GitLab

* Performance improvment for distributed kvstore

* update

* update

* upd

* damn it

* fuck

* fuck pylint

* fudge

* remove some comments about MXNet

* upd

* upd

* damn it

* damn it

* fuck

* fuck

* upd

* upd

* pylint bastard

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* [BUG] Fix #717

* fix mxnet test

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test for mxnet TAGCov

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* reproduce the bug

* Fix concurrency bug reported at #755.
Also make test_shared_mem_store.py more deterministic.

* Update test_shared_mem_store.py

* Update dmlc/core

* Update Knowledge Graph CI with new Docker image

* Remove unused line_profierx

* Poke Jenkins

* Update test with exit code check and simplify docker

* Update Jenkinsfile to make app test a standalone stage

* Update kg_test

* Update Jenkinsfile

* Make some KG test parallel

* Update

* KG MXNet does not support ComplEx

* Update Jenkinsfile

* Update Jenkins file

* Change torch-1.2 to torch-1.2-cu92

* ci

* Update ubuntu_install_mxnet_cpu.sh

* Update ubuntu_install_mxnet_gpu.sh

* We only need to test train and eval script.
Delete some test code

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test for mxnet TAGCov

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* reproduce the bug

* Fix concurrency bug reported at #755.
Also make test_shared_mem_store.py more deterministic.

* Update test_shared_mem_store.py

* Update dmlc/core

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* style fixes & undefined name fix

* transpose=False in test

* check the number of columns.

* add test.

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test for mxnet TAGCov

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* init version.

* change default value of regularization.

* avoid specifying adversarial_temperature

* use default eval_interval.

* remove original model.

* remove optimizer.

* set default value of num_proc

* set default value of log_interval.

* don't need to set neg_sample_size_valid.

* remove unused code.

* use uni_weight by default.

* unify model.

* rename model.

* remove unnecessary data sampler.

* remove the code for checkpoint.

* fix eval.

* raise exception in invalid arguments.

* remove RowAdagrad.

* remove unsupported score function for now.

* Fix bugs of kg
Update README

* Update Readme for mxnet distmult

* Update README.md

* Update README.md

* revert changes on dmlc

* add tests.

* update CI.

* add tests script.

* reorder tests in CI.

* measure performance.

* add results on wn18

* remove some code.

* rename the training script.

* new results on TransE.

* remove --train.

* add format.

* fix.

* use EdgeSubgraph.

* create PBGNegEdgeSubgraph to simplify the code.

* fix test

* fix CI.

* run nose for unit tests.

* remove unused code in dataset.

* change argument to save embeddings.

* test training and eval scripts in CI.

* check Pytorch version.

* fix a minor problem in config.

* fix a minor bug.

* fix readme.

* Update README.md

* Update README.md

* Update README.md

* reduce test.

* increase timeout for unittest.

* edge sampler return head nodes and tail nodes.

* edge subgraph.

* use VecToIdArray

* example.

* fix compile error.

* [Hetero] Create heterograph from dictionary of edge lists

* rename

* address comments

* sort

* sort in parallel.

* fix a bug in sorting adj

* rename.

* add more comments.

* accelerate GetData

* fix tests.

* avoid sorting multiple times.

* add test.

* change back.

* sort.

* add sort_csr.

* Fix a bug.

* fix.

* revert modifcation.

* rename

* speed up EdgeIds.

* Add HAN

* Fix

* WIP; load raw ACM dataset

* DGL's own preprocessing with metapath coalescer

* various fixes

* comparison against simple logistic regression

* rename

* fix test

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix

* fix

* fix lint

* fixlint

* metapath sampler

* lint & fixes

* lint x2

* lint x3

* fix windows

* remove max_cycle argument

* add todo note

* optimizer for sampling all negative edges.

* allow to disable checking false negative.

* fix lint.

* fix docstring.

* fix for comment.

* add comments.

* find the existence of negative edges.

* add comment.

* fix test.

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix

* WIP. remove graph arg in NodeBatch and EdgeBatch

* refactor: use graph adapter for scheduler

* WIP: recv

* draft impl

* stuck at bipartite

* bipartite->unitgraph; support dsttype == srctype

* pass test_query

* pass test_query

* pass test_view

* test apply

* pass udf message passing tests

* pass quan's test using builtins

* WIP: wildcard slicing

* new construct methods

* broken

* good

* add stack cross reducer

* fix bug; fix mx

* fix bug in csrmm2 when the CSR is not square

* lint

* removed FlattenedHeteroGraph class

* WIP

* prop nodes, prop edges, filter nodes/edges

* add DGLGraph tests to heterograph. Fix several bugs

* finish nx<->hetero graph conversion

* create bipartite from nx

* more spec on hetero/homo conversion

* silly fixes

* check node and edge types

* repr

* to api

* adj APIs

* inc

* fix some lints and bugs

* fix some lints

* hetero/homo conversion

* fix flatten test

* more spec in hetero_from_homo and test

* flatten using concat names

* WIP: creators

* rewrite hetero_from_homo in a more efficient way

* remove useless variables

* fix lint

* subgraphs and typed subgraphs

* lint & removed heterosubgraph class

* lint x2

* disable heterograph mutation test

* docstring update

* add edge id for nx graph test

* fix mx unittests

* fix bug

* try fix

* fix unittest when cross_reducer is stack

* fix ci

* fix nx bipartite bug; docstring

* fix scipy creation bug

* lint

* fix bug when converting heterograph from homograph

* fix bug in hetero_from_homo about ntype order

* trailing white

* docstring fixes for add_foo and data views

* docstring for relation slice

* to_hetero and to_homo with feature support

* lint

* lint

* DGLGraph compatibility

* incidence matrix & docstring fixes

* example string fixes

* feature in hetero_from_relations

* deduplication of edge types in to_hetero

* fix lint

* fix

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Start implementing masked-mm kernel.

Add base control flow code.

* Add masked dot declare

* Update func/variable name

* Skeleton compile OK

* Update Implement. Unify BinaryDot with BinaryReduce

* New Impl of x_dot_x, reuse binary reduce template

* Compile OK.

TODO:
1. make sure x_add_x, x_sub_x, x_mul_x, x_div_x work
2. let x_dot_x work
3. make sure backward of x_add_x, x_sub_x, x_mul_x, x_div_x work
4. let x_dot_x backward work

* Fix code style

* Now we can pass the tests/compute/test_kernel.py for add/sub/mul/div forward and backward

* Fix mxnet test code

* Add u_dot_v, u_dot_e, v_dot_e unitest.

* Update doc

* Now also support v_dot_u, e_dot_u, e_dot_v

* Add unroll for some loop

* Add some Opt for cuda backward of dot builtin.

Backward is still slow for dot

* Apply UnravelRavel opt for broadcast backward

* update docstring

* PBG negative edge sampler.

* add a positive edge to make it regular, handle last batch.

* exclude all positive edges in the parent graph.

* just uniformly sample negative nodes.

* fix lint.

* shuffle one-side nodes of positive edges.

* just uniformly sample negative nodes.

* change the data type.

* address comment.

* remove commented code.

* update

* speedup

* add some comments

* Add serialization

* add serialization

* add serialization

* lalalalalalalala

* lalalalalalalala

* serialize

* serialize

* nnn

* WIP: import tvm runtime node system

* WIP: object system

* containers

* tested basic container composition

* tested custom object

* tmp

* fix setattr bug

* tested object container return

* fix lint

* some comments about get/set state

* fix lint

* fix lint

* update cython

* fix cython

* ffi doc

* fix doc

* WIP: using object system for graph

* c++ side refactoring done; compiled

* remove stale apis

* fix bug in DGLGraphCreate; passed test_graph.py

* fix bug in python modify; passed utest for pytorch/cpu

* fix lint

* Add serialization

* Add serialization

* fix

* fix typo

* serialize with new ffi

* commit

* commit

* commit

* save

* save

* save

* save

* commit

* clean

* Delete tt2.py

* fix lint

* Add serialization

* fix lint 2

* fix lint

* fix lint

* fix lint

* fix lint

* Fix Lint

* Add serialization

* Change to Macro

* fix

* fix

* fix bugs

* refactor

* refactor

* updating dmlc-core to include force flag

* trying tempfile

* delete leaked pointer

* Fix assert

* fix assert

* add comment and test case

* add graph labels

* add load labels

* lint

* lint

* add graph labels

* lint

*  fix windows

* fix

* update dmlc-core to latest

* fix

* fix camel naming

* upd

* add test

* fix

* upd

* merge

* hotfix

* upd

* fix

* upd

* up

* upd

* upd

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* upd

* stable

* refactor

* upd mpnn

* gat

* upd

* upd sage

* upd

* upd

* upd

* upd

* upd

* add gmmconv

* upd ggnn

* upd

* upd

* upd

* upd

* add citation examples

* add README

* fix cheb

* improve doc

* formula

* upd

* trigger

* lint

* lint

* upd

* add test for transform

* add test

* check

* upd

* improve doc

* shape check

* upd

* densechebconv, currently not correct (?)

* fix cheb

* fix

* upd

* upd sgc-reddit

* upd

* trigger

* upd

* upd

* upd

* upd

* upd

* passed test

* add note

* upd

* trigger

* slight change

* upd

* upd

* trigger

* fix

* simplify

* upd

* upd

* fudge

* upd

* trigger

* test partial

* upd

* trigger

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* rgcn module

* support id input

* WIP: model codes

* use faster index select

* dropout

* self loop

* WIP: link prediction

* fix lint

* WIP: docs

* docstring

* docstring

* merge two child classes

* mxnet rgcn module

* fix lint

* fix lint

* fix rename bug

* add uniform edge sampler

* fix fn name

* docstring

* fix mxnet rgcn module

* fix mx rgcn

* enable test on cuda

* moving heterograph index to another file

* node view

* python interfaces

* heterograph init

* bug fixes

* docstring for readonly

* more docstring

* unit tests & lint

* oops

* oops x2

* removed node/edge addition

* addressed comments

* lint

* rw on frames with one node/edge type

* homograph with underlying heterograph demo

* view is not necessary

* bugfix

* replace

* scheduler, builtins not working yet

* moving bipartite.h to header

* moving back bipartite to bipartite.h

* oops

* asbits and copyto for bipartite

* tested update_all and send_and_recv

* lightweight node & edge type retrieval

* oops

* sorry

* removing obsolete code

* oops

* lint

* various bug fixes & more tests

* UDF tests

* multiple type number_of_nodes and number_of_edges

* docstring fixes

* more tests

* going for dict in initialization

* lint

* updated api as per discussions

* lint

* bug

* bugfix

* moving back bipartite impl to cc

* note on views

* fix

* add edge sampler.

* add test and run.

* add negative sampling.

* remap the edge subgraph vid.

* negative graph excludes edges of positive edgs.

* remove print.

* avoid sampling NodeFlow when expand_factor or num_hops is 0.

* fix a bug when excluding nodes in negative graph.

* support multigraph.

* exclude positive edges.

* fix memory leak.

* return subgraph object directly.

* fix many problems.

* add comments.

* address comments

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand