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0a56d65223
* 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
61 lines
724 B
ReStructuredText
61 lines
724 B
ReStructuredText
.. _apifunction:
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Builtin functions
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=================
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.. automodule:: dgl.function
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Message functions
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-----------------
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.. autosummary::
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:toctree: ../../generated/
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copy_src
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copy_edge
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src_mul_edge
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copy_u
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copy_e
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u_add_v
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u_sub_v
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u_mul_v
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u_div_v
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u_add_e
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u_sub_e
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u_mul_e
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u_div_e
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v_add_u
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v_sub_u
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v_mul_u
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v_div_u
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v_add_e
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v_sub_e
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v_mul_e
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v_div_e
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e_add_u
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e_sub_u
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e_mul_u
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e_div_u
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e_add_v
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e_sub_v
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e_mul_v
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e_div_v
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u_dot_v
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u_dot_e
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v_dot_e
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v_dot_u
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e_dot_u
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e_dot_v
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Reduce functions
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----------------
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.. autosummary::
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:toctree: ../../generated/
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sum
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max
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min
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prod
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mean
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