//! kfd gpu driver integration tests. //! //! run: cargo test --release --test kfd_gpu -- --nocapture //! //! these tests require /dev/kfd (amd gpu with kfd driver). //! they share a single HsaDevice to avoid acquire_vm conflicts. //! each test validates correctness against a cpu reference. use isis::host::device::kfd::{self, HsaDevice}; use isis::host::device::kfd::dispatch::KernArgs; use std::sync::OnceLock; /// single shared gpu device for all tests. /// tests run sequentially (--test-threads=1 enforced by kfd constraint). static GPU: OnceLock>> = OnceLock::new(); fn gpu() -> Option> { GPU.get_or_init(|| { if !kfd::is_available() { return None; } HsaDevice::open().ok().map(std::sync::Mutex::new) }).as_ref().map(|m| m.lock().unwrap()) } // ─── store kernel ─────────────────────────────────────────── #[test] fn store42_writes_correct_values() { let mut dev = match gpu() { Some(d) => d, None => return }; let n = 32u32; let y = dev.alloc.alloc_userptr_public((n as u64) * 4).unwrap(); // write sentinel — must be overwritten y.write_f32(0, &vec![-1.0f32; n as usize]); let mut args = KernArgs::new(); args.push_ptr(&y); let args_buf = args.upload(&dev.alloc).unwrap(); assert!(dev.dispatch_kernel("test_store", &args_buf, [n, 1, 1], [n, 1, 1]), "dispatch timed out"); let result = y.read_f32(0, n as usize); for i in 0..n as usize { assert_eq!(result[i], 42.0, "y[{i}] = {} (sentinel was -1.0)", result[i]); } } // ─── matvec kernel ────────────────────────────────────────── /// dispatch matvec and return output vector. fn gpu_matvec(dev: &mut HsaDevice, w: &[f32], b: &[f32], x: &[f32], out_dim: u32, in_dim: u32) -> Vec { let w_buf = dev.upload_f32(w).unwrap(); let b_buf = dev.upload_f32(b).unwrap(); let x_buf = dev.upload_f32(x).unwrap(); let y_buf = dev.alloc.alloc_userptr_public(((out_dim as u64) * 4 + 4095) & !4095).unwrap(); let mut args = KernArgs::new(); args.push_ptr(&w_buf); args.push_ptr(&b_buf); args.push_ptr(&x_buf); args.push_ptr(&y_buf); args.push_u32(out_dim); args.push_u32(in_dim); let args_buf = args.upload(&dev.alloc).unwrap(); let block = out_dim.min(256); assert!(dev.dispatch_kernel("matvec", &args_buf, [out_dim, 1, 1], [block, 1, 1]), "matvec dispatch timed out ({}x{})", out_dim, in_dim); y_buf.read_f32(0, out_dim as usize) } /// cpu reference: y = W*x + b fn cpu_matvec(w: &[f32], b: &[f32], x: &[f32], out_dim: usize, in_dim: usize) -> Vec { let mut y = vec![0.0f32; out_dim]; for row in 0..out_dim { let mut sum = b[row]; for col in 0..in_dim { sum += w[row * in_dim + col] * x[col]; } y[row] = sum; } y } fn assert_close(gpu: &[f32], cpu: &[f32], tol: f32, label: &str) { assert_eq!(gpu.len(), cpu.len(), "{label}: length mismatch {} vs {}", gpu.len(), cpu.len()); for i in 0..gpu.len() { let err = (gpu[i] - cpu[i]).abs(); assert!(err < tol, "{label}: y[{i}] gpu={} cpu={} err={err}", gpu[i], cpu[i]); } } #[test] fn matvec_identity_4x4() { let mut dev = match gpu() { Some(d) => d, None => return }; let n = 4; let mut w = vec![0.0f32; n * n]; for i in 0..n { w[i * n + i] = 1.0; } let b = vec![0.5; n]; let x = vec![1.0, 2.0, 3.0, 4.0]; let gpu_y = gpu_matvec(&mut dev, &w, &b, &x, n as u32, n as u32); let cpu_y = cpu_matvec(&w, &b, &x, n, n); assert_close(&gpu_y, &cpu_y, 1e-5, "identity_4x4"); } #[test] fn matvec_dense_2x3() { let mut dev = match gpu() { Some(d) => d, None => return }; let w = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0f32]; let b = vec![0.1, 0.2]; let x = vec![1.0, 1.0, 1.0]; let gpu_y = gpu_matvec(&mut dev, &w, &b, &x, 2, 3); let cpu_y = cpu_matvec(&w, &b, &x, 2, 3); assert_close(&gpu_y, &cpu_y, 1e-4, "dense_2x3"); } #[test] fn matvec_32x32_vs_cpu() { let mut dev = match gpu() { Some(d) => d, None => return }; let n = 32usize; // deterministic pseudo-random weights let w: Vec = (0..n*n).map(|i| ((i * 7 + 3) % 100) as f32 / 100.0 - 0.5).collect(); let b: Vec = (0..n).map(|i| (i % 10) as f32 * 0.1).collect(); let x: Vec = (0..n).map(|i| ((i * 13 + 5) % 100) as f32 / 100.0).collect(); let gpu_y = gpu_matvec(&mut dev, &w, &b, &x, n as u32, n as u32); let cpu_y = cpu_matvec(&w, &b, &x, n, n); assert_close(&gpu_y, &cpu_y, 1e-3, "random_32x32"); } #[test] fn matvec_nonsquare_8x16() { let mut dev = match gpu() { Some(d) => d, None => return }; let out = 8usize; let inp = 16usize; let w = vec![1.0f32; out * inp]; // all ones let b = vec![0.0; out]; let x = vec![1.0; inp]; // all ones let gpu_y = gpu_matvec(&mut dev, &w, &b, &x, out as u32, inp as u32); let cpu_y = cpu_matvec(&w, &b, &x, out, inp); assert_close(&gpu_y, &cpu_y, 1e-3, "nonsquare_8x16"); // each row should sum to 16.0 (16 ones) for i in 0..out { assert!((gpu_y[i] - 16.0).abs() < 1e-3, "y[{i}] = {}", gpu_y[i]); } } #[test] fn matvec_scalar_1x1() { let mut dev = match gpu() { Some(d) => d, None => return }; let gpu_y = gpu_matvec(&mut dev, &[3.0], &[0.5], &[2.0], 1, 1); assert!((gpu_y[0] - 6.5).abs() < 1e-5, "1x1: {} expected 6.5", gpu_y[0]); } // ─── async future ─────────────────────────────────────────── #[test] fn dispatch_async_returns_future() { use isis::host::device::kfd::compute::GpuFuture; let mut dev = match gpu() { Some(d) => d, None => return }; let y = dev.alloc.alloc_userptr_public(4096).unwrap(); let mut args = KernArgs::new(); args.push_ptr(&y); let args_buf = args.upload(&dev.alloc).unwrap(); let future = dev.dispatch_async("test_store", &args_buf, [4, 1, 1], [4, 1, 1]); assert!(future.is_some(), "dispatch_async returned None"); let future = future.unwrap(); assert!(!future.poll() || true, "poll should not panic"); // may already be done let elapsed = future.wait(1_000_000); assert!(elapsed.is_some(), "future timed out after 1s"); let result = y.read_f32(0, 4); assert_eq!(result, vec![42.0; 4]); } #[test] fn dispatch_nonexistent_kernel_returns_none() { let mut dev = match gpu() { Some(d) => d, None => return }; let y = dev.alloc.alloc_userptr_public(4096).unwrap(); let mut args = KernArgs::new(); args.push_ptr(&y); let args_buf = args.upload(&dev.alloc).unwrap(); let future = dev.dispatch_async("nonexistent_kernel", &args_buf, [1, 1, 1], [1, 1, 1]); assert!(future.is_none(), "should return None for unknown kernel"); }