplace优化的问题,后来禁止了inplace还是一样,之前我在cxxnet上做过类似的实验,这个数据集都是能很好收敛的,是不是初始化参数权重的地方要特别注意的地方?能不能提供像cxxnet那么精确参数设置?
例如:
momentum = 0.9
wmat:lr = 0.05
wmat:wd = 0.0001
bias:wd = 0.000
bias:lr = 0.1

@junranhe junranhe changed the title 可不可以让设置参数初始化更加精确一点? 参数初始化的问题? Sep 29, 2015
@junranhe junranhe reopened this Sep 29, 2015
@tqchen
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tqchen commented Sep 29, 2015

You should be able to change the optimizer behavior easily by https://github.com/dmlc/mxnet/blob/master/python/mxnet/optimizer.py#L85

Consider to submit a PR on this :)

@antinucleon
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Thanks for letting us know We figure out a bug in initialization and we are fixing it. To set params for model, you may find this doc to be useful. https://mxnet.readthedocs.org/en/latest/python/model.html

@tqchen
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tqchen commented Sep 29, 2015

Was caused by inconsistent scale xavier initialization. Batchnorm makes things scale invariant so it poses a less problem

@antinucleon
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I changed default Initializer, Now it works on many nets without BN. Could you try again?

@junranhe
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更新代码不收敛的现象没了,btw,model.py 里,eval_metric.update() 可不可以挪到 调用epoch_end_callback 前, 这样比较方便我测试的时候传入自己的eval_metric和epochcallback来检查迭代情况,否则容易第一个batch抛除零异常。
还有一个发现一个奇怪的现象,可能跟机器相关,用的都是cudnn-v3,alexnet的计算时间是比cxxnet上快了很多,之前我的780是每秒180张,现在时260张,但inception-bn的速度却跟之前cxxnet的一样,都是34张每秒,没发现明显提高

@antinucleon
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What is your batch size of Inception BN?

On Tue, Sep 29, 2015 at 21:47 junranhe notifications@github.com wrote:

更新代码不收敛的现象没了,btw,model.py 里,eval_metric.update() 可不可以挪到
调用epoch_end_callback 前,
这样比较方便我测试的时候传入自己的eval_metric和epochcallback来检查迭代情况,否则容易第一个batch抛除零异常。

还有一个发现一个奇怪的现象,可能跟机器相关,用的都是cudnn-v3,alexnet的计算时间是比cxxnet上快了很多,之前我的780是每秒180张,现在时260张,但inception-bn的速度却跟之前cxxnet的一样,都是34张每秒,没发现明显提高


Reply to this email directly or view it on GitHub
#182 (comment).

@junranhe
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two 780,mxnet is 64batch,so each gpu 32batch, cxxnet is 20 batch each(I modify the cxxnet so it can run 20batch on 3GB memory)

@tqchen
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tqchen commented Sep 30, 2015

Normally you cannot expect mxnet to be faster than cxxnet in all cases as cxxnet is already heavily optimized in c++. But you can expect they run at same performance, with mxnet more memory efficient and more flexible.

You proposal seems to be valid, please open a PR on this

Tianqi

@junranhe
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well, when I run the same inception-bn on purine,and it run 50 sample per sec

@tqchen
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tqchen commented Sep 30, 2015

hmm, maybe you can try set update_on_kvstore=True http://mxnet.readthedocs.org/en/latest/python/model.html#mxnet.model.FeedForward.fit which will change default behavior. But I do not have tried 780 before so don't know if that would really help :)

@tqchen
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tqchen commented Sep 30, 2015

Sorry update_on_kvstore=False

@junranhe
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Oh, I try update_on_kvstore both True and False, it has no effect, and I try to run mxnet on single gpu, and inception-bn is 65 sample per sec... compare with two gpu 68 sample per sec(so 34 sample/sec), It seem just like used one gpu, Is there some lock make it slowdown? (I use dmesg can not find any error message, and I test alexnet is no any problem like inception-bn),

@tqchen
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tqchen commented Sep 30, 2015

I see what you mean, there is no such lock that I am aware of. What you observed was interesting.
Could because the synchronization was caped somehow on your machine, mxnet indeed uses cpu heavily because we need to catch up with dynamic dependencies issued in python, maybe you can set the env variables in https://github.com/dmlc/mxnet/blob/master/doc/env_var.md related to NTHREADS to 1. and see if that helps.

@junranhe
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junranhe commented Oct 2, 2015

I try it , nthreads seems no effect, the main effect on speed are batch_size and mxnet_engine_type, and NaiveEngine is 30 per sec, EngineThread is 50 per sec, and Default is 68 per sec(all on two 780 gpu)

@tqchen
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tqchen commented Oct 2, 2015

Thanks. I have no great idea so far. Seems it was due to synchronization cost(maybe PCI bandwith or memory speed). You can try use the most recent version and set

kvstore_type = 'device'

This will do GPU to GPU copy instead of copy to mainmemory
You will need to assign more to GPU worker threads in this setting.

@junranhe
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junranhe commented Oct 3, 2015

well, kvstore_type = 'device' is work, and My mechine run 120 sample per sec now, thanks for you help:)

@antinucleon
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Guess it is caused by your PCI-E bandwith . Good to see it works.

@tqchen
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tqchen commented Oct 3, 2015

You may also want to set
MXNET_GPU_WORKER_NTHREADS to be two, which I observed would help in this setting. Your problem was caused by the PCI-E bandwith, so the device side kvstore works better on your setting.

Glad that things works out for you. Thanks for using MXNet in the very early stage and help us improve it

@tqchen tqchen closed this as completed Oct 3, 2015
@junranhe
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junranhe commented Oct 3, 2015

Thanks to let me know, My GPU'memory is so poor, and out of memory tor more worker :(

@tqchen
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tqchen commented Oct 3, 2015

I see, I have created #204 so memory allocation algorithm is not strictly tied with GPU threads. You can try

MXNET_EXEC_NUM_TEMP=1
MXNET_GPU_WORKER_NTHREADS=2

after the PR is merged in

@junranhe
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junranhe commented Oct 4, 2015

Thanks, I have one more question, When I build the VGGNet 13layer, on 2 batch_size on 1 gpu used 1500M memory, but I set 3 batch_size, It raise out of memory(780 has 3000M), How the memory allocation algorighm work?

@tqchen
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tqchen commented Oct 4, 2015

The memory allocator will try to re-use the memory as much as possible. I do not know in particular what happens in your case though. This could due to the memory demand of workspace of CUDNN is nonlinear to batch size(for example, rounded to size of 4)

@junranhe
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junranhe commented Oct 4, 2015

Year, I try
MXNET_EXEC_NUM_TEMP=1
MXNET_GPU_WORKER_NTHREADS=2

and It is 5% faster on my machine

@tqchen
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tqchen commented Oct 4, 2015

Yeah, that is what it can get you

stefanhenneking pushed a commit to stefanhenneking/mxnet that referenced this issue Jun 30, 2017
Includes:
1. transpose_indices
2. Fix a bug in SortByKey on CPU
3. Use float for start/stop/step for the range OP
4. index_fill
cjolivier01 pushed a commit to cjolivier01/mxnet that referenced this issue Aug 21, 2017
* add clarification for csr

* cr comments
piiswrong pushed a commit that referenced this issue Aug 22, 2017
* [WIP] Sparse Tensor  (#5800)

* squash

merge with 38f7c55

compiles on GPU

update check alloc:

Checkpoint. Pass elem-sum gpu test

bug fix for copyfromto. sparse sgd test pass on gpu

inefficient implementation for csr copy

update submodule

fix lint

Simple bind with infer storage type (#32)

* Symbol binding for sparse tensor development. (#31)

* Initial checkin

* Add init functions for simple bind in graph_executor

* Add simple_bind c_api

* Add simple bind c-api

* Assign zeros to in_args, arg_grads, and aux_states

* Add simple_bind2 python interface

* Fix python interface bugs

* Interface changes

* Fix

* Fix core dump

* Add bind_ith_exec c_api

* Change simple_bind2

* Fix seg fault

* Finish simple_bind

* Change _bind_ith_exec

* Refactor simple_bind initialization flow for bind

* Consolidate bind and simple_bind graph init flow

* Fix bug

* Clean up

* Add comments

* Clean up

* Clean up

* Minor correction

* Rename APIs in graph executor

* Refactor

* Rebase

* Delete deprecated functions

* Move more front-end work to backend

* Bug fix

* Fix failed tests

* Minor fix

* Fix lint

* Fix lint

* Revert unnecessary changes

* Revert

* Revert

* Clean up

* Fix lint

Conflicts:
	python/mxnet/symbol.py
	src/executor/graph_executor.cc

* Add inferstorage to graph executor

* re-enable tests for sparse embedding with simple_bind

* type switch fix in sparse embedding"
;

change `default` to `default_storage` for cast storage op (#33)

* change default to default_storage

* disable cpp test build temporarily

attempt to fix windows build error, and fix lint (#34)

update nnvm submodule (#37)

Scipy build (#38)

* update nnvm submodule

* add scipy pip install for dockerfile

Python3 unit tests (#39)

* change xrange to range for python3 compatiblity"

* remove more xrange from tests

replace long with int for python3 (#40)

fix the rest of TShape constructor errors (#41)

fix lint (#42)

fix wrong usage of mshadow::Shape1" (#43)

implementation for Csr slice on cpu (#36)

* CPU implementation for CSR

remove seg_len from csr slice

add some docs for slice csr

change indptr, values, etc to be private member

bug fix in sparse embedding

update nnvm submoduel

fix lint

update unit test for sparse nd"

* add const for SliceCsrIndPtr kernel

Fix sparse dot according to the new RSP definition (#35)

* Fix csr dot dns

* Fix sparse dot

* Add fallback and test cases for dot(csr, dns)=dns

* Add int type switch

* Fix

* Fix

* Fix

update mshadow submodule (#44)

Fix dns to rsp (#46)

fix lint (#47)

add runtime storage fallback detection" (#48)

* add runtime storage fallback detection"

* replace cast storage ex with cast storage impl

Fm example (#45)

* update csr slice logic to avoid confusion. add more exmaples.

* add hint to module.update

* more testcases(fallback) for sparse_nd

* add to_csr() and to_rsp() method. More unit test (fallback now)

* add fm test. fix lint

* register sparse sgd under Optim.SGD

* update dmlc-core submoduel

* change indptr to _indptr temporarily. add const ref to fname

fix lint

fix lint; (#51)

Guard gpu cast storage (#50)

* Clean up

* Fix typo

Rearrange unit test files (#52)

fix lint. add scipy for python_test. fix scipy.sparse import error. fix truediv for python3

fix travis test (#54)

* remove pyc files

* add verbose for travis nosetests

cleanup some testing code and enums (#57)

* update Makefile

* refactor test_sparse_operator

* change `default_storage` back to `default`

* remove unused cpp tests

port libsvm parser to mxnet as libsvm iter (#55)

* copied csv iter to libsvm iter

test

libsvm iter draft

handle round batch == false for csr batch loader

code refactoring

add get stype, shape interface to iiter

separate class for sparse iter

add missing file

fix mem corruption'

rename variables

add comments

also read label from libsvm

add test. update docs. update submodule

Conflicts:
	python/mxnet/sparse_ndarray.py

* update submodule

* fix lint

* update test

* revert naming change

add benchmark scritp for dot (#59)

* add benchmark scritp for dot

add gpu option for bench

add get_data funciton for benchmark

print t_sparse, too;

add comment

change nnz to dnesity

add backward

* add comment

update fm test (#62)

introduce CSRNDarray and rowsparseNDarray to python frontend api (#58)

* introduce CSRNDarray and rowsparseNDarray to python frontend api

* temporarily disable fm_module test

fix lint (#64)

fix typo. disable libsvm io test (#65)

Improve dot (#61)

* Init checkin

* Fix

* Adjust dot parallelization methods

* Set num_omp_threads for benchmark from command line

* Fix omp thread number

* Clean up

* Add scipy as dot baseline

* Fix format

sparse_retain op (#66)

* Initial checkin

* Fix bugs

* Add unit test for sparse_retain

* Add example and modify test

add storage cast for outputs that have non-default storage (#67)

fix gpu build (#69)

Fix test_sparse_retain python3 issue (#68)

revert nnvm version

* draft for sgd rsp rsp (#75)

support sgd(rsp, rsp)

support dot(csr, rsp) when rsp is full

add ref to const ndarray params

support sparse embedding with rsp weight'

fix lint

modify embedding backward to produce dense grad

remove invalid_rid for rsp->dns

remove previous embedding op changes

pass sparse embedding test

add STORAGE_TYPE_ASSIGN_CHECK

remove backward storage infer

* fix lint (#78)

* fix lint (#79)

* serial elemwise sum impl (#80)

update module kvstore interface

add other missing params and functions

revert some interface changes

revert some more changes

reomve explicit casting for gradients on kvstore

update Comm interface

update fm example

Conflicts:
	python/mxnet/model.py
	python/mxnet/ndarray.py

* bug fix for initializing module with row_sparse weight (#81)

* bug fix for initializing module with row_sparse weight

* update log message

* Sparse ndarray serialization and deserialization (#77)

* Initial checkin

* Add unit tests

* Fix lint

* Fix lint (#84)

* Sgd with row_sparse weight, dns gradient (#83)

* sgd rsp dns draft

* support sgd_mom(rsp, dns, rsp)

* update doc

* remove cast storage for kv updater

* code refactoring

* update mshadow version (#88)

* csr slice bug fix (#90)

* benchmark dot code refactor (#87)

* q^x6x add some code in benchmark

* refactor

* minor fixes

* fix

* lint fix

* Add unit test (#91)

* add unittest

* minor fix

* remove commented lines

* change test func name

* add test rsp

* kvstore push row sparse (#93)

* Add multi-thread cpu elemwise sum for rsps

* Minor fix

* Add flag to switch between serial and multi-thread kvstore push

* Fix lint in sparse_ndarray.py

* Revert "Fix lint in sparse_ndarray.py"

This reverts commit d7225ec.

* Fix ndarray init in copy(ctx)

* Add env var to control the flow of serial/parallel reduce

* Refactor

* Fix copy ndarray bug

* Fix lint

* Refactor

* Fix windows openmp build failure (#94)

* update mshadow submoduel (#95)

* Revert "update mshadow submoduel (#95)" (#96)

This reverts commit 1a129e4.

* Refactor sparse tensor code (#99)

* Initial checkin test_sparse_ndarray passes

* Fix test failure

* Clean up

* Clean up

* Move init backend op to ndarray_utils

* Fix lint

* Eliminate circular dependency on headers

* More refactor

* Fix gpu build and consolidate Slice for dense and sparse

* Clean up

* More refactor

* Clean up

* Fix gpu build

* Fix comment

* fix pylint (#100)

* Fix refactor sparse gpu test (#104)

* Fix gpu build

* Fix

* Fix gpu test failure

* change idx types from int32 to int64 (#101)

Conflicts:
	python/mxnet/test_utils.py
	tests/python/unittest/test_sparse_operator.py

update mshadow submodule

fix extra quotes in test script

change indptr type to int64

better err message for rsp"

* revert LOG(DEBUG) change (#105)

* fix undefined zeros in optimizer.py (#106)

* move init dns zeros to init_op.h for kvstore to use (#107)

* Refactor cast storage (#109)

* Refactor cast_storage

* Add cast_storage cc and cu files

* Remove redundant comments

* Replace std::accumulate with ParallelAccumulate

* Clean up

* Fix windows build

* Rowsparse kv (#111)

* update kvstore unit test

Conflicts:
	tests/python/unittest/test_kvstore.py

update model/module.py

Conflicts:
	python/mxnet/model.py
	python/mxnet/module/module.py

fix lint

resolve conflict

remove int keys in kvstore

update cast to str function

* fix failed dist_sync_kv test

* bug fix in comm to ensure merged gradient is of the right type

bug fix in comm

* row sparse dist kvstore draft (push only)

row_sparse pull

* add ndarray row sparse shared mem constructor

* code refactoring

* add test for row_sparse weight

bug fix for kv server slicing

add async support

rsolve race condition in kvstore

* resolve error after reb ase

* fix lint (#113)

* rename some python funciton (#114)

* _to_rsp

* _to_csr. raise NotImplementedError

* todense

* fix lint (#115)

enable libsvm uniit test (#6839)

remove shared mem slice for csr

add csr ndarray iter test

make osx nose test verbose

disable libsvm iter test

Move InferAttr to mxnet from nnvm (#6830)

* Move InferAttr to mxnet from nnvm

Replace nnvm infer attr functions in c_api

Initial checkin

Clean up

Remove nnvm namespace for FInferShape, FInferType, and FInferStorageType

Add new interface for InferStorageType

Revert "Remove nnvm namespace for FInferShape, FInferType, and FInferStorageType"

This reverts commit 8aedf05.

Fix and clean up

Fix lint

Add nnvm changes

Change infer function interface to accept only rvalue reference of graph

Clean up

Flush commits to show up in PR

Add error handling for storage type inference failure

Update nnvm

* Fix pylint

Change idx type switch for aux data (#6860)

* Change idx type switch for aux data

* Add mshadow commit

Sparse dot enhancement (#6842)

* Initial checkin

Initial checkin

Fix sparse dot test

Fix unitest and add fallback for sparse dot

* Add benchmark code

* Revert "Add benchmark code"

This reverts commit be009fe.

* Fix bug

* Fix storage shape

* Remove unnecessary test code

* Use idx type switch

Implement dot(csr, rsp)=dns and dot(csr.T, rsp)=rsp and refactor (#6902)

* Initial checkin

Add dot(csr.T, rsp)=rsp2

Add infer storage for dot(csr, rsp)=dns and dot(csr.T, rsp)=rsp2

* Fix comments

* Replace std::lower_bound with own impl for gpu use too

* Add time profiling

* Revert "Add time profiling"

This reverts commit 8f5bb98.

* Move dot and batch_dot to a single file

* Move dot gpu impl to a .cuh file

* More refactor

* Fix include error

LibsvmIter fix (#6898)

* fix bug in libsvm iter which causes mem corruption

* add test for news dataset

* fix wrong path in test

* fix import error for urllib

* update url

* replace bz command with bz module

Optimized gpu dot kernels (#6937)

* pulled update to mshadow

* mshadow update

* added optimized gpu kernels for dot(csr,dns)=dns and dot(csr.T,dns)=dns, and unit test

* added __syncwarp to vector kernel and reduced number of writes to shared memory

Refactor sparse tensor code (#6955)

* Save stype in frontend to avoid c-api call for stype

* Change storage_type to stype

* Revert "Change storage_type to stype"

This reverts commit 90db7d1.

* Revert "Revert "Change storage_type to stype""

This reverts commit 0932838.

Move ndarray.py, sparse_ndarray.py, ndarray_utils.py, and _ndarray_internal to ndarrary folder

More refactor

Move elementwise sum for rsp to ndarray_function.cc

Remove unnecessary import in ndarray module

Fix pylint

Remove redundant code

Remove _stype from slots

Fix cpp-package build error caused by the change to imperative invoke interface

Use relative import

Remove print line

Rename _ndarray_internal.py to _internal.py

* Relaunch test...

minor bug fix in warp synchronous code (#7029)

* move storage type vector from nnvm to mxnet (#7054)

* move storage type vector from nnvm to mxnet

* update nnvm

* update nnvm

* Improve copy sparse tensors (#7003)

* Use cast_storage when copying ndarrays of different stypes on same context

* Relaunch test

* fix failed tests. add back 64bit support for dot

fix lint

* bug fix for IdentityComputeRsp

* fix lint

fix lint

fix lint

* add data partition for libsvm iter (#7027)

* remove sparse embedding (#7165)

* fix ndarray namespace

* remove untested gpu operators (#7172)

* skip sparse dot gpu tset. add sparse_nd_zeros gpu test

* remove sparse_retain gpu

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Fix ndarray aux data issue (#7098)

* Fix getting sparse ndarray data/aux_data issues

* Add tests for func csr and row_sparse

* Make get/set data/aux_data thread safe

* Fix a bug

* Fix typo and comment

* More comments

* Correct comment

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Support K-dimensional row-sparse tensor (#7179)

* remove check for k dimensional rowsparse tensor

* change var name for rsp sgd operator

* add checks for sparse dot

* bug fix for kdim rowsparse cast storage cpu

* update IdentityLikeRhsComputeEx interface

* remove set_storage_shape from ndarray. support elemwise_add with kdim row_sparse tensor

* use get_with_shape instead of reshape

* update according to comments

Conflicts:
	src/operator/tensor/elemwise_unary_op.h

* Improve sparse ndarray error message (#7181)

* add test for broadcast_to

* add comments

Conflicts:
	python/mxnet/base.py

* construct row_sparse ndarray for dist-async

fix bug in rsp add

rsp sync push

race condition for push

fix bug in rsp pull. refactor test

cleanup comments

refactor dist server

fix lint

fix storage shape issue with the new ndarray constructor

data sharding draft;

fix lint. add comment

add support for zeros gradients

use std::upper_bound/lower_bound

remove special init function for rowsparse dist kvstore

temporary support for inplace operators for sparse

add test. fix return type

store kRowSparseNDArray in kv server

remove fcomp_ex sgd with dns weight and rsp gradient

bug fix in sparse retain

sparse pull c_api

revise rowsparse pull api

use engine to compute unique to ensure thread safety

add rowsparse pull to dist-kv

fix lint

add example for rsp_pull

remove name2idx;

add sparse_pull_dict param to module

fix unit test and  c rowid conversion

support str key type in kvstore (#6765)

* update kvstore unit test

* update model/module.py

* fix lint

* remove int keys in kvstore

* update cast to str function

* remove _cast_to_str_keys

* fix lint

* always cast to str

Conflicts:
	include/mxnet/c_api.h
	include/mxnet/kvstore.h
	python/mxnet/kvstore.py
	python/mxnet/model.py
	python/mxnet/module/module.py
	src/c_api/c_api.cc
	src/kvstore/kvstore_local.h
	tests/python/unittest/test_kvstore.py

update module API for other submodules

update stypes in kvstore after refactoring

change type of size from size_t to int64_t

add sparse linear regression example

remove sparse_pull_dict from module

fix init_optim for seq_module. update sparse example

resolve conflict for binary add rsp rsp

Conflicts:
	python/mxnet/kvstore.py
	tests/python/unittest/test_kvstore.py

* fix DotCsrRspRspImpl error message (#7191)

* GPU implementation of cast_storage (dense to csr) (#7081)

* Added gpu implementation for cast_storage dense to csr, unit tests, and benchmark. Additionally, cast_storage interface change to accommodate the need of temporary storage in cuda kernels.

* fixed whitespace

* minor unittest update

* removed whitespace

* add cast storage benchmark params info

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Sparse square sum (#7206)

* Add square_sum op

* Add unit test and fix check_numeric_gradient

* Add .cu file and example

* Fix lint

* Remove gpu registration

* Use square_sum in test_module_fm

* Modify and Add documentation for mx.nd.zeros (#7197)

* Modify and Add documentation for mx.nd.zeros

* Change context to cpu

* Change stype to optional

* Change ordering and remove optional for _zeros_sparse_ndarray

* Expose kWriteInplace for imperative execution (fcompute_ex and fstatefulcompute_ex) (#133)

* expose kWriteInplace to FComputeEx and FStatefulComputeEx

* refactor ccode

* remove duplicated test

* Operator add_n for row sparse ndarrays (#7244)

* Add add_n op for row-sparse ndarrays and identity FComputeEx

* Fix bug in square_sum

* Remove test_cast_storage_ex from gpu test since it's not implemented yet

* Fix according to the cr

Conflicts:
	src/operator/tensor/elemwise_sum.cc
	src/operator/tensor/elemwise_unary_op.cc
	tests/python/gpu/test_operator_gpu.py

resolve conflict

* GPU implementation of cast_storage (dense to rsp) (#7223)

* CastStorageDnsRsp GPU Implementation

* updating function doc and some variable types and names

* adding cuda_get_device_prop() util function

* added rand_shape function for n-dimensional tensors

* updated cast storage unit test

* added dns_to_rsp to cast storage benchmark script

* removing redundant unit test

* fix lint

* minor change in benchmark script

* fix lint

* correct function description

* change storage_type to stype

* changed scope of using namespaces

* changed variable types from index_t to dim_t

* resolve merge conflict in ndarray.load

* Improve StatefulOp/FCompute storage fallback (#134)

* test for fcomp fallback

add storage fallback test and optimize fallback logic

rename function, add comments

use std size()

* add autograd test with sparse inputs

* update sparse ndarray api (#139)

* support mx.nd.empty for sparse ndarray

Change SparseNDArray to BaseSparseNDArray

support mx.nd.array with BaseSparseNDArray inputs. Update documentation with explicit subclasses of NDArrays

Conflicts:
	python/mxnet/ndarray/__init__.py
	python/mxnet/ndarray/ndarray.py
	python/mxnet/ndarray/sparse_ndarray.py
	tests/python/unittest/test_sparse_ndarray.py

* fix print msg in test

* Handle ograd_stype='row_sparse' for square_sum backward (#143)

* Add one kernel for square_sum backward pass to take rsp ograd

* Add kNullOp and change to use type_assign in infer stype fallback

* Sparse retain improvement (#138)

* Add one more kernel for sparse retain

* Fix compile

* Change STORAGE_TYPE_ASSIGN_CHECK to type_assign for fallback

* Fix

* Add gpu compile

* ignoring variables in SimpleBind that is used on python's sparse branch for now. (#135)

* add bias term to fm test (#145)

* update ndarray.nd, remove `invoke` from excluded members (#137)

remove __weakref__ from SparseNDArray

add data indice to doc

revert dlpack update

revert mxdoc changes

move methods from BaseSparseNDarray to csrndarray and rwosparse ndarray

* support storage fallback with mutable inputs (#147)

* include mutatable inputs in storage fallback. refactor executor

add fallback test for rms prop and adam

fix lint

fix lint

fix test in optimizer

*  update according to comments

* fix unit tests

* fix gpu compilation err

* Code changes based on reviews (#144)

* code changes according to review comments

remove executor debug. add doc to optimizer

update sparse sgd test

add dtype option to rand_sparse_ndarray

* overhauled reqs for sparse operators

* patch FCompExFallback with mutable inputs. update test_optimizer with more fallback cases

* change executor debug macro to env var

* add comment

* update doc

* change ndarray.aux_shape() to return const reference

* remove todense to_rsp to_csr. replace with tostype

* replace manual calls to cast_storage with tostype

* disable gpu fallback test for optimizer

* fix lint

* add backward pass for cast_storage. refactor cast_storage test

* rand_sparse_ndarray bug fix

* fix cast_storage for gpu

* disable csr test for fp16

* update row sparse ndarray doc

* update doc

* small edits according to reviews (#151)

* fix lint (#152)

* add license to all new files in sparse brnach (#154)

* Allocate temp data on the fly for some casting operations (#149)

* fix utf8 encoding in sparse ndarray

* Extending the GPU dot operator (#7226)

* Added GPU DotCsrRspDnsImpl declaration and TODOs

* cleaning up function doc, variable types, and code-style

* minor bug fixes

* enable GPU dot(csr,rsp)=dns unit test

* extend sparse dot unit test

* adding GPU impl of DotCsrRspDns and its kernels

* add TODO

* changed variable types from index_t to dim_t

* fix function description

* added DotCsrRspRspImpl and its kernels (baseline, functionality)

* added DotCsrDnsRspImpl and its kernels (baseline, functionality); plus code documentation

* refactored dot benchmark

* optimized DotCsrTransDnsRsp GPU kernel

* change of dot impl interface to include OpContext, for temp storage

* removing __device__ flag from CPU kernels

* minor fixes and changing variable data types

* minor fixes based on code reviews

Conflicts:
	benchmark/python/sparse_op.py
	tests/python/gpu/test_operator_gpu.py
	tests/python/unittest/test_sparse_operator.py

* Add get_synthetic_dataset function to util (#146)

* Add get_synthetic_datasets

* Move to test_utils

* Remove _get_uniform_dataset

* Move validation to its own function

* Refactor the validation code for csr generation

* Make test_powerlaw a nested function

* Change SparseNDArray to CSRNDArray

* Merge with dtype specific changes in test_utils

* temporary fix for batch norm storage fallback (#156)

* support random_uniform/normal/gamma with row_sparse output (#155)

* add support for initilazer with rowsparse output

* add scalar assignment to row_sparse

* add setitem test to gpu

* Revert "add scalar assignment to row_sparse"

This reverts commit 8aef7a5.

* Revert "add setitem test to gpu"

This reverts commit 3b969ac.

* Square sum backward support one more case (#161)

* Add documentation for sparse ops (#148)

*  draft doc for sparse op

* add more stype doc for operators

* add doc for cast_storage

* see also cast_storage. remove base sparse ndarray. fix aux_types comemtn

* grammar / spelling fix

* A few fixes (#163)

* fix batch norm gpu kernel. register random operators on gpu

* register sparse random op on gpu, too

* Minor fixes sparse ops (#160)

* change CPU kernel inline directives, data types, and function doc

* update dot dtype switch to use 32 and 64bit floating point only

* use type_assign instead of STORAGE_TYPE_ASSIGN_CHECK

* added tensor_util-inl.cuh file for common tensor operator GPU kernels

* sparse Adam optimizer (#164)

*  add sparse adam

* register gpu op

* add comments

* cr comments

* kvstore.row_sparse_pull for GPU and end-to-end benchmark: CPU vs. multi-GPUs (#150)

* Add gpu support for BroadcastRowSparse

* Fix bugs

* Add benchmark script

* Increase output dim size

* Update weight on CPU using single GPU for sparse tensors

* More fix

* Optimize sparse_retain for special case

* Change row sparse pull locations

* Avoid sparse retain on cpu if possible

* Use acc for metric

* Fix misc

* fix bug in adam update (#167)

fix a bug in adam update

* change sparse example from regression to classification (#165)

* fix python import (#166)

* Add waitall to sparse_end2end.py (#169)

* Add waitall()

* Add dummy metric option

* Add header license

* Dot script changes (#159)

* Add get_synthetic_datasets

* Move to test_utils

* Remove _get_uniform_dataset

* Move validation to its own function

* Refactor the validation code for csr generation

* Make test_powerlaw a nested function

* Change SparseNDArray to CSRNDArray

* Refactoring changes to dot.py

* Fix mxnet test_utils changes

* Remove pdb statement

* Add distribution parameter

* Refactor benchmarking script

* Remove unused code

* Make style changes and remove unused code

* Change typo in comment

* Add transpose support

* Change typo

* 4 decimal points needed for density

* Add rsp support for real datasets

* Correct variable name mini_file_name

* Move wait_to_read outside if

* Seperate out scipy and mxnet logic in bench_dot

* Fix lhs_trans issue

* Move transpose outside measure_cost

* Compute transpose inside measure_cost

* Remove unused variables

* Transpose only if trans_lhs (#171)

* fix default val for distribution (#172)

* fix lint (#175)

* avoid cast_storage in dist-kvstore-server (#174)

* avoid cast_storage in dist-kvstore-server

* add stream arg to mshadow;;copy

* fix copy order

* Add sparse namespace to ndarray and symbol (#177)

* Register dot, cast_storage, and sparse_retain under mxnet.ndarray.sparse

* Add sparse to symbol namespace

* Delete commented code

* mv sparse_ndarray.py sparse.py

* Clean up

* Change docstring

* changes based on code reviews (#176)

* remove scipy dependency

* move kvstore checks to backned

* add const to lambda

* temp fix to ndarray.md (#178)

* Fix sparse namespace pylint (#179)

* add comments and error msg (#181)

* add clarification for csr (#182)

* add clarification for csr

* cr comments

* revert change in test util (#183)

* fix amalgamation (#184)

* fix lint
crazy-cat pushed a commit to crazy-cat/incubator-mxnet that referenced this issue Oct 26, 2017
* [WIP] Sparse Tensor  (apache#5800)

* squash

merge with 38f7c55

compiles on GPU

update check alloc:

Checkpoint. Pass elem-sum gpu test

bug fix for copyfromto. sparse sgd test pass on gpu

inefficient implementation for csr copy

update submodule

fix lint

Simple bind with infer storage type (apache#32)

* Symbol binding for sparse tensor development. (apache#31)

* Initial checkin

* Add init functions for simple bind in graph_executor

* Add simple_bind c_api

* Add simple bind c-api

* Assign zeros to in_args, arg_grads, and aux_states

* Add simple_bind2 python interface

* Fix python interface bugs

* Interface changes

* Fix

* Fix core dump

* Add bind_ith_exec c_api

* Change simple_bind2

* Fix seg fault

* Finish simple_bind

* Change _bind_ith_exec

* Refactor simple_bind initialization flow for bind

* Consolidate bind and simple_bind graph init flow

* Fix bug

* Clean up

* Add comments

* Clean up

* Clean up

* Minor correction

* Rename APIs in graph executor

* Refactor

* Rebase

* Delete deprecated functions

* Move more front-end work to backend

* Bug fix

* Fix failed tests

* Minor fix

* Fix lint

* Fix lint

* Revert unnecessary changes

* Revert

* Revert

* Clean up

* Fix lint

Conflicts:
	python/mxnet/symbol.py
	src/executor/graph_executor.cc

* Add inferstorage to graph executor

* re-enable tests for sparse embedding with simple_bind

* type switch fix in sparse embedding"
;

change `default` to `default_storage` for cast storage op (apache#33)

* change default to default_storage

* disable cpp test build temporarily

attempt to fix windows build error, and fix lint (apache#34)

update nnvm submodule (apache#37)

Scipy build (apache#38)

* update nnvm submodule

* add scipy pip install for dockerfile

Python3 unit tests (apache#39)

* change xrange to range for python3 compatiblity"

* remove more xrange from tests

replace long with int for python3 (apache#40)

fix the rest of TShape constructor errors (apache#41)

fix lint (apache#42)

fix wrong usage of mshadow::Shape1" (apache#43)

implementation for Csr slice on cpu (apache#36)

* CPU implementation for CSR

remove seg_len from csr slice

add some docs for slice csr

change indptr, values, etc to be private member

bug fix in sparse embedding

update nnvm submoduel

fix lint

update unit test for sparse nd"

* add const for SliceCsrIndPtr kernel

Fix sparse dot according to the new RSP definition (apache#35)

* Fix csr dot dns

* Fix sparse dot

* Add fallback and test cases for dot(csr, dns)=dns

* Add int type switch

* Fix

* Fix

* Fix

update mshadow submodule (apache#44)

Fix dns to rsp (apache#46)

fix lint (apache#47)

add runtime storage fallback detection" (apache#48)

* add runtime storage fallback detection"

* replace cast storage ex with cast storage impl

Fm example (apache#45)

* update csr slice logic to avoid confusion. add more exmaples.

* add hint to module.update

* more testcases(fallback) for sparse_nd

* add to_csr() and to_rsp() method. More unit test (fallback now)

* add fm test. fix lint

* register sparse sgd under Optim.SGD

* update dmlc-core submoduel

* change indptr to _indptr temporarily. add const ref to fname

fix lint

fix lint; (apache#51)

Guard gpu cast storage (apache#50)

* Clean up

* Fix typo

Rearrange unit test files (apache#52)

fix lint. add scipy for python_test. fix scipy.sparse import error. fix truediv for python3

fix travis test (apache#54)

* remove pyc files

* add verbose for travis nosetests

cleanup some testing code and enums (apache#57)

* update Makefile

* refactor test_sparse_operator

* change `default_storage` back to `default`

* remove unused cpp tests

port libsvm parser to mxnet as libsvm iter (apache#55)

* copied csv iter to libsvm iter

test

libsvm iter draft

handle round batch == false for csr batch loader

code refactoring

add get stype, shape interface to iiter

separate class for sparse iter

add missing file

fix mem corruption'

rename variables

add comments

also read label from libsvm

add test. update docs. update submodule

Conflicts:
	python/mxnet/sparse_ndarray.py

* update submodule

* fix lint

* update test

* revert naming change

add benchmark scritp for dot (apache#59)

* add benchmark scritp for dot

add gpu option for bench

add get_data funciton for benchmark

print t_sparse, too;

add comment

change nnz to dnesity

add backward

* add comment

update fm test (apache#62)

introduce CSRNDarray and rowsparseNDarray to python frontend api (apache#58)

* introduce CSRNDarray and rowsparseNDarray to python frontend api

* temporarily disable fm_module test

fix lint (apache#64)

fix typo. disable libsvm io test (apache#65)

Improve dot (apache#61)

* Init checkin

* Fix

* Adjust dot parallelization methods

* Set num_omp_threads for benchmark from command line

* Fix omp thread number

* Clean up

* Add scipy as dot baseline

* Fix format

sparse_retain op (apache#66)

* Initial checkin

* Fix bugs

* Add unit test for sparse_retain

* Add example and modify test

add storage cast for outputs that have non-default storage (apache#67)

fix gpu build (apache#69)

Fix test_sparse_retain python3 issue (apache#68)

revert nnvm version

* draft for sgd rsp rsp (apache#75)

support sgd(rsp, rsp)

support dot(csr, rsp) when rsp is full

add ref to const ndarray params

support sparse embedding with rsp weight'

fix lint

modify embedding backward to produce dense grad

remove invalid_rid for rsp->dns

remove previous embedding op changes

pass sparse embedding test

add STORAGE_TYPE_ASSIGN_CHECK

remove backward storage infer

* fix lint (apache#78)

* fix lint (apache#79)

* serial elemwise sum impl (apache#80)

update module kvstore interface

add other missing params and functions

revert some interface changes

revert some more changes

reomve explicit casting for gradients on kvstore

update Comm interface

update fm example

Conflicts:
	python/mxnet/model.py
	python/mxnet/ndarray.py

* bug fix for initializing module with row_sparse weight (apache#81)

* bug fix for initializing module with row_sparse weight

* update log message

* Sparse ndarray serialization and deserialization (apache#77)

* Initial checkin

* Add unit tests

* Fix lint

* Fix lint (apache#84)

* Sgd with row_sparse weight, dns gradient (apache#83)

* sgd rsp dns draft

* support sgd_mom(rsp, dns, rsp)

* update doc

* remove cast storage for kv updater

* code refactoring

* update mshadow version (apache#88)

* csr slice bug fix (apache#90)

* benchmark dot code refactor (apache#87)

* q^x6x add some code in benchmark

* refactor

* minor fixes

* fix

* lint fix

* Add unit test (apache#91)

* add unittest

* minor fix

* remove commented lines

* change test func name

* add test rsp

* kvstore push row sparse (apache#93)

* Add multi-thread cpu elemwise sum for rsps

* Minor fix

* Add flag to switch between serial and multi-thread kvstore push

* Fix lint in sparse_ndarray.py

* Revert "Fix lint in sparse_ndarray.py"

This reverts commit d7225ec.

* Fix ndarray init in copy(ctx)

* Add env var to control the flow of serial/parallel reduce

* Refactor

* Fix copy ndarray bug

* Fix lint

* Refactor

* Fix windows openmp build failure (apache#94)

* update mshadow submoduel (apache#95)

* Revert "update mshadow submoduel (apache#95)" (apache#96)

This reverts commit 1a129e4.

* Refactor sparse tensor code (apache#99)

* Initial checkin test_sparse_ndarray passes

* Fix test failure

* Clean up

* Clean up

* Move init backend op to ndarray_utils

* Fix lint

* Eliminate circular dependency on headers

* More refactor

* Fix gpu build and consolidate Slice for dense and sparse

* Clean up

* More refactor

* Clean up

* Fix gpu build

* Fix comment

* fix pylint (apache#100)

* Fix refactor sparse gpu test (apache#104)

* Fix gpu build

* Fix

* Fix gpu test failure

* change idx types from int32 to int64 (apache#101)

Conflicts:
	python/mxnet/test_utils.py
	tests/python/unittest/test_sparse_operator.py

update mshadow submodule

fix extra quotes in test script

change indptr type to int64

better err message for rsp"

* revert LOG(DEBUG) change (apache#105)

* fix undefined zeros in optimizer.py (apache#106)

* move init dns zeros to init_op.h for kvstore to use (apache#107)

* Refactor cast storage (apache#109)

* Refactor cast_storage

* Add cast_storage cc and cu files

* Remove redundant comments

* Replace std::accumulate with ParallelAccumulate

* Clean up

* Fix windows build

* Rowsparse kv (apache#111)

* update kvstore unit test

Conflicts:
	tests/python/unittest/test_kvstore.py

update model/module.py

Conflicts:
	python/mxnet/model.py
	python/mxnet/module/module.py

fix lint

resolve conflict

remove int keys in kvstore

update cast to str function

* fix failed dist_sync_kv test

* bug fix in comm to ensure merged gradient is of the right type

bug fix in comm

* row sparse dist kvstore draft (push only)

row_sparse pull

* add ndarray row sparse shared mem constructor

* code refactoring

* add test for row_sparse weight

bug fix for kv server slicing

add async support

rsolve race condition in kvstore

* resolve error after reb ase

* fix lint (apache#113)

* rename some python funciton (apache#114)

* _to_rsp

* _to_csr. raise NotImplementedError

* todense

* fix lint (apache#115)

enable libsvm uniit test (apache#6839)

remove shared mem slice for csr

add csr ndarray iter test

make osx nose test verbose

disable libsvm iter test

Move InferAttr to mxnet from nnvm (apache#6830)

* Move InferAttr to mxnet from nnvm

Replace nnvm infer attr functions in c_api

Initial checkin

Clean up

Remove nnvm namespace for FInferShape, FInferType, and FInferStorageType

Add new interface for InferStorageType

Revert "Remove nnvm namespace for FInferShape, FInferType, and FInferStorageType"

This reverts commit 8aedf05.

Fix and clean up

Fix lint

Add nnvm changes

Change infer function interface to accept only rvalue reference of graph

Clean up

Flush commits to show up in PR

Add error handling for storage type inference failure

Update nnvm

* Fix pylint

Change idx type switch for aux data (apache#6860)

* Change idx type switch for aux data

* Add mshadow commit

Sparse dot enhancement (apache#6842)

* Initial checkin

Initial checkin

Fix sparse dot test

Fix unitest and add fallback for sparse dot

* Add benchmark code

* Revert "Add benchmark code"

This reverts commit be009fe.

* Fix bug

* Fix storage shape

* Remove unnecessary test code

* Use idx type switch

Implement dot(csr, rsp)=dns and dot(csr.T, rsp)=rsp and refactor (apache#6902)

* Initial checkin

Add dot(csr.T, rsp)=rsp2

Add infer storage for dot(csr, rsp)=dns and dot(csr.T, rsp)=rsp2

* Fix comments

* Replace std::lower_bound with own impl for gpu use too

* Add time profiling

* Revert "Add time profiling"

This reverts commit 8f5bb98.

* Move dot and batch_dot to a single file

* Move dot gpu impl to a .cuh file

* More refactor

* Fix include error

LibsvmIter fix (apache#6898)

* fix bug in libsvm iter which causes mem corruption

* add test for news dataset

* fix wrong path in test

* fix import error for urllib

* update url

* replace bz command with bz module

Optimized gpu dot kernels (apache#6937)

* pulled update to mshadow

* mshadow update

* added optimized gpu kernels for dot(csr,dns)=dns and dot(csr.T,dns)=dns, and unit test

* added __syncwarp to vector kernel and reduced number of writes to shared memory

Refactor sparse tensor code (apache#6955)

* Save stype in frontend to avoid c-api call for stype

* Change storage_type to stype

* Revert "Change storage_type to stype"

This reverts commit 90db7d1.

* Revert "Revert "Change storage_type to stype""

This reverts commit 0932838.

Move ndarray.py, sparse_ndarray.py, ndarray_utils.py, and _ndarray_internal to ndarrary folder

More refactor

Move elementwise sum for rsp to ndarray_function.cc

Remove unnecessary import in ndarray module

Fix pylint

Remove redundant code

Remove _stype from slots

Fix cpp-package build error caused by the change to imperative invoke interface

Use relative import

Remove print line

Rename _ndarray_internal.py to _internal.py

* Relaunch test...

minor bug fix in warp synchronous code (apache#7029)

* move storage type vector from nnvm to mxnet (apache#7054)

* move storage type vector from nnvm to mxnet

* update nnvm

* update nnvm

* Improve copy sparse tensors (apache#7003)

* Use cast_storage when copying ndarrays of different stypes on same context

* Relaunch test

* fix failed tests. add back 64bit support for dot

fix lint

* bug fix for IdentityComputeRsp

* fix lint

fix lint

fix lint

* add data partition for libsvm iter (apache#7027)

* remove sparse embedding (apache#7165)

* fix ndarray namespace

* remove untested gpu operators (apache#7172)

* skip sparse dot gpu tset. add sparse_nd_zeros gpu test

* remove sparse_retain gpu

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Fix ndarray aux data issue (apache#7098)

* Fix getting sparse ndarray data/aux_data issues

* Add tests for func csr and row_sparse

* Make get/set data/aux_data thread safe

* Fix a bug

* Fix typo and comment

* More comments

* Correct comment

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Support K-dimensional row-sparse tensor (apache#7179)

* remove check for k dimensional rowsparse tensor

* change var name for rsp sgd operator

* add checks for sparse dot

* bug fix for kdim rowsparse cast storage cpu

* update IdentityLikeRhsComputeEx interface

* remove set_storage_shape from ndarray. support elemwise_add with kdim row_sparse tensor

* use get_with_shape instead of reshape

* update according to comments

Conflicts:
	src/operator/tensor/elemwise_unary_op.h

* Improve sparse ndarray error message (apache#7181)

* add test for broadcast_to

* add comments

Conflicts:
	python/mxnet/base.py

* construct row_sparse ndarray for dist-async

fix bug in rsp add

rsp sync push

race condition for push

fix bug in rsp pull. refactor test

cleanup comments

refactor dist server

fix lint

fix storage shape issue with the new ndarray constructor

data sharding draft;

fix lint. add comment

add support for zeros gradients

use std::upper_bound/lower_bound

remove special init function for rowsparse dist kvstore

temporary support for inplace operators for sparse

add test. fix return type

store kRowSparseNDArray in kv server

remove fcomp_ex sgd with dns weight and rsp gradient

bug fix in sparse retain

sparse pull c_api

revise rowsparse pull api

use engine to compute unique to ensure thread safety

add rowsparse pull to dist-kv

fix lint

add example for rsp_pull

remove name2idx;

add sparse_pull_dict param to module

fix unit test and  c rowid conversion

support str key type in kvstore (apache#6765)

* update kvstore unit test

* update model/module.py

* fix lint

* remove int keys in kvstore

* update cast to str function

* remove _cast_to_str_keys

* fix lint

* always cast to str

Conflicts:
	include/mxnet/c_api.h
	include/mxnet/kvstore.h
	python/mxnet/kvstore.py
	python/mxnet/model.py
	python/mxnet/module/module.py
	src/c_api/c_api.cc
	src/kvstore/kvstore_local.h
	tests/python/unittest/test_kvstore.py

update module API for other submodules

update stypes in kvstore after refactoring

change type of size from size_t to int64_t

add sparse linear regression example

remove sparse_pull_dict from module

fix init_optim for seq_module. update sparse example

resolve conflict for binary add rsp rsp

Conflicts:
	python/mxnet/kvstore.py
	tests/python/unittest/test_kvstore.py

* fix DotCsrRspRspImpl error message (apache#7191)

* GPU implementation of cast_storage (dense to csr) (apache#7081)

* Added gpu implementation for cast_storage dense to csr, unit tests, and benchmark. Additionally, cast_storage interface change to accommodate the need of temporary storage in cuda kernels.

* fixed whitespace

* minor unittest update

* removed whitespace

* add cast storage benchmark params info

Conflicts:
	tests/python/gpu/test_operator_gpu.py

* Sparse square sum (apache#7206)

* Add square_sum op

* Add unit test and fix check_numeric_gradient

* Add .cu file and example

* Fix lint

* Remove gpu registration

* Use square_sum in test_module_fm

* Modify and Add documentation for mx.nd.zeros (apache#7197)

* Modify and Add documentation for mx.nd.zeros

* Change context to cpu

* Change stype to optional

* Change ordering and remove optional for _zeros_sparse_ndarray

* Expose kWriteInplace for imperative execution (fcompute_ex and fstatefulcompute_ex) (apache#133)

* expose kWriteInplace to FComputeEx and FStatefulComputeEx

* refactor ccode

* remove duplicated test

* Operator add_n for row sparse ndarrays (apache#7244)

* Add add_n op for row-sparse ndarrays and identity FComputeEx

* Fix bug in square_sum

* Remove test_cast_storage_ex from gpu test since it's not implemented yet

* Fix according to the cr

Conflicts:
	src/operator/tensor/elemwise_sum.cc
	src/operator/tensor/elemwise_unary_op.cc
	tests/python/gpu/test_operator_gpu.py

resolve conflict

* GPU implementation of cast_storage (dense to rsp) (apache#7223)

* CastStorageDnsRsp GPU Implementation

* updating function doc and some variable types and names

* adding cuda_get_device_prop() util function

* added rand_shape function for n-dimensional tensors

* updated cast storage unit test

* added dns_to_rsp to cast storage benchmark script

* removing redundant unit test

* fix lint

* minor change in benchmark script

* fix lint

* correct function description

* change storage_type to stype

* changed scope of using namespaces

* changed variable types from index_t to dim_t

* resolve merge conflict in ndarray.load

* Improve StatefulOp/FCompute storage fallback (apache#134)

* test for fcomp fallback

add storage fallback test and optimize fallback logic

rename function, add comments

use std size()

* add autograd test with sparse inputs

* update sparse ndarray api (apache#139)

* support mx.nd.empty for sparse ndarray

Change SparseNDArray to BaseSparseNDArray

support mx.nd.array with BaseSparseNDArray inputs. Update documentation with explicit subclasses of NDArrays

Conflicts:
	python/mxnet/ndarray/__init__.py
	python/mxnet/ndarray/ndarray.py
	python/mxnet/ndarray/sparse_ndarray.py
	tests/python/unittest/test_sparse_ndarray.py

* fix print msg in test

* Handle ograd_stype='row_sparse' for square_sum backward (apache#143)

* Add one kernel for square_sum backward pass to take rsp ograd

* Add kNullOp and change to use type_assign in infer stype fallback

* Sparse retain improvement (apache#138)

* Add one more kernel for sparse retain

* Fix compile

* Change STORAGE_TYPE_ASSIGN_CHECK to type_assign for fallback

* Fix

* Add gpu compile

* ignoring variables in SimpleBind that is used on python's sparse branch for now. (apache#135)

* add bias term to fm test (apache#145)

* update ndarray.nd, remove `invoke` from excluded members (apache#137)

remove __weakref__ from SparseNDArray

add data indice to doc

revert dlpack update

revert mxdoc changes

move methods from BaseSparseNDarray to csrndarray and rwosparse ndarray

* support storage fallback with mutable inputs (apache#147)

* include mutatable inputs in storage fallback. refactor executor

add fallback test for rms prop and adam

fix lint

fix lint

fix test in optimizer

*  update according to comments

* fix unit tests

* fix gpu compilation err

* Code changes based on reviews (apache#144)

* code changes according to review comments

remove executor debug. add doc to optimizer

update sparse sgd test

add dtype option to rand_sparse_ndarray

* overhauled reqs for sparse operators

* patch FCompExFallback with mutable inputs. update test_optimizer with more fallback cases

* change executor debug macro to env var

* add comment

* update doc

* change ndarray.aux_shape() to return const reference

* remove todense to_rsp to_csr. replace with tostype

* replace manual calls to cast_storage with tostype

* disable gpu fallback test for optimizer

* fix lint

* add backward pass for cast_storage. refactor cast_storage test

* rand_sparse_ndarray bug fix

* fix cast_storage for gpu

* disable csr test for fp16

* update row sparse ndarray doc

* update doc

* small edits according to reviews (apache#151)

* fix lint (apache#152)

* add license to all new files in sparse brnach (apache#154)

* Allocate temp data on the fly for some casting operations (apache#149)

* fix utf8 encoding in sparse ndarray

* Extending the GPU dot operator (apache#7226)

* Added GPU DotCsrRspDnsImpl declaration and TODOs

* cleaning up function doc, variable types, and code-style

* minor bug fixes

* enable GPU dot(csr,rsp)=dns unit test

* extend sparse dot unit test

* adding GPU impl of DotCsrRspDns and its kernels

* add TODO

* changed variable types from index_t to dim_t

* fix function description

* added DotCsrRspRspImpl and its kernels (baseline, functionality)

* added DotCsrDnsRspImpl and its kernels (baseline, functionality); plus code documentation

* refactored dot benchmark

* optimized DotCsrTransDnsRsp GPU kernel

* change of dot impl interface to include OpContext, for temp storage

* removing __device__ flag from CPU kernels

* minor fixes and changing variable data types

* minor fixes based on code reviews

Conflicts:
	benchmark/python/sparse_op.py
	tests/python/gpu/test_operator_gpu.py
	tests/python/unittest/test_sparse_operator.py

* Add get_synthetic_dataset function to util (apache#146)

* Add get_synthetic_datasets

* Move to test_utils

* Remove _get_uniform_dataset

* Move validation to its own function

* Refactor the validation code for csr generation

* Make test_powerlaw a nested function

* Change SparseNDArray to CSRNDArray

* Merge with dtype specific changes in test_utils

* temporary fix for batch norm storage fallback (apache#156)

* support random_uniform/normal/gamma with row_sparse output (apache#155)

* add support for initilazer with rowsparse output

* add scalar assignment to row_sparse

* add setitem test to gpu

* Revert "add scalar assignment to row_sparse"

This reverts commit 8aef7a5.

* Revert "add setitem test to gpu"

This reverts commit 3b969ac.

* Square sum backward support one more case (apache#161)

* Add documentation for sparse ops (apache#148)

*  draft doc for sparse op

* add more stype doc for operators

* add doc for cast_storage

* see also cast_storage. remove base sparse ndarray. fix aux_types comemtn

* grammar / spelling fix

* A few fixes (apache#163)

* fix batch norm gpu kernel. register random operators on gpu

* register sparse random op on gpu, too

* Minor fixes sparse ops (apache#160)

* change CPU kernel inline directives, data types, and function doc

* update dot dtype switch to use 32 and 64bit floating point only

* use type_assign instead of STORAGE_TYPE_ASSIGN_CHECK

* added tensor_util-inl.cuh file for common tensor operator GPU kernels

* sparse Adam optimizer (apache#164)

*  add sparse adam

* register gpu op

* add comments

* cr comments

* kvstore.row_sparse_pull for GPU and end-to-end benchmark: CPU vs. multi-GPUs (apache#150)

* Add gpu support for BroadcastRowSparse

* Fix bugs

* Add benchmark script

* Increase output dim size

* Update weight on CPU using single GPU for sparse tensors

* More fix

* Optimize sparse_retain for special case

* Change row sparse pull locations

* Avoid sparse retain on cpu if possible

* Use acc for metric

* Fix misc

* fix bug in adam update (apache#167)

fix a bug in adam update

* change sparse example from regression to classification (apache#165)

* fix python import (apache#166)

* Add waitall to sparse_end2end.py (apache#169)

* Add waitall()

* Add dummy metric option

* Add header license

* Dot script changes (apache#159)

* Add get_synthetic_datasets

* Move to test_utils

* Remove _get_uniform_dataset

* Move validation to its own function

* Refactor the validation code for csr generation

* Make test_powerlaw a nested function

* Change SparseNDArray to CSRNDArray

* Refactoring changes to dot.py

* Fix mxnet test_utils changes

* Remove pdb statement

* Add distribution parameter

* Refactor benchmarking script

* Remove unused code

* Make style changes and remove unused code

* Change typo in comment

* Add transpose support

* Change typo

* 4 decimal points needed for density

* Add rsp support for real datasets

* Correct variable name mini_file_name

* Move wait_to_read outside if

* Seperate out scipy and mxnet logic in bench_dot

* Fix lhs_trans issue

* Move transpose outside measure_cost

* Compute transpose inside measure_cost

* Remove unused variables

* Transpose only if trans_lhs (apache#171)

* fix default val for distribution (apache#172)

* fix lint (apache#175)

* avoid cast_storage in dist-kvstore-server (apache#174)

* avoid cast_storage in dist-kvstore-server

* add stream arg to mshadow;;copy

* fix copy order

* Add sparse namespace to ndarray and symbol (apache#177)

* Register dot, cast_storage, and sparse_retain under mxnet.ndarray.sparse

* Add sparse to symbol namespace

* Delete commented code

* mv sparse_ndarray.py sparse.py

* Clean up

* Change docstring

* changes based on code reviews (apache#176)

* remove scipy dependency

* move kvstore checks to backned

* add const to lambda

* temp fix to ndarray.md (apache#178)

* Fix sparse namespace pylint (apache#179)

* add comments and error msg (apache#181)

* add clarification for csr (apache#182)

* add clarification for csr

* cr comments

* revert change in test util (apache#183)

* fix amalgamation (apache#184)

* fix lint
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