[MXNET-107] Add Fused Vanilla RNN and dropout for CPU

[lihaofd]

Description

In this PR, it creates Fused Vanilla RNN(tanh/relu) operator and dropout of GRU/LSTM/vRNN for CPU.
@pengzhao-intel, @TaoLv

Feature changes

New features

• Single layer/Multiple layer and unidirectional/bidirectional Vanilla RNN(tanh/relu), including both forward and backward computation.
• Support dropout of GRU/LSTM/vRNN

Unit-test changes

• Create new testcase in tests/python/unittests/test_operator.py.
• update testcase in example/rnn/bucketing/cudnn_rnn_bucketing.py
• Check consistency with original RNNCell implementation.

Performance

We have tested performance of FusedRNN and NonFused RNNCell on our local Skylake-8180 with 2 Sockets and 56 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	Inference time(fwd, samples/sec)	Training time(fwd + bwd, samples/sec)
rnn.RNNCell - NoFusedRNN(Tanh, CPU)	492.61	198.02
this PR - FusedRNN(Tanh, CPU)	952.38	318.98
speedup	1.93x	1.61x

API	Inference time(fwd, samples/sec)	Training time(fwd + bwd, samples/sec)
rnn.RNNCell - NoFusedRNN(Relu, CPU)	277.78	104.17
this PR - FusedRNN(Relu, CPU)	740.74	177
speedup	2.67x	1.7x

Layer=5 bidirectional = True

API	Inference time(fwd, samples/sec)	Training time(fwd + bwd, samples/sec)
rnn.RNNCell - NoFusedRNN(Tanh, CPU)	38.91	22.73
rnn.RNNCell (Tanh, cuda)	47.85	26.95
rnn.RNNCell (Tanh, cudnn)	208.33	81.63
this PR - FusedRNN(Tanh, CPU)	104.17	34.01
speedup -this PR/RNNCell (Tanh, CPU)	267.7%	149.7%
speedup -this PR/RNNCell (Tanh, cuda)	217.7%	126.2%
speedup -this PR/RNNCell (Tanh, cudnn)	50%	41.7%

API	Inference time(fwd, samples/sec)	Training time(fwd + bwd, samples/sec)
rnn.RNNCell - NoFusedRNN(Relu, CPU)	40.73	22.6
rnn.RNNCell (Relu, cuda)	52.91	26.81
rnn.RNNCell (Relu, cudnn)	206.83	82.64
this PR - FusedRNN(Relu, CPU)	134.23	35.97
speedup -this PR/RNNCell (Relu, CPU)	329.5%	159.2%
speedup -this PR/RNNCell (Relu, cuda)	253.7%	134.2%
speedup -this PR/RNNCell (Relu, cudnn)	64.9%	43.5%

Convergency Curve

We have tested Convergency of FusedGRU/LSTM(dropout = 0.5) on our CPU-Skylake-8180 with 2 Sockets and 56 cores and GPU-P100 by using example/rnn/bucketing/cudnn_rnn_bucketing.py
Test input size is layer = 3, batch_size = 32, num-embed = 800, num-hidden = 800, num-epochs 20

@szha: resolves #10870, #10872

[TaoLv]

Please remove [WIP] from the title and add the JIRA number to it. https://issues.apache.org/jira/browse/MXNET-107