web-rwkv 0.10.20

An implementation of the RWKV language model in pure WebGPU.
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struct View {
    shape: vec4<u32>,
    stride: vec4<u32>,
    offset: vec4<u32>,
};

@group(0) @binding(0) var<uniform> shape: vec4<u32>;                        // [C, R, B]
@group(0) @binding(1) var<uniform> source: View;                            // [R, T, B]
@group(0) @binding(2) var<uniform> destination: View;                       // [R, T, B]

@group(0) @binding(3) var<storage, read> matrix: array<u32>;                // (B, R, C)
@group(0) @binding(4) var<storage, read> minmax: array<u32>;

#ifdef IN_FP16
@group(0) @binding(5) var<storage, read> input: array<vec2<u32>>;           // (B, T, C)
#else
@group(0) @binding(5) var<storage, read> input: array<vec4<f32>>;           // (B, T, C)
#endif
#ifdef OUT_FP16
@group(0) @binding(6) var<storage, read_write> output: array<vec2<u32>>;    // (B, T, R)
#else
@group(0) @binding(6) var<storage, read_write> output: array<vec4<f32>>;    // (B, T, R)
#endif

const INT8_BLOCK_STEP: u32 = INT8_BLOCK_SIZE / 4u;

var<workgroup> sketch: array<vec4<f32>, BLOCK_SIZE>;

// ACTIVATION_DEFINE

fn compute_index(view: View, batch: u32, token: u32, index: u32) -> u32 {
    let stride = view.stride.x >> 2u;
    let offset = vec3<u32>(view.offset.zy, view.offset.x >> 2u);
    return dot(vec3<u32>(batch, token, index) + offset, vec3<u32>(view.stride.y * stride, stride, 1u));
}

fn pack4x16float(x: vec4<f32>) -> vec2<u32> {
    return vec2<u32>(pack2x16float(x.xy), pack2x16float(x.zw));
}

fn unpack4x16float(x: vec2<u32>) -> vec4<f32> {
    return vec4<f32>(unpack2x16float(x.x), unpack2x16float(x.y));
}

fn unpack_minmax(index: u32) -> vec2<f32> {
    let i = index / INT8_BLOCK_STEP;
    return unpack2x16float(minmax[i]);
}

fn reduce_sum(index: u32, stride: u32) {
    if index < stride {
        sketch[index] += sketch[index + stride];
    }
    workgroupBarrier();
}

@compute @workgroup_size(BLOCK_SIZE, 1, 1)
fn matmul(@builtin(global_invocation_id) invocation_id: vec3<u32>) {
    let stride = shape.x / 4u;
    let index = invocation_id.x % BLOCK_SIZE;
    let channel = invocation_id.x / BLOCK_SIZE;     // 1 channel: 4 rows in matrix
    let token = invocation_id.y;
    let batch = invocation_id.z;

    let bb = compute_index(source, batch, token, 0u);
    let cb = batch * shape.y * stride + channel * 4u * stride;

    var local_sum = vec4<f32>(0.0);
    for (var i = index; i < stride; i += BLOCK_SIZE) {
        let bti = bb + i;
        var ci = cb + i;

        // read 4 elements from the input
#ifdef IN_FP16
        let x = unpack4x16float(input[bti]);
#else
        let x = input[bti];
#endif

#ifdef SPARSE_INPUT
        if all(x == vec4<f32>(0.0)) {
            continue;
        }
#endif

        // read 4 rows from the matrix, each with 4 unpacked floats, forming a 4x4 sub-block
        var m: mat4x4<f32>;
        var b: vec2<f32>;
        b = unpack_minmax(ci); m[0] = fma(unpack4x8unorm(matrix[ci]), vec4<f32>(b[1] - b[0]), vec4<f32>(b[0])); ci += stride;
        b = unpack_minmax(ci); m[1] = fma(unpack4x8unorm(matrix[ci]), vec4<f32>(b[1] - b[0]), vec4<f32>(b[0])); ci += stride;
        b = unpack_minmax(ci); m[2] = fma(unpack4x8unorm(matrix[ci]), vec4<f32>(b[1] - b[0]), vec4<f32>(b[0])); ci += stride;
        b = unpack_minmax(ci); m[3] = fma(unpack4x8unorm(matrix[ci]), vec4<f32>(b[1] - b[0]), vec4<f32>(b[0]));
        local_sum += transpose(m) * x;
    }
    sketch[index] = local_sum;
    workgroupBarrier();

    reduce_sum(index, 64u);
    reduce_sum(index, 32u);
    reduce_sum(index, 16u);
    reduce_sum(index, 8u);
    reduce_sum(index, 4u);
    reduce_sum(index, 2u);
    reduce_sum(index, 1u);

    if index == 0u {
        let btc = compute_index(destination, batch, token, channel);
        let out = ACT(sketch[0]);
#ifdef OUT_FP16
        output[btc] = pack4x16float(out);
#else
        output[btc] = out;
#endif
    }
}