Libdevice (tl_extra.libdevice) 函数
Triton 可以调用外部库中的自定义函数。在这个例子中，我们将使用 libdevice 库在张量上应用 asin 函数。请参考以下链接获取关于所有可用 libdevice 函数语义的详细信息：
- CUDA：https://docs.nvidia.com/cuda/libdevice-users-guide/index.html
- HIP：https://github.com/ROCm/llvm-project/tree/amd-staging/amd/device-libs/ocml/src
在 libdevice.py 中，我们试图将相同计算但不同数据类型的函数聚合在一起。例如，__nv_asin 和 __nv_asinf 都计算输入的反正弦的主值，但 __nv_asin 适用于 double 类型，而 __nv_asinf 适用于 float 类型。使用 Triton，您可以简单 地调用 tl.math.asin。根据输入和输出类型，Triton 会自动选择正确的底层设备函数来调用。
asin 内核
import torch
import triton
import triton.language as tl
from triton.language.extra import libdevice
@triton.jit
def asin_kernel(
x_ptr,
y_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
x = libdevice.asin(x)
tl.store(y_ptr + offsets, x, mask=mask)
使用默认的 libdevice 库路径
可以使用 triton/language/math.py 中编码的默认 libdevice 库路径。
torch.manual_seed(0)
size = 98432
x = torch.rand(size, device='cuda')
output_triton = torch.zeros(size, device='cuda')
output_torch = torch.asin(x)
assert x.is_cuda and output_triton.is_cuda
n_elements = output_torch.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
print(output_torch)
print(output_triton)
print(f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}')
Out:
tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0') tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0') The maximum difference between torch and triton is 2.384185791015625e-07
定制 libdevice 库路径
可以通过将 libdevice 库的路径传递给 asin 内核来定制 libdevice 库的路径。
output_triton = torch.empty_like(x)
asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
print(output_torch)
print(output_triton)
print(f'The maximum difference between torch and triton is '
f'{torch.max(torch.abs(output_torch - output_triton))}')
Out:
tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0') tensor([0.4105, 0.5430, 0.0249, ..., 0.0424, 0.5351, 0.8149], device='cuda:0') The maximum difference between torch and triton is 2.384185791015625e-07
Download Jupyter notebook: 07-extern-functions.ipynb