GitLab

* upd

* upd

* upd

* relax

* relax too

* upd

* add sparse embedding.

* fix

* add test.

* man fixes.

* many fixes

* fix sparse emb.

* fix.

* fix lint.

* fix lint.

* fix kvstore.

* expose DistTensor.

* test sparse embeddings.

* add attach_grad to the backends.

* remove part_id

* fix.

* move backward computation.

* move more computation to backend.

* fix a bug when applying learning rate.

* fix a few things.

* fix a few things.

* add docstring

* fix.

* apply no_grad.

* fix tests.

* fix for other frameworks.

* add examples in docstring.

* udp

* simplify

* sddmm dot cpu

* upd

* format

* upd

* compatible with MJ's PR

* lint

* upd

* upd

* upd

* python end

* upd

* upd

* lint

* lint

* upd

* upd

* upd

* upd

* upd

* lint

* fix mxnet

* upd

* lint

* use minjie's ptr

* macro

* upd

* reorg

* lint

* fix corner cases

* upd

* enrich cpu docs

* upd

* upd

* lint

* lint

* pylint

* sx review

* improve docstring

* python doc

* upd

* restructure

* lint

* upd test

* upd

* pylint

* fix corner cases and 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

* 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

* 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

* fix gat code to use latest edge softmax module

* avoid transpose

* update README

* use edge_softmax op

* mxnet edge softmax op

* mxnet gat

* update README

* fix unittest

* fix ci

* fix mxnet nn test; relax criteria for prod reducer

* add graph_to

* use backend copy_to

* add test

* fix test

* framework agnostic to() test

* disable pylint complaint

* add examples

* fix docstring

* formatting

* Format

* Update test_to_device.py

* [Kernel] Minigun integration and fused kernel support (#519)

* kernel interface

* add minigun

* Add cuda build

* functors

* working on binary elewise

* binary reduce

* change kernel interface

* WIP

* wip

* fix minigun

* compile

* binary reduce kernels

* compile

* simple test passed

* more reducers

* fix thrust problem

* fix cmake

* fix cmake; add proper guard for atomic

* WIP: bcast

* WIP

* bcast kernels

* update to new minigun pass-by-value practice

* broadcasting dim

* add copy src and copy edge

* fix linking

* fix none array problem

* fix copy edge

* add device_type and device_id to backend operator

* cache csr adj, remove cache for adjmat and incmat

* custom ops in backend and pytorch impl

* change dgl-mg kernel python interface

* add id_mapping var

* clean up plus v2e spmv schedule

* spmv schedule & clean up fall back

* symbolic message and reduce func...

* add rtfd

* rrr

* update

* change env

* temp fix

* update

* fix

* fix

* add

* conf

* Move file_pattern from Makefile to conf.py

* remove yml

* fix

* fix

* fix

* fix

* remove yml

* remove yml

* add doc docker

* add dgl install script

* change name

* change dockerfile

* fix

* name

* add

* fix

* fix

* fix

* fix

* fix docker

* delete sphinx.py for doc-build backend

* Add softmax to test backend

* Add group apply function and tests

* Delete unnecessary file

* Update comments and test

* Fix lint

* remove unused bucketing code

* group apply edge bucketing code

* gen degree bucket schedule for group apply edge

* schedule and graph code

* fix compiling

* fix

* fix lint

* naming

* harder test case

* fix comments

* more comments

* tweak function name

* test basics

* batched graph & filter, mxnet filter fix

* frame and function; bugfix

* test graph adj and inc matrices

* fixing start = 0 for mxnet

* test index

* inplace update & line graph

* multi send recv

* more tests

* oops

* more tests

* removing old test files; readonly graphs for mxnet still kept

* modifying test scripts

* adding a placeholder for pytorch to reserve directory

* torch 0.4.1 compat fixes

* moving backend out of compute to avoid nose detection

* tests guide

* mx sparse-to-dense/sparse-to-numpy is buggy

* oops

* contribution guide for unit tests

* printing incmat

* printing dlpack

* small push

* typo

* fixing duplicate entries that causes undefined behavior

* move equal comparison to backend