Update PyTorch to 2.9.0+cu129 (#24994)

Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

.buildkite/test-pipeline.yaml

@@ -172,6 +172,8 @@ steps:
   - tests/v1/engine/test_engine_core_client.py
   - tests/distributed/test_symm_mem_allreduce.py
   commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
@@ -349,7 +351,8 @@ steps:
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -534,7 +537,7 @@ steps:
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.13.0
+  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: LM Eval Small Models # 53min
@@ -975,6 +978,8 @@ steps:
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py

.pre-commit-config.yaml

@@ -38,7 +38,7 @@ repos:
   rev: 0.9.1
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128, --python-platform, x86_64-manylinux_2_28]
+      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu129, --python-platform, x86_64-manylinux_2_28]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:

CMakeLists.txt

@@ -49,8 +49,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.8.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.9.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.8.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.9.0")
 
 #
 # Try to find python package with an executable that exactly matches

docker/Dockerfile

@@ -5,7 +5,7 @@
 # docs/contributing/dockerfile/dockerfile.md and
 # docs/assets/contributing/dockerfile-stages-dependency.png
 
-ARG CUDA_VERSION=12.8.1
+ARG CUDA_VERSION=12.9.1
 ARG PYTHON_VERSION=3.12
 
 # By parameterizing the base images, we allow third-party to use their own
@@ -275,6 +275,7 @@ WORKDIR /vllm-workspace
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETPLATFORM
 
+# TODO (huydhn): There is no prebuilt gdrcopy package on 12.9 at the moment
 ARG GDRCOPY_CUDA_VERSION=12.8
 # Keep in line with FINAL_BASE_IMAGE
 ARG GDRCOPY_OS_VERSION=Ubuntu22_04
@@ -360,6 +361,13 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
     && uv pip install --system dist/*.whl --verbose \
         --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
+# TODO (huydhn): Remove this once xformers is released for 2.9.0
+RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
+    . /etc/environment
+    export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
+    uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
+BASH
+
 # Install FlashInfer pre-compiled kernel cache and binaries
 # https://docs.flashinfer.ai/installation.html
 RUN --mount=type=cache,target=/root/.cache/uv \
@@ -426,6 +434,7 @@ ARG PYTHON_VERSION
 
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
 
 # This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
 # Reference: https://github.com/astral-sh/uv/pull/1694
@@ -438,7 +447,8 @@ ENV UV_LINK_MODE=copy
 RUN --mount=type=cache,target=/root/.cache/uv \
     CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
     if [ "$CUDA_MAJOR" -ge 12 ]; then \
-        uv pip install --system -r requirements/dev.txt; \
+        uv pip install --system -r requirements/dev.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
     fi
 
 # install development dependencies (for testing)

docker/Dockerfile.cpu

@@ -199,9 +199,13 @@ FROM base AS vllm-test-deps
 
 WORKDIR /workspace/vllm
 
+# TODO: Update to 2.9.0 when there is a new build for intel_extension_for_pytorch for that version
 RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
     cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
+    sed -i 's/^torch==.*/torch==2.8.0/g' requirements/cpu-test.in && \
+    sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
+    sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
 
 RUN --mount=type=cache,target=/root/.cache/uv \

docs/contributing/ci/update_pytorch_version.md

@@ -87,7 +87,7 @@ is ineffective.
 
 While ongoing efforts like <https://github.com/vllm-project/vllm/issues/17419>
 address the long build time at its source, the current workaround is to set `VLLM_CI_BRANCH`
-to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
+to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/long_build`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
 
 1. Increase the timeout limit to 10 hours so that the build doesn't time out.
@@ -100,35 +100,17 @@ to warm it up so that future builds are faster.
 
 ## Update dependencies
 
-Several vLLM dependencies, such as FlashInfer, also depend on PyTorch and need
+Several vLLM dependencies like xFormers depend on PyTorch and need
 to be updated accordingly. Rather than waiting for all of them to publish new
 releases (which would take too much time), they can be built from
 source to unblock the update process.
 
-### FlashInfer
-
-Here is how to build and install it from source with `torch2.7.0+cu128` in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
-
-```bash
-export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
-export FLASHINFER_ENABLE_SM90=1
-uv pip install --system \
-    --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
-```
-
-One caveat is that building FlashInfer from source adds approximately 30
-minutes to the vLLM build time. Therefore, it's preferable to cache the wheel in a
-public location for immediate installation, such as [this FlashInfer wheel link](https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.6.post1%2Bcu128torch2.7-cp39-abi3-linux_x86_64.whl). For future releases, contact the PyTorch release
-team if you want to get the package published there.
-
 ### xFormers
 
-Similar to FlashInfer, here is how to build and install xFormers from source:
-
 ```bash
-export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
+export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
 MAX_JOBS=16 uv pip install --system \
-    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
+    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
 ```
 
 ## Update all the different vLLM platforms

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
     "packaging>=24.2",
     "setuptools>=77.0.3,<80.0.0",
     "setuptools-scm>=8.0",
-    "torch == 2.8.0",
+    "torch == 2.9.0",
     "wheel",
     "jinja2",
 ]

requirements/build.txt

@@ -4,7 +4,7 @@ ninja
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 setuptools-scm>=8
-torch==2.8.0
+torch==2.9.0
 wheel
 jinja2>=3.1.6
 regex

requirements/cuda.txt

@@ -5,11 +5,11 @@ numba == 0.61.2 # Required for N-gram speculative decoding
 
 # Dependencies for NVIDIA GPUs
 ray[cgraph]>=2.48.0 # Ray Compiled Graph, required for pipeline parallelism in V1.
-torch==2.8.0
+torch==2.9.0
-torchaudio==2.8.0
+torchaudio==2.9.0
 # These must be updated alongside torch
-torchvision==0.23.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
+torchvision==0.24.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # https://github.com/facebookresearch/xformers/releases/tag/v0.0.32.post1
-xformers==0.0.32.post1; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch >= 2.8
+# xformers==0.0.32.post1; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch >= 2.8
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.4.1

requirements/rocm-build.txt

@@ -1,12 +1,12 @@
 # Common dependencies
 -r common.txt
 
---extra-index-url https://download.pytorch.org/whl/rocm6.3
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
-torch==2.8.0
+torch==2.9.0
-torchvision==0.23.0
+torchvision==0.24.0
-torchaudio==2.8.0
+torchaudio==2.9.0
 
-triton==3.3.0
+triton==3.5.0
 cmake>=3.26.1,<4
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0

requirements/test.in

@@ -24,9 +24,9 @@ soundfile # required for audio tests
 jiwer # required for audio tests
 tblib # for pickling test exceptions
 timm >=1.0.17 # required for internvl and gemma3n-mm test
-torch==2.8.0
+torch==2.9.0
-torchaudio==2.8.0
+torchaudio==2.9.0
-torchvision==0.23.0
+torchvision==0.24.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[image,audio] >= 1.8.5 # required for voxtral test

requirements/test.txt

@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match --torch-backend cu128 --python-platform x86_64-manylinux_2_28
+#    uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match --torch-backend cu129 --python-platform x86_64-manylinux_2_28
 absl-py==2.1.0
     # via rouge-score
 accelerate==1.0.1
@@ -573,42 +573,44 @@ numpy==1.26.4
     #   tritonclient
     #   vocos
     #   xarray
-nvidia-cublas-cu12==12.8.4.1
+nvidia-cublas-cu12==12.9.1.4
     # via
     #   nvidia-cudnn-cu12
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-cupti-cu12==12.9.79
     # via torch
-nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-nvrtc-cu12==12.9.86
     # via torch
-nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cuda-runtime-cu12==12.9.79
     # via torch
 nvidia-cudnn-cu12==9.10.2.21
     # via torch
-nvidia-cufft-cu12==11.3.3.83
+nvidia-cufft-cu12==11.4.1.4
     # via torch
-nvidia-cufile-cu12==1.13.1.3
+nvidia-cufile-cu12==1.14.1.1
     # via torch
-nvidia-curand-cu12==10.3.9.90
+nvidia-curand-cu12==10.3.10.19
     # via torch
-nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusolver-cu12==11.7.5.82
     # via torch
-nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparse-cu12==12.5.10.65
     # via
     #   nvidia-cusolver-cu12
     #   torch
 nvidia-cusparselt-cu12==0.7.1
     # via torch
-nvidia-nccl-cu12==2.27.3
+nvidia-nccl-cu12==2.27.5
     # via torch
-nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvjitlink-cu12==12.9.86
     # via
     #   nvidia-cufft-cu12
     #   nvidia-cusolver-cu12
     #   nvidia-cusparse-cu12
     #   torch
-nvidia-nvtx-cu12==12.8.90
+nvidia-nvshmem-cu12==3.3.20
+    # via torch
+nvidia-nvtx-cu12==12.9.79
     # via torch
 omegaconf==2.3.0
     # via
@@ -1017,7 +1019,6 @@ setuptools==77.0.3
     #   lightning-utilities
     #   pytablewriter
     #   torch
-    #   triton
 shapely==2.1.1
     # via
     #   geopandas
@@ -1122,7 +1123,7 @@ tomli==2.2.1
     # via schemathesis
 tomli-w==1.2.0
     # via schemathesis
-torch==2.8.0+cu128
+torch==2.9.0+cu129
     # via
     #   -r requirements/test.in
     #   accelerate
@@ -1151,7 +1152,7 @@ torch==2.8.0+cu128
     #   torchvision
     #   vector-quantize-pytorch
     #   vocos
-torchaudio==2.8.0+cu128
+torchaudio==2.9.0+cu129
     # via
     #   -r requirements/test.in
     #   encodec
@@ -1164,7 +1165,7 @@ torchmetrics==1.7.4
     #   pytorch-lightning
     #   terratorch
     #   torchgeo
-torchvision==0.23.0+cu128
+torchvision==0.24.0+cu129
     # via
     #   -r requirements/test.in
     #   lightly
@@ -1205,7 +1206,7 @@ transformers==4.56.2
     #   transformers-stream-generator
 transformers-stream-generator==0.0.5
     # via -r requirements/test.in
-triton==3.4.0
+triton==3.5.0
     # via torch
 tritonclient==2.51.0
     # via

tests/lora/test_chatglm3_tp.py

@@ -109,7 +109,7 @@ def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files):
         tensor_parallel_size=4,
         trust_remote_code=True,
         fully_sharded_loras=True,
-        gpu_memory_utilization=0.85,
+        gpu_memory_utilization=0.8,
         compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
             cudagraph_specialize_lora=False,
         ),

tests/v1/e2e/test_async_sched_and_preempt.py

@@ -3,6 +3,7 @@
 from typing import Any
 
 import pytest
+import torch._dynamo.config as dynamo_config
 
 from vllm import SamplingParams
 
@@ -12,6 +13,7 @@ from ...models.utils import check_outputs_equal
 MODEL = "Qwen/Qwen3-0.6B"
 
 
+@dynamo_config.patch(cache_size_limit=16)
 def test_preempt_and_async_scheduling_e2e(monkeypatch: pytest.MonkeyPatch):
     """Test consistency of combos of async scheduling, preemption,
     uni/multiproc executor, and various sampling parameters."""

tools/install_gdrcopy.sh

@@ -7,18 +7,15 @@ set -euo pipefail
 # Requires: curl, apt-get, root privileges
 if [[ $(id -u) -ne 0 ]]; then
   echo "Must be run as root" >&2
-  
   exit 1
 fi
 if [[ $# -ne 3 ]]; then
   echo "Usage: $0 <GDRCOPY_OS_VERSION> <GDRCOPY_CUDA_VERSION> <uuarch(x64|aarch64)>" >&2
   exit 1
 fi
-
 OS_VER="$1"
 CUDA_VER="$2"
 UUARCH_RAW="$3"
-
 # Normalize/validate arch
 case "${UUARCH_RAW,,}" in
   aarch64|arm64)