[Feature] x_dot_x builtin kernel support #831
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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+.
…ide.
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
1. clear all agnostic related code in dgl.nn 2. make test_graph_conv agnostic to cpu/gpu
work on both CPU and GPU.
Add base control flow code.
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
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MXNet CI test may have some problem due to adapting to new version. I'll try to fix this tomorrow. |
…forward and backward
yzh119
reviewed
Sep 5, 2019
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@jermainewang , I've verified the correctness with STT. The GPU memory footprint is about the same but builtin function is 2x slower than my custom kernels(node parallel strategy). |
classicsong
changed the title
Backward is still slow for dot
| static DGLDEVICE DGLINLINE DType Call(DType *lhs, DType *rhs, int64_t len) { | ||
| DType out = 0; | ||
| // simple vector dot vector | ||
| #pragma unroll |
There was a problem hiding this comment.
There is already a pragma unroll in the graph level in minigun. Nested pragma unroll usually does not give benefit. Consider remove this.
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Description
Adding support for u_dot_v, u_dot_e, v_dot_e, v_dot_u, e_dot_u and e_dot_v as builtin kernel.
Implementation will base on current binary_reduce structure.
#659
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Checklist
Please feel free to remove inapplicable items for your PR.
or have been fixed to be compatible with this change
Changes