WebGPU-Neural-Network-Trainer/shaders/neural/softmax_cross_entropy_backward.wgsl

@group(0) @binding(0) var<storage, read> outputLogits : array<f32>;
@group(0) @binding(1) var<storage, read> targetLabels : array<u32>;
@group(0) @binding(2) var<storage, read_write> gradientLogits : array<f32>;
@group(0) @binding(3) var<storage, read_write> crossEntropyLoss : array<f32>;
@group(0) @binding(4) var<uniform> layer2Sizes : vec3<u32>;
// x = hiddenDim, y = numClasses, z = batchSize

@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) id: vec3<u32>) {
    let sampleIndex  = id.x;
    let batchSize    = layer2Sizes.z;
    let numClasses   = layer2Sizes.y;

    if (sampleIndex >= batchSize) { return; }

    let base = sampleIndex * numClasses;
    let label = targetLabels[sampleIndex];

    var maxLogit : f32 = -1e9;
    for (var c : u32 = 0u; c < numClasses; c++) {
        let v = outputLogits[base + c];
        if (v > maxLogit) { maxLogit = v; }
    }

    var sumExp : f32 = 0.0;
    for (var c : u32 = 0u; c < numClasses; c++) {
        sumExp += exp(outputLogits[base + c] - maxLogit);
    }

    var loss : f32 = 0.0;
    for (var c : u32 = 0u; c < numClasses; c++) {
        let sm = exp(outputLogits[base + c] - maxLogit) / sumExp;
        let oneHot = f32(c == label);
        gradientLogits[base + c] = sm - oneHot;
        if (oneHot == 1.0) { loss = -log(sm); }
    }

    crossEntropyLoss[sampleIndex] = loss;
}