07d0e60fe4
- Fork cudarc locally (vendor/cudarc): add CudaContext::load_cubin() that calls cuModuleLoadData directly — zero nvrtc dependency - Remove "nvrtc" feature from ml-core, ml-dqn, ml-ppo Cargo.toml - Replace all 89 Ptx::from_binary + load_module calls with load_cubin - ml-core cuda_autograd: wire 9 stub constructors to precompiled cubins (activation, elementwise, linear, loss, reduction, dropout, layer_norm, optimizer) - ml-core build.rs: compile 8 BF16-native CUDA kernels via nvcc - cubin_loader.rs: thin wrapper around CudaContext::load_cubin() - Fix size_of::<f32> in gpu_tensor.rs, stream_ops.rs, layer_norm.rs - Fix test data: Vec<f32> → Vec<half::bf16> for memcpy_htod - Stub ml-ppo/ml-dqn runtime compile_ptx calls (dead code) - backtest_metrics_kernel.cu: full native BF16 rewrite (no float) - backtest_env_kernel.cu: shared memory → __nv_bfloat16 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
74 lines
2.3 KiB
Rust
74 lines
2.3 KiB
Rust
use cudarc::{
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driver::{CudaContext, DriverError, LaunchConfig, PushKernelArg},
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nvrtc::compile_ptx,
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};
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fn main() -> Result<(), DriverError> {
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let ctx = CudaContext::new(0)?;
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let stream = ctx.default_stream();
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// Load the module containing the kernel with constant memory
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let ptx = compile_ptx(include_str!("./constant_memory.cu")).expect("compile failure");
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let module = ctx.load_module(ptx)?;
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// Get the constant memory symbol as a CudaSlice<u8>
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let mut coefficients_symbol = module.get_global("coefficients", &stream)?;
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println!(
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"Constant memory symbol 'coefficients' has {} bytes",
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coefficients_symbol.len()
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);
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// Set up polynomial coefficients: 1.0 + 2.0*x + 3.0*x^2 + 4.0*x^3
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let coefficients = [1.0f32, 2.0, 3.0, 4.0];
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// Transmute the symbol to f32 and copy coefficients to constant memory
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let mut symbol_f32 = unsafe { coefficients_symbol.transmute_mut::<f32>(4).unwrap() };
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stream.memcpy_htod(&coefficients, &mut symbol_f32)?;
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// Load the kernel function
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let polynomial_kernel = module.load_function("polynomial_kernel")?;
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// Prepare input data
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let input = vec![0.0f32, 1.0, 2.0, 3.0, 4.0, 5.0];
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let n = input.len();
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// Copy input to device
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let input_dev = stream.clone_htod(&input)?;
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let mut output_dev = stream.alloc_zeros::<f32>(n)?;
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// Launch kernel
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let cfg = LaunchConfig::for_num_elems(n as u32);
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unsafe {
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stream
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.launch_builder(&polynomial_kernel)
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.arg(&mut output_dev)
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.arg(&input_dev)
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.arg(&(n as i32))
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.launch(cfg)
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}?;
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// Copy results back
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let output = stream.clone_dtoh(&output_dev)?;
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// Verify results
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println!("\nPolynomial evaluation (1.0 + 2.0*x + 3.0*x^2 + 4.0*x^3):");
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for (i, (&x, &y)) in input.iter().zip(output.iter()).enumerate() {
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let expected = coefficients[0]
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+ coefficients[1] * x
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+ coefficients[2] * x * x
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+ coefficients[3] * x * x * x;
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println!(" f({:.1}) = {:.1} (expected {:.1})", x, y, expected);
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assert!(
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(y - expected).abs() < 1e-4,
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"Mismatch at index {}: got {}, expected {}",
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i,
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y,
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expected
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);
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}
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println!("\nAll results match expected values!");
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Ok(())
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}
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