450c23a6d0
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences) - Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250) - Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.) - Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum) - Move compile_ptx_for_device() to ml-core for shared access - Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs, training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics - Replace unwrap_or(Device::Cpu) with hard errors everywhere - Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers - Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline) - Port IQL value network to GPU kernel (5 CUDA entry points) - Port HER goal relabeling to GPU kernel (warp-per-sample) - Wire DSR GPU-to-CPU sync in training loop - cfg!(feature = "cuda") → true in inference_validator Zero warnings, zero errors across entire workspace. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
376 lines
17 KiB
Rust
376 lines
17 KiB
Rust
//! CUDA Kernel Direct Testing
|
||
//!
|
||
//! Tests CUDA kernels directly using cudarc for low-level GPU operations
|
||
|
||
use super::utils::*;
|
||
use log::{info, warn};
|
||
|
||
use cudarc::driver::{CudaDevice, DevicePtr, LaunchAsync, LaunchConfig};
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
#[test]
|
||
fn test_cuda_kernel_manager_creation() {
|
||
init_gpu_test_env();
|
||
info!("🔧 Testing CUDA kernel manager creation");
|
||
|
||
if !cuda_available() {
|
||
info!("⚠️ Skipping CUDA kernel test - CUDA not available");
|
||
return;
|
||
}
|
||
|
||
match CudaDevice::new(0) {
|
||
Ok(device) => {
|
||
info!("✅ CUDA device initialized for kernel testing");
|
||
|
||
// Test basic device operations
|
||
let device_name = device.name().unwrap_or("Unknown".to_string());
|
||
info!("Device name: {}", device_name);
|
||
|
||
// Test memory allocation
|
||
match device.alloc_zeros::<f32>(1024) {
|
||
Ok(memory) => {
|
||
info!("✅ CUDA memory allocation successful: {} elements", memory.len());
|
||
|
||
// Test memory deallocation (automatic when memory goes out of scope)
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ CUDA memory allocation failed: {}", e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to initialize CUDA device: {}", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_cuda_memory_operations() {
|
||
init_gpu_test_env();
|
||
info!("💾 Testing CUDA memory operations");
|
||
|
||
if !cuda_available() {
|
||
info!("⚠️ Skipping CUDA memory test - CUDA not available");
|
||
return;
|
||
}
|
||
|
||
match CudaDevice::new(0) {
|
||
Ok(device) => {
|
||
info!("✅ CUDA device ready for memory testing");
|
||
|
||
// Test different memory allocation sizes
|
||
let sizes = vec![1024, 4096, 16384, 65536];
|
||
|
||
for size in sizes {
|
||
match device.alloc_zeros::<f32>(size) {
|
||
Ok(gpu_memory) => {
|
||
info!("✅ Allocated {} f32 elements on GPU", size);
|
||
|
||
// Test host-to-device transfer
|
||
let host_data: Vec<f32> = (0..size).map(|i| i as f32).collect();
|
||
|
||
match device.htod_copy(host_data.clone(), &gpu_memory) {
|
||
Ok(_) => {
|
||
info!("✅ Host-to-device transfer successful for {} elements", size);
|
||
|
||
// Test device-to-host transfer
|
||
match device.dtoh_sync_copy(&gpu_memory) {
|
||
Ok(result_data) => {
|
||
info!("✅ Device-to-host transfer successful");
|
||
|
||
// Verify data integrity
|
||
if result_data.len() == host_data.len() {
|
||
let matches = result_data.iter()
|
||
.zip(host_data.iter())
|
||
.take(100) // Check first 100 elements
|
||
.all(|(a, b)| (a - b).abs() < 1e-6);
|
||
|
||
if matches {
|
||
info!("✅ Data integrity verified for {} elements", size);
|
||
} else {
|
||
warn!("❌ Data integrity check failed for {} elements", size);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Device-to-host transfer failed: {}", e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Host-to-device transfer failed: {}", e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to allocate {} elements: {}", size, e);
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to initialize CUDA device: {}", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_cuda_kernel_compilation() {
|
||
init_gpu_test_env();
|
||
info!("🔨 Testing CUDA kernel compilation");
|
||
|
||
if !cuda_available() {
|
||
info!("⚠️ Skipping CUDA kernel compilation test - CUDA not available");
|
||
return;
|
||
}
|
||
|
||
match CudaDevice::new(0) {
|
||
Ok(device) => {
|
||
info!("✅ CUDA device ready for kernel compilation");
|
||
|
||
// Simple CUDA kernel for vector addition
|
||
let ptx_source = r#"
|
||
.version 7.0
|
||
.target sm_50
|
||
.address_size 64
|
||
|
||
.visible .entry vector_add(
|
||
.param .u64 vector_add_param_0, // a
|
||
.param .u64 vector_add_param_1, // b
|
||
.param .u64 vector_add_param_2, // c
|
||
.param .u32 vector_add_param_3 // n
|
||
)
|
||
{
|
||
.reg .u32 %tid;
|
||
.reg .u64 %a_addr, %b_addr, %c_addr;
|
||
.reg .f32 %a_val, %b_val, %c_val;
|
||
.reg .u32 %n;
|
||
.reg .pred %p1;
|
||
|
||
ld.param.u64 %a_addr, [vector_add_param_0];
|
||
ld.param.u64 %b_addr, [vector_add_param_1];
|
||
ld.param.u64 %c_addr, [vector_add_param_2];
|
||
ld.param.u32 %n, [vector_add_param_3];
|
||
|
||
mov.u32 %tid, %tid.x;
|
||
setp.lt.u32 %p1, %tid, %n;
|
||
@!%p1 bra END;
|
||
|
||
mul.wide.u32 %a_addr, %tid, 4;
|
||
add.u64 %a_addr, %a_addr, vector_add_param_0;
|
||
ld.global.f32 %a_val, [%a_addr];
|
||
|
||
mul.wide.u32 %b_addr, %tid, 4;
|
||
add.u64 %b_addr, %b_addr, vector_add_param_1;
|
||
ld.global.f32 %b_val, [%b_addr];
|
||
|
||
add.f32 %c_val, %a_val, %b_val;
|
||
|
||
mul.wide.u32 %c_addr, %tid, 4;
|
||
add.u64 %c_addr, %c_addr, vector_add_param_2;
|
||
st.global.f32 [%c_addr], %c_val;
|
||
|
||
END:
|
||
ret;
|
||
}
|
||
"#;
|
||
|
||
// Try to load the kernel
|
||
match device.load_ptx(ptx_source.into(), "vector_add_module", &["vector_add"]) {
|
||
Ok(module) => {
|
||
info!("✅ CUDA kernel compiled successfully");
|
||
|
||
// Test kernel execution
|
||
let n = 1024u32;
|
||
let a_host: Vec<f32> = (0..n).map(|i| i as f32).collect();
|
||
let b_host: Vec<f32> = (0..n).map(|i| (i * 2) as f32).collect();
|
||
|
||
match (
|
||
device.htod_copy(a_host.clone()),
|
||
device.htod_copy(b_host.clone()),
|
||
device.alloc_zeros::<f32>(n as usize)
|
||
) {
|
||
(Ok(a_gpu), Ok(b_gpu), Ok(c_gpu)) => {
|
||
info!("✅ Test data uploaded to GPU");
|
||
|
||
// Launch kernel
|
||
let config = LaunchConfig {
|
||
grid_dim: ((n + 255) / 256, 1, 1),
|
||
block_dim: (256, 1, 1),
|
||
shared_mem_bytes: 0,
|
||
};
|
||
|
||
// SAFETY: Unsafe operation validated - invariants maintained by surrounding code
|
||
unsafe {
|
||
match module.get_func("vector_add") {
|
||
Ok(func) => {
|
||
match func.launch(config, (&a_gpu, &b_gpu, &c_gpu, n)) {
|
||
Ok(_) => {
|
||
info!("✅ CUDA kernel launched successfully");
|
||
|
||
// Get results
|
||
match device.dtoh_sync_copy(&c_gpu) {
|
||
Ok(result) => {
|
||
info!("✅ Kernel results retrieved");
|
||
|
||
// Verify first few results
|
||
let mut correct = true;
|
||
for i in 0..10.min(n as usize) {
|
||
let expected = a_host[i] + b_host[i];
|
||
if (result[i] - expected).abs() > 1e-6 {
|
||
correct = false;
|
||
break;
|
||
}
|
||
}
|
||
|
||
if correct {
|
||
info!("✅ CUDA kernel execution verified");
|
||
} else {
|
||
warn!("❌ CUDA kernel results incorrect");
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to retrieve kernel results: {}", e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to launch CUDA kernel: {}", e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to get kernel function: {}", e);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
_ => {
|
||
warn!("❌ Failed to allocate GPU memory for kernel test");
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ CUDA kernel compilation failed: {}", e);
|
||
info!("ℹ️ This may be due to PTX version compatibility or kernel syntax");
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to initialize CUDA device: {}", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_cuda_stream_operations() {
|
||
init_gpu_test_env();
|
||
info!("🌊 Testing CUDA stream operations");
|
||
|
||
if !cuda_available() {
|
||
info!("⚠️ Skipping CUDA stream test - CUDA not available");
|
||
return;
|
||
}
|
||
|
||
match CudaDevice::new(0) {
|
||
Ok(device) => {
|
||
info!("✅ CUDA device ready for stream testing");
|
||
|
||
// Test multiple streams for concurrent operations
|
||
let stream_count = 4;
|
||
let data_size = 1024;
|
||
|
||
for stream_id in 0..stream_count {
|
||
info!("Testing stream {}", stream_id);
|
||
|
||
// Allocate memory for this stream
|
||
match (
|
||
device.alloc_zeros::<f32>(data_size),
|
||
device.alloc_zeros::<f32>(data_size)
|
||
) {
|
||
(Ok(gpu_mem1), Ok(gpu_mem2)) => {
|
||
info!("✅ Allocated GPU memory for stream {}", stream_id);
|
||
|
||
// Create host data
|
||
let host_data: Vec<f32> = (0..data_size).map(|i| (i + stream_id * 1000) as f32).collect();
|
||
|
||
// Test asynchronous operations
|
||
match device.htod_copy(host_data.clone(), &gpu_mem1) {
|
||
Ok(_) => {
|
||
info!("✅ Stream {} host-to-device transfer", stream_id);
|
||
|
||
// Simulate some GPU work by copying data
|
||
// In a real scenario, this would be a kernel launch
|
||
match device.dtoh_sync_copy(&gpu_mem1) {
|
||
Ok(result) => {
|
||
if result.len() == host_data.len() {
|
||
info!("✅ Stream {} operations completed successfully", stream_id);
|
||
} else {
|
||
warn!("❌ Stream {} data size mismatch", stream_id);
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Stream {} device-to-host failed: {}", stream_id, e);
|
||
}
|
||
}
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Stream {} host-to-device failed: {}", stream_id, e);
|
||
}
|
||
}
|
||
}
|
||
_ => {
|
||
warn!("❌ Failed to allocate memory for stream {}", stream_id);
|
||
}
|
||
}
|
||
}
|
||
|
||
info!("✅ CUDA stream operations test completed");
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ Failed to initialize CUDA device: {}", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
#[test]
|
||
fn test_cuda_kernel_integration_framework() {
|
||
init_gpu_test_env();
|
||
info!("🔧 Testing CUDA kernel integration framework");
|
||
|
||
// Test the integration between our ML models and CUDA kernels
|
||
if !cuda_available() {
|
||
info!("⚠️ Testing CPU fallback for kernel integration");
|
||
|
||
// Verify that ML models can work without CUDA
|
||
let cpu_device = candle_core::Device::Cpu;
|
||
match candle_core::Tensor::zeros((10, 10), candle_core::DType::F32, &cpu_device) {
|
||
Ok(_tensor) => {
|
||
info!("✅ CPU fallback integration working");
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ CPU fallback integration failed: {}", e);
|
||
}
|
||
}
|
||
} else {
|
||
info!("✅ CUDA available for kernel integration testing");
|
||
|
||
// Test integration with actual CUDA device
|
||
let gpu_device = candle_core::Device::cuda_if_available(0).unwrap();
|
||
match candle_core::Tensor::zeros((10, 10), candle_core::DType::F32, &gpu_device) {
|
||
Ok(_tensor) => {
|
||
info!("✅ GPU kernel integration working");
|
||
}
|
||
Err(e) => {
|
||
warn!("❌ GPU kernel integration failed: {}", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
info!("✅ CUDA kernel integration framework test completed");
|
||
}
|
||
} |