1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181

use crate::optimizers::*;
use crate::cpu_params::*;
use crate::util::*;

use log::debug;

use std::collections::HashMap;

pub struct OptimizerAdaGrad {
    pub learn_rate: f32,
    pub theta: f32,
    pub g: HashMap<u64, HashMap<i32, VariantParam>>,
}

impl OptimizerAdaGrad {
    pub fn new(learn_rate: f32) -> Self {
        Self {
            learn_rate,
            theta: 1e-8,
            g: HashMap::new(),
        }
    }
}

impl Default for OptimizerAdaGrad {
    fn default() -> Self {
        Self {
            learn_rate: 1e-2,
            theta: 1e-6,
            g: HashMap::new(),
        }
    }
}

impl OptimizerAdaGrad {
    fn optimize_layer(
        buf: &mut [f32],
        buf_grad: &[f32],
        g: &mut [f32],
        learn_rate: &f32,
        theta: &f32,
    ) {
        for ((buf_v, buf_grad_v), g_v) in buf.iter_mut().zip(buf_grad.iter()).zip(g.iter_mut()) {
            if *buf_grad_v == 0.0 {
                continue;
            }

            *g_v += buf_grad_v.powf(2.0);
            *buf_v += (learn_rate / (*g_v + theta).sqrt()) * buf_grad_v;
        }
    }
}

impl Optimizer for OptimizerAdaGrad {
    fn optimize_params(&mut self, lp: &mut CpuParams, opt_prms: TrainableBufsIds) {
        if !self.g.contains_key(&lp.id) {
            self.g.insert(lp.id, HashMap::new());
            debug!("[opt_ada_grad] Inserted learn_params with id {}", lp.id);
        }

        for (buf_id, buf_grad_id) in opt_prms.0.iter().zip(opt_prms.1.iter()) {
            let buf_grad = lp.get_param(*buf_grad_id);
            let g_val = self.g.get_mut(&lp.id).unwrap();

            if !g_val.contains_key(buf_grad_id) {
                let zeroed_param = VariantParam::copy_zeroed_shape_from(&buf_grad);
                g_val.insert(*buf_grad_id, zeroed_param);
            }

            let g_m = g_val.get_mut(buf_grad_id).unwrap();

            match g_m {
                VariantParam::Array1(arr1) => {
                    let buf_grad_slice = buf_grad.get_arr_1d();
                    let buf_grad_slice = buf_grad_slice.borrow();
                    let buf_grad_slice = buf_grad_slice.as_slice().unwrap();

                    let buf_slice = lp.get_1d_buf(*buf_id);
                    let mut buf_slice = buf_slice.borrow_mut();
                    let buf_slice = buf_slice.as_slice_mut().unwrap();

                    let v_slice = arr1.as_slice_mut().unwrap();

                    OptimizerAdaGrad::optimize_layer(
                        buf_slice,
                        buf_grad_slice,
                        v_slice,
                        &self.learn_rate,
                        &self.theta,
                    );
                }
                VariantParam::Array2(arr2) => {
                    let buf_grad_slice = buf_grad.get_arr_2d();
                    let buf_grad_slice = buf_grad_slice.borrow();
                    let buf_grad_slice = buf_grad_slice.as_slice().unwrap();

                    let buf_slice = lp.get_2d_buf(*buf_id);
                    let mut buf_slice = buf_slice.borrow_mut();
                    let buf_slice = buf_slice.as_slice_mut().unwrap();

                    let v_slice = arr2.as_slice_mut().unwrap();

                    OptimizerAdaGrad::optimize_layer(
                        buf_slice,
                        buf_grad_slice,
                        v_slice,
                        &self.learn_rate,
                        &self.theta,
                    );
                }
            }
        }

        // match g_m {
        //     VariantParam::Array1(arr1) => {
        //         let buf_grad_slice = lp.get_1d_buf(*buf_grad_id);
        //         let buf_grad_slice = buf_grad_slice.borrow();
        //         let buf_grad_slice = buf_grad_slice.as_slice().unwrap();

        //         let buf_slice = lp.get_1d_buf(*buf_id);
        //         let mut buf_slice = buf_slice.borrow_mut();
        //         let buf_slice = buf_slice.as_slice_mut().unwrap();

        //         let g_slice = arr1.as_slice_mut().unwrap();

        //         OptimizerAdaGrad::optimize_layer(
        //             buf_slice,
        //             buf_grad_slice,
        //             g_slice,
        //             &self.learn_rate,
        //             &self.theta,
        //         );
        //     },
        //     VariantParam::Array2(mut arr2) => {
        //         let buf_grad_slice = lp.get_2d_buf(*buf_grad_id);
        //         let buf_grad_slice = buf_grad_slice.borrow();
        //         let buf_grad_slice = buf_grad_slice.as_slice().unwrap();

        //         let mut buf_slice = lp.get_2d_buf(*buf_id);
        //         let mut buf_slice = buf_slice.borrow_mut();
        //         let mut buf_slice = buf_slice.as_slice_mut().unwrap();

        //         let g_slice = arr2.as_slice_mut().unwrap();

        //         OptimizerAdaGrad::optimize_layer(
        //             buf_slice,
        //             buf_grad_slice,
        //             g_slice,
        //             &self.learn_rate,
        //             &self.theta,
        //         );
        //     }
        // }
    }
}

impl WithParams for OptimizerAdaGrad {
    fn cfg(&self) -> HashMap<String, Variant> {
        let mut cfg_params = HashMap::new();

        cfg_params.insert("type".to_string(), Variant::String("adagrad".to_string()));
        cfg_params.insert("learning_rate".to_string(), Variant::Float(self.learn_rate));
        cfg_params.insert("theta".to_string(), Variant::Float(self.theta));

        cfg_params
    }

    fn set_cfg(&mut self, args: &HashMap<String, Variant>) {
        if args.contains_key("learning_rate") {
            if let Variant::Float(v) = args.get("learning_rate").unwrap() {
                self.learn_rate = *v;
            }
        }

        if args.contains_key("theta") {
            if let Variant::Float(v) = args.get("theta").unwrap() {
                self.theta = *v;
            }
        }
    }
}