A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 5, 06:00 UTC
[XPU] revert torch-xpu to 2.10 (#39656)
Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>
Kunshang Ji committed
Apr 13, 2026 at 10:50 UTC
dc02271d760137d3f58fb4c2378f44b2619daee5
2 files changed
+24
-24
requirements/test/xpu.txt
+23
-23
@@ -90,7 +90,7 @@ docker==7.1.0
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# via gpt-oss
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docopt==0.6.2
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# via num2words
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-dpcpp-cpp-rt==2025.3.2
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+dpcpp-cpp-rt==2025.3.1
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# via
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# onemkl-sycl-blas
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# onemkl-sycl-dft
@@ -171,27 +171,27 @@ idna==3.11
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# yarl
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imageio==2.37.3
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# via scikit-image
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-impi-rt==2021.17.2
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+impi-rt==2021.17.0
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# via
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# oneccl
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# torch
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iniconfig==2.3.0
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# via pytest
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-intel-cmplr-lib-rt==2025.3.2
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+intel-cmplr-lib-rt==2025.3.1
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# via
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# intel-sycl-rt
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# torch
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-intel-cmplr-lib-ur==2025.3.2
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+intel-cmplr-lib-ur==2025.3.1
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# via
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# intel-openmp
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# intel-sycl-rt
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# torch
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-intel-cmplr-lic-rt==2025.3.2
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+intel-cmplr-lic-rt==2025.3.1
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# via
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# intel-opencl-rt
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# intel-sycl-rt
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# torch
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-intel-opencl-rt==2025.3.2
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+intel-opencl-rt==2025.3.1
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# via
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# dpcpp-cpp-rt
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# onemkl-sycl-blas
@@ -200,14 +200,14 @@ intel-opencl-rt==2025.3.2
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# onemkl-sycl-rng
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# onemkl-sycl-sparse
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# torch
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-intel-openmp==2025.3.2
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+intel-openmp==2025.3.1
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# via
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# dpcpp-cpp-rt
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# mkl
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# torch
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-intel-pti==0.16.0
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+intel-pti==0.15.0
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# via torch
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-intel-sycl-rt==2025.3.2
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+intel-sycl-rt==2025.3.1
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# via
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# dpcpp-cpp-rt
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# oneccl
@@ -269,7 +269,7 @@ mistral-common==1.11.0
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# via
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# -c requirements/common.txt
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# -r requirements/test/xpu.in
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-mkl==2025.3.1
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+mkl==2025.3.0
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# via
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# onemkl-sycl-blas
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# onemkl-sycl-dft
@@ -334,28 +334,28 @@ numpy==2.2.6
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# tifffile
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# torchvision
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# transformers
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-oneccl==2021.17.2
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+oneccl==2021.17.1
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# via
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# oneccl-devel
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# torch
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-oneccl-devel==2021.17.2
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+oneccl-devel==2021.17.1
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# via torch
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-onemkl-license==2025.3.1
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+onemkl-license==2025.3.0
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# via
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# mkl
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# torch
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-onemkl-sycl-blas==2025.3.1
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+onemkl-sycl-blas==2025.3.0
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# via
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# onemkl-sycl-lapack
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# onemkl-sycl-sparse
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# torch
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-onemkl-sycl-dft==2025.3.1
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+onemkl-sycl-dft==2025.3.0
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# via torch
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-onemkl-sycl-lapack==2025.3.1
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+onemkl-sycl-lapack==2025.3.0
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# via torch
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-onemkl-sycl-rng==2025.3.1
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+onemkl-sycl-rng==2025.3.0
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# via torch
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-onemkl-sycl-sparse==2025.3.1
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+onemkl-sycl-sparse==2025.3.0
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# via torch
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openai-harmony==0.0.8
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# via
@@ -606,7 +606,7 @@ tabledata==1.3.4
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# via pytablewriter
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tabulate==0.10.0
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# via sacrebleu
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-tbb==2022.3.1
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+tbb==2022.3.0
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# via
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# intel-opencl-rt
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# mkl
@@ -643,7 +643,7 @@ tokenizers==0.22.2
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# via
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# -c requirements/common.txt
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# transformers
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-torch==2.11.0+xpu
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+torch==2.10.0+xpu
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# via
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# -c requirements/xpu.txt
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# accelerate
@@ -651,7 +651,7 @@ torch==2.11.0+xpu
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# sentence-transformers
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# timm
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# torchvision
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-torchvision==0.26.0+xpu
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+torchvision==0.25.0+xpu
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# via timm
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tqdm==4.67.3
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# via
@@ -669,7 +669,7 @@ transformers==4.57.6
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# via
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# -c requirements/common.txt
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# sentence-transformers
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-triton-xpu==3.7.0
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+triton-xpu==3.6.0
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# via torch
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typepy==1.3.4
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# via
@@ -704,7 +704,7 @@ typing-inspection==0.4.2
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# via
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# fastapi
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# pydantic
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-umf==1.0.3
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+umf==1.0.2
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# via
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# intel-cmplr-lib-ur
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# torch
requirements/xpu.txt
+1
-1
@@ -11,7 +11,7 @@ jinja2>=3.1.6
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datasets # for benchmark scripts
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numba == 0.61.2 # Required for N-gram speculative decoding
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--extra-index-url=https://download.pytorch.org/whl/xpu
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-torch==2.11.0+xpu
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+torch==2.10.0+xpu
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torchaudio
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torchvision
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