rnn/lstm/gru dynamic quantization

[nihui]

• rnn
• rnn-arm
• lstm
• lstm-arm
• lstm-x86
• gru
• gru-arm
• fix over load s8
• coverage
• doc
• speed test
• rnn aq
• rnn-arm aq
• lstm aq
• lstm-arm aq
• lstm-x86 aq
• gru aq
• gru-arm aq

[nihui]

import torch
import torch.nn as nn
import torch.nn.functional as F
import pnnx

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

        self.rnn = nn.RNN(input_size=256, hidden_size=256, num_layers=30)
        self.lstm = nn.LSTM(input_size=256, hidden_size=256, num_layers=30)
        self.gru = nn.GRU(input_size=256, hidden_size=256, num_layers=30)

    def forward(self, x):
        out0, _ = self.rnn(x)
        out1, _ = self.lstm(x)
        out2, _ = self.gru(x)
        return out0, out1, out2

net = Model().half().float()
net.eval()

torch.manual_seed(0)
x = torch.rand(300, 1, 256)

pnnx.export(net, "rnn.pt", x)

ncnn2int8 rnn.ncnn.param rnn.ncnn.bin rnn-int8.ncnn.param rnn-int8.ncnn.bin /dev/null

rnn/rnn-int8.bin	fp16	int8
模型体积	60.1M	30.6M

qcom855plus MAE	fp32	fp16	int8
30层rnn	0	2.29E-08	7.31E-08
30层lstm	0	4.39E-09	5.54E-09
30层gru	0	6.75E-09	1.96E-08

qcom855plus 单线程耗时	fp32	fp16	int8
30层rnn	45.16	24.81	19.87
30层lstm	256.51	121.99	60.7
30层gru	167.52	94.68	46.29

i5-12400 单线程跑30层lstm-int8模型耗时
naive(sse2)	95.24
sse2	87.02
avx	64.85
avx2	42.22
avxvnni	23.24
avx512	27.95
avx512vnni	15.8

[nihui]

imx6d 单线程耗时	fp32	int8
30层rnn	1392.22	504.83
30层lstm	6063.91	1833.46
30层gru	4357.59	1300.93