lihaofd opened a new pull request #14713: MKLDNN RNN Inference Integration(fp32 LSTM and vRNN with tanh and relu) URL: https://github.com/apache/incubator-mxnet/pull/14713 ## Description ## In this PR, it integrated MKLDNN RNN Inference Integration(fp32 lstm and vRNN with tanh and relu) @pengzhao-intel, @TaoLv , @ciyongch ## Feature changes ## ### New features ### - Single layer/Multiple layer and unidirectional/bidirectional inference by mkldnn lstm and vrnn with tanh and relu) ### Unit-test changes ### - Using existing test case in test_operator.py to check consistency with original RNN Cell implementation. ### Performance ### We have tested performance of FusedRNN (USE_MKLDNN = 0 and 1) on our local Skylake-8180 with 1 Sockets and 28 cores. Use MKL as blas lib in this performance test. Test input size is from DS2 default parameters(seq_length = 300, batch_size = 20, input_size = 800, hidden_size = 800). Layer=1 bidirectional = False | API | MKLDNN = 0 (sample/sec) | MKLDNN = 1 (sample/sec) | speedup | -------- | :-----: | :----: | :----: | | FusedLSTM | 255 | 637 | 2.5x | | FusedvRNN with tanh | 989 | 1449 | 1.47x | | FusedvRNN with relu | 1296 | 1442 | 1.11x | Layer=5 bidirectional = True | API | MKLDNN = 0 (sample/sec) | MKLDNN = 1 (sample/sec) | speedup | -------- | :-----: | :----: | :----: | | FusedLSTM | 26 | 56 | 2.15x | | FusedvRNN with tanh | 83 | 157 | 1.9x | | FusedvRNN with relu | 104 | 152 | 1.46x | ## Checklist ## - [X] Passed code style checking (make lint). - [X] All changes have test coverage. - [ ] Code is well-documented.
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