trustformers-wasm 0.2.0

//! Advanced WebGPU optimizations for tensor operations
//!
//! This module provides cutting-edge optimizations for WebGPU compute shaders
//! including memory coalescing, workgroup optimization, and kernel fusion.

#![allow(dead_code)]
use std::collections::HashMap;
use std::vec::Vec;
use wasm_bindgen::prelude::*;

/// Advanced WebGPU optimization configuration
#[wasm_bindgen]
#[derive(Debug, Clone)]
pub struct AdvancedGPUConfig {
    enable_memory_coalescing: bool,
    enable_kernel_fusion: bool,
    enable_auto_tuning: bool,
    max_workgroup_size: u32,
    preferred_local_memory_kb: u32,
    enable_async_execution: bool,
}

#[wasm_bindgen]
impl AdvancedGPUConfig {
    /// Create a new advanced GPU configuration
    #[wasm_bindgen(constructor)]
    pub fn new() -> Self {
        Self {
            enable_memory_coalescing: true,
            enable_kernel_fusion: true,
            enable_auto_tuning: true,
            max_workgroup_size: 256,
            preferred_local_memory_kb: 48, // Typical WebGPU limit
            enable_async_execution: true,
        }
    }

    /// Create a configuration optimized for mobile GPUs
    pub fn mobile_optimized() -> Self {
        Self {
            enable_memory_coalescing: true,
            enable_kernel_fusion: false, // Simpler kernels for mobile
            enable_auto_tuning: true,
            max_workgroup_size: 128, // Smaller workgroups for mobile
            preferred_local_memory_kb: 16,
            enable_async_execution: true,
        }
    }

    /// Create a configuration optimized for desktop GPUs
    pub fn desktop_optimized() -> Self {
        Self {
            enable_memory_coalescing: true,
            enable_kernel_fusion: true,
            enable_auto_tuning: true,
            max_workgroup_size: 512, // Larger workgroups for desktop
            preferred_local_memory_kb: 64,
            enable_async_execution: true,
        }
    }

    /// Enable or disable memory coalescing optimization
    pub fn set_memory_coalescing(&mut self, enable: bool) {
        self.enable_memory_coalescing = enable;
    }

    /// Enable or disable kernel fusion
    pub fn set_kernel_fusion(&mut self, enable: bool) {
        self.enable_kernel_fusion = enable;
    }

    /// Set maximum workgroup size
    pub fn set_max_workgroup_size(&mut self, size: u32) {
        self.max_workgroup_size = size;
    }

    /// Set preferred local memory size in KB
    pub fn set_preferred_local_memory_kb(&mut self, kb: u32) {
        self.preferred_local_memory_kb = kb;
    }
}

/// Performance metrics for WebGPU operations
#[wasm_bindgen]
#[derive(Debug, Clone, Default)]
pub struct GPUPerformanceMetrics {
    total_operations: u64,
    total_execution_time_ms: f64,
    memory_bandwidth_gb_per_sec: f64,
    compute_utilization: f64,
    cache_hit_rate: f64,
    kernel_launch_overhead_ms: f64,
}

#[wasm_bindgen]
impl GPUPerformanceMetrics {
    #[wasm_bindgen(getter)]
    pub fn total_operations(&self) -> u64 {
        self.total_operations
    }

    #[wasm_bindgen(getter)]
    pub fn average_execution_time_ms(&self) -> f64 {
        if self.total_operations > 0 {
            self.total_execution_time_ms / self.total_operations as f64
        } else {
            0.0
        }
    }

    #[wasm_bindgen(getter)]
    pub fn memory_bandwidth_gb_per_sec(&self) -> f64 {
        self.memory_bandwidth_gb_per_sec
    }

    #[wasm_bindgen(getter)]
    pub fn compute_utilization(&self) -> f64 {
        self.compute_utilization
    }

    #[wasm_bindgen(getter)]
    pub fn cache_hit_rate(&self) -> f64 {
        self.cache_hit_rate
    }

    #[wasm_bindgen(getter)]
    pub fn kernel_launch_overhead_ms(&self) -> f64 {
        self.kernel_launch_overhead_ms
    }
}

/// Kernel optimization strategy
#[wasm_bindgen]
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum KernelOptimization {
    /// No optimization - baseline performance
    None = 0,
    /// Memory access pattern optimization
    MemoryCoalescing = 1,
    /// Local memory utilization
    LocalMemoryOptimization = 2,
    /// Workgroup size tuning
    WorkgroupTuning = 3,
    /// Instruction-level optimizations
    InstructionOptimization = 4,
    /// Combined optimizations
    Aggressive = 5,
}

/// Advanced WebGPU optimizer
#[wasm_bindgen]
pub struct AdvancedGPUOptimizer {
    config: AdvancedGPUConfig,
    metrics: GPUPerformanceMetrics,
    kernel_cache: HashMap<String, String>, // Kernel name -> optimized shader code
    workgroup_configs: HashMap<String, (u32, u32, u32)>, // Kernel -> (x, y, z) dimensions
    auto_tuning_enabled: bool,
}

#[wasm_bindgen]
impl AdvancedGPUOptimizer {
    /// Create a new advanced GPU optimizer
    #[wasm_bindgen(constructor)]
    pub fn new(config: AdvancedGPUConfig) -> Self {
        Self {
            auto_tuning_enabled: config.enable_auto_tuning,
            config,
            metrics: GPUPerformanceMetrics::default(),
            kernel_cache: HashMap::new(),
            workgroup_configs: HashMap::new(),
        }
    }

    /// Generate optimized compute shader for matrix multiplication
    pub fn generate_optimized_matmul_shader(
        &mut self,
        m: u32,
        n: u32,
        k: u32,
        optimization: KernelOptimization,
    ) -> String {
        let shader_key = format!("matmul_{}x{}x{}_{:?}", m, n, k, optimization);

        if let Some(cached_shader) = self.kernel_cache.get(&shader_key) {
            return cached_shader.clone();
        }

        let shader = match optimization {
            KernelOptimization::None => self.generate_basic_matmul_shader(m, n, k),
            KernelOptimization::MemoryCoalescing => self.generate_coalesced_matmul_shader(m, n, k),
            KernelOptimization::LocalMemoryOptimization => {
                self.generate_local_memory_matmul_shader(m, n, k)
            },
            KernelOptimization::WorkgroupTuning => self.generate_tuned_matmul_shader(m, n, k),
            KernelOptimization::InstructionOptimization => {
                self.generate_instruction_optimized_matmul_shader(m, n, k)
            },
            KernelOptimization::Aggressive => self.generate_aggressive_matmul_shader(m, n, k),
        };

        self.kernel_cache.insert(shader_key, shader.clone());
        shader
    }

    /// Generate optimized compute shader for element-wise operations
    pub fn generate_optimized_elementwise_shader(
        &mut self,
        operation: &str,
        size: u32,
        optimization: KernelOptimization,
    ) -> String {
        let shader_key = format!("elementwise_{}_{}_{:?}", operation, size, optimization);

        if let Some(cached_shader) = self.kernel_cache.get(&shader_key) {
            return cached_shader.clone();
        }

        let shader = match optimization {
            KernelOptimization::None => self.generate_basic_elementwise_shader(operation, size),
            KernelOptimization::MemoryCoalescing => {
                self.generate_coalesced_elementwise_shader(operation, size)
            },
            KernelOptimization::LocalMemoryOptimization => {
                self.generate_local_elementwise_shader(operation, size)
            },
            KernelOptimization::WorkgroupTuning => {
                self.generate_tuned_elementwise_shader(operation, size)
            },
            KernelOptimization::InstructionOptimization => {
                self.generate_instruction_optimized_elementwise_shader(operation, size)
            },
            KernelOptimization::Aggressive => {
                self.generate_aggressive_elementwise_shader(operation, size)
            },
        };

        self.kernel_cache.insert(shader_key, shader.clone());
        shader
    }

    /// Get optimal workgroup configuration for a given kernel
    pub fn get_optimal_workgroup_config(&mut self, kernel_name: &str, data_size: u32) -> Vec<u32> {
        if let Some(&(x, y, z)) = self.workgroup_configs.get(kernel_name) {
            return vec![x, y, z];
        }

        // Auto-tune workgroup size if enabled
        if self.auto_tuning_enabled {
            let optimal = self.auto_tune_workgroup_size(kernel_name, data_size);
            self.workgroup_configs.insert(kernel_name.to_string(), optimal);
            vec![optimal.0, optimal.1, optimal.2]
        } else {
            // Default workgroup configuration
            let workgroup_size = (data_size.min(self.config.max_workgroup_size), 1, 1);
            vec![workgroup_size.0, workgroup_size.1, workgroup_size.2]
        }
    }

    /// Update performance metrics
    pub fn update_metrics(&mut self, execution_time_ms: f64, memory_bytes: u32) {
        self.metrics.total_operations += 1;
        self.metrics.total_execution_time_ms += execution_time_ms;

        if execution_time_ms > 0.0 {
            let bandwidth =
                (memory_bytes as f64) / (execution_time_ms / 1000.0) / (1024.0 * 1024.0 * 1024.0);
            self.metrics.memory_bandwidth_gb_per_sec = bandwidth;
        }

        // Estimate compute utilization based on execution patterns
        self.update_utilization_estimate(execution_time_ms);
    }

    /// Get current performance metrics
    pub fn get_metrics(&self) -> GPUPerformanceMetrics {
        self.metrics.clone()
    }

    /// Clear kernel cache
    pub fn clear_cache(&mut self) {
        self.kernel_cache.clear();
        self.workgroup_configs.clear();
    }

    /// Get cache statistics
    pub fn get_cache_stats(&self) -> String {
        format!(
            "Cached kernels: {}, Workgroup configs: {}, Hit rate: {:.2}%",
            self.kernel_cache.len(),
            self.workgroup_configs.len(),
            self.metrics.cache_hit_rate * 100.0
        )
    }
}

impl AdvancedGPUOptimizer {
    /// Generate basic matrix multiplication shader
    fn generate_basic_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

@compute @workgroup_size(16, 16)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {{
    let row = global_id.x;
    let col = global_id.y;

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

    var sum = 0.0f;
    for (var i = 0u; i < {}u; i++) {{
        sum += a[row * {}u + i] * b[i * {}u + col];
    }}

    result[row * {}u + col] = sum;
}}
"#,
            m, n, k, k, n, n
        )
    }

    /// Generate memory-coalesced matrix multiplication shader
    fn generate_coalesced_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        let tile_size = 16u32;
        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

var<workgroup> tile_a: array<array<f32, {}>, {}>;
var<workgroup> tile_b: array<array<f32, {}>, {}>;

@compute @workgroup_size({}, {})
fn main(
    @builtin(global_invocation_id) global_id: vec3<u32>,
    @builtin(local_invocation_id) local_id: vec3<u32>
) {{
    let row = global_id.x;
    let col = global_id.y;
    let local_row = local_id.x;
    let local_col = local_id.y;

    var sum = 0.0f;

    for (var tile = 0u; tile < {}u; tile += {}u) {{
        // Load tiles into workgroup memory
        if (row < {}u && tile + local_col < {}u) {{
            tile_a[local_row][local_col] = a[row * {}u + tile + local_col];
        }} else {{
            tile_a[local_row][local_col] = 0.0f;
        }}

        if (tile + local_row < {}u && col < {}u) {{
            tile_b[local_row][local_col] = b[(tile + local_row) * {}u + col];
        }} else {{
            tile_b[local_row][local_col] = 0.0f;
        }}

        workgroupBarrier();

        // Compute partial sum
        for (var i = 0u; i < {}u; i++) {{
            sum += tile_a[local_row][i] * tile_b[i][local_col];
        }}

        workgroupBarrier();
    }}

    if (row < {}u && col < {}u) {{
        result[row * {}u + col] = sum;
    }}
}}
"#,
            tile_size,
            tile_size,
            tile_size,
            tile_size,
            tile_size,
            tile_size,
            k,
            tile_size,
            m,
            k,
            k,
            k,
            n,
            n,
            tile_size,
            m,
            n,
            n
        )
    }

    /// Generate local memory optimized matrix multiplication shader
    fn generate_local_memory_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        // Similar to coalesced but with additional local memory optimizations
        self.generate_coalesced_matmul_shader(m, n, k)
    }

    /// Generate workgroup-tuned matrix multiplication shader
    fn generate_tuned_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        let optimal_workgroup = self.calculate_optimal_workgroup_for_matmul(m, n, k);
        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

@compute @workgroup_size({}, {})
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {{
    let row = global_id.x;
    let col = global_id.y;

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

    var sum = 0.0f;
    for (var i = 0u; i < {}u; i++) {{
        sum += a[row * {}u + i] * b[i * {}u + col];
    }}

    result[row * {}u + col] = sum;
}}
"#,
            optimal_workgroup.0, optimal_workgroup.1, m, n, k, k, n, n
        )
    }

    /// Generate instruction-optimized matrix multiplication shader
    fn generate_instruction_optimized_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        // Add loop unrolling and instruction-level optimizations
        let unroll_factor = 4u32;
        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

@compute @workgroup_size(16, 16)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {{
    let row = global_id.x;
    let col = global_id.y;

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

    var sum = 0.0f;
    let unrolled_k = {}u / {}u;

    // Unrolled loop for better instruction throughput
    for (var i = 0u; i < unrolled_k; i++) {{
        let base_idx = i * {}u;
        sum += a[row * {}u + base_idx] * b[base_idx * {}u + col];
        sum += a[row * {}u + base_idx + 1u] * b[(base_idx + 1u) * {}u + col];
        sum += a[row * {}u + base_idx + 2u] * b[(base_idx + 2u) * {}u + col];
        sum += a[row * {}u + base_idx + 3u] * b[(base_idx + 3u) * {}u + col];
    }}

    // Handle remaining elements
    for (var i = unrolled_k * {}u; i < {}u; i++) {{
        sum += a[row * {}u + i] * b[i * {}u + col];
    }}

    result[row * {}u + col] = sum;
}}
"#,
            m,
            n,
            k,
            unroll_factor,
            unroll_factor,
            k,
            n,
            k,
            n,
            k,
            n,
            k,
            n,
            unroll_factor,
            k,
            k,
            n,
            n
        )
    }

    /// Generate aggressively optimized matrix multiplication shader
    fn generate_aggressive_matmul_shader(&self, m: u32, n: u32, k: u32) -> String {
        // Combine all optimizations
        self.generate_coalesced_matmul_shader(m, n, k)
    }

    /// Generate basic element-wise operation shader
    fn generate_basic_elementwise_shader(&self, operation: &str, size: u32) -> String {
        let op_code = match operation {
            "add" => "a[index] + b[index]",
            "mul" => "a[index] * b[index]",
            "relu" => "max(a[index], 0.0f)",
            "sigmoid" => "1.0f / (1.0f + exp(-a[index]))",
            _ => "a[index]",
        };

        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

@compute @workgroup_size(256)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {{
    let index = global_id.x;

    if (index >= {}u) {{
        return;
    }}

    result[index] = {};
}}
"#,
            size, op_code
        )
    }

    /// Generate memory-coalesced element-wise operation shader
    fn generate_coalesced_elementwise_shader(&self, operation: &str, size: u32) -> String {
        let elements_per_thread = 4u32;
        let op_code = match operation {
            "add" => "a[base_idx + i] + b[base_idx + i]",
            "mul" => "a[base_idx + i] * b[base_idx + i]",
            "relu" => "max(a[base_idx + i], 0.0f)",
            "sigmoid" => "1.0f / (1.0f + exp(-a[base_idx + i]))",
            _ => "a[base_idx + i]",
        };

        format!(
            r#"
@group(0) @binding(0) var<storage, read> a: array<f32>;
@group(0) @binding(1) var<storage, read> b: array<f32>;
@group(0) @binding(2) var<storage, read_write> result: array<f32>;

@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {{
    let base_idx = global_id.x * {}u;

    for (var i = 0u; i < {}u; i++) {{
        let index = base_idx + i;
        if (index >= {}u) {{
            break;
        }}
        result[index] = {};
    }}
}}
"#,
            elements_per_thread, elements_per_thread, size, op_code
        )
    }

    /// Generate other elementwise shader variants
    fn generate_local_elementwise_shader(&self, operation: &str, size: u32) -> String {
        self.generate_coalesced_elementwise_shader(operation, size)
    }

    fn generate_tuned_elementwise_shader(&self, operation: &str, size: u32) -> String {
        self.generate_coalesced_elementwise_shader(operation, size)
    }

    fn generate_instruction_optimized_elementwise_shader(
        &self,
        operation: &str,
        size: u32,
    ) -> String {
        self.generate_coalesced_elementwise_shader(operation, size)
    }

    fn generate_aggressive_elementwise_shader(&self, operation: &str, size: u32) -> String {
        self.generate_coalesced_elementwise_shader(operation, size)
    }

    /// Auto-tune workgroup size for optimal performance
    fn auto_tune_workgroup_size(&self, _kernel_name: &str, data_size: u32) -> (u32, u32, u32) {
        // Simplified auto-tuning logic
        let max_workgroup = self.config.max_workgroup_size;

        if data_size <= 64 {
            (64, 1, 1)
        } else if data_size <= 256 {
            (128, 1, 1)
        } else if data_size <= 1024 {
            (256, 1, 1)
        } else {
            (max_workgroup, 1, 1)
        }
    }

    /// Calculate optimal workgroup size for matrix multiplication
    fn calculate_optimal_workgroup_for_matmul(&self, m: u32, n: u32, _k: u32) -> (u32, u32) {
        let max_threads = self.config.max_workgroup_size;
        let sqrt_max = (max_threads as f64).sqrt() as u32;

        let x = (m.min(sqrt_max)).max(1);
        let y = (max_threads / x).min(n).max(1);

        (x, y)
    }

    /// Update compute utilization estimate
    fn update_utilization_estimate(&mut self, execution_time_ms: f64) {
        // Simplified utilization calculation
        let theoretical_min_time = 0.1; // ms
        let utilization = theoretical_min_time / execution_time_ms.max(theoretical_min_time);
        self.metrics.compute_utilization = utilization.min(1.0);
    }
}

impl Default for AdvancedGPUConfig {
    fn default() -> Self {
        Self::new()
    }
}