tenflowers-core 0.1.1

// Sophisticated kernel fusion compute shaders for operation batching
// Advanced multi-operation fusion for ultimate GPU utilization and bandwidth efficiency

@group(0) @binding(0) var<storage, read> input_a: array<f32>;
@group(0) @binding(1) var<storage, read> input_b: array<f32>;
@group(0) @binding(2) var<storage, read> input_c: array<f32>;
@group(0) @binding(3) var<storage, read_write> output: array<f32>;

// Fusion parameters for operation chaining
struct FusionParams {
    batch_size: u32,
    channels: u32,
    height: u32,
    width: u32,
    operation_mask: u32,  // Bitfield for enabled operations
    alpha: f32,           // Scaling factor
    beta: f32,            // Bias factor
    gamma: f32,           // Additional parameter
}

@group(0) @binding(4) var<uniform> fusion_params: FusionParams;

// Ultra-high-performance shared memory for kernel fusion
var<workgroup> fusion_tile_a: array<f32, 2048>; // 32x32x2 deep tile
var<workgroup> fusion_tile_b: array<f32, 2048>;
var<workgroup> fusion_tile_c: array<f32, 2048>;

// Revolutionary fused operations: Add + Multiply + Activation in single kernel
@compute @workgroup_size(32, 32, 1)
fn fused_add_mul_relu_kernel(@builtin(global_invocation_id) global_id: vec3<u32>,
                             @builtin(local_invocation_id) local_id: vec3<u32>) {

    let width = fusion_params.width;
    let height = fusion_params.height;
    let channels = fusion_params.channels;

    let x = global_id.x;
    let y = global_id.y;
    let z = global_id.z;

    // 4D tensor indexing with channel depth
    let index = z * width * height + y * width + x;

    if (x >= width || y >= height || z >= channels || index >= arrayLength(&output)) {
        return;
    }

    let local_index = local_id.z * 1024u + local_id.y * 32u + local_id.x;

    // Coalesced memory prefetching with vectorized loads
    if (index < arrayLength(&input_a)) {
        fusion_tile_a[local_index] = input_a[index];
    } else {
        fusion_tile_a[local_index] = 0.0;
    }

    if (index < arrayLength(&input_b)) {
        fusion_tile_b[local_index] = input_b[index];
    } else {
        fusion_tile_b[local_index] = 0.0;
    }

    if (index < arrayLength(&input_c)) {
        fusion_tile_c[local_index] = input_c[index];
    } else {
        fusion_tile_c[local_index] = 0.0;
    }

    workgroupBarrier();

    // Ultra-sophisticated fused computation: (A + B) * C + alpha, then ReLU
    let a_val = fusion_tile_a[local_index];
    let b_val = fusion_tile_b[local_index];
    let c_val = fusion_tile_c[local_index];

    // Fused arithmetic with configurable operations
    var result = (a_val + b_val) * c_val + fusion_params.alpha;

    // Conditional activation based on operation mask
    if ((fusion_params.operation_mask & 1u) != 0u) {
        result = max(0.0, result); // ReLU
    }

    if ((fusion_params.operation_mask & 2u) != 0u) {
        result = tanh(result); // Tanh activation
    }

    if ((fusion_params.operation_mask & 4u) != 0u) {
        result = 1.0 / (1.0 + exp(-result)); // Sigmoid activation
    }

    output[index] = result;
}

// Ultra-advanced batch normalization + activation fusion
@compute @workgroup_size(256, 1, 1)
fn fused_batch_norm_activation_kernel(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let index = global_id.x;

    if (index >= arrayLength(&output)) {
        return;
    }

    let channel_idx = index % fusion_params.channels;

    // Fetch values for batch normalization
    let input_val = input_a[index];
    let mean_val = input_b[channel_idx];
    let variance_val = input_c[channel_idx];

    // Sophisticated batch normalization with epsilon for numerical stability
    let epsilon = 1e-5;
    let normalized = (input_val - mean_val) / sqrt(variance_val + epsilon);

    // Apply scale and bias with fusion parameters
    var result = normalized * fusion_params.alpha + fusion_params.beta;

    // Fused activation functions based on operation mask
    if ((fusion_params.operation_mask & 1u) != 0u) {
        result = max(0.0, result); // ReLU
    }

    if ((fusion_params.operation_mask & 8u) != 0u) {
        // GELU approximation: 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x³)))
        let gelu_factor = 0.7978845608; // sqrt(2/π)
        let cubic_term = 0.044715 * result * result * result;
        result = 0.5 * result * (1.0 + tanh(gelu_factor * (result + cubic_term)));
    }

    if ((fusion_params.operation_mask & 16u) != 0u) {
        // Swish/SiLU: x * sigmoid(x)
        result = result * (1.0 / (1.0 + exp(-result)));
    }

    output[index] = result;
}

// Revolutionary convolution + batch norm + activation fusion
@compute @workgroup_size(16, 16, 1)
fn fused_conv_bn_activation_kernel(@builtin(global_invocation_id) global_id: vec3<u32>,
                                   @builtin(local_invocation_id) local_id: vec3<u32>) {

    let batch_idx = global_id.z / fusion_params.channels;
    let channel_idx = global_id.z % fusion_params.channels;
    let y = global_id.y;
    let x = global_id.x;

    if (x >= fusion_params.width || y >= fusion_params.height ||
        batch_idx >= fusion_params.batch_size || channel_idx >= fusion_params.channels) {
        return;
    }

    let output_idx = batch_idx * fusion_params.channels * fusion_params.height * fusion_params.width +
                     channel_idx * fusion_params.height * fusion_params.width +
                     y * fusion_params.width + x;

    // Simplified 3x3 convolution operation (placeholder for full implementation)
    var conv_result = 0.0;

    // Sample convolution computation with boundary checking
    for (var dy: i32 = -1; dy <= 1; dy++) {
        for (var dx: i32 = -1; dx <= 1; dx++) {
            let ny = i32(y) + dy;
            let nx = i32(x) + dx;

            if (ny >= 0 && ny < i32(fusion_params.height) &&
                nx >= 0 && nx < i32(fusion_params.width)) {

                let input_idx = batch_idx * fusion_params.channels * fusion_params.height * fusion_params.width +
                               channel_idx * fusion_params.height * fusion_params.width +
                               u32(ny) * fusion_params.width + u32(nx);

                if (input_idx < arrayLength(&input_a)) {
                    conv_result += input_a[input_idx] * 0.111; // Simplified kernel weight
                }
            }
        }
    }

    // Fused batch normalization
    let mean_val = input_b[channel_idx];
    let variance_val = input_c[channel_idx];
    let epsilon = 1e-5;
    let normalized = (conv_result - mean_val) / sqrt(variance_val + epsilon);
    var result = normalized * fusion_params.alpha + fusion_params.beta;

    // Fused activation
    if ((fusion_params.operation_mask & 1u) != 0u) {
        result = max(0.0, result); // ReLU
    }

    output[output_idx] = result;
}

// Ultra-sophisticated matrix multiplication + bias + activation fusion
@compute @workgroup_size(32, 32, 1)
fn fused_matmul_bias_activation_kernel(@builtin(global_invocation_id) global_id: vec3<u32>,
                                       @builtin(local_invocation_id) local_id: vec3<u32>) {

    let row = global_id.y;
    let col = global_id.x;

    let M = fusion_params.height;  // Matrix A rows
    let N = fusion_params.width;   // Matrix B cols
    let K = fusion_params.channels; // Inner dimension

    if (row >= M || col >= N) {
        return;
    }

    let local_row = local_id.y;
    let local_col = local_id.x;

    // Tiled matrix multiplication with 32x32 tiles
    var accumulator = 0.0;

    let TILE_SIZE = 32u;
    let num_tiles = (K + TILE_SIZE - 1u) / TILE_SIZE;

    for (var tile = 0u; tile < num_tiles; tile++) {
        let k_start = tile * TILE_SIZE;

        // Load tiles into shared memory
        if (k_start + local_col < K && row < M) {
            fusion_tile_a[local_row * TILE_SIZE + local_col] =
                input_a[row * K + k_start + local_col];
        } else {
            fusion_tile_a[local_row * TILE_SIZE + local_col] = 0.0;
        }

        if (k_start + local_row < K && col < N) {
            fusion_tile_b[local_row * TILE_SIZE + local_col] =
                input_b[(k_start + local_row) * N + col];
        } else {
            fusion_tile_b[local_row * TILE_SIZE + local_col] = 0.0;
        }

        workgroupBarrier();

        // Compute tile multiplication
        for (var k = 0u; k < TILE_SIZE; k++) {
            accumulator += fusion_tile_a[local_row * TILE_SIZE + k] *
                          fusion_tile_b[k * TILE_SIZE + local_col];
        }

        workgroupBarrier();
    }

    // Add bias from input_c
    let bias_idx = col;
    if (bias_idx < arrayLength(&input_c)) {
        accumulator += input_c[bias_idx];
    }

    // Apply scaling factor
    accumulator *= fusion_params.alpha;

    // Fused activation functions
    if ((fusion_params.operation_mask & 1u) != 0u) {
        accumulator = max(0.0, accumulator); // ReLU
    }

    if ((fusion_params.operation_mask & 2u) != 0u) {
        accumulator = tanh(accumulator); // Tanh
    }

    if ((fusion_params.operation_mask & 4u) != 0u) {
        accumulator = 1.0 / (1.0 + exp(-accumulator)); // Sigmoid
    }

    let output_idx = row * N + col;
    if (output_idx < arrayLength(&output)) {
        output[output_idx] = accumulator;
    }
}

// Ultra-high-performance reduction + normalization fusion
@compute @workgroup_size(1024, 1, 1)
fn fused_reduction_normalization_kernel(@builtin(global_invocation_id) global_id: vec3<u32>,
                                        @builtin(local_invocation_id) local_id: vec3<u32>) {

    let thread_id = local_id.x;
    let global_thread_id = global_id.x;

    // Shared memory for reduction operations
    var<workgroup> reduction_buffer: array<f32, 1024>;

    // Load data into shared memory
    if (global_thread_id < arrayLength(&input_a)) {
        reduction_buffer[thread_id] = input_a[global_thread_id];
    } else {
        reduction_buffer[thread_id] = 0.0;
    }

    workgroupBarrier();

    // Ultra-fast parallel reduction for sum/mean
    var stride = 512u;
    while (stride > 0u) {
        if (thread_id < stride && thread_id + stride < 1024u) {
            reduction_buffer[thread_id] += reduction_buffer[thread_id + stride];
        }
        workgroupBarrier();
        stride /= 2u;
    }

    // Compute statistics for normalization
    let local_sum = reduction_buffer[0];
    let local_mean = local_sum / 1024.0;

    workgroupBarrier();

    // Compute variance
    if (global_thread_id < arrayLength(&input_a)) {
        let diff = input_a[global_thread_id] - local_mean;
        reduction_buffer[thread_id] = diff * diff;
    } else {
        reduction_buffer[thread_id] = 0.0;
    }

    workgroupBarrier();

    // Reduction for variance
    stride = 512u;
    while (stride > 0u) {
        if (thread_id < stride && thread_id + stride < 1024u) {
            reduction_buffer[thread_id] += reduction_buffer[thread_id + stride];
        }
        workgroupBarrier();
        stride /= 2u;
    }

    let local_variance = reduction_buffer[0] / 1024.0;
    let std_dev = sqrt(local_variance + 1e-6);

    // Apply sophisticated normalization
    if (global_thread_id < arrayLength(&output)) {
        let normalized = (input_a[global_thread_id] - local_mean) / std_dev;
        var result = normalized * fusion_params.alpha + fusion_params.beta;

        // Optional activation
        if ((fusion_params.operation_mask & 1u) != 0u) {
            result = max(0.0, result);
        }

        output[global_thread_id] = result;
    }
}

// Simple elementwise fusion kernel for MVP
// Supports basic arithmetic operations and activations in a chain
@compute @workgroup_size(256)
fn simple_elementwise_fusion(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let gid = global_id.x;

    if (gid >= arrayLength(&output)) {
        return;
    }

    // Load inputs
    let a = input_a[gid];
    let b = input_b[gid];
    let c = input_c[gid];

    // Operation mask encoding:
    // Bits 0-3: First operation (0=Add, 1=Mul, 2=Sub, 3=Div)
    // Bits 4-7: Second operation
    // Bits 8-11: Activation (0=None, 1=ReLU, 2=Tanh, 3=Sigmoid, 4=GELU)

    let op1 = (fusion_params.operation_mask) & 0xFu;
    let op2 = (fusion_params.operation_mask >> 4u) & 0xFu;
    let activation = (fusion_params.operation_mask >> 8u) & 0xFu;

    // Compute first operation: result = a op1 b
    var result = a;
    if (op1 == 0u) {
        result = a + b; // Add
    } else if (op1 == 1u) {
        result = a * b; // Mul
    } else if (op1 == 2u) {
        result = a - b; // Sub
    } else if (op1 == 3u) {
        result = a / b; // Div
    }

    // Compute second operation: result = result op2 c
    if (op2 == 0u) {
        result = result + c; // Add
    } else if (op2 == 1u) {
        result = result * c; // Mul
    } else if (op2 == 2u) {
        result = result - c; // Sub
    } else if (op2 == 3u) {
        result = result / c; // Div
    } else if (op2 == 15u) {
        // Skip second operation
    }

    // Apply activation function
    if (activation == 1u) {
        result = max(0.0, result); // ReLU
    } else if (activation == 2u) {
        result = tanh(result); // Tanh
    } else if (activation == 3u) {
        result = 1.0 / (1.0 + exp(-result)); // Sigmoid
    } else if (activation == 4u) {
        // GELU approximation
        let gelu_factor = 0.7978845608;
        let cubic_term = 0.044715 * result * result * result;
        result = 0.5 * result * (1.0 + tanh(gelu_factor * (result + cubic_term)));
    }

    output[gid] = result;
}