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Implement the 2 empty function bodies in specs/ml/layers/dropout_layer.t27 #4061

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Merged
gHashTag merged 1 commit into from
Sep 17, 2026
Merged

Implement the 2 empty function bodies in specs/ml/layers/dropout_layer.t27 #4061

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59 changes: 50 additions & 9 deletions specs/ml/layers/dropout_layer.t27
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Original file line number Diff line number Diff line change
Expand Up @@ -26,14 +26,49 @@ module Dropout;
// 3. Core Functions
// ═══════════════════════════════════════════════════════════

// forward(input: []const f32) → void
fn forward(input: []const f32) -> void {
// TODO: Implement from .tri spec
// forward(input: []const f32, output: []f32, mask: []bool, training: bool, config: DropoutConfig) → void
fn forward(input: []const f32, output: []f32, mask: []bool, training: bool, config: DropoutConfig) -> void {
const n = input.len;
var i : usize = 0;
while (i < n) {
if (training) {
if (mask[i]) {
// Neuron is kept, scale during training if configured
if (config.scale_during_training) {
output[i] = input[i] * (1.0 / (1.0 - config.p));
} else {
output[i] = input[i];
}
} else {
// Neuron is dropped
output[i] = 0.0;
}
} else {
// During inference, always scale output
output[i] = input[i] * (1.0 - config.p);
}
i = i + 1;
}
}

// backward(grad_output: []const f32) → void
fn backward(grad_output: []const f32) -> void {
// TODO: Implement from .tri spec
// backward(grad_output: []const f32, mask: []const bool, grad_input: []f32, config: DropoutConfig) → void
fn backward(grad_output: []const f32, mask: []const bool, grad_input: []f32, config: DropoutConfig) -> void {
const n = grad_output.len;
var i : usize = 0;
while (i < n) {
if (mask[i]) {
// Gradient flows through kept neurons
if (config.scale_during_training) {
grad_input[i] = grad_output[i] * (1.0 / (1.0 - config.p));
} else {
grad_input[i] = grad_output[i];
}
} else {
// Gradient is zero for dropped neurons
grad_input[i] = 0.0;
}
i = i + 1;
}
}

// ═══════════════════════════════════════════════════════════
Expand All @@ -42,11 +77,17 @@ module Dropout;

test forward_basic_case
given input = default_input()
when result = forward(input)
output = allocf32(input.len)
mask = allocbool(input.len)
config = DropoutConfig { p = 0.5, inplace = false, scale_during_training = true }
when result = forward(input, output, mask, true, config)
then result != undefined

test backward_basic_case
given input = default_input()
when result = backward(input)
given grad_output = default_input()
mask = allocbool(grad_output.len)
grad_input = allocf32(grad_output.len)
config = DropoutConfig { p = 0.5, inplace = false, scale_during_training = true }
when result = backward(grad_output, mask, grad_input, config)
then result != undefined

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