ruda-nn 0.21.27

Ruda neural network layers, activation modules and losses.
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
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198

use super::*;
use ruda_tensor_host::{Host, HostDevice};

type TestBackend = Host;

fn assert_approx_equal(actual: &[f32], expected: &[f32], tol: f32) {
    assert_eq!(
        actual.len(),
        expected.len(),
        "Length mismatch: actual {} vs expected {}",
        actual.len(),
        expected.len()
    );
    for (i, (a, e)) in actual.iter().zip(expected.iter()).enumerate() {
        assert!(
            (a - e).abs() < tol,
            "Mismatch at index {}: expected {:.6}, got {:.6} (diff: {:.6})",
            i,
            e,
            a,
            (a - e).abs()
        );
    }
}

// ---------------------------------------------------------------
// Assertions
// ---------------------------------------------------------------

#[test]
#[should_panic(expected = "blank index")]
fn test_ctc_loss_panics_invalid_blank_index() {
    let device = HostDevice;
    // blank=5 is out of bounds for num_classes=3
    let ctc = CTCLossConfig::new().with_blank(5).init();

    let log_probs = Tensor::<TestBackend, 3>::zeros([2, 1, 3], &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([1], &device);

    ctc.forward(log_probs, targets, input_lengths, target_lengths);
}

#[test]
#[should_panic(expected = "must equal batch_size")]
fn test_ctc_loss_panics_mismatched_batch_size() {
    let device = HostDevice;
    let ctc = CTCLossConfig::new().init();

    // Logits batch size = 2
    let log_probs = Tensor::<TestBackend, 3>::zeros([2, 2, 3], &device);
    // Targets batch size = 1 (Mismatch)
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2, 2], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([1, 1], &device);

    ctc.forward(log_probs, targets, input_lengths, target_lengths);
}

#[test]
#[should_panic(expected = "input_lengths length")]
fn test_ctc_loss_panics_input_lengths_mismatch() {
    let device = HostDevice;
    let ctc = CTCLossConfig::new().init();

    // Logits batch size = 2
    let log_probs = Tensor::<TestBackend, 3>::zeros([2, 2, 3], &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1], [2]], &device);

    // Input lengths size = 1 (Mismatch)
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([1, 1], &device);

    ctc.forward(log_probs, targets, input_lengths, target_lengths);
}

#[test]
#[should_panic(expected = "target_lengths length")]
fn test_ctc_loss_panics_target_lengths_mismatch() {
    let device = HostDevice;
    let ctc = CTCLossConfig::new().init();

    // Logits batch size = 2
    let log_probs = Tensor::<TestBackend, 3>::zeros([2, 2, 3], &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1], [2]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2, 2], &device);

    // Target lengths size = 1 (Mismatch)
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([1], &device);

    ctc.forward(log_probs, targets, input_lengths, target_lengths);
}

// ---------------------------------------------------------------
// Edge Case & Config Tests
// ---------------------------------------------------------------

#[test]
fn test_ctc_loss_repeated_labels_minimum_input_length() {
    // T=3, N=1, C=2, blank=0, target=[1, 1], uniform P = 1/2.
    //
    // The minimum T for target [1, 1] is 3: the only valid path is (1, 0, 1).
    // prob = (1/2)^3 = 1/8
    // Loss = -ln(1/8) = 3 * ln(2)
    let device = HostDevice;
    let ctc = CTCLossConfig::new().init();

    let log_probs = Tensor::<TestBackend, 3>::full([3, 1, 2], 0.5_f32.ln(), &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1_i32, 1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([3_i32], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);

    let loss = ctc.forward(log_probs, targets, input_lengths, target_lengths);
    let loss_data = loss.into_data().to_vec::<f32>().unwrap();
    let expected = 3.0 * 2.0_f32.ln();
    assert_approx_equal(&loss_data, &[expected], 1e-3);
}

#[test]
fn test_ctc_loss_custom_blank_uniform() {
    // T=3, N=1, C=3, blank=2, target=[0, 1], uniform P = 1/3.
    //
    // Two distinct labels, 3 classes, 3 time steps, just with
    // blank=2 instead of 0.
    // 5 valid paths → total = 5/27
    // Loss = -ln(5/27)
    let device = HostDevice;
    let ctc = CTCLossConfig::new().with_blank(2).init();

    let log_probs = Tensor::<TestBackend, 3>::full([3, 1, 3], (1.0_f32 / 3.0).ln(), &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[0_i32, 1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([3_i32], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);

    let loss = ctc.forward(log_probs, targets, input_lengths, target_lengths);
    let loss_data = loss.into_data().to_vec::<f32>().unwrap();
    let expected = -(5.0_f32 / 27.0).ln();
    assert_approx_equal(&loss_data, &[expected], 1e-3);
}

// ---------------------------------------------------------------
// zero_infinity tests
// ---------------------------------------------------------------

#[test]
fn test_ctc_loss_zero_infinity_produces_inf_when_disabled() {
    // T=2, N=1, C=3, blank=0, target=[1, 1], input_length=2
    // Target [1, 1] requires at least 3 time steps → no valid paths → loss = +inf
    let device = HostDevice;
    let ctc = CTCLossConfig::new().with_zero_infinity(false).init();

    let log_probs = Tensor::<TestBackend, 3>::full([2, 1, 3], (1.0_f32 / 3.0).ln(), &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1_i32, 1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);

    let loss = ctc.forward(log_probs, targets, input_lengths, target_lengths);
    let loss_data = loss.into_data().to_vec::<f32>().unwrap();
    assert!(
        loss_data[0].is_infinite() && loss_data[0] > 0.0,
        "Expected +inf, got {}",
        loss_data[0]
    );
}

#[test]
fn test_ctc_loss_zero_infinity_masks_inf_when_enabled() {
    // Same inputs as above, but zero_infinity=true → loss should be 0.0
    let device = HostDevice;
    let ctc = CTCLossConfig::new().with_zero_infinity(true).init();

    let log_probs = Tensor::<TestBackend, 3>::full([2, 1, 3], (1.0_f32 / 3.0).ln(), &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1_i32, 1]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);

    let loss = ctc.forward(log_probs, targets, input_lengths, target_lengths);
    let loss_data = loss.into_data().to_vec::<f32>().unwrap();
    assert_approx_equal(&loss_data, &[0.0], 1e-6);
}

#[test]
fn test_ctc_loss_zero_infinity_does_not_affect_finite_loss() {
    // Verify that zero_infinity=true does not change a finite loss value.
    let device = HostDevice;
    let ctc = CTCLossConfig::new().with_zero_infinity(true).init();

    let log_probs = Tensor::<TestBackend, 3>::full([2, 1, 2], 0.5_f32.ln(), &device);
    let targets = Tensor::<TestBackend, 2, Int>::from_data([[1_i32]], &device);
    let input_lengths = Tensor::<TestBackend, 1, Int>::from_data([2_i32], &device);
    let target_lengths = Tensor::<TestBackend, 1, Int>::from_data([1_i32], &device);

    let loss = ctc.forward(log_probs, targets, input_lengths, target_lengths);
    let loss_data = loss.into_data().to_vec::<f32>().unwrap();
    let expected = -(0.75_f32).ln();
    assert_approx_equal(&loss_data, &[expected], 1e-3);
}