use crate::{DType, Result, TensorError};
use rayon::prelude::*;
use scirs2_core::profiling::Profiler;
use std::collections::HashMap;
use super::types::{FusableOp, FusedOperation};
pub mod ultra_scheduler {
use super::*;
use scirs2_core::parallel_ops::{par_chunks, par_join};
use scirs2_core::profiling::Profiler;
pub struct UltraKernelScheduler {
kernel_queue: Vec<ScheduledKernel>,
predictor: PerformancePredictor,
resource_tracker: ResourceTracker,
}
#[derive(Debug)]
struct ScheduledKernel {
kernel_id: String,
fusion_pattern: FusedOperation,
priority: u8,
estimated_runtime_ms: f64,
memory_requirement_mb: u64,
}
#[derive(Debug)]
struct PerformancePredictor {
historical_data: HashMap<String, Vec<f64>>,
prediction_accuracy: f64,
}
#[derive(Debug)]
struct ResourceTracker {
gpu_utilization: f64,
memory_utilization: f64,
bandwidth_utilization: f64,
compute_units_active: u32,
}
impl UltraKernelScheduler {
pub fn new_ultra_performance() -> Self {
Self {
kernel_queue: Vec::new(),
predictor: PerformancePredictor {
historical_data: HashMap::new(),
prediction_accuracy: 0.95,
},
resource_tracker: ResourceTracker {
gpu_utilization: 0.0,
memory_utilization: 0.0,
bandwidth_utilization: 0.0,
compute_units_active: 0,
},
}
}
pub fn schedule_kernel(&mut self, fusion_pattern: FusedOperation) -> Result<()> {
let _profiler = Profiler::new();
let estimated_runtime = self.predict_runtime(&fusion_pattern)?;
let memory_requirement = self.estimate_memory_usage(&fusion_pattern)?;
let scheduled_kernel = ScheduledKernel {
kernel_id: fusion_pattern.kernel_id.clone(),
fusion_pattern,
priority: self.calculate_priority(estimated_runtime, memory_requirement),
estimated_runtime_ms: estimated_runtime,
memory_requirement_mb: memory_requirement,
};
self.kernel_queue.push(scheduled_kernel);
self.optimize_queue()?;
Ok(())
}
pub fn execute_optimized_batch(&mut self) -> Result<Vec<String>> {
let _profiler = Profiler::new();
let mut executed_kernels = Vec::new();
self.kernel_queue.sort_by(|a, b| {
b.priority.cmp(&a.priority).then(
a.estimated_runtime_ms
.partial_cmp(&b.estimated_runtime_ms)
.unwrap_or(std::cmp::Ordering::Equal),
)
});
while let Some(kernel) = self.kernel_queue.pop() {
if self.can_execute_kernel(&kernel)? {
println!("🚀 Executing ultra-optimized kernel: {}", kernel.kernel_id);
self.update_resource_utilization(&kernel)?;
executed_kernels.push(kernel.kernel_id);
}
}
Ok(executed_kernels)
}
fn predict_runtime(&self, fusion_pattern: &FusedOperation) -> Result<f64> {
let base_time = match fusion_pattern.operations.len() {
1..=2 => 0.1,
3..=5 => 0.5,
6..=10 => 1.5,
_ => 3.0,
};
let complexity_multiplier = fusion_pattern
.operations
.iter()
.map(|op| match op {
FusableOp::MatMul | FusableOp::Conv2D => 2.0,
FusableOp::MultiHeadAttention => 3.0,
FusableOp::FP8MatMul => 1.5,
_ => 1.0,
})
.sum::<f64>()
/ fusion_pattern.operations.len() as f64;
Ok(base_time * complexity_multiplier)
}
fn estimate_memory_usage(&self, fusion_pattern: &FusedOperation) -> Result<u64> {
let base_memory_mb = match fusion_pattern.operations.len() {
1..=2 => 16,
3..=5 => 64,
6..=10 => 256,
_ => 512,
};
Ok(base_memory_mb)
}
fn calculate_priority(&self, runtime_ms: f64, memory_mb: u64) -> u8 {
match (runtime_ms, memory_mb) {
(r, m) if r < 1.0 && m < 64 => 255,
(r, m) if r < 2.0 && m < 128 => 200,
(r, m) if r < 5.0 && m < 256 => 150,
_ => 100,
}
}
fn optimize_queue(&mut self) -> Result<()> {
self.kernel_queue.sort_by(|a, b| {
let efficiency_a = a.priority as f64
/ (a.estimated_runtime_ms + a.memory_requirement_mb as f64 * 0.01);
let efficiency_b = b.priority as f64
/ (b.estimated_runtime_ms + b.memory_requirement_mb as f64 * 0.01);
efficiency_b
.partial_cmp(&efficiency_a)
.expect("partial_cmp should not return None for valid values")
});
Ok(())
}
fn can_execute_kernel(&self, kernel: &ScheduledKernel) -> Result<bool> {
let memory_available =
1024 - (self.resource_tracker.memory_utilization * 1024.0) as u64;
let can_execute = memory_available >= kernel.memory_requirement_mb
&& self.resource_tracker.gpu_utilization < 0.9;
Ok(can_execute)
}
fn update_resource_utilization(&mut self, kernel: &ScheduledKernel) -> Result<()> {
self.resource_tracker.memory_utilization = (self.resource_tracker.memory_utilization
+ kernel.memory_requirement_mb as f64 * 0.001)
.min(1.0);
self.resource_tracker.gpu_utilization =
(self.resource_tracker.gpu_utilization + 0.1).min(1.0);
self.resource_tracker.compute_units_active += 1;
Ok(())
}
pub fn get_performance_analytics(&self) -> HashMap<String, f64> {
let mut analytics = HashMap::new();
analytics.insert("queue_length".to_string(), self.kernel_queue.len() as f64);
analytics.insert(
"prediction_accuracy".to_string(),
self.predictor.prediction_accuracy,
);
analytics.insert(
"gpu_utilization".to_string(),
self.resource_tracker.gpu_utilization,
);
analytics.insert(
"memory_utilization".to_string(),
self.resource_tracker.memory_utilization,
);
analytics.insert(
"active_compute_units".to_string(),
self.resource_tracker.compute_units_active as f64,
);
analytics
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_fused_operation_creation() {
let fused_op = FusedOperation::fused_dense_layer(Some(FusableOp::ReLU));
assert_eq!(
fused_op.operations,
vec![FusableOp::MatMul, FusableOp::Add, FusableOp::ReLU]
);
assert_eq!(fused_op.input_count, 3);
assert_eq!(fused_op.kernel_id, "fused_matmul_add_relu");
}
#[test]
fn test_elementwise_fusion() {
let fused_op =
FusedOperation::fused_elementwise_activation(FusableOp::Add, FusableOp::Sigmoid);
assert_eq!(
fused_op.operations,
vec![FusableOp::Add, FusableOp::Sigmoid]
);
assert_eq!(fused_op.input_count, 2);
assert_eq!(fused_op.kernel_id, "fused_add_sigmoid");
}
#[test]
fn test_kernel_id_generation() {
let ops = vec![FusableOp::Mul, FusableOp::ReLU];
let kernel_id = FusedOperation::generate_kernel_id(&ops);
assert_eq!(kernel_id, "fused_mul_relu");
}
}