"""
Libdevice (`tl.math`) function
==============================
Triton can invoke a custom function from an external library.
In this example, we will use the `libdevice` library (a.k.a `math` in triton) to apply `asin` on a tensor.
Please refer to https://docs.nvidia.com/cuda/libdevice-users-guide/index.html regarding the semantics of all available libdevice functions.
In `triton/language/math.py`, we try to aggregate functions with the same computation but different data types together.
For example, both `__nv_asin` and `__nvasinf` calculate the principal value of the arc sine of the input, but `__nv_asin` operates on `double` and `__nv_asinf` operates on `float`.
Using triton, you can simply call `tl.math.asin`.
Triton automatically selects the correct underlying device function to invoke based on input and output types.
"""

# %%
#  asin Kernel
# ------------

import torch

import triton
import triton.language as tl


@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 = tl.math.asin(x)
    tl.store(y_ptr + offsets, x, mask=mask)

# %%
#  Using the default libdevice library path
# -----------------------------------------
# We can use the default libdevice library path encoded in `triton/language/math.py`


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))}'
)

# %%
#  Customize the libdevice library path
# -------------------------------------
# We can also customize the libdevice library path by passing the path to the `libdevice` library to the `asin` kernel.

output_triton = torch.empty_like(x)
asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024,
                  extern_libs={'libdevice': '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'})
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))}'
)
