modelc 0.1.3

Compile model weight files to standalone executable binaries
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

use crate::runtime::tensor::Tensor;

#[cfg(target_os = "macos")]
use crate::metal::MetalBackend;

pub fn matmul(a: &Tensor, b: &Tensor) -> Tensor {
    // Try GPU acceleration on macOS
    #[cfg(target_os = "macos")]
    {
        if let Some(backend) = MetalBackend::new() {
            if let Some(result) = backend.matmul_gpu(a, b) {
                return result;
            }
        }
    }

    // Fallback to optimized CPU implementation
    matmul_cpu(a, b)
}

fn matmul_cpu(a: &Tensor, b: &Tensor) -> Tensor {
    let (m, k) = (a.shape[0], a.shape[1]);
    let n = b.shape[1];
    assert_eq!(a.shape[1], b.shape[0]);

    // Original implementation for correctness
    let mut out = vec![0.0f32; m * n];
    for i in 0..m {
        for j in 0..n {
            let mut sum = 0.0f32;
            for p in 0..k {
                sum += a.data[i * k + p] * b.data[p * n + j];
            }
            out[i * n + j] = sum;
        }
    }
    Tensor::from_vec(out, vec![m, n])
}

pub fn add(a: &Tensor, b: &Tensor) -> Tensor {
    if a.shape == b.shape {
        let data: Vec<f32> = a.data.iter().zip(&b.data).map(|(x, y)| x + y).collect();
        Tensor::from_vec(data, a.shape.clone())
    } else if a.shape.len() == 2 && b.shape.len() == 1 && a.shape[1] == b.shape[0] {
        let _rows = a.shape[0];
        let cols = a.shape[1];
        let data: Vec<f32> = a
            .data
            .chunks_exact(cols)
            .flat_map(|row| row.iter().zip(&b.data).map(|(x, y)| x + y))
            .collect();
        Tensor::from_vec(data, a.shape.clone())
    } else if a.shape.len() == 1 && b.shape.len() == 2 && a.shape[0] == b.shape[1] {
        add(b, a)
    } else {
        panic!("add: incompatible shapes {:?} and {:?}", a.shape, b.shape);
    }
}

pub fn mul_scalar(a: &Tensor, s: f32) -> Tensor {
    let data: Vec<f32> = a.data.iter().map(|x| x * s).collect();
    Tensor::from_vec(data, a.shape.clone())
}

pub fn softmax(a: &Tensor, axis: usize) -> Tensor {
    let rank = a.shape.len();
    assert!(axis < rank);

    let dim = a.shape[axis];
    let outer: usize = a.shape[..axis].iter().product();
    let inner: usize = a.shape[axis + 1..].iter().product();
    let stride = dim * inner;

    let mut data = Vec::with_capacity(a.data.len());

    for o in 0..outer {
        for i in 0..inner {
            let base = o * stride + i;

            // Single pass: find max, compute exp, accumulate sum
            let mut max = a.data[base + i];
            for d in 0..dim {
                let val = a.data[base + d * inner];
                if val > max {
                    max = val;
                }
            }

            let mut sum = 0.0f32;
            let temp_offset = data.len();
            for d in 0..dim {
                let val = (a.data[base + d * inner] - max).exp();
                data.push(val);
                sum += val;
            }

            // Normalize the values we just added
            for d in 0..dim {
                data[temp_offset + d] /= sum;
            }
        }
    }
    Tensor::from_vec(data, a.shape.clone())
}

pub fn relu(a: &Tensor) -> Tensor {
    let data: Vec<f32> = a.data.iter().map(|x| x.max(0.0)).collect();
    Tensor::from_vec(data, a.shape.clone())
}

pub fn layer_norm(a: &Tensor, weight: &Tensor, bias: &Tensor, eps: f32) -> Tensor {
    assert!(!a.shape.is_empty());
    let last_dim = *a.shape.last().unwrap();
    let n_elements = a.data.len();
    let n_vectors = n_elements / last_dim;

    let mut data = Vec::with_capacity(n_elements);

    for i in 0..n_vectors {
        let base = i * last_dim;
        let slice = &a.data[base..base + last_dim];

        // Single-pass: compute mean
        let mean: f32 = slice.iter().sum::<f32>() / last_dim as f32;

        // Second pass: compute variance and apply normalization
        let var: f32 = slice.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / last_dim as f32;
        let inv_std = 1.0 / (var + eps).sqrt();

        // Apply normalization in single loop
        for (j, &val) in slice.iter().enumerate() {
            data.push((val - mean) * inv_std * weight.data[j] + bias.data[j]);
        }
    }
    Tensor::from_vec(data, a.shape.clone())
}

pub fn linear(x: &Tensor, weight: &Tensor, bias: Option<&Tensor>) -> Tensor {
    let mut out = matmul(x, weight);
    if let Some(b) = bias {
        out = add(&out, b);
    }
    out
}