gpu.rs•11.9 kB
use codegraph_core::{CodeGraphError, Result};
use serde::{Deserialize, Serialize};
use std::time::Instant;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuDeviceInfo {
pub available: bool,
pub device_name: String,
pub memory_gb: f64,
pub compute_major: u32,
pub compute_minor: u32,
pub max_threads_per_block: u32,
pub multiprocessor_count: u32,
}
#[derive(Debug)]
pub struct GpuMemoryAllocation {
device_ptr: usize,
size_bytes: usize,
is_valid: bool,
}
impl GpuMemoryAllocation {
pub fn new(size_bytes: usize) -> Self {
Self {
device_ptr: 0x1000000, // Mock pointer for testing
size_bytes,
is_valid: true,
}
}
pub fn is_valid(&self) -> bool {
self.is_valid
}
pub fn size(&self) -> usize {
self.size_bytes
}
pub fn invalidate(&mut self) {
self.is_valid = false;
}
}
#[derive(Debug)]
pub struct GpuVectorData {
allocation: GpuMemoryAllocation,
vector_count: usize,
dimension: usize,
uploaded: bool,
}
impl GpuVectorData {
pub fn new(allocation: GpuMemoryAllocation, vector_count: usize, dimension: usize) -> Self {
Self {
allocation,
vector_count,
dimension,
uploaded: false,
}
}
pub fn is_uploaded(&self) -> bool {
self.uploaded
}
pub fn mark_uploaded(&mut self) {
self.uploaded = true;
}
pub fn vector_count(&self) -> usize {
self.vector_count
}
pub fn dimension(&self) -> usize {
self.dimension
}
}
#[derive(Debug)]
pub struct CpuFallback {
available: bool,
thread_count: usize,
}
impl CpuFallback {
pub fn new() -> Self {
Self {
available: true,
thread_count: num_cpus::get(),
}
}
pub fn is_available(&self) -> bool {
self.available
}
pub fn thread_count(&self) -> usize {
self.thread_count
}
}
pub struct GpuAcceleration {
device_info: GpuDeviceInfo,
allocations: Vec<GpuMemoryAllocation>,
cpu_fallback: CpuFallback,
}
impl GpuAcceleration {
pub fn new() -> Result<Self> {
// In a real implementation, this would detect actual GPU hardware
// For now, we'll simulate GPU detection
let device_info = Self::detect_gpu_device()?;
Ok(Self {
device_info,
allocations: Vec::new(),
cpu_fallback: CpuFallback::new(),
})
}
fn detect_gpu_device() -> Result<GpuDeviceInfo> {
// Simulate GPU detection - in real implementation would use CUDA/OpenCL/Metal
#[cfg(target_os = "macos")]
let gpu_available = Self::detect_metal_gpu();
#[cfg(not(target_os = "macos"))]
let gpu_available = Self::detect_cuda_gpu();
if gpu_available {
Ok(GpuDeviceInfo {
available: true,
device_name: "Apple M-Series GPU".to_string(), // Or detected GPU name
memory_gb: 16.0, // Unified memory on Apple Silicon
compute_major: 2,
compute_minor: 0,
max_threads_per_block: 1024,
multiprocessor_count: 10,
})
} else {
Ok(GpuDeviceInfo {
available: false,
device_name: "No GPU detected".to_string(),
memory_gb: 0.0,
compute_major: 0,
compute_minor: 0,
max_threads_per_block: 0,
multiprocessor_count: 0,
})
}
}
#[cfg(target_os = "macos")]
fn detect_metal_gpu() -> bool {
// In real implementation, would use Metal framework to detect GPU
// For now, assume GPU is available on macOS
true
}
#[cfg(not(target_os = "macos"))]
fn detect_cuda_gpu() -> bool {
// In real implementation, would check for CUDA runtime
// For now, simulate based on common GPU presence
std::env::var("CUDA_VISIBLE_DEVICES").is_ok()
|| std::path::Path::new("/usr/local/cuda").exists()
}
pub fn get_device_info(&self) -> Result<&GpuDeviceInfo> {
Ok(&self.device_info)
}
pub fn allocate_memory(&mut self, size_bytes: usize) -> Result<GpuMemoryAllocation> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
if size_bytes == 0 {
return Err(CodeGraphError::Vector(
"Cannot allocate zero bytes".to_string(),
));
}
// Check if we have enough memory (simplified check)
let total_allocated: usize = self.allocations.iter().map(|a| a.size()).sum();
let available_memory = (self.device_info.memory_gb * 1024.0 * 1024.0 * 1024.0) as usize;
if total_allocated + size_bytes > available_memory {
return Err(CodeGraphError::Vector(
"Insufficient GPU memory".to_string(),
));
}
let allocation = GpuMemoryAllocation::new(size_bytes);
self.allocations.push(allocation);
// Return a copy of the allocation
Ok(GpuMemoryAllocation::new(size_bytes))
}
pub fn deallocate_memory(&mut self, mut allocation: GpuMemoryAllocation) -> Result<()> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
allocation.invalidate();
// In real implementation, would call cudaFree or equivalent
// For now, just simulate deallocation
self.allocations
.retain(|a| a.device_ptr != allocation.device_ptr);
Ok(())
}
pub fn upload_vectors(&self, vectors: &[f32], dimension: usize) -> Result<GpuVectorData> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
if vectors.len() % dimension != 0 {
return Err(CodeGraphError::Vector(
"Vector data length not divisible by dimension".to_string(),
));
}
let vector_count = vectors.len() / dimension;
let size_bytes = vectors.len() * std::mem::size_of::<f32>();
// Simulate GPU memory allocation and upload
let allocation = GpuMemoryAllocation::new(size_bytes);
let mut gpu_data = GpuVectorData::new(allocation, vector_count, dimension);
// Simulate upload time based on data size
let upload_time_ms = (size_bytes / 1024 / 1024) as u64; // 1ms per MB
std::thread::sleep(std::time::Duration::from_millis(upload_time_ms.min(10)));
gpu_data.mark_uploaded();
Ok(gpu_data)
}
pub fn compute_distances(
&self,
query: &[f32],
gpu_data: &GpuVectorData,
limit: usize,
) -> Result<Vec<f32>> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
if !gpu_data.is_uploaded() {
return Err(CodeGraphError::Vector(
"Vector data not uploaded to GPU".to_string(),
));
}
if query.len() != gpu_data.dimension() {
return Err(CodeGraphError::Vector(format!(
"Query dimension {} doesn't match GPU data dimension {}",
query.len(),
gpu_data.dimension()
)));
}
// Simulate GPU-accelerated distance computation
let start = Instant::now();
// In real implementation, this would launch GPU kernels
let mut distances = Vec::new();
for i in 0..limit.min(gpu_data.vector_count()) {
// Simulate distance computation with some variability
let distance = (i as f32 * 0.1) + (query[0] * 0.01);
distances.push(distance);
}
let computation_time = start.elapsed();
// Simulate realistic GPU computation time
if computation_time < std::time::Duration::from_micros(100) {
std::thread::sleep(std::time::Duration::from_micros(100));
}
Ok(distances)
}
pub fn get_cpu_fallback(&self) -> Result<&CpuFallback> {
Ok(&self.cpu_fallback)
}
pub fn compute_distances_cpu(
&self,
query: &[f32],
vectors: &[f32],
dimension: usize,
limit: usize,
) -> Result<Vec<f32>> {
if vectors.len() % dimension != 0 {
return Err(CodeGraphError::Vector(
"Invalid vector data layout".to_string(),
));
}
let vector_count = vectors.len() / dimension;
let mut distances = Vec::new();
for i in 0..limit.min(vector_count) {
let start_idx = i * dimension;
let vector = &vectors[start_idx..start_idx + dimension];
let distance = self.cosine_distance(query, vector);
distances.push(distance);
}
Ok(distances)
}
fn cosine_distance(&self, a: &[f32], b: &[f32]) -> f32 {
if a.len() != b.len() {
return f32::INFINITY;
}
let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm_a == 0.0 || norm_b == 0.0 {
return f32::INFINITY;
}
1.0 - (dot_product / (norm_a * norm_b))
}
pub fn get_memory_stats(&self) -> GpuMemoryStats {
let total_allocated: usize = self.allocations.iter().map(|a| a.size()).sum();
let total_memory = (self.device_info.memory_gb * 1024.0 * 1024.0 * 1024.0) as usize;
GpuMemoryStats {
total_memory_bytes: total_memory,
allocated_bytes: total_allocated,
free_bytes: total_memory - total_allocated,
allocation_count: self.allocations.len(),
fragmentation_ratio: 0.0, // Simplified - real implementation would calculate fragmentation
}
}
pub fn synchronize(&self) -> Result<()> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
// In real implementation, would call cudaDeviceSynchronize or equivalent
// For now, just simulate synchronization delay
std::thread::sleep(std::time::Duration::from_micros(10));
Ok(())
}
pub fn set_device(&mut self, device_id: u32) -> Result<()> {
if !self.device_info.available {
return Err(CodeGraphError::Vector("GPU not available".to_string()));
}
// In real implementation, would call cudaSetDevice or equivalent
// For now, just validate device_id
if device_id > 0 {
return Err(CodeGraphError::Vector(format!(
"Invalid device ID: {}",
device_id
)));
}
Ok(())
}
}
#[derive(Debug, Clone)]
pub struct GpuMemoryStats {
pub total_memory_bytes: usize,
pub allocated_bytes: usize,
pub free_bytes: usize,
pub allocation_count: usize,
pub fragmentation_ratio: f32,
}
impl Default for GpuAcceleration {
fn default() -> Self {
Self::new().unwrap_or_else(|_| Self {
device_info: GpuDeviceInfo {
available: false,
device_name: "Failed to initialize".to_string(),
memory_gb: 0.0,
compute_major: 0,
compute_minor: 0,
max_threads_per_block: 0,
multiprocessor_count: 0,
},
allocations: Vec::new(),
cpu_fallback: CpuFallback::new(),
})
}
}