use crate::cuda_sys::cudaStream_t;
use crate::tensor::CudaTensor;
use crate::DType;
use tl_backend::BackendResult;
extern "C" {
fn launch_neg_kernel(x: *const f32, y: *mut f32, n: i32, stream: cudaStream_t);
fn launch_abs_kernel(x: *const f32, y: *mut f32, n: i32, stream: cudaStream_t);
fn launch_exp_kernel(x: *const f32, y: *mut f32, n: i32, stream: cudaStream_t);
fn launch_log_kernel(x: *const f32, y: *mut f32, n: i32, stream: cudaStream_t);
fn launch_sqrt_kernel(x: *const f32, y: *mut f32, n: i32, stream: cudaStream_t);
}
impl CudaTensor {
fn unary_kernel_op(
&self,
launch: unsafe extern "C" fn(*const f32, *mut f32, i32, cudaStream_t),
) -> BackendResult<CudaTensor> {
let n = self.elem_count();
let output = CudaTensor::uninit(self.shape(), DType::F32);
let stream = crate::stream::get_stream().raw();
unsafe {
launch(
self.buffer.ptr() as *const f32,
output.buffer.ptr() as *mut f32,
n as i32,
stream,
);
}
crate::stream::sync_stream();
Ok(output)
}
pub fn neg_impl(&self) -> BackendResult<CudaTensor> {
self.unary_kernel_op(launch_neg_kernel)
}
pub fn abs_impl(&self) -> BackendResult<CudaTensor> {
self.unary_kernel_op(launch_abs_kernel)
}
pub fn exp_impl(&self) -> BackendResult<CudaTensor> {
self.unary_kernel_op(launch_exp_kernel)
}
pub fn log_impl(&self) -> BackendResult<CudaTensor> {
self.unary_kernel_op(launch_log_kernel)
}
pub fn sqrt_impl(&self) -> BackendResult<CudaTensor> {
self.unary_kernel_op(launch_sqrt_kernel)
}
}