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NVPTX: Add f16 SIMD intrinsics

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Kjetil Kjeka 2024-08-12 14:40:32 +02:00 • committed by Amanieu d'Antras
parent f23656fbb6
commit 869ddbeb66
2 changed files with 148 additions and 0 deletions
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5
library/stdarch/crates/core_arch/src/nvptx/mod.rs
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@ -13,6 +13,11 @@
use crate::ffi::c_void;
mod packed;
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub use packed::*;
#[allow(improper_ctypes)]
extern "C" {
#[link_name = "llvm.nvvm.barrier0"]

143
library/stdarch/crates/core_arch/src/nvptx/packed.rs Normal file
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@ -0,0 +1,143 @@
//! NVPTX Packed data types (SIMD)
//!
//! Packed Data Types is what PTX calls SIMD types. See [PTX ISA (Packed Data Types)](https://docs.nvidia.com/cuda/parallel-thread-execution/#packed-data-types) for a full reference.
// Note: #[assert_instr] tests are not actually being run on nvptx due to being a `no_std` target incapable of running tests. Something like FileCheck would be appropriate for verifying the correct instruction is used.
use crate::intrinsics::simd::*;
#[allow(improper_ctypes)]
extern "C" {
#[link_name = "llvm.minnum.v2f16"]
fn llvm_f16x2_minnum(a: f16x2, b: f16x2) -> f16x2;
#[link_name = "llvm.minimum.v2f16"]
fn llvm_f16x2_minimum(a: f16x2, b: f16x2) -> f16x2;
#[link_name = "llvm.maxnum.v2f16"]
fn llvm_f16x2_maxnum(a: f16x2, b: f16x2) -> f16x2;
#[link_name = "llvm.maximum.v2f16"]
fn llvm_f16x2_maximum(a: f16x2, b: f16x2) -> f16x2;
}
types! {
#![unstable(feature = "stdarch_nvptx", issue = "111199")]
/// PTX-specific 32-bit wide floating point (f16 x 2) vector type
pub struct f16x2(2 x f16);
}
/// Add two values, round to nearest even
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-add>
///
/// Corresponds to the CUDA C intrinsics:
/// - [`__hadd2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g921c795176eaa31265bd80ef4fe4b8e6)
/// - [`__hadd2_rn`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g6cd8ddb2c3d670e1a10c3eb2e7644f82)
#[inline]
#[cfg_attr(test, assert_instr(add.rn.f16x22))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_add(a: f16x2, b: f16x2) -> f16x2 {
simd_add(a, b)
}
/// Subtract two values, round to nearest even
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-sub>
///
/// Corresponds to the CUDA C intrinsics:
/// - [`__hsub2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1ga5536c9c3d853d8c8b9de60e18b41e54)
/// - [`__hsub2_rn`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g8adc164c68d553354f749f0f0645a874)
#[inline]
#[cfg_attr(test, assert_instr(sub.rn.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_sub(a: f16x2, b: f16x2) -> f16x2 {
simd_sub(a, b)
}
/// Multiply two values, round to nearest even
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-mul>
///
/// Corresponds to the CUDA C intrinsics:
/// - [`__hmul2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g70de3f2ee48babe4e0969397ac17708e)
/// - [`__hmul2_rn`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g99f8fe23a4b4c6898d6faf999afaa76e)
#[inline]
#[cfg_attr(test, assert_instr(mul.rn.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_mul(a: f16x2, b: f16x2) -> f16x2 {
simd_mul(a, b)
}
/// Fused multiply-add, round to nearest even
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-fma>
///
/// Corresponds to the CUDA C intrinsics:
/// - [`__fma2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g43628ba21ded8b1e188a367348008dab)
/// - [`__fma2_rn`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__ARITHMETIC.html#group__CUDA__MATH____HALF2__ARITHMETIC_1g43628ba21ded8b1e188a367348008dab)
#[inline]
#[cfg_attr(test, assert_instr(fma.rn.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_fma(a: f16x2, b: f16x2, c: f16x2) -> f16x2 {
simd_fma(a, b, c)
}
/// Arithmetic negate
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-neg>
///
/// Corresponds to the CUDA C intrinsic [`__hmin2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__COMPARISON.html#group__CUDA__MATH____HALF2__COMPARISON_1g9e17a33f96061804166f3fbd395422b6)
#[inline]
#[cfg_attr(test, assert_instr(neg.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_neg(a: f16x2) -> f16x2 {
simd_neg(a)
}
/// Find the minimum of two values
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-min>
///
/// Corresponds to the CUDA C intrinsic [`__hmin2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__COMPARISON.html#group__CUDA__MATH____HALF2__COMPARISON_1g9e17a33f96061804166f3fbd395422b6)
#[inline]
#[cfg_attr(test, assert_instr(min.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_min(a: f16x2, b: f16x2) -> f16x2 {
llvm_f16x2_minnum(a, b)
}
/// Find the minimum of two values, NaNs pass through.
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-min>
///
/// Corresponds to the CUDA C intrinsic [`__hmin2_nan`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__COMPARISON.html#group__CUDA__MATH____HALF2__COMPARISON_1g8bb8f58e9294cc261d2f42c4d5aecd6b)
#[inline]
#[cfg_attr(test, assert_instr(min.NaN.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_min_nan(a: f16x2, b: f16x2) -> f16x2 {
llvm_f16x2_minimum(a, b)
}
/// Find the maximum of two values
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-max>
///
/// Corresponds to the CUDA C intrinsic [`__hmax2`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__COMPARISON.html#group__CUDA__MATH____HALF2__COMPARISON_1g59fc7fc7975d8127b202444a05e57e3d)
#[inline]
#[cfg_attr(test, assert_instr(max.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_max(a: f16x2, b: f16x2) -> f16x2 {
llvm_f16x2_maxnum(a, b)
}
/// Find the maximum of two values, NaNs pass through.
///
/// <https://docs.nvidia.com/cuda/parallel-thread-execution/#half-precision-floating-point-instructions-max>
///
/// Corresponds to the CUDA C intrinsic [`__hmax2_nan`](https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH____HALF2__COMPARISON.html#group__CUDA__MATH____HALF2__COMPARISON_1g41623db7850e3074fd9daa80a14c3897)
#[inline]
#[cfg_attr(test, assert_instr(max.NaN.f16x2))]
#[unstable(feature = "stdarch_nvptx", issue = "111199")]
pub unsafe fn f16x2_max_nan(a: f16x2, b: f16x2) -> f16x2 {
llvm_f16x2_maximum(a, b)
}