cbalint13 commented on PR #14468:
URL: https://github.com/apache/tvm/pull/14468#issuecomment-1499637329
@tqchen , @junrushao
In continuation of previous comment, I also attach here some test result.
Comparative test confirms that ```rank-binary``` (binarized only at eval
step) behaves identically with ```rank``` (original):
loss_type="rank-binary" (xgboost-2.0.0-dev 20230403 git hash 15e073ca)
[Task 1/54] (conv2d_nchw_spatial_pack.mali) {17.75 GFLOPS / #4912
records} SKIP
[Task 2/54] (conv2d_nchw_spatial_pack.mali) {40.74 GFLOPS / #1040
records} SKIP
[Task 3/54] (conv2d_nchw_spatial_pack.mali) {19.63 GFLOPS / #2032
records} SKIP
loss_type="rank" (xgboost-1.7.5 20230328 git hash 21d95f3d)
[Task 1/54] (conv2d_nchw_spatial_pack.mali) {11.71 GFLOPS / #1680
records} SKIP
[Task 2/54] (conv2d_nchw_spatial_pack.mali) {26.17 GFLOPS / #1024
records} SKIP
[Task 3/54] (conv2d_nchw_spatial_pack.mali) {13.15 GFLOPS / #1040
records} SKIP
Note:
* In the case of rank-binary there was more steps (bit prologed / see
amount of records) hence bit better results.
* The tuned network was first three layers of yolov8s using float16 for
half a day on a rk3399 board (nanopc-t4).
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