python3Packages.vllm: 0.10.2 -> 0.11.0 (#448598)
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@@ -1,12 +1,14 @@
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diff --git a/requirements/cpu.txt b/requirements/cpu.txt
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index d80354342..7434f32f0 100644
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index 2db6d87ee..37f816170 100644
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--- a/requirements/cpu.txt
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+++ b/requirements/cpu.txt
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@@ -21,7 +21,4 @@ torchvision; platform_machine != "ppc64le" and platform_machine != "s390x"
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torchvision==0.22.0; platform_machine == "ppc64le"
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@@ -21,9 +21,6 @@ torchvision; platform_machine != "ppc64le" and platform_machine != "s390x"
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torchvision==0.23.0; platform_machine == "ppc64le"
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datasets # for benchmark scripts
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-# Intel Extension for PyTorch, only for x86_64 CPUs
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-intel-openmp==2024.2.1; platform_machine == "x86_64"
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-intel_extension_for_pytorch==2.6.0; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
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-intel_extension_for_pytorch==2.8.0; platform_machine == "x86_64"
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triton==3.2.0; platform_machine == "x86_64" # Triton is required for torch 2.6+cpu, as it is imported in torch.compile.
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# Use this to gather CPU info and optimize based on ARM Neoverse cores
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@@ -115,8 +115,8 @@ let
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src = fetchFromGitHub {
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owner = "vllm-project";
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repo = "FlashMLA";
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rev = "a757314c04eedd166e329e846c820eb1bdd702de";
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hash = "sha256-KT9R6ju7XzgqKHPGQwzw0yNiKL3DNW6qJrEBvmLn4hY=";
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rev = "5f65b85703c7ed75fda01e06495077caad207c3f";
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hash = "sha256-DO9EFNSoAgyfRRc095v1UjT+Zdzk4cFY0+n28FVEwI0=";
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};
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dontConfigure = true;
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@@ -271,7 +271,7 @@ in
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buildPythonPackage rec {
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pname = "vllm";
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version = "0.10.2";
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version = "0.11.0";
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pyproject = true;
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stdenv = torch.stdenv;
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@@ -280,7 +280,7 @@ buildPythonPackage rec {
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owner = "vllm-project";
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repo = "vllm";
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tag = "v${version}";
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hash = "sha256-m9P4cxxdAToGKKIyTQdFupG3vZ3sEueMMxjugYfjKbo=";
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hash = "sha256-uYK/e9McEyrDTACMk5S0cGCjai9rf6HMR9dpPL7ISYc=";
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};
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patches = [
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