From e034c55e6b294125412e133ff29cf7391f800291 Mon Sep 17 00:00:00 2001 From: Break Yang Date: Sun, 18 May 2025 17:04:48 -0700 Subject: [PATCH] treefmt --- .../python-modules/flashinfer/default.nix | 23 +++++++++++-------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/pkgs/development/python-modules/flashinfer/default.nix b/pkgs/development/python-modules/flashinfer/default.nix index d05a07a7f2b1..fb2004be007f 100644 --- a/pkgs/development/python-modules/flashinfer/default.nix +++ b/pkgs/development/python-modules/flashinfer/default.nix @@ -4,7 +4,8 @@ # # This means that if you plan to use flashinfer, you will need to set the # environment varaible `CUDA_HOME` to `cudatoolkit`. -{ lib, +{ + lib, buildPythonPackage, symlinkJoin, fetchFromGitHub, @@ -12,7 +13,7 @@ cmake, ninja, numpy, - torch + torch, }: assert torch.cudaSupport; @@ -34,7 +35,8 @@ let hash = "sha256-d4czDoEv0Focf1bJHOVGX4BDS/h5O7RPoM/RrujhgFQ="; }; -in buildPythonPackage { +in +buildPythonPackage { inherit pname version; src = fetchFromGitHub { @@ -64,7 +66,7 @@ in buildPythonPackage { # compiled kernels cached for future use. JIT mode allows fast installation, # as no CUDA kernels are pre-compiled, making it ideal for development and # testing. JIT version is also available as a sdist in PyPI. - # + # # AOT mode: Core CUDA kernels are pre-compiled and included in the library, # reducing runtime compilation overhead. If a required kernel is not # pre-compiled, it will be compiled at runtime using JIT. AOT mode is @@ -86,12 +88,13 @@ in buildPythonPackage { meta = with lib; { homepage = "https://flashinfer.ai/"; - description = ''; - FlashInfer is a library and kernel generator for Large Language Models - that provides high-performance implementation of LLM GPU kernels such as - FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving - and inference, and delivers state-of-the-art performance across diverse - scenarios. + description = '' + ; + FlashInfer is a library and kernel generator for Large Language Models + that provides high-performance implementation of LLM GPU kernels such as + FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving + and inference, and delivers state-of-the-art performance across diverse + scenarios. ''; license = licenses.asl20; maintainers = with maintainers; [ breakds ];