GitLab

* 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

* break nn modules into files

* break mxnet nn modules

* fix lint

* fix lint

* 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

* Update

* Update

* Update

* Update

* Update

* 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

* fix

* poke ci

* 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

* 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

* Test performance of udf

* trigger

* Fix doc

* Add nn.TGConv and example

* Update test code

* 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

* Minor opt for URRevel

* Delete test code

* Update code style and notes.

* Fix func name

* Update

* Update

* Update

* Update fix

* Update

* Update

* Refactor

* Update

* Update

* Update

* Update

* Update

* Update

* Fix style

* upd

* fig edgebatch edges

* add test

* trigger

* upd

* upd

* upd

* Add serialization

* fix gin

* Add serialization

* fix gin

* fix

* Prepare for metapath sampler

This file is just for reviewing metapath sampling algorithm (Python version).

* Delete metapath_sampler

* Prepare for metapath sampler

This file is just for reviewing metapath sampling algorithm (Python code).

* Add files via upload

* Create metapath2vec.md

* Add files via upload

* Delete data_handler.py

* Delete word2vec.py

* Delete word_train.py

* Add files via upload

Metapath2vec implementations. Metapath2vec++ needs negative sampler optimization.

* Delete shuffle_training.py

* Delete test.py

* Add files via upload

* Delete sampler.py

* Delete metapath_sampler.md

* Add files via upload

* Update and rename shuffle_training.py to metapath2vec.py

* Update reading_data.py

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Update metapath2vec.md

* Create label 2

* Delete label 2

* Create testing.md

* Add files via upload

* Create sample.md

* Add files via upload

* Delete sampler.py

* Add files via upload

* Delete googlescholar.8area.author.label.txt

* Delete googlescholar.8area.venue.label.txt

* Delete testing.md

* Delete id_author.txt

* Delete id_conf.txt

* Delete paper.txt

* Delete paper_author.txt

* Delete paper_conf.txt

* Delete sample.md

* Delete sampler.py

* Add files via upload

* Add files via upload

* Add files via upload

* Delete reading_data.py

* Add files via upload

* Add files via upload

* Delete metapath2vec.py

* Add files via upload

* Rename shuffle_training.py to metapath2vec.py

* Update metapath2vec.md

* Delete reading_data.py

* add comments and remov e commented codes

* Update conv.py

* Update conv.py

change `oto` to `to

* fix doc

* poke ci

* poke ci

* upd

* fix doc

* upd

* add title for all nn

* upd

* fix warning

* fix

* fix doc

* poke ci

* poke ci

* upd

* fix doc

* upd

* add title for all nn

* upd

* upd

* up

* upd

* upd

* fix.

* update

* [Conda] Update license

* doc fixes

* 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

* initial commit

* second commit

* another commit

* change docstring

* migrating to dgl.nn

* fixes

* docs

* lint

* multiple fixes

* doc

* renaming nearest neighbor graph

* initial commit

* second commit

* another commit

* change docstring

* migrating to dgl.nn

* fixes

* docs

* lint

* multiple fixes

* doc

* GAT

* Fix mistake

* Fix

* hotfix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Update

* Update

* Fix style

* Hotfix

* Hotfix

* Hotfix

* Fix

* Fix

* Update

* CI trial

* Update

* Update

* Update

* upd

* stable