Instructions to use galqiwi/hadamard_transform_kernels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use galqiwi/hadamard_transform_kernels with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("galqiwi/hadamard_transform_kernels") - Notebooks
- Google Colab
- Kaggle
hadamard_transform_kernels
Forward Hadamard transform CUDA kernel, packaged for the
kernels library.
fp32 / fp16 / bf16, last dim from 1 up to 32768 (zero-padded to the
next power of two internally).
Use
import torch
from kernels import get_kernel
hadamard = get_kernel("galqiwi/hadamard_transform_kernels", version=1)
x = torch.randn(4, 4096, device="cuda", dtype=torch.float16)
y = hadamard.hadamard_transform(x, scale=1.0)
API
hadamard_transform(x: torch.Tensor, scale: float = 1.0) -> torch.Tensor
x is a CUDA tensor of shape (..., dim). The output has the same shape and
dtype.
Attribution
CUDA code is adapted from Dao-AILab/fast-hadamard-transform (Tri Dao, BSD-3-Clause).
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# !pip install kernels from kernels import get_kernel kernel = get_kernel("galqiwi/hadamard_transform_kernels")