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>
39 lines
1.1 KiB
Rust
39 lines
1.1 KiB
Rust
use cudarc::{
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driver::{CudaContext, DriverError, LaunchConfig, PushKernelArg},
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nvrtc::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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// You can load a function from a pre-compiled PTX like so:
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let module = ctx.load_module(Ptx::from_file("./examples/sin.ptx"))?;
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// and then load a function from it:
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let f = module.load_function("sin_kernel").unwrap();
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let a_host = [1.0, 2.0, 3.0];
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let a_dev = stream.clone_htod(&a_host)?;
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let mut b_dev = a_dev.clone();
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// we use a buidler pattern to launch kernels.
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let n = 3i32;
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let cfg = LaunchConfig::for_num_elems(n as u32);
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let mut launch_args = stream.launch_builder(&f);
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launch_args.arg(&mut b_dev);
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launch_args.arg(&a_dev);
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launch_args.arg(&n);
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unsafe { launch_args.launch(cfg) }?;
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let a_host_2 = stream.clone_dtoh(&a_dev)?;
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let b_host = stream.clone_dtoh(&b_dev)?;
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println!("Found {b_host:?}");
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println!("Expected {:?}", a_host.map(f32::sin));
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assert_eq!(&a_host, a_host_2.as_slice());
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Ok(())
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}
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