"...git@developer.sourcefind.cn:renzhc/diffusers_dcu.git" did not exist on "1a04812439c82a9dd318d14a800bb04e84dbbfc0"
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xiang song(charlie.song) authored
* 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 docstring0a56d652
