* 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
* 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
* 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
* 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 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+.
* nonuniform sampler
* unit test
* test on out neighbors
* error checks
* lint
* fix
* clarification
* use macro switcher
* use empty array for uniform sampling
* oops
* Revert "oops"
This reverts commit a11f9ae707aaeb67fb5921c887a17d3711d5b04a.
* Revert "use empty array for uniform sampling"
This reverts commit 8526ce4cade89f2c1b09a08aca8830375ebafb31.
* re-reverting
* use a method
* rng refactor
* fix bugs
* unit test
* remove setsize
* lint
* fix test
* use explicit instantiation instead of inlining
* stricter test
* use tvm solution
* moved python interface to dgl.random
* lint
* address comments
* make getthreadid an inline function
* 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
* address comments
Quan Gan