Support Mod in elementwise system

[JamesLim-sy]

PR types

Performance optimization

PR changes

OPs

Describe

1. Basing on new elementwise + broadcast system support binary functors below : Mod

2. The performance variation is recorded in the statics table below:
   

3. As can be seen from the table, in most cases, the mod operation costs less CUDA operation time after adopting new elementwise + broadcast system. However, the old broadcast branch work better in the 2nd test case, the old broadcast branch consists of perf-optimized branch and common broadcast branch, the former one works well when the quantity of input tensor data is not big enough and the input tensor`s dim meet the special demands. Apparently, 2nd test case perfectly meets the that demands and data quantity is relatively small, therefore, it beats the new elementwise + broadcast op in 2nd case. But introducing of this branch dose make the code less compactness and hard to maintain, and working area of this is not big enough. Furthermore, the elementwise op in paddle is suggested to dealing with NN whose data quantity is often large, so i suggest Approval of 2nd test case and adopt the new elementwise + broadcast system in mod op.

[paddle-bot-old]

Thanks for your contribution!
Please wait for the result of CI firstly. See Paddle CI Manual for details.

[paddle-bot-old]

Sorry to inform you that 8b06d3c's CIs have passed for more than 7 days. To prevent PR conflicts, you need to re-run all CIs manually.

[Xreki]

2021-06-22 20:01:35 [check_op_benchmark_result.py:80] [INFO] ------ OP: remainder_2 (forward) ------
2021-06-22 20:01:35 [check_op_benchmark_result.py:82] [INFO] GPU time change: 7.25394% (develop: 0.0223620 -> PR: 0.0239841)
2021-06-22 20:01:35 [check_op_benchmark_result.py:84] [INFO] Total time change: 1.92571% (develop: 0.0402996 -> PR: 0.0410757)
2021-06-22 20:01:35 [check_op_benchmark_result.py:85] [INFO] backward: False
2021-06-22 20:01:35 [check_op_benchmark_result.py:86] [INFO] parameters:
2021-06-22 20:01:35 [check_op_benchmark_result.py:88] [INFO] x (Variable) - dtype: float32, shape: [16, 2048, 7, 7]
2021-06-22 20:01:35 [check_op_benchmark_result.py:88] [INFO] y (Variable) - dtype: float32, shape: [16, 2048]
2021-06-22 20:01:35 [check_op_benchmark_result.py:88] [INFO] axis (int): 0
2021-06-22 20:01:35 [check_op_benchmark_result.py:153] [ERROR] Check speed result with case "remainder_2 (forward)" failed.

CI中该配置性能下降7%，但其他配置均有性能提升，故可以先合入该PR。