// WebGPU Compute Shaders: Advanced Neural Network Operations
// High-performance GPU implementations of specialized neural operations
// Includes convolution, pooling, attention, and optimization kernels
@group(0) @binding(0) var<storage, read> input_data: array<f32>;
@group(0) @binding(1) var<storage, read> weights: array<f32>;
@group(0) @binding(2) var<storage, read> bias: array<f32>;
@group(0) @binding(3) var<storage, read_write> output_data: array<f32>;
@group(0) @binding(4) var<storage, read_write> scratch_buffer: array<f32>;
struct AdvancedUniforms {
input_height: u32,
input_width: u32,
input_channels: u32,
output_channels: u32,
kernel_size: u32,
stride: u32,
padding: u32,
batch_size: u32,
sequence_length: u32, // For attention mechanisms
head_dimension: u32, // For multi-head attention
num_heads: u32,
scale_factor: f32, // For attention scaling
}
@group(0) @binding(5) var<uniform> uniforms: AdvancedUniforms;
// 2D Convolution operation optimized for GPU
@compute @workgroup_size(16, 16, 1)
fn conv2d_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
let out_y = global_id.y;
let out_x = global_id.z;
if (batch_idx >= uniforms.batch_size ||
out_y >= uniforms.input_height ||
out_x >= uniforms.input_width) {
return;
}
let output_height = (uniforms.input_height + 2u * uniforms.padding - uniforms.kernel_size) / uniforms.stride + 1u;
let output_width = (uniforms.input_width + 2u * uniforms.padding - uniforms.kernel_size) / uniforms.stride + 1u;
if (out_y >= output_height || out_x >= output_width) {
return;
}
// Compute convolution for each output channel
for (var out_c = 0u; out_c < uniforms.output_channels; out_c++) {
var sum: f32 = 0.0;
// Convolve with kernel
for (var ky = 0u; ky < uniforms.kernel_size; ky++) {
for (var kx = 0u; kx < uniforms.kernel_size; kx++) {
for (var in_c = 0u; in_c < uniforms.input_channels; in_c++) {
let in_y = out_y * uniforms.stride + ky - uniforms.padding;
let in_x = out_x * uniforms.stride + kx - uniforms.padding;
// Check bounds (padding)
if (in_y < uniforms.input_height && in_x < uniforms.input_width) {
let input_idx = batch_idx * (uniforms.input_channels * uniforms.input_height * uniforms.input_width) +
in_c * (uniforms.input_height * uniforms.input_width) +
in_y * uniforms.input_width + in_x;
let weight_idx = out_c * (uniforms.input_channels * uniforms.kernel_size * uniforms.kernel_size) +
in_c * (uniforms.kernel_size * uniforms.kernel_size) +
ky * uniforms.kernel_size + kx;
sum += input_data[input_idx] * weights[weight_idx];
}
}
}
}
// Add bias and store result
let output_idx = batch_idx * (uniforms.output_channels * output_height * output_width) +
out_c * (output_height * output_width) +
out_y * output_width + out_x;
output_data[output_idx] = sum + bias[out_c];
}
}
// Max pooling operation
@compute @workgroup_size(16, 16, 1)
fn max_pool2d_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
let out_y = global_id.y;
let out_x = global_id.z;
let pool_size = uniforms.kernel_size;
let output_height = uniforms.input_height / pool_size;
let output_width = uniforms.input_width / pool_size;
if (batch_idx >= uniforms.batch_size ||
out_y >= output_height ||
out_x >= output_width) {
return;
}
for (var c = 0u; c < uniforms.input_channels; c++) {
var max_val: f32 = -3.4e38; // Negative infinity approximation
// Find maximum in pool window
for (var py = 0u; py < pool_size; py++) {
for (var px = 0u; px < pool_size; px++) {
let in_y = out_y * pool_size + py;
let in_x = out_x * pool_size + px;
let input_idx = batch_idx * (uniforms.input_channels * uniforms.input_height * uniforms.input_width) +
c * (uniforms.input_height * uniforms.input_width) +
in_y * uniforms.input_width + in_x;
max_val = max(max_val, input_data[input_idx]);
}
}
let output_idx = batch_idx * (uniforms.input_channels * output_height * output_width) +
c * (output_height * output_width) +
out_y * output_width + out_x;
output_data[output_idx] = max_val;
}
}
// Average pooling operation
@compute @workgroup_size(16, 16, 1)
fn avg_pool2d_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
let out_y = global_id.y;
let out_x = global_id.z;
let pool_size = uniforms.kernel_size;
let output_height = uniforms.input_height / pool_size;
let output_width = uniforms.input_width / pool_size;
if (batch_idx >= uniforms.batch_size ||
out_y >= output_height ||
out_x >= output_width) {
return;
}
for (var c = 0u; c < uniforms.input_channels; c++) {
var sum: f32 = 0.0;
// Sum values in pool window
for (var py = 0u; py < pool_size; py++) {
for (var px = 0u; px < pool_size; px++) {
let in_y = out_y * pool_size + py;
let in_x = out_x * pool_size + px;
let input_idx = batch_idx * (uniforms.input_channels * uniforms.input_height * uniforms.input_width) +
c * (uniforms.input_height * uniforms.input_width) +
in_y * uniforms.input_width + in_x;
sum += input_data[input_idx];
}
}
// Average and store
let avg_val = sum / f32(pool_size * pool_size);
let output_idx = batch_idx * (uniforms.input_channels * output_height * output_width) +
c * (output_height * output_width) +
out_y * output_width + out_x;
output_data[output_idx] = avg_val;
}
}
// Softmax operation with numerical stability
@compute @workgroup_size(256)
fn softmax_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
if (batch_idx >= uniforms.batch_size) {
return;
}
let vector_size = uniforms.input_channels;
let base_idx = batch_idx * vector_size;
// Find maximum for numerical stability
var max_val: f32 = -3.4e38;
for (var i = 0u; i < vector_size; i++) {
max_val = max(max_val, input_data[base_idx + i]);
}
// Compute sum of exponentials
var exp_sum: f32 = 0.0;
for (var i = 0u; i < vector_size; i++) {
let exp_val = exp(input_data[base_idx + i] - max_val);
scratch_buffer[base_idx + i] = exp_val;
exp_sum += exp_val;
}
// Normalize
for (var i = 0u; i < vector_size; i++) {
output_data[base_idx + i] = scratch_buffer[base_idx + i] / exp_sum;
}
}
// Layer normalization
@compute @workgroup_size(256)
fn layer_norm_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
if (batch_idx >= uniforms.batch_size) {
return;
}
let feature_size = uniforms.input_channels;
let base_idx = batch_idx * feature_size;
let epsilon = 1e-5;
// Compute mean
var mean: f32 = 0.0;
for (var i = 0u; i < feature_size; i++) {
mean += input_data[base_idx + i];
}
mean /= f32(feature_size);
// Compute variance
var variance: f32 = 0.0;
for (var i = 0u; i < feature_size; i++) {
let diff = input_data[base_idx + i] - mean;
variance += diff * diff;
}
variance /= f32(feature_size);
// Normalize
let inv_std = 1.0 / sqrt(variance + epsilon);
for (var i = 0u; i < feature_size; i++) {
let normalized = (input_data[base_idx + i] - mean) * inv_std;
// Apply learned scale and bias if available
let scale = select(1.0, weights[i], i < uniforms.input_channels);
let bias_val = select(0.0, bias[i], i < uniforms.input_channels);
output_data[base_idx + i] = normalized * scale + bias_val;
}
}
// Scaled dot-product attention (simplified)
@compute @workgroup_size(16, 16)
fn scaled_dot_product_attention(@builtin(global_invocation_id) global_id: vec3<u32>) {
let batch_idx = global_id.x;
let head_idx = global_id.y;
let seq_pos = global_id.z;
if (batch_idx >= uniforms.batch_size ||
head_idx >= uniforms.num_heads ||
seq_pos >= uniforms.sequence_length) {
return;
}
let head_dim = uniforms.head_dimension;
let scale = uniforms.scale_factor;
// This is a simplified version - full attention would require Q, K, V matrices
let base_offset = batch_idx * uniforms.num_heads * uniforms.sequence_length * head_dim +
head_idx * uniforms.sequence_length * head_dim +
seq_pos * head_dim;
// Compute attention scores (simplified)
var attention_sum: f32 = 0.0;
for (var i = 0u; i < uniforms.sequence_length; i++) {
let query_offset = base_offset;
let key_offset = batch_idx * uniforms.num_heads * uniforms.sequence_length * head_dim +
head_idx * uniforms.sequence_length * head_dim +
i * head_dim;
// Compute dot product
var dot_product: f32 = 0.0;
for (var d = 0u; d < head_dim; d++) {
dot_product += input_data[query_offset + d] * weights[key_offset + d];
}
let attention_score = exp(dot_product * scale);
scratch_buffer[seq_pos * uniforms.sequence_length + i] = attention_score;
attention_sum += attention_score;
}
// Normalize attention weights and compute output
for (var d = 0u; d < head_dim; d++) {
var weighted_sum: f32 = 0.0;
for (var i = 0u; i < uniforms.sequence_length; i++) {
let attention_weight = scratch_buffer[seq_pos * uniforms.sequence_length + i] / attention_sum;
let value_offset = batch_idx * uniforms.num_heads * uniforms.sequence_length * head_dim +
head_idx * uniforms.sequence_length * head_dim +
i * head_dim + d;
weighted_sum += attention_weight * bias[value_offset]; // Reuse bias buffer for values
}
output_data[base_offset + d] = weighted_sum;
}
}
// Element-wise operations
@compute @workgroup_size(256)
fn element_wise_add(@builtin(global_invocation_id) global_id: vec3<u32>) {
let index = global_id.x;
if (index >= uniforms.batch_size * uniforms.input_channels) {
return;
}
output_data[index] = input_data[index] + weights[index];
}
@compute @workgroup_size(256)
fn element_wise_multiply(@builtin(global_invocation_id) global_id: vec3<u32>) {
let index = global_id.x;
if (index >= uniforms.batch_size * uniforms.input_channels) {
return;
}
output_data[index] = input_data[index] * weights[index];
}
// GELU activation: f(x) = x * Phi(x) where Phi is cumulative distribution function
@compute @workgroup_size(256)
fn gelu_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let index = global_id.x;
if (index >= uniforms.batch_size * uniforms.input_channels) {
return;
}
let x = input_data[index];
// GELU approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
let sqrt_2_over_pi = 0.7978845608;
let a = 0.044715;
let x_cubed = x * x * x;
let inner = sqrt_2_over_pi * (x + a * x_cubed);
let gelu_approx = 0.5 * x * (1.0 + tanh(inner));
output_data[index] = gelu_approx;
}
// Swish/SiLU activation: f(x) = x * sigmoid(x)
@compute @workgroup_size(256)
fn swish_main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let index = global_id.x;
if (index >= uniforms.batch_size * uniforms.input_channels) {
return;
}
let x = input_data[index];
let sigmoid_x = 1.0 / (1.0 + exp(-x));
output_data[index] = x * sigmoid_x;
}