* PPIDataset
* Revert "PPIDataset"
This reverts commit 264bd0c960.
* data pipeline user guide
* remove chapter numbers
* Update data.rst
* image in dataset userguide
* update links using ref
* modify the link of save_graphs and load_graphs in dataset user guide
* move image to s3 server.
* fix links and ref
* PPIDataset
* Revert "PPIDataset"
This reverts commit 264bd0c960.
* data pipeline user guide
* remove chapter numbers
* Update data.rst
* image in dataset userguide
* update links using ref
* modify the link of save_graphs and load_graphs in dataset user guide
* go through gcn, relgcn
* fix tagconv formula
* fix doc in sageconv
* fix sgconv doc
* replace hat with tilde
* more comments on gmmconv
* fix agnnconv chebconv doc
* modify nnconv doc
* remove &
* add nn conv examples
* Rebase master
* More merge conflicts
* check homo
* add back self loop for some convs, check homo in tranform
* add example for denseconv
* add example and doc for dotgat and cfconv
* check in-degree for graphconv
* add language fix
* gconv address all comments
* another round of change based on api template
* change agnn
* go through agnn, appnp, atomic, cf, cheb, dense, gat, sage modules
* finish pytorch part of nn conv
* mxnet graphconv done
* tensorflow graphconv works
* add new modules into doc
* add comments to not split code
* refine doc
* resr
* more comments
* more fix
* finish conv and dense conv part api
* pylint fix
* fix pylink
* fix pylint
* more fix
* fix
* fix test fail because zere in degree
* fix test fail
* sage is not update for mxnet tf
Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-81.us-east-2.compute.internal>
* clean commit
* oops forgot the most important files
* use einsum
* copy feature from frontier to block
* Revert "copy feature from frontier to block"
This reverts commit 5224ec963e.
* temp fix
* unit test
* fix
* revert jtnn
* lint
* fix win64
* docstring fixes and doc indexing
* revert einsum in sparse bidecoder
* fix some examples
* lint
* fix due to some tediousness in remove_edges
* addresses comments
* fix
* more jtnn fixes
* 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
* 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
* 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
