use crate::error::{Error, Result};
use scirs2_core::ndarray::{s, Array, Array1, Array2, ArrayD};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::Duration;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceInfo {
pub id: u32,
pub name: String,
pub memory_total: u64,
pub memory_free: u64,
pub compute_capability: (u32, u32),
pub multiprocessor_count: u32,
pub warp_size: u32,
pub is_available: bool,
}
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct MemoryStats {
pub allocated: u64,
pub reserved: u64,
pub active: u64,
pub inactive: u64,
pub cached: u64,
pub struct MixedPrecisionConfig {
pub enabled: bool,
pub loss_scale: f32,
pub loss_scale_window: u32,
pub min_loss_scale: f32,
pub max_loss_scale: f32,
pub scale_factor: f32,
impl Default for MixedPrecisionConfig {
fn default() -> Self {
Self {
enabled: false,
loss_scale: 65536.0,
loss_scale_window: 2000,
min_loss_scale: 1.0,
max_loss_scale: 2.0_f32.powi(16),
scale_factor: 2.0,
}
}
}
#[derive(Debug)]
pub struct GpuContext {
devices: Vec<DeviceInfo>,
current_device: u32,
memory_stats: Arc<RwLock<HashMap<u32, MemoryStats>>>,
mixed_precision: MixedPrecisionConfig,
#[allow(dead_code)]
stream_pool: Arc<Mutex<Vec<u32>>>, impl GpuContext {
pub fn new() -> Result<Self> {
let devices = Self::discover_devices()?;
let device_count = devices.len() as u32;
if device_count == 0 {
return Err(Error::ComputationError("No GPU devices found".to_string()));
let mut memory_stats = HashMap::new();
for device in &devices {
memory_stats.insert(device.id, MemoryStats::default());
Ok(Self {
devices,
current_device: 0,
memory_stats: Arc::new(RwLock::new(memory_stats)),
mixed_precision: MixedPrecisionConfig::default(),
stream_pool: Arc::new(Mutex::new(Vec::new())),
})
fn discover_devices() -> Result<Vec<DeviceInfo>> {
let mut devices = Vec::new();
for i in 0..Self::get_device_count() {
devices.push(DeviceInfo {
id: i,
name: format!("GPU Device {}", i),
memory_total: 8 * 1024 * 1024 * 1024, // 8GB
memory_free: 7 * 1024 * 1024 * 1024, // 7GB free
compute_capability: (7, 5), // Simulated compute capability
multiprocessor_count: 68,
warp_size: 32,
is_available: true,
});
Ok(devices)
fn get_device_count() -> u32 {
1
pub fn set_device(&mut self, deviceid: u32) -> Result<()> {
if device_id >= self.devices.len() as u32 {
return Err(Error::InvalidArgument(format!(
"Device ID {} not found. Available devices: 0-{}",
device_id,
self.devices.len() - 1
)));
self.current_device = device_id;
Ok(())
pub fn current_device_info(&self) -> &DeviceInfo {
&self.devices[self.current_device as usize]
pub fn all_devices(&self) -> &[DeviceInfo] {
&self.devices
pub fn enable_mixed_precision(&mut self, config: MixedPrecisionConfig) {
self.mixed_precision = config;
pub fn memory_stats(&self, deviceid: u32) -> Result<MemoryStats> {
let stats = self.memory_stats.read().expect("Operation failed");
stats
.get(&device_id)
.cloned()
.ok_or_else(|| Error::InvalidArgument(format!("Device {} not found", device_id)))
pub fn allocate_memory(&self, size: u64, deviceid: u32) -> Result<GpuMemoryHandle> {
"Invalid device ID: {}",
device_id
// Update memory statistics
{
let mut stats = self.memory_stats.write().expect("Operation failed");
if let Some(device_stats) = stats.get_mut(&device_id) {
device_stats.allocated += size;
device_stats.active += size;
}
Ok(GpuMemoryHandle {
ptr: size as *mut u8, // Simulated pointer
size,
device_id,
/// Free GPU memory
pub fn free_memory(&self, handle: &GpuMemoryHandle) -> Result<()> {
let mut stats = self.memory_stats.write().expect("Operation failed");
if let Some(device_stats) = stats.get_mut(&handle.device_id) {
device_stats.allocated = device_stats.allocated.saturating_sub(handle.size);
device_stats.active = device_stats.active.saturating_sub(handle.size);
}
Ok(())
}
}
/// GPU memory handle
pub struct GpuMemoryHandle {
ptr: *mut u8,
size: u64,
device_id: u32,
unsafe impl Send for GpuMemoryHandle {}
unsafe impl Sync for GpuMemoryHandle {}
/// CUDA safe wrapper for tensor operations
pub struct CudaTensor<T> {
data: GpuMemoryHandle,
shape: Vec<usize>,
strides: Vec<usize>, _phantom: std::marker::PhantomData<T>,
impl<T> CudaTensor<T>
where
T: Copy + Default + Send + Sync,
{
/// Create new CUDA tensor
pub fn new(shape: Vec<usize>, deviceid: u32, context: &GpuContext) -> Result<Self> {
let size = shape.iter().product::<usize>() * std::mem::size_of::<T>();
let data = context.allocate_memory(size as u64, device_id)?;
let mut strides = vec![1; shape.len()];
for i in (0..shape.len().saturating_sub(1)).rev() {
strides[i] = strides[i + 1] * shape[i + 1];
data,
shape,
strides_phantom: std::marker::PhantomData,
/// Get tensor shape
pub fn shape(&self) -> &[usize] {
&self.shape
/// Get device ID
pub fn device_id(&self) -> u32 {
self.device_id
/// Copy tensor to different device
pub fn to_device(&self, targetdevice: u32, context: &GpuContext) -> Result<Self> {
let new_tensor = Self::new(self.shape.clone(), target_device, context)?;
// In real implementation, would perform _device-to-_device copy
Ok(new_tensor)
}
}
/// Multi-GPU training coordinator
pub struct MultiGpuTrainer {
contexts: Vec<Arc<GpuContext>>,
communication_backend: String,
reduction_strategy: ReductionStrategy,
/// Gradient reduction strategy for multi-GPU training
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ReductionStrategy {
/// All-reduce algorithm
AllReduce,
/// Parameter server algorithm
ParameterServer,
/// Hierarchical reduction algorithm
Hierarchical,
impl MultiGpuTrainer {
/// Create new multi-GPU trainer
pub fn new(_deviceids: Vec<u32>) -> Result<Self> {
let mut contexts = Vec::new();
for &device_id in &_device_ids {
let mut context = GpuContext::new()?;
context.set_device(device_id)?;
contexts.push(Arc::new(context));
contexts,
communication_backend: "NCCL".to_string(),
reduction_strategy: ReductionStrategy::AllReduce,
/// Set reduction strategy
pub fn set_reduction_strategy(&mut self, strategy: ReductionStrategy) {
self.reduction_strategy = strategy;
/// Perform all-reduce operation across GPUs
pub fn all_reduce<T>(&self, tensors: &mut [CudaTensor<T>]) -> Result<()>
where
T: Copy
+ Default
+ Send
+ Sync
+ std::ops::Add<Output = T>
+ std::ops::Div<Output = T>
+ From<usize>,
{
if tensors.len() != self.contexts.len() {
return Err(Error::InvalidArgument(
"Number of tensors must match number of devices".to_string(),
));
match self.reduction_strategy {
ReductionStrategy::AllReduce => self.all_reduce_impl(tensors),
ReductionStrategy::ParameterServer => self.parameter_server_reduce(tensors),
ReductionStrategy::Hierarchical => self.hierarchical_reduce(tensors),
}
}
fn all_reduce_impl<T>(selftensors: &mut [CudaTensor<T>]) -> Result<()>
where
T: Copy + Default + Send + Sync,
{
// Simulate all-reduce operation
// In real implementation, would use NCCL or similar
thread::sleep(Duration::from_millis(1)); // Simulate communication latency
Ok(())
}
fn parameter_server_reduce<T>(selftensors: &mut [CudaTensor<T>]) -> Result<()> {
// Simulate parameter server reduction
thread::sleep(Duration::from_millis(2));
Ok(())
}
fn hierarchical_reduce<T>(selftensors: &mut [CudaTensor<T>]) -> Result<()> {
// Simulate hierarchical reduction
thread::sleep(Duration::from_millis(1));
Ok(())
}
/// Get number of devices
pub fn device_count(&self) -> usize {
self.contexts.len()
}
/// Get memory statistics for all devices
pub fn memory_stats_all(&self) -> Result<Vec<MemoryStats>> {
let mut stats = Vec::new();
for (i, context) in self.contexts.iter().enumerate() {
stats.push(context.memory_stats(i as u32)?);
}
Ok(stats)
}
}
/// Enhanced neural operations accelerator with full GPU support
pub struct NeuralOps {
/// GPU context for device management
gpu_context: Option<Arc<GpuContext>>,
/// Backend identifier
backend_type: String,
/// Mixed precision enabled
mixed_precision: bool,
impl NeuralOps {
/// Create new neural operations context with CPU backend
gpu_context: None,
backend_type: "CPU".to_string(),
mixed_precision: false,
/// Create with GPU backend
pub fn with_gpu() -> Result<Self> {
let gpu_context = GpuContext::new().ok();
let backend_type = if gpu_context.is_some() { "GPU" } else { "CPU" };
gpu_context: gpu_context.map(Arc::new),
backend_type: backend_type.to_string(),
/// Create with specified backend preference
pub fn with_backend(backend: &str) -> Result<Self> {
match backend.to_uppercase().as_str() {
"GPU" | "CUDA" => Self::with_gpu(),
"CPU" => Self::new(, _ => {
println!("Unknown _backend '{}', falling back to CPU", backend);
Self::new()
pub fn enable_mixed_precision(&mut self, config: MixedPrecisionConfig) -> Result<()> {
if self.gpu_context.is_none() {
return Err(Error::ComputationError(
"Mixed precision requires GPU backend".to_string(),
if let Some(ref gpu_context) = self.gpu_context {
// In a real implementation, we'd need mutable access to the context
// For now, we'll store the mixed precision flag locally
self.mixed_precision = config.enabled;
/// Check if GPU is available
pub fn is_gpu_available(&self) -> bool {
self.gpu_context.is_some()
/// Get GPU context (if available)
pub fn gpu_context(&self) -> Option<&Arc<GpuContext>> {
self.gpu_context.as_ref()
/// Optimized matrix multiplication
pub fn matrix_multiply(&self, a: &Array2<f32>, b: &Array2<f32>) -> Result<Array2<f32>> {
let (m, k) = a.dim();
let (k2, n) = b.dim();
if k != k2 {
return Err(Error::DimensionMismatch(format!(
"Matrix dimensions don't match for multiplication: {}x{} * {}x{}",
m, k, k2, n
// Use ndarray's optimized BLAS implementation
Ok(a.dot(b))
/// Batch matrix multiplication for neural network layers
pub fn batch_matrix_multiply(&self, a: &ArrayD<f32>, b: &ArrayD<f32>) -> Result<ArrayD<f32>> {
let ashape = a.shape();
let bshape = b.shape();
if ashape.len() != 3 || bshape.len() != 3 {
return Err(Error::DimensionMismatch(
"Batch matrix multiply requires 3D arrays (batch, rows, cols)".to_string(),
let batch_size = ashape[0];
let m = ashape[1];
let _k = ashape[2];
let n = bshape[2];
if ashape[0] != bshape[0] || ashape[2] != bshape[1] {
"Batch matrix dimensions don't match: {:?} * {:?}",
ashape, bshape
let mut result = Array::zeros((batch_size, m, n));
// Process each batch
for i in 0..batch_size {
let a_slice = a.slice(s![i, .., ..]);
let b_slice = b.slice(s![i, .., ..]);
let mut result_slice = result.slice_mut(s![i, .., ..]);
// Convert to 2D for matrix multiplication
let a_2d = a_slice
.into_dimensionality::<scirs2_core::ndarray::Ix2>()
.map_err(|e| Error::ComputationError(format!("Failed to convert to 2D: {}", e)))?;
let b_2d = b_slice
result_slice.assign(&a_2d.dot(&b_2d));
Ok(result.into_dyn())
/// ReLU activation function
pub fn relu_forward(&self, input: &ArrayD<f32>) -> Result<ArrayD<f32>> {
Ok(input.mapv(|x| x.max(0.0)))
/// ReLU derivative for backpropagation
pub fn relu_backward(
&self,
input: &ArrayD<f32>,
grad_output: &ArrayD<f32>,
) -> Result<ArrayD<f32>> {
if input.shape() != grad_output.shape() {
"Input and gradient shapes must match for ReLU backward".to_string(),
Ok(scirs2_core::ndarray::Zip::from(input)
.and(grad_output)
.map_collect(|&x, &grad| if x > 0.0 { grad } else { 0.0 }))
/// Sigmoid activation function
pub fn sigmoid_forward(&self, input: &ArrayD<f32>) -> Result<ArrayD<f32>> {
Ok(input.mapv(|x| 1.0 / (1.0 + (-x).exp())))
/// Sigmoid derivative
pub fn sigmoid_backward(
output: &ArrayD<f32>,
if output.shape() != grad_output.shape() {
"Output and gradient shapes must match for sigmoid backward".to_string(),
Ok(scirs2_core::ndarray::Zip::from(output)
.map_collect(|&sigmoid_out, &grad| grad * sigmoid_out * (1.0 - sigmoid_out)))
/// Batch normalization forward pass
pub fn batch_normalize(
mean: &Array1<f32>,
var: &Array1<f32>,
gamma: &Array1<f32>,
beta: &Array1<f32>,
epsilon: f32,
let inputshape = input.shape();
let channels = mean.len();
// Check that all parameter arrays have the same length
if var.len() != channels || gamma.len() != channels || beta.len() != channels {
"All batch norm parameters must have the same length".to_string(),
// Assume channel-last format (NHWC) - last dimension is channels
if inputshape[inputshape.len() - 1] != channels {
"Channel dimension mismatch in batch normalization".to_string(),
let mut normalized = input.clone();
// Apply normalization per channel
for c in 0..channels {
let channel_mean = mean[c];
let channel_var = var[c];
let channel_gamma = gamma[c];
let channel_beta = beta[c];
let std_dev = (channel_var + epsilon).sqrt();
// Create a slice for the current channel across all other dimensions
let mut channel_slice = normalized.slice_mut(s![.., c]);
channel_slice
.mapv_inplace(|x| (x - channel_mean) / std_dev * channel_gamma + channel_beta);
Ok(normalized)
/// Softmax activation function
pub fn softmax_forward(&self, input: &ArrayD<f32>) -> Result<ArrayD<f32>> {
if inputshape.len() < 2 {
"Softmax requires at least 2D input (batch_size, features)".to_string(),
let mut output = input.clone();
let _last_axis = inputshape.len() - 1;
// Apply softmax along the last axis (features)
for mut row in output.axis_iter_mut(scirs2_core::ndarray::Axis(0)) {
// Find max for numerical stability
let max_val = row.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
// Subtract max and compute exp
row.mapv_inplace(|x| (x - max_val).exp());
// Compute sum and normalize
let sum: f32 = row.sum();
row.mapv_inplace(|x| x / sum);
Ok(output)
/// Convolution forward pass (simplified 2D implementation)
pub fn conv2d_forward(
kernel: &ArrayD<f32>,
stride: (usize, usize),
padding: (usize, usize),
let kernelshape = kernel.shape();
// Check input format: (batch, channels, height, width)
if inputshape.len() != 4 || kernelshape.len() != 4 {
"Conv2D requires 4D input and kernel (batch, channels, height, width)".to_string(),
let (batch_size, in_channels, in_height, in_width) = (
inputshape[0],
inputshape[1],
inputshape[2],
inputshape[3],
);
let (out_channels, kernel_in_channels, kernel_height, kernel_width) = (
kernelshape[0],
kernelshape[1],
kernelshape[2],
kernelshape[3],
if in_channels != kernel_in_channels {
"Input and kernel channel dimensions must match".to_string(),
// Calculate output dimensions
let out_height = (in_height + 2 * padding.0 - kernel_height) / stride.0 + 1;
let out_width = (in_width + 2 * padding.1 - kernel_width) / stride.1 + 1;
let mut output = Array::zeros((batch_size, out_channels, out_height, out_width));
// Simplified convolution (for demonstration - real implementation would be optimized)
for b in 0..batch_size {
for out_c in 0..out_channels {
for out_h in 0..out_height {
for out_w in 0..out_width {
let mut sum = 0.0;
for in_c in 0..in_channels {
for k_h in 0..kernel_height {
for k_w in 0..kernel_width {
let in_h = out_h * stride.0 + k_h;
let in_w = out_w * stride.1 + k_w;
// Apply padding
if in_h >= padding.0
&& in_w >= padding.1
&& in_h < in_height + padding.0
&& in_w < in_width + padding.1
{
let actual_h = in_h - padding.0;
let actual_w = in_w - padding.1;
if actual_h < in_height && actual_w < in_width {
sum += input[[b, in_c, actual_h, actual_w]]
* kernel[[out_c, in_c, k_h, k_w]];
}
}
}
}
}
output[[b, out_c, out_h, out_w]] = sum;
}
}
Ok(output.into_dyn())
/// Optimized GPU matrix multiplication
pub fn gpu_matrix_multiply<T>(
a: &CudaTensor<T>,
b: &CudaTensor<T>,
) -> Result<CudaTensor<T>>
T: Copy + Default + Send + Sync + std::ops::Add<Output = T> + std::ops::Mul<Output = T>,
"GPU context not available".to_string(),
// Validate dimensions for matrix multiplication
if ashape.len() != 2 || bshape.len() != 2 {
"Matrix multiplication requires 2D tensors".to_string(),
if ashape[1] != bshape[0] {
"Matrix dimensions incompatible: {}x{} * {}x{}",
ashape[0], ashape[1], bshape[0], bshape[1]
let resultshape = vec![ashape[0], bshape[1]];
let result = CudaTensor::new(
resultshape,
a.device_id(),
self.gpu_context.as_ref().expect("Operation failed"),
)?;
// In real implementation, would launch CUDA kernels
thread::sleep(Duration::from_micros(100)); // Simulate GPU computation
Ok(result)
/// GPU-accelerated ReLU activation
pub fn gpu_relu<T>(&self, input: &CudaTensor<T>) -> Result<CudaTensor<T>>, T: Copy + Default + Send + Sync + PartialOrd + From<f32>,
input.shape().to_vec(),
input.device_id(),
// Simulate GPU kernel launch
thread::sleep(Duration::from_micros(10));
/// GPU-accelerated softmax
pub fn gpu_softmax<T>(&self, input: &CudaTensor<T>) -> Result<CudaTensor<T>>
// Simulate GPU softmax kernel
thread::sleep(Duration::from_micros(50));
/// GPU-accelerated convolution
pub fn gpu_conv2d<T>(
input: &CudaTensor<T>,
kernel: &CudaTensor<T>,
"Conv2D requires 4D tensors (N, C, H, W)".to_string(),
let out_height = (inputshape[2] + 2 * padding.0 - kernelshape[2]) / stride.0 + 1;
let out_width = (inputshape[3] + 2 * padding.1 - kernelshape[3]) / stride.1 + 1;
let outputshape = vec![inputshape[0], kernelshape[0], out_height, out_width];
outputshape,
// Simulate GPU convolution kernel (would use cuDNN in real implementation)
/// Synchronize GPU operations
pub fn synchronize(&self) -> Result<()> {
if self.gpu_context.is_some() {
// Simulate GPU synchronization
thread::sleep(Duration::from_micros(1));
/// Get backend information
pub fn backend_info(&self) -> String {
let precision = if self.mixed_precision {
" (Mixed Precision)"
} else {
""
};
format!(
"Neural operations running on: {}{}",
self.backend_type, precision
)
/// Get detailed GPU information
pub fn gpu_info(&self) -> Result<String> {
if let Some(ref gpu_context) = self.gpu_context {
let device_info = gpu_context.current_device_info();
Ok(format!(
"GPU: {} ({}GB, Compute {}.{}, {} SMs)",
device_info.name,
device_info.memory_total / (1024 * 1024 * 1024),
device_info.compute_capability.0,
device_info.compute_capability.1,
device_info.multiprocessor_count
))
} else {
Err(Error::ComputationError(
"No GPU context available".to_string(),
))
}
}
}
impl Default for NeuralOps {
fn default() -> Self {
Self::new().expect("Failed to create default NeuralOps")
}
}
/// Helper function to create neural operations with automatic backend detection
#[allow(dead_code)]
pub fn create_neural_ops() -> Result<NeuralOps> {
// For now, always use CPU. Future versions will detect GPU availability
NeuralOps::new()
/// Helper function to create neural operations with preferred backend
#[allow(dead_code)]
pub fn create_neural_ops_with_backend(backend: &str) -> Result<NeuralOps> {
NeuralOps::with_backend(_backend)
#[cfg(test)]
mod tests {
use super::*;
use scirs2_core::ndarray::array;
#[test]
fn test_matrix_multiply() {
let ops = create_neural_ops().expect("Operation failed");
let a = array![[1.0, 2.0], [3.0, 4.0]];
let b = array![[5.0, 6.0], [7.0, 8.0]];
let result = ops.matrix_multiply(&a, &b).expect("Operation failed");
let expected = array![[19.0, 22.0], [43.0, 50.0]];
assert_eq!(result, expected);
}
#[test]
fn test_relu_forward() {
let ops = create_neural_ops().expect("Operation failed");
let input = array![[-1.0, 0.0, 1.0, 2.0]].into_dyn();
let result = ops.relu_forward(&input).expect("Operation failed");
let expected = array![[0.0, 0.0, 1.0, 2.0]].into_dyn();
assert_eq!(result, expected);
}
#[test]
fn test_sigmoid_forward() {
let ops = create_neural_ops().expect("Operation failed");
let input = array![[0.0, 1.0, -1.0]].into_dyn();
let result = ops.sigmoid_forward(&input).expect("Operation failed");
// Check that outputs are in valid sigmoid range (0, 1)
for &val in result.iter() {
assert!(val > 0.0 && val < 1.0);
}
// Check that sigmoid(0) ≈ 0.5
assert!((result[[0, 0]] - 0.5).abs() < 1e-6);
}
#[test]
fn test_batch_normalize() {
let ops = create_neural_ops().expect("Operation failed");
let input = array![[1.0, 2.0], [3.0, 4.0]].into_dyn();
let mean = array![2.0, 3.0];
let var = array![1.0, 1.0];
let gamma = array![1.0, 1.0];
let beta = array![0.0, 0.0];
let result = ops
.batch_normalize(&input, &mean, &var, &gamma, &beta, 1e-5)
.expect("Operation failed");
// Result should be normalized
assert!(result.shape() == input.shape());
}
#[test]
fn test_softmax_forward() {
let ops = create_neural_ops().expect("Operation failed");
let input = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]].into_dyn();
let result = ops.softmax_forward(&input).expect("Operation failed");
// Check that each row sums to 1
for row in result.axis_iter(scirs2_core::ndarray::Axis(0)) {
let sum: f64 = row.iter().map(|&x| x as f64).sum();
assert!((sum - 1.0).abs() < 1e-6);
}
// Check that all values are positive
for &val in result.iter() {
assert!(val > 0.0);
}
}
#[test]
fn test_gpu_context_creation() {
// Test both success and failure cases
match GpuContext::new() {
Ok(context) => {
assert!(!context.all_devices().is_empty());
assert!(context.current_device_info().is_available);
}
Err(_) => {
// GPU not available, which is expected in CI environments
println!("GPU not available, skipping GPU context test");
}
}
}
#[test]
fn test_neural_ops_with_gpu() {
match NeuralOps::with_gpu() {
Ok(ops) => {
if ops.is_gpu_available() {
assert_eq!(ops.backend_type, "GPU");
assert!(ops.gpu_context().is_some());
} else {
assert_eq!(ops.backend_type, "CPU");
}
}
Err(_) => {
// Expected when GPU is not available
println!("GPU not available for neural ops test");
}
}
}
#[test]
fn test_mixed_precision_config() {
let config = MixedPrecisionConfig {
enabled: true,
loss_scale: 1024.0,
..Default::default()
};
assert!(config.enabled);
assert_eq!(config.loss_scale, 1024.0);
assert_eq!(config.loss_scale_window, 2000);
}
#[test]
fn test_device_info_serialization() {
let device_info = DeviceInfo {
id: 0,
name: "Test GPU".to_string(),
memory_total: 8 * 1024 * 1024 * 1024,
memory_free: 7 * 1024 * 1024 * 1024,
compute_capability: (7, 5),
multiprocessor_count: 68,
warp_size: 32,
is_available: true,
};
// Test serialization/deserialization
let serialized = serde_json::to_string(&device_info).expect("Operation failed");
let deserialized: DeviceInfo = serde_json::from_str(&serialized).expect("Operation failed");
assert_eq!(device_info.id, deserialized.id);
assert_eq!(device_info.name, deserialized.name);
}
#[test]
fn test_memory_stats() {
let stats = MemoryStats {
allocated: 1024,
active: 512,
};
assert_eq!(stats.allocated, 1024);
assert_eq!(stats.active, 512);
}
#[test]
fn test_multi_gpu_trainer_creation() {
let device_ids = vec![0];
match MultiGpuTrainer::new(device_ids) {
Ok(trainer) => {
assert_eq!(trainer.device_count(), 1);
assert_eq!(trainer.reduction_strategy, ReductionStrategy::AllReduce);
}
Err(_) => {
println!("GPU not available for multi-GPU trainer test");
}
}
}
#[test]
fn test_cuda_tensor_creation() {
if let Ok(context) = GpuContext::new() {
let shape = vec![2, 3, 4];
match CudaTensor::<f32>::new(shape.clone(), 0, &context) {
Ok(tensor) => {
assert_eq!(tensor.shape(), &shape);
assert_eq!(tensor.device_id(), 0);
}
Err(_) => {
println!("CUDA tensor creation failed (expected without real GPU)");
}
}
}
}
#[test]
fn test_reduction_strategies() {
let strategies = [
ReductionStrategy::AllReduce,
ReductionStrategy::ParameterServer,
ReductionStrategy::Hierarchical,
];
for strategy in &strategies {
let serialized = serde_json::to_string(strategy).expect("Operation failed");
let _deserialized: ReductionStrategy = serde_json::from_str(&serialized).expect("Operation failed");
}
}
#[test]
fn test_backend_info() {
let cpu_ops = NeuralOps::new().expect("Operation failed");
assert!(cpu_ops.backend_info().contains("CPU"));
if let Ok(mut gpu_ops) = NeuralOps::with_gpu() {
let info = gpu_ops.backend_info();
assert!(info.contains("GPU") || info.contains("CPU"));
// Test mixed precision info
if gpu_ops
.enable_mixed_precision(MixedPrecisionConfig::default())
.is_ok()
{
// Mixed precision should be mentioned in backend info
let info_with_mp = gpu_ops.backend_info();
assert!(info_with_mp.len() >= info.len());
}
}
}
#[test]
fn test_synchronize() {
let ops = create_neural_ops().expect("Operation failed");
assert!(ops.synchronize().is_ok());
if let Ok(gpu_ops) = NeuralOps::with_gpu() {
assert!(gpu_ops.synchronize().is_ok());
}
}
}