rumus 0.1.2

A native-Rust deep learning framework with explicit memory safety and hardware acceleration
Documentation 
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

//! Adam optimizer (Kingma & Ba, 2014).
//!
//! When parameters are GPU-resident, dispatches a fused WGSL kernel that
//! updates moments and weights in a single pass — zero D2H transfers.

use std::collections::HashMap;

use crate::autograd::{AutogradError, GradientStore};
use crate::nn::Parameter;
use crate::optim::Optimizer;
use crate::tensor::{ParamId, Tensor};

pub struct Adam {
    params: Vec<Parameter>,
    lr: f32,
    beta1: f32,
    beta2: f32,
    epsilon: f32,
    step_count: usize,
    first_moment: HashMap<ParamId, Tensor>,
    second_moment: HashMap<ParamId, Tensor>,
}

impl Adam {
    pub fn new(params: Vec<Parameter>, lr: f32) -> Self {
        Self::with_betas(params, lr, 0.9, 0.999, 1e-8)
    }

    pub fn with_betas(
        params: Vec<Parameter>,
        lr: f32,
        beta1: f32,
        beta2: f32,
        epsilon: f32,
    ) -> Self {
        Self {
            params,
            lr,
            beta1,
            beta2,
            epsilon,
            step_count: 0,
            first_moment: HashMap::new(),
            second_moment: HashMap::new(),
        }
    }
}

impl Optimizer for Adam {
    fn step(&mut self, grads: &mut GradientStore) -> Result<(), AutogradError> {
        self.step_count += 1;
        let t = self.step_count;
        let bc1 = 1.0 - self.beta1.powi(t as i32);
        let bc2 = 1.0 - self.beta2.powi(t as i32);

        for param in &self.params {
            let grad = grads
                .remove(param.grad_id())
                .ok_or(AutogradError::MissingGrad {
                    grad_id: param.grad_id(),
                })?;
            let numel = param.tensor.numel();
            let shape = param.tensor.shape().to_vec();

            let m = self
                .first_moment
                .entry(param.id)
                .or_insert_with(|| Tensor::new(vec![0.0; numel], shape.clone()));
            let v = self
                .second_moment
                .entry(param.id)
                .or_insert_with(|| Tensor::new(vec![0.0; numel], shape));

            // ---- GPU path ----
            #[cfg(feature = "gpu")]
            if param.tensor.storage.is_gpu() {
                use crate::backend::gpu::{
                    compute as gpu_compute,
                    context::GpuContext,
                };

                let ctx = GpuContext::get().expect("GPU required for GPU param");

                // Ensure grad and moment buffers are on GPU.
                grad.to_gpu();
                m.to_gpu();
                v.to_gpu();

                let grad_buf = grad.storage.gpu_buffer();
                let m_buf = m.storage.gpu_buffer();
                let v_buf = v.storage.gpu_buffer();
                let param_buf = param.tensor.storage.gpu_buffer();

                gpu_compute::adam_step(
                    ctx,
                    &grad_buf,
                    &m_buf,
                    &v_buf,
                    &param_buf,
                    numel as u32,
                    self.lr,
                    self.beta1,
                    self.beta2,
                    self.epsilon,
                    bc1,
                    bc2,
                );

                drop(grad_buf);
                drop(m_buf);
                drop(v_buf);
                drop(param_buf);

                // GPU wrote to m, v, and param — mark CPU copies stale.
                m.storage.mark_gpu_dirty();
                v.storage.mark_gpu_dirty();
                param.tensor.storage.mark_gpu_dirty();
                param.tensor.storage.bump_version();
                continue;
            }

            // ---- CPU path (unchanged) ----
            let grad_guard = grad.storage.data();
            {
                let mut m_data = m.storage.data_write();
                let mut v_data = v.storage.data_write();
                let mut param_data = param.tensor.storage.data_write();

                let beta1 = self.beta1;
                let beta2 = self.beta2;
                let lr = self.lr;
                let eps = self.epsilon;

                for i in 0..numel {
                    let g = grad_guard[i];
                    m_data[i] = beta1 * m_data[i] + (1.0 - beta1) * g;
                    v_data[i] = beta2 * v_data[i] + (1.0 - beta2) * g * g;
                    let m_hat = m_data[i] / bc1;
                    let v_hat = v_data[i] / bc2;
                    param_data[i] -= lr * m_hat / (v_hat.sqrt() + eps);
                }
            }
            drop(grad_guard);
            param.tensor.storage.bump_version();
        }

        Ok(())
    }
}