afd85b2f8f
- Delete 14 unused example files (-3,543 lines): config, adaptive-strategy, data, storage, trading_engine, api_gateway, backtesting, trading_service, chaos - Update ML training/eval binaries: improved CLI args, completion tracking, CUDA test cleanup, hyperopt enhancements - Fix KAN network and TFT module adjustments - Update risk test assertions for consistency - Fix backtesting repositories and promotion manager - Update .serena project config and Cargo dependencies Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
92 lines
2.9 KiB
Rust
92 lines
2.9 KiB
Rust
//! Simple CUDA functionality test to verify compatibility
|
|
//!
|
|
//! This test verifies that the updated candle-core with CUDA support
|
|
//! can successfully create tensors and perform basic operations.
|
|
#![allow(unused_crate_dependencies)]
|
|
|
|
use candle_core::{Device, Tensor};
|
|
use candle_nn::{linear, Module, VarBuilder, VarMap};
|
|
|
|
/// Format a tensor's dimensions as a display string without using Debug formatting.
|
|
fn fmt_dims(dims: &[usize]) -> String {
|
|
let parts: Vec<String> = dims.iter().map(|d| d.to_string()).collect();
|
|
format!("[{}]", parts.join(", "))
|
|
}
|
|
|
|
/// Test basic CUDA tensor operations
|
|
pub fn test_cuda_basic() -> Result<(), Box<dyn std::error::Error>> {
|
|
println!("Testing CUDA compatibility...");
|
|
|
|
// Try to get CUDA device
|
|
match Device::new_cuda(0) {
|
|
Ok(device) => {
|
|
println!("[OK] CUDA device 0 available");
|
|
|
|
// Create a simple tensor
|
|
let tensor = Tensor::randn(0_f32, 1.0, (4, 4), &device)?;
|
|
println!("[OK] Created CUDA tensor: {}", fmt_dims(tensor.dims()));
|
|
|
|
// Perform basic operations
|
|
let result = tensor.matmul(&tensor.t()?)?;
|
|
println!(
|
|
"[OK] Matrix multiplication successful: {}",
|
|
fmt_dims(result.dims())
|
|
);
|
|
|
|
Ok(())
|
|
},
|
|
Err(e) => {
|
|
println!("[WARN] CUDA device not available: {}", e);
|
|
println!("This is expected if no GPU is present, but CUDA compilation succeeded");
|
|
Ok(())
|
|
},
|
|
}
|
|
}
|
|
|
|
/// Test candle-nn components with CUDA
|
|
pub fn test_cuda_neural_network() -> Result<(), Box<dyn std::error::Error>> {
|
|
println!("Testing CUDA neural network components...");
|
|
|
|
if let Ok(device) = Device::new_cuda(0) {
|
|
let varmap = VarMap::new();
|
|
let vs = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, &device);
|
|
|
|
// Create a simple linear layer
|
|
let linear_layer = linear(10, 5, vs.pp("linear"))?;
|
|
let input = Tensor::randn(0_f32, 1.0, (1, 10), &device)?;
|
|
|
|
let output = linear_layer.forward(&input)?;
|
|
println!(
|
|
"[OK] Neural network forward pass successful: {}",
|
|
fmt_dims(output.dims())
|
|
);
|
|
} else {
|
|
println!("[WARN] CUDA neural network test skipped (no GPU)");
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn verify_cuda_compilation() {
|
|
// This test just verifies that CUDA code compiles
|
|
// The actual runtime test is optional since CI may not have GPU
|
|
println!("CUDA compilation test passed!");
|
|
|
|
// Try to run basic test but don't fail if no GPU
|
|
let _ = test_cuda_basic();
|
|
let _ = test_cuda_neural_network();
|
|
}
|
|
}
|
|
|
|
fn main() -> Result<(), Box<dyn std::error::Error>> {
|
|
test_cuda_basic()?;
|
|
test_cuda_neural_network()?;
|
|
println!("[DONE] CUDA compatibility verification complete!");
|
|
Ok(())
|
|
}
|