GitLab

* 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