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Author SHA1 Message Date
Woosuk KwonandClaude Fable 5 9feb5a94be [Spec Decode] Keep draft_gumbel_pos at the top of spec_decode utils
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
2026-07-02 15:56:23 +00:00
Woosuk KwonandClaude Fable 5 e83808768c [Spec Decode] Move draft_gumbel_pos to spec_decode utils
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
2026-07-02 15:54:15 +00:00
Woosuk KwonandClaude Fable 5 7fd9f22fcb [Spec Decode] Salt DSpark's draft Gumbel stream too
DSparkSpeculator's sequential Markov sampling calls gumbel_sample
directly with key Q-1 (the verification key), bypassing sample_draft.
Route it through draft_gumbel_pos so its probabilistic drafts get the
same disjoint stream.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
2026-07-02 15:49:18 +00:00
Woosuk Kwon d61cfb1452 Merge branch 'main' into woosuk/mrv2-draft-gumbel-decouple 2026-07-02 15:46:57 +00:00
Woosuk KwonandClaude Fable 5 214a2054a8 [Model Runner V2][Spec Decode] Decouple draft Gumbel stream from acceptance/recovery noise
With draft_sample_method="probabilistic", the draft token for position P
was sampled with Gumbel noise keyed by Philox offset P -- the same offset
that keys the acceptance uniform (u == float of the very Philox draw used
as the draft's Gumbel key) and the recovery Gumbel noise in the rejection
sampler. On rejection, the recovery draw therefore reused the exact noise
vector that selected the rejected draft token, violating the independence
assumption of rejection sampling and biasing the output marginal toward
draft-favored tokens (measured TV distance from the target 0.0125 vs a
0.0028 noise floor; 0.0014 after the fix). Acceptance rate is unchanged.

Salt the draft-side Philox offsets into a range disjoint from the
target-side streams (positions are bounded by max_model_len << 2**30).
The default greedy draft mode consumes no Gumbel noise and is unaffected.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
2026-07-02 04:33:42 +00:00
994 changed files with 12497 additions and 46153 deletions
-1
View File
@@ -3,7 +3,6 @@ job_dirs:
- ".buildkite/intel_jobs"
run_all_patterns:
- ".buildkite/ci_config_intel.yaml"
- ".buildkite/scripts/hardware_ci/run-intel-test.sh"
- "docker/Dockerfile"
- "docker/Dockerfile.xpu"
- "CMakeLists.txt"
-1
View File
@@ -8,7 +8,6 @@ run_all_patterns:
- "docker/docker-bake-rocm.hcl"
- ".buildkite/hardware_tests/amd.yaml"
- ".buildkite/scripts/ci-bake-rocm.sh"
- ".buildkite/scripts/rocm/"
- ".buildkite/scripts/hardware_ci/run-amd-test.py"
- ".buildkite/scripts/hardware_ci/run-amd-test.sh"
- "CMakeLists.txt"
+29 -32
View File
@@ -1,28 +1,5 @@
group: Hardware - AMD Build
# ROCm image flow:
# 1. Refresh the long-lived ROCm base image only when Dockerfile.rocm_base changes.
# 2. Build ci_base from either the stable base or the freshly refreshed base.
# 3. Build the per-commit ROCm CI image and smoke-test it before GPU jobs run.
steps:
- label: "AMD: :docker: refresh ROCm base"
key: refresh-rocm-base-amd
depends_on: []
device: amd_cpu
no_plugin: true
commands:
- bash .buildkite/scripts/rocm/refresh-base-image.sh
env:
DOCKER_BUILDKIT: "1"
BUILDKIT_PROGRESS: "tty"
TERM: "xterm-256color"
retry:
automatic:
- exit_status: -1 # Agent was lost
limit: 1
- exit_status: -10 # Agent was lost
limit: 1
# Ensure ci_base is up-to-date before building the test image.
# Compares a content hash of ci_base-affecting files against the remote
# image label. If hashes match the build is skipped (< 30 s); if they
@@ -30,16 +7,13 @@ steps:
- label: "AMD: :docker: ensure ci_base"
key: ensure-ci-base-amd
soft_fail: false
depends_on:
- refresh-rocm-base-amd
depends_on: []
device: amd_cpu
no_plugin: true
commands:
- bash .buildkite/scripts/rocm/build-ci-base.sh
- bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
env:
DOCKER_BUILDKIT: "1"
BUILDKIT_PROGRESS: "tty"
TERM: "xterm-256color"
VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
REMOTE_VLLM: "1"
@@ -59,12 +33,35 @@ steps:
device: amd_cpu
no_plugin: true
commands:
- bash .buildkite/scripts/rocm/build-test-image.sh
- bash .buildkite/scripts/rocm/smoke-test-image.sh
- |
if [[ "${ROCM_CI_ARTIFACT_ONLY:-0}" == "1" ]]; then
echo "ROCM_CI_ARTIFACT_ONLY=1; building ROCm wheel artifact only"
IMAGE_TAG="" bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-artifacts
else
bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-wheel
fi
- |
docker run --rm --network=none --entrypoint /bin/bash "rocm/vllm-ci:${BUILDKITE_COMMIT}" -ec '
if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
command -v python3
command -v uv
command -v pytest
if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
echo No ROCm CLI found in image >&2
exit 1
fi
python3 - <<PY
import torch, vllm
print(torch.__version__)
print(vllm.__version__)
PY
echo AMD image smoke OK
'
env:
DOCKER_BUILDKIT: "1"
BUILDKIT_PROGRESS: "tty"
TERM: "xterm-256color"
VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
IMAGE_TAG: "rocm/vllm-ci:$BUILDKITE_COMMIT"
+1 -16
View File
@@ -17,14 +17,12 @@ steps:
- tests/kernels/test_awq_int4_to_int8.py
- tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
- tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
- tests/kernels/mamba/test_cpu_short_conv.py
commands:
- |
bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
pytest -x -v -s tests/kernels/moe/test_cpu_quant_fused_moe.py
pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py
pytest -x -v -s tests/kernels/test_onednn.py
pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
@@ -141,22 +139,9 @@ steps:
commands:
- |
bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py --ignore=tests/models/multimodal/generation/test_qwen2_5_vl.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
parallelism: 4
- label: CPU-Qwen2.5-VL Multimodal Tests
depends_on: []
device: intel_cpu
no_plugin: true
source_file_dependencies:
# - vllm/
- vllm/model_executor/layers/rotary_embedding
- tests/models/multimodal/generation/
commands:
- |
bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
VLLM_CI_ENV=0 pytest -x -v -s tests/models/multimodal/generation/test_qwen2_5_vl.py"
- label: "Arm CPU Test"
depends_on: []
soft_fail: false
@@ -45,7 +45,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
-20
View File
@@ -79,18 +79,12 @@ setup_buildx_builder() {
docker buildx ls | grep -E '^\*|^NAME' || docker buildx ls
}
annotate_image_tags() {
.buildkite/scripts/annotate-image-build.sh \
"${IMAGE_TAG:-}" "${IMAGE_TAG_LATEST:-}"
}
check_and_skip_if_image_exists() {
if [[ -n "${IMAGE_TAG:-}" ]]; then
echo "--- :mag: Checking if image exists"
if docker manifest inspect "${IMAGE_TAG}" >/dev/null 2>&1; then
echo "Image already exists: ${IMAGE_TAG}"
echo "Skipping build"
annotate_image_tags
exit 0
fi
echo "Image not found, proceeding with build"
@@ -177,18 +171,6 @@ BRANCH=$4
IMAGE_TAG=$5
IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional
# When TORCH_NIGHTLY=1, build the base CI image against PyTorch nightly so the
# entire existing pipeline runs on nightly torch (CUDA/GPU lane only). Delegate
# to the dedicated nightly build (PYTORCH_NIGHTLY=1, CUDA 13.0) and tag it at the
# normal IMAGE_TAG that every test step already pulls -- no separate image tag,
# no duplicate "vLLM Against PyTorch Nightly" pipeline section.
if [[ "${TORCH_NIGHTLY:-0}" == "1" ]]; then
echo "--- :warning: TORCH_NIGHTLY=1 -- building base image on PyTorch nightly"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
exec "${SCRIPT_DIR}/image_build_torch_nightly.sh" \
"${REGISTRY}" "${REPO}" "${BUILDKITE_COMMIT}" "${BRANCH}" "${IMAGE_TAG}"
fi
# build config
TARGET="test-ci"
VLLM_BAKE_FILE_PATH="${VLLM_BAKE_FILE_PATH:-docker/docker-bake.hcl}"
@@ -272,5 +254,3 @@ echo "--- :docker: Building ${TARGET}"
docker --debug buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"
echo "--- :white_check_mark: Build complete"
annotate_image_tags
+19 -20
View File
@@ -9,31 +9,30 @@ fi
REGISTRY=$1
REPO=$2
BUILDKITE_COMMIT=$3
IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64"
# authenticate with AWS ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
# skip build if image already exists
if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
echo "Image found"
else
if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64) ]]; then
echo "Image not found, proceeding with build..."
# build for arm64 GPU targets: Grace/GH200 (sm_90),
# Blackwell/Thor (sm_100/sm_103/sm_110), and DGX Spark/GB10
# (sm_121, family-covered by 12.0 under CUDA 13)
docker build --file docker/Dockerfile \
--platform linux/arm64 \
--build-arg max_jobs=16 \
--build-arg nvcc_threads=4 \
--build-arg torch_cuda_arch_list="9.0 10.0 11.0 12.0" \
--build-arg USE_SCCACHE=1 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$IMAGE" \
--target test \
--progress plain .
# push
docker push "$IMAGE"
else
echo "Image found"
exit 0
fi
.buildkite/scripts/annotate-image-build.sh "$IMAGE"
# build for arm64 GPU targets: Grace/GH200 (sm_90) and DGX Spark/GB10
# (sm_121, family-covered by 12.0 under CUDA 13)
docker build --file docker/Dockerfile \
--platform linux/arm64 \
--build-arg max_jobs=16 \
--build-arg nvcc_threads=4 \
--build-arg torch_cuda_arch_list="9.0 12.0" \
--build-arg USE_SCCACHE=1 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64 \
--target test \
--progress plain .
# push
docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64
+15 -15
View File
@@ -9,26 +9,26 @@ fi
REGISTRY=$1
REPO=$2
BUILDKITE_COMMIT=$3
IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu"
# authenticate with AWS ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
# skip build if image already exists
if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
echo "Image found"
else
if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
echo "Image not found, proceeding with build..."
# build
docker build --file docker/Dockerfile.cpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--build-arg VLLM_CPU_X86=true \
--tag "$IMAGE" \
--target vllm-test \
--progress plain .
# push
docker push "$IMAGE"
else
echo "Image found"
exit 0
fi
.buildkite/scripts/annotate-image-build.sh "$IMAGE"
# build
docker build --file docker/Dockerfile.cpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--build-arg VLLM_CPU_X86=true \
--tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
--target vllm-test \
--progress plain .
# push
docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
+14 -14
View File
@@ -9,25 +9,25 @@ fi
REGISTRY=$1
REPO=$2
BUILDKITE_COMMIT=$3
IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64-cpu"
# authenticate with AWS ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
# skip build if image already exists
if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
echo "Image found"
else
if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
echo "Image not found, proceeding with build..."
# build
docker build --file docker/Dockerfile.cpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$IMAGE" \
--target vllm-test \
--progress plain .
# push
docker push "$IMAGE"
else
echo "Image found"
exit 0
fi
.buildkite/scripts/annotate-image-build.sh "$IMAGE"
# build
docker build --file docker/Dockerfile.cpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
--target vllm-test \
--progress plain .
# push
docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
+15 -15
View File
@@ -9,26 +9,26 @@ fi
REGISTRY=$1
REPO=$2
BUILDKITE_COMMIT=$3
IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu"
# authenticate with AWS ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
# skip build if image already exists
if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
echo "Image found"
else
if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
echo "Image not found, proceeding with build..."
# build
docker build \
--file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$IMAGE" \
--progress plain \
https://github.com/vllm-project/vllm-gaudi.git
# push
docker push "$IMAGE"
else
echo "Image found"
exit 0
fi
.buildkite/scripts/annotate-image-build.sh "$IMAGE"
# build
docker build \
--file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
--progress plain \
https://github.com/vllm-project/vllm-gaudi.git
# push
docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu
@@ -40,7 +40,6 @@ docker buildx ls
echo "--- :mag: Checking if image already exists"
if docker manifest inspect "$IMAGE_TAG" >/dev/null 2>&1; then
echo "Image found: $IMAGE_TAG — skipping build"
.buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"
exit 0
fi
echo "Image not found, proceeding with build..."
@@ -67,5 +66,3 @@ docker buildx build --file docker/Dockerfile \
--progress plain .
echo "--- :white_check_mark: Torch nightly image build complete: $IMAGE_TAG"
.buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"
+14 -14
View File
@@ -9,26 +9,26 @@ fi
REGISTRY=$1
REPO=$2
BUILDKITE_COMMIT=$3
IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-xpu"
# authenticate with AWS ECR
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
# skip build if image already exists
if docker manifest inspect "$IMAGE" &> /dev/null; then
echo "Image found"
else
if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
echo "Image not found, proceeding with build..."
# build
docker build \
--file docker/Dockerfile.xpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$IMAGE" \
--progress plain .
# push
docker push "$IMAGE"
else
echo "Image found"
exit 0
fi
.buildkite/scripts/annotate-image-build.sh "$IMAGE"
# build
docker build \
--file docker/Dockerfile.xpu \
--build-arg max_jobs=16 \
--build-arg buildkite_commit="$BUILDKITE_COMMIT" \
--tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
--progress plain .
# push
docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu
+6 -28
View File
@@ -38,7 +38,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
working_dir: "."
env:
@@ -72,31 +72,9 @@ steps:
pytest -v -s v1/test_oracle.py &&
pytest -v -s v1/test_request.py &&
pytest -v -s v1/test_outputs.py &&
pytest -v -s v1/sample'
- label: Basic Models Tests (Initialization)
timeout_in_minutes: 60
device: intel_gpu
agent_tags:
label: production
gpu: 1+
mem: 16+
no_plugin: true
working_dir: "."
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
REPO: "vllm-ci-test-repo"
VLLM_TEST_DEVICE: "xpu"
source_file_dependencies:
- vllm/
- tests/models/test_initialization.py
- tests/models/registry.py
commands:
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'export VLLM_XPU_FUSED_MOE_USE_REF=1 &&
cd tests &&
pytest -v -s models/test_initialization.py::test_can_initialize_large_subset[Eagle3MiniMaxM2ForCausalLM]'
pytest -v -s v1/sample/test_topk_topp_sampler.py &&
pytest -v -s v1/sample/test_logprobs.py &&
pytest -v -s v1/sample/test_logprobs_e2e.py'
- label: XPU CPU Offload
timeout_in_minutes: 60
@@ -132,7 +110,7 @@ steps:
agent_tags:
label: production
gpu: 2+
mem: 16+
mem: 24+
no_plugin: true
working_dir: "."
env:
@@ -180,7 +158,7 @@ steps:
commands:
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'pip install modelscope\<1.38 &&
'pip install modelscope &&
cd tests &&
pytest -v -s test_regression.py'
@@ -9,7 +9,7 @@ steps:
agent_tags:
label: production
gpu: 2+
mem: 16+
mem: 24+
no_plugin: true
working_dir: "."
env:
@@ -9,7 +9,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
working_dir: "."
env:
@@ -81,7 +81,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
working_dir: "."
env:
@@ -105,7 +105,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
working_dir: "."
env:
-28
View File
@@ -1,28 +0,0 @@
group: Quantization
depends_on:
- image-build-xpu
steps:
- label: Quantization
key: quantization
timeout_in_minutes: 30
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
REPO: "vllm-ci-test-repo"
VLLM_TEST_DEVICE: "xpu"
no_plugin: true
working_dir: "."
device: intel_gpu
agent_tags:
label: production
gpu: 1+
mem: 16+
source_file_dependencies:
- csrc/
- vllm/model_executor/layers/quantization
- tests/quantization
commands:
# - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s tests/quantization/test_per_token_kv_cache.py --deselect="tests/quantization/test_per_token_kv_cache.py::test_triton_unified_attention_per_token_head_scale[int4-16-128-num_heads0-seq_lens1]"'
+2 -50
View File
@@ -42,37 +42,12 @@ steps:
python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --kv-cache-dtype fp8 &&
python3 examples/basic/offline_inference/generate.py --model nvidia/Llama-3.1-8B-Instruct-FP8 --block-size 64 --enforce-eager --quantization modelopt --kv-cache-dtype fp8 --attention-backend TRITON_ATTN --max-model-len 4096 &&
python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192 &&
python3 examples/basic/offline_inference/generate.py --model TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ --block-size 64 --enforce-eager &&
python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 &&
python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel &&
python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --max-model-len 8192 &&
VLLM_XPU_FUSED_MOE_USE_REF=1 python3 examples/basic/offline_inference/generate.py --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 --enforce-eager -tp 2 --max-model-len 8192 &&
python3 examples/basic/offline_inference/generate.py --model INCModel/Qwen3-30B-A3B-Instruct-2507-MXFP4-LLMC --enforce-eager -tp 2 --max-model-len 8192
'
- label: "XPU W8A8 FP8 Linear Examples"
depends_on:
- image-build-xpu
timeout_in_minutes: 60
device: intel_gpu
agent_tags:
label: production
gpu: 1+
mem: 24+
no_plugin: true
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
REPO: "vllm-ci-test-repo"
VLLM_TEST_DEVICE: "xpu"
source_file_dependencies:
- vllm/
- .buildkite/intel_jobs/test-intel.yaml
commands:
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 --enforce-eager --max-model-len 4096 &&
python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic --enforce-eager --max-model-len 4096 &&
python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model meta-llama/Llama-3.2-1B-Instruct --quantization fp8 --enforce-eager --max-model-len 4096
'
- label: "XPU V1 test"
depends_on:
- image-build-xpu
@@ -81,7 +56,7 @@ steps:
agent_tags:
label: production
gpu: 1+
mem: 24+
mem: 16+
no_plugin: true
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
@@ -144,27 +119,4 @@ steps:
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'cd tests &&
pytest -v -s quantization/test_auto_round.py'
- label: "XPU compressed tensors FP8 test"
depends_on:
- image-build-xpu
timeout_in_minutes: 60
device: intel_gpu
agent_tags:
label: production
gpu: 1+
mem: 16+
no_plugin: true
env:
REGISTRY: "public.ecr.aws/q9t5s3a7"
REPO: "vllm-ci-test-repo"
VLLM_TEST_DEVICE: "xpu"
source_file_dependencies:
- vllm/
- tests/quantization/test_compressed_tensors.py
- .buildkite/intel_jobs/test-intel.yaml
commands:
- >-
bash .buildkite/scripts/hardware_ci/run-intel-test.sh
'cd tests &&
pytest -v -s quantization/test_compressed_tensors.py::test_compressed_tensors_fp8'
pytest -v -s quantization/test_auto_round.py'
-47
View File
@@ -350,25 +350,6 @@ steps:
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
- 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"'
- label: ":docker: Build release image - x86_64 - XPU"
depends_on: ~
id: build-xpu-release-image
agents:
queue: cpu_queue_release
commands:
- "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
- |
DOCKER_BUILDKIT=1 docker build \
$(bash .buildkite/scripts/docker-build-metadata-args.sh xpu) \
--build-arg GIT_REPO_CHECK=1 \
--target vllm-openai \
--progress plain \
-f docker/Dockerfile.xpu .
- "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"
- 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"'
env:
DOCKER_BUILDKIT: "1"
- block: "Build release image for x86_64 CPU"
key: block-cpu-release-image-build
depends_on: ~
@@ -464,16 +445,6 @@ steps:
- "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
- 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 12.9 Ubuntu 24.04" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"'
- label: "Create manifest - XPU"
depends_on:
- build-xpu-release-image
id: create-manifest-xpu
agents:
queue: small_cpu_queue_release
commands:
- "bash .buildkite/scripts/xpu/create-xpu-ecr-manifest.sh"
- 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: XPU" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-xpu"'
- label: "Publish nightly multi-arch image to DockerHub"
depends_on:
- create-multi-arch-manifest
@@ -848,23 +819,6 @@ steps:
DOCKER_BUILDKIT: "1"
S3_BUCKET: "vllm-wheels"
- label: "Publish nightly XPU image to DockerHub"
depends_on:
- create-manifest-xpu
if: build.env("NIGHTLY") == "1"
agents:
queue: small_cpu_queue_release
commands:
- "bash .buildkite/scripts/xpu/push-nightly-builds-xpu.sh"
- "bash .buildkite/scripts/cleanup-nightly-builds.sh nightly- vllm/vllm-openai-xpu"
plugins:
- docker-login#v3.0.0:
username: vllmbot
password-env: DOCKERHUB_TOKEN
env:
DOCKER_BUILDKIT: "1"
DOCKERHUB_USERNAME: "vllmbot"
- label: "Publish nightly ROCm image to DockerHub"
depends_on:
- build-rocm-release-image
@@ -895,7 +849,6 @@ steps:
- create-multi-arch-manifest-cuda-12-9
- create-multi-arch-manifest-ubuntu2404
- create-multi-arch-manifest-cuda-12-9-ubuntu2404
- create-manifest-xpu
- build-rocm-release-image
- input-release-version
# Wait for CPU builds if their block steps were unblocked, so publish
@@ -1,36 +0,0 @@
#!/bin/bash
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
#
# Append the Docker image tag(s) an image-build step pushed to a Buildkite
# annotation, so the built image tags show up on the build page instead of
# being buried in the job logs.
#
# Usage: annotate-image-build.sh <image_tag> [<image_tag> ...]
set -euo pipefail
# buildkite-agent only exists on Buildkite agents; no-op elsewhere so the
# image build scripts stay runnable locally.
if ! command -v buildkite-agent >/dev/null 2>&1; then
echo "buildkite-agent not found; skipping image tag annotation"
exit 0
fi
label="${BUILDKITE_LABEL:-Image build}"
content=""
for image in "$@"; do
[[ -n "$image" ]] || continue
content+="- **${label}**: \`${image}\`"$'\n'
done
if [[ -z "$content" ]]; then
echo "No image tags provided; nothing to annotate"
exit 0
fi
# Best-effort: a flaky annotation must never fail an otherwise successful
# (and expensive) image build.
if ! printf '%s' "$content" | \
buildkite-agent annotate --append --style 'info' --context 'docker-images'; then
echo "warning: failed to annotate build with image tags"
fi
@@ -29,11 +29,7 @@ if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
TORCH_INDEX_URL=""
fi
else
if [ "${TORCH_NIGHTLY:-0}" = "1" ]; then
TORCH_INDEX_URL="https://download.pytorch.org/whl/nightly/cu130"
else
TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
fi
TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
fi
echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
+3 -25
View File
@@ -15,7 +15,7 @@ set -euo pipefail
DEFAULT_REPO_SLUG="vllm-project/vllm"
DEFAULT_CI_HCL_SOURCE="docker/ci-rocm.hcl"
DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh .buildkite/scripts/rocm/build-ci-base.sh"
DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh"
DEFAULT_CI_BASE_DOCKERFILE="docker/Dockerfile.rocm"
DEFAULT_CI_BASE_DOCKERFILE_STAGES="base build_rixl build_rocshmem build_deepep mori_base ci_base"
DEFAULT_CI_BASE_METADATA_VERSION="1"
@@ -393,16 +393,6 @@ should_upload_wheel_artifacts() {
|| "${TARGET}" == *"artifact"* ]]
}
set_buildkite_metadata() {
local key="$1"
local value="$2"
[[ -n "${value}" ]] || return 0
if command -v buildkite-agent >/dev/null 2>&1; then
buildkite-agent meta-data set "${key}" "${value}" || true
fi
}
get_remote_image_label() {
local image_ref="$1"
local label_key="$2"
@@ -743,8 +733,6 @@ configure_ci_base_image_refs() {
if is_ci_base_target; then
IMAGE_TAG="${primary_tag}"
CI_BASE_IMAGE="${primary_tag}"
export CI_BASE_IMAGE
export IMAGE_TAG
echo "ci_base primary image tag: ${CI_BASE_IMAGE_TAG}"
@@ -762,10 +750,6 @@ configure_ci_base_image_refs() {
echo "ci_base stable alias will not be pushed for this build"
echo "Set NIGHTLY=1 on ${CI_BASE_STABLE_BRANCH:-main} to refresh ${stable_tag}"
fi
set_buildkite_metadata "rocm-ci-base-image" "${CI_BASE_IMAGE_TAG}"
set_buildkite_metadata "rocm-ci-base-image-content" "${content_tag}"
set_buildkite_metadata "rocm-ci-base-image-commit" "${CI_BASE_IMAGE_TAG_COMMIT:-}"
set_buildkite_metadata "rocm-ci-base-image-stable" "${CI_BASE_IMAGE_TAG_STABLE:-}"
return 0
fi
@@ -1795,10 +1779,7 @@ seed_dependency_caches_if_needed() {
echo "--- :docker: Seeding ${target}"
echo "Expected cache ref: ${cache_ref}"
docker buildx bake \
"${BAKE_FILES[@]}" \
--progress "${BUILDKIT_PROGRESS:-plain}" \
"${target}"
docker buildx bake "${BAKE_FILES[@]}" --progress plain "${target}"
verify_dependency_cache_ref "${cache_ref}"
done
}
@@ -1826,10 +1807,7 @@ run_bake() {
local build_rc=0
echo "--- :docker: Building ${TARGET}"
docker buildx bake \
"${BAKE_FILES[@]}" \
--progress "${BUILDKIT_PROGRESS:-plain}" \
"${BAKE_TARGETS[@]}" || build_rc=$?
docker buildx bake "${BAKE_FILES[@]}" --progress plain "${BAKE_TARGETS[@]}" || build_rc=$?
if [[ ${build_rc} -eq 0 ]]; then
echo "--- :white_check_mark: Build complete"
+2 -29
View File
@@ -28,17 +28,6 @@
###############################################################################
set -o pipefail
: "${BUILDKIT_PROGRESS:=plain}"
: "${TERM:=xterm-256color}"
: "${FORCE_COLOR:=1}"
: "${CLICOLOR_FORCE:=1}"
: "${PY_COLORS:=1}"
: "${ROCM_DOCKER_TTY:=1}"
if [[ " ${PYTEST_ADDOPTS:-} " != *" --color"* ]]; then
PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }--color=yes"
fi
export BUILDKIT_PROGRESS TERM FORCE_COLOR CLICOLOR_FORCE PY_COLORS PYTEST_ADDOPTS ROCM_DOCKER_TTY
# Export Python path for commands that run directly on the host. Containerized
# tests set this to /vllm-workspace below so spawned Python processes do not
# depend on their current working directory.
@@ -160,7 +149,6 @@ EOF
echo "--- Building local ROCm test image"
docker build \
--pull=false \
--progress "${BUILDKIT_PROGRESS}" \
--build-arg "BASE_IMAGE=${base_image}" \
-t "${artifact_image}" \
"${context_dir}" || return 1
@@ -460,11 +448,9 @@ checkout="${BUILDKITE_BUILD_CHECKOUT_PATH:-}"
if [[ -z "${checkout}" || ! -d "${checkout}" ]]; then
checkout="."
fi
# Pass safe.directory per-command (-c) because buildkite runs will always fail
# the next check on git 2.35.2+ due to mixed uses of root and buildkite-agent/uids.
if git -c "safe.directory=${checkout}" -C "${checkout}" rev-parse --is-inside-work-tree >/dev/null 2>&1; then
if git -C "${checkout}" rev-parse --is-inside-work-tree >/dev/null 2>&1; then
vllm_standalone_merge_base="$(
git -c "safe.directory=${checkout}" -C "${checkout}" merge-base HEAD origin/main 2>/dev/null || true
git -C "${checkout}" merge-base HEAD origin/main 2>/dev/null || true
)"
fi
if [[ -z "${vllm_standalone_merge_base}" ]]; then
@@ -547,13 +533,6 @@ if is_multi_node "$commands"; then
else
echo "--- Single-node job"
echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
docker_run_terminal_args=(-i)
if [[ "${ROCM_DOCKER_TTY}" == "1" ]]; then
docker_run_terminal_args+=(-t)
echo "Docker interactive stdin: enabled; TTY allocation: enabled"
else
echo "Docker interactive stdin: enabled; TTY allocation: disabled"
fi
ulimit_core_hard=$(ulimit -H -c)
if [[ "$ulimit_core_hard" == "unlimited" ]]; then
@@ -570,7 +549,6 @@ else
fi
docker run \
"${docker_run_terminal_args[@]}" \
--device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
$RDMA_FLAGS \
--network=host \
@@ -585,11 +563,6 @@ else
-e AWS_SECRET_ACCESS_KEY \
-e BUILDKITE_PARALLEL_JOB \
-e BUILDKITE_PARALLEL_JOB_COUNT \
-e TERM \
-e FORCE_COLOR \
-e CLICOLOR_FORCE \
-e PY_COLORS \
-e PYTEST_ADDOPTS \
-v "${HF_CACHE}:${HF_MOUNT}" \
-e "HF_HOME=${HF_MOUNT}" \
-e "PYTHONPATH=${MYPYTHONPATH}" \
@@ -38,9 +38,7 @@ function cpu_tests() {
pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
pytest -x -v -s tests/kernels/core/test_cpu_activation.py
pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py
pytest -x -v -s tests/kernels/moe/test_cpu_int4_moe.py
pytest -x -v -s tests/kernels/mamba/test_cpu_short_conv.py"
pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"
# skip tests requiring model downloads if HF_TOKEN is not set
# due to rate-limits
@@ -64,6 +62,7 @@ function cpu_tests() {
set -e
pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
# basic online serving
docker exec cpu-test bash -c '
set -e
@@ -21,7 +21,6 @@ case "${test_suite}" in
python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --kv-cache-dtype fp8
python3 examples/basic/offline_inference/generate.py --model nvidia/Llama-3.1-8B-Instruct-FP8 --block-size 64 --enforce-eager --quantization modelopt --kv-cache-dtype fp8 --attention-backend TRITON_ATTN --max-model-len 4096
python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192
python3 examples/basic/offline_inference/generate.py --model TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ --block-size 64 --enforce-eager
python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2
python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --max-model-len 8192
@@ -360,7 +360,7 @@ export HF_TOKEN ZE_AFFINITY_MASK
--ipc=host \
--privileged \
-v /dev/dri/by-path:/dev/dri/by-path \
-v "/data/huggingface:/root/.cache/huggingface" \
-v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
--entrypoint='' \
-e HF_TOKEN \
-e ZE_AFFINITY_MASK \
@@ -85,7 +85,7 @@ RUN pip config set global.index-url http://cache-service-vllm.nginx-pypi-cache.s
# Install for pytest to make the docker build cache layer always valid
RUN --mount=type=cache,target=/root/.cache/pip \
pip install pytest>=6.0 'modelscope<1.38'
pip install pytest>=6.0 modelscope
WORKDIR /workspace/vllm
@@ -130,22 +130,6 @@ docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm
docker push vllm/vllm-openai-rocm:latest-base
docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
# ---- XPU ----
docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:latest-x86_64
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
docker push vllm/vllm-openai-xpu:latest-x86_64
docker push vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
docker manifest rm vllm/vllm-openai-xpu:latest || true
docker manifest rm vllm/vllm-openai-xpu:v${RELEASE_VERSION} || true
docker manifest create vllm/vllm-openai-xpu:latest vllm/vllm-openai-xpu:latest-x86_64 --amend
docker manifest create vllm/vllm-openai-xpu:v${RELEASE_VERSION} vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64 --amend
docker manifest push vllm/vllm-openai-xpu:latest
docker manifest push vllm/vllm-openai-xpu:v${RELEASE_VERSION}
# ---- CPU ----
# CPU images are behind separate block steps and may not have been built.
# All-or-nothing: inspect both arches first, then either publish everything
-32
View File
@@ -1,32 +0,0 @@
#!/usr/bin/env bash
# Build the ROCm ci_base image, optionally from a freshly rebuilt ROCm base.
set -euo pipefail
metadata_get() {
local key="$1"
if command -v buildkite-agent >/dev/null 2>&1; then
buildkite-agent meta-data get "${key}" 2>/dev/null || true
fi
}
main() {
local base_refreshed=""
base_refreshed="$(metadata_get rocm-base-refresh)"
if [[ "${base_refreshed}" == "1" ]]; then
export BASE_IMAGE
export CI_BASE_PUSH_STABLE_TAG
BASE_IMAGE="$(metadata_get rocm-base-image)"
CI_BASE_PUSH_STABLE_TAG="$(metadata_get rocm-base-push-stable-tag)"
CI_BASE_PUSH_STABLE_TAG="${CI_BASE_PUSH_STABLE_TAG:-0}"
echo "Using refreshed ROCm base image for ci_base: ${BASE_IMAGE}"
echo "Push stable ci_base tag: ${CI_BASE_PUSH_STABLE_TAG}"
fi
bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
}
main "$@"
@@ -1,57 +0,0 @@
#!/usr/bin/env bash
# Build the ROCm CI test image or wheel artifact.
#
# When Dockerfile.rocm_base changes, always build the full image so downstream
# ROCm tests can validate the freshly rebuilt base -> ci_base -> ci image chain.
set -euo pipefail
metadata_get() {
local key="$1"
if command -v buildkite-agent >/dev/null 2>&1; then
buildkite-agent meta-data get "${key}" 2>/dev/null || true
fi
}
use_refreshed_base_if_present() {
local base_refreshed=""
base_refreshed="$(metadata_get rocm-base-refresh)"
if [[ "${base_refreshed}" != "1" ]]; then
return 1
fi
export BASE_IMAGE
export CI_BASE_IMAGE
export IMAGE_TAG_LATEST
BASE_IMAGE="$(metadata_get rocm-base-image)"
CI_BASE_IMAGE="$(metadata_get rocm-ci-base-image)"
IMAGE_TAG_LATEST="$(metadata_get rocm-ci-image-descriptive)"
echo "Using refreshed ROCm base image for test image: ${BASE_IMAGE}"
echo "Using refreshed ROCm ci_base image for test image: ${CI_BASE_IMAGE}"
if [[ -n "${IMAGE_TAG_LATEST}" ]]; then
echo "Also tagging full ROCm CI image as: ${IMAGE_TAG_LATEST}"
fi
return 0
}
main() {
local base_refreshed=0
if use_refreshed_base_if_present; then
base_refreshed=1
fi
if [[ "${ROCM_CI_ARTIFACT_ONLY:-0}" == "1" && "${base_refreshed}" != "1" ]]; then
echo "ROCM_CI_ARTIFACT_ONLY=1; building ROCm wheel artifact only"
IMAGE_TAG="" bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-artifacts
return
fi
bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-wheel
}
main "$@"
@@ -1,513 +0,0 @@
#!/usr/bin/env bash
# Build and publish a fresh ROCm base image when Dockerfile.rocm_base changes.
#
# Normal AMD CI builds should not pay for this path. The script no-ops unless
# docker/Dockerfile.rocm_base changed relative to the branch base, the previous
# main commit, or ROCM_BASE_REFRESH_FORCE=1 is set.
set -euo pipefail
DOCKERFILE="${ROCM_BASE_DOCKERFILE:-docker/Dockerfile.rocm_base}"
BASE_REPO="${ROCM_BASE_IMAGE_REPO:-rocm/vllm-dev}"
CI_IMAGE_REPO="${ROCM_CI_IMAGE_REPO:-rocm/vllm-ci}"
BUILDER_NAME="${ROCM_BASE_BUILDER_NAME:-vllm-rocm-base-builder}"
DEFAULT_ROCM_BASE_METADATA_VERSION="1"
DEFAULT_ROCM_BASE_CONTENT_FILES="${DOCKERFILE}"
DEFAULT_ROCM_BASE_CONTENT_ARGS="BASE_IMAGE TRITON_BRANCH TRITON_REPO PYTORCH_BRANCH PYTORCH_REPO PYTORCH_VISION_BRANCH PYTORCH_VISION_REPO PYTORCH_AUDIO_BRANCH PYTORCH_AUDIO_REPO FA_BRANCH FA_REPO AITER_BRANCH AITER_REPO MORI_BRANCH MORI_REPO PYTORCH_ROCM_ARCH PYTHON_VERSION USE_SCCACHE"
metadata_set() {
local key="$1"
local value="$2"
[[ -n "${value}" ]] || return 0
if command -v buildkite-agent >/dev/null 2>&1; then
buildkite-agent meta-data set "${key}" "${value}" || true
fi
}
compute_content_hash() {
local path=""
local file=""
for path in "$@"; do
if [[ -d "${path}" ]]; then
while IFS= read -r -d '' file; do
printf 'file:%s\n' "${file}"
sha256sum "${file}"
done < <(find "${path}" -type f -print0 | sort -z)
elif [[ -f "${path}" ]]; then
printf 'file:%s\n' "${path}"
sha256sum "${path}"
else
printf 'missing:%s\n' "${path}"
fi
done | sha256sum | cut -d' ' -f1
}
clean_docker_tag() {
local input="$1"
echo "${input}" | sed 's/[^a-zA-Z0-9._-]/_/g' | cut -c1-128
}
tag_component() {
local input="$1"
local max_chars="${2:-24}"
clean_docker_tag "${input:-unknown}" | cut -c1-"${max_chars}"
}
extract_arg_default() {
local arg_name="$1"
sed -n -E "s/^[[:space:]]*ARG[[:space:]]+${arg_name}=\"?([^\"[:space:]]+)\"?.*/\\1/p" \
"${DOCKERFILE}" | head -1
}
resolve_image_digest() {
local image_ref="$1"
docker buildx imagetools inspect "${image_ref}" 2>/dev/null \
| sed -n -E 's/^Digest:[[:space:]]+//p' \
| head -1 || true
}
resolve_rocm_base_arg_value() {
local arg_name="$1"
local use_sccache="$2"
case "${arg_name}" in
USE_SCCACHE)
printf '%s\n' "${use_sccache}"
;;
*)
extract_arg_default "${arg_name}"
;;
esac
}
hash_rocm_base_arg_values() {
local use_sccache="$1"
local base_image_digest="$2"
local arg_name=""
local arg_value=""
shift 2 || true
for arg_name in "$@"; do
[[ -n "${arg_name}" ]] || continue
arg_value=$(resolve_rocm_base_arg_value "${arg_name}" "${use_sccache}")
printf 'arg:%s=%s\n' "${arg_name}" "${arg_value:-<empty>}"
if [[ "${arg_name}" == "BASE_IMAGE" && -n "${arg_value}" ]]; then
printf 'arg:%s.digest=%s\n' "${arg_name}" "${base_image_digest:-unknown}"
fi
done
}
rocm_version_from_base_image() {
local base_image="$1"
local version=""
version="$(sed -n -E 's/.*:([0-9]+\.[0-9]+(\.[0-9]+)?)-.*/\1/p' <<<"${base_image}")"
tag_component "${version:-${base_image}}" 16
}
git_diff_changed_base() {
local range="$1"
[[ -n "$(git diff --name-only "${range}" -- "${DOCKERFILE}" 2>/dev/null)" ]]
}
short_git_ref() {
local ref="$1"
git rev-parse --short "${ref}" 2>/dev/null || printf '%s\n' "${ref}"
}
extract_arg_default_from_ref() {
local ref="$1"
local arg_name="$2"
local content=""
content="$(git show "${ref}:${DOCKERFILE}" 2>/dev/null || true)"
sed -n -E "s/^[[:space:]]*ARG[[:space:]]+${arg_name}=\"?([^\"[:space:]]+)\"?.*/\\1/p" \
<<<"${content}" | head -1
}
log_arg_default_changes() {
local old_ref="$1"
local new_ref="$2"
local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
local arg_name=""
local old_value=""
local new_value=""
local changed=0
echo "Changed ROCm base ARG defaults:"
for arg_name in ${content_args}; do
old_value="$(extract_arg_default_from_ref "${old_ref}" "${arg_name}")"
new_value="$(extract_arg_default_from_ref "${new_ref}" "${arg_name}")"
if [[ "${old_value}" != "${new_value}" ]]; then
echo " - ${arg_name}: ${old_value:-<unset>} -> ${new_value:-<unset>}"
changed=1
fi
done
if [[ "${changed}" == "0" ]]; then
echo " - none detected; Dockerfile instructions changed outside tracked ARG defaults"
fi
}
log_arg_line_diff() {
local range="$1"
local arg_diff=""
arg_diff="$(
git diff --unified=0 "${range}" -- "${DOCKERFILE}" 2>/dev/null \
| awk '/^[+-][[:space:]]*ARG[[:space:]]/ && $0 !~ /^(---|\+\+\+)/ { print " " $0 }' \
|| true
)"
if [[ -n "${arg_diff}" ]]; then
echo "Changed Dockerfile ARG lines:"
printf '%s\n' "${arg_diff}"
fi
}
log_rocm_base_change_check() {
local context="$1"
local range="$2"
local old_ref="$3"
local old_short=""
local head_short=""
old_short="$(short_git_ref "${old_ref}")"
head_short="$(short_git_ref HEAD)"
echo "--- :mag: ROCm base refresh check"
echo "Context: ${context}"
echo "Dockerfile: ${DOCKERFILE}"
echo "Base revision: ${old_short}"
echo "Head revision: ${head_short}"
echo "Git diff range: ${range}"
}
log_rocm_base_rebuild_reason() {
local context="$1"
local range="$2"
local old_ref="$3"
local changed_files=""
log_rocm_base_change_check "${context}" "${range}" "${old_ref}"
changed_files="$(git diff --name-only "${range}" -- "${DOCKERFILE}" 2>/dev/null || true)"
echo "Changed files:"
if [[ -n "${changed_files}" ]]; then
sed 's/^/ - /' <<<"${changed_files}"
else
echo " - ${DOCKERFILE}"
fi
log_arg_default_changes "${old_ref}" HEAD
log_arg_line_diff "${range}"
echo "Decision: rebuilding ROCm base image because ${DOCKERFILE} changed."
}
rocm_base_changed_in_range() {
local context="$1"
local range="$2"
local old_ref="$3"
if git_diff_changed_base "${range}"; then
log_rocm_base_rebuild_reason "${context}" "${range}" "${old_ref}"
return 0
fi
log_rocm_base_change_check "${context}" "${range}" "${old_ref}"
echo "Decision: ROCm base refresh not required; ${DOCKERFILE} is unchanged."
return 1
}
rocm_base_changed() {
local base_branch="${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}"
local base_ref="refs/remotes/origin/${base_branch}"
local merge_base=""
if [[ "${ROCM_BASE_REFRESH_SKIP:-0}" == "1" ]]; then
echo "ROCM_BASE_REFRESH_SKIP=1 set; skipping ROCm base refresh"
return 1
fi
if [[ "${ROCM_BASE_REFRESH_FORCE:-0}" == "1" ]]; then
echo "ROCM_BASE_REFRESH_FORCE=1 set; refreshing ROCm base image"
return 0
fi
if ! git rev-parse --is-inside-work-tree >/dev/null 2>&1; then
echo "Not in a git checkout; skipping ROCm base refresh unless forced"
return 1
fi
if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" ]]; then
git fetch --no-tags --depth=200 origin \
"+refs/heads/${base_branch}:${base_ref}" >/dev/null 2>&1 || true
merge_base=$(git merge-base HEAD "${base_ref}" 2>/dev/null || true)
if [[ -z "${merge_base}" ]]; then
echo "Unable to determine merge base with PR base ${base_ref}; skipping ROCm base refresh unless forced"
return 1
fi
if rocm_base_changed_in_range \
"pull request build against ${base_ref}" \
"${merge_base}...HEAD" \
"${merge_base}"; then
return 0
fi
elif [[ "${BUILDKITE_BRANCH:-}" == "${ROCM_BASE_STABLE_BRANCH:-main}" ]] \
&& git rev-parse --verify HEAD~1 >/dev/null 2>&1; then
if rocm_base_changed_in_range \
"stable branch build; comparing against previous ${ROCM_BASE_STABLE_BRANCH:-main} commit" \
"HEAD~1..HEAD" \
"HEAD~1"; then
return 0
fi
else
git fetch --no-tags --depth=200 origin \
"+refs/heads/${base_branch}:${base_ref}" >/dev/null 2>&1 || true
merge_base=$(git merge-base HEAD "${base_ref}" 2>/dev/null || true)
if [[ -z "${merge_base}" ]]; then
echo "Unable to determine merge base with branch base ${base_ref}; skipping ROCm base refresh unless forced"
return 1
fi
if rocm_base_changed_in_range \
"branch build against ${base_ref}" \
"${merge_base}...HEAD" \
"${merge_base}"; then
return 0
fi
fi
return 1
}
should_push_stable_tag() {
if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" ]]; then
return 1
fi
if [[ "${ROCM_BASE_PUSH_STABLE_TAG:-}" == "1" ]]; then
return 0
fi
if [[ "${ROCM_BASE_PUSH_STABLE_TAG:-}" == "0" ]]; then
return 1
fi
[[ "${BUILDKITE_PULL_REQUEST:-false}" == "false" \
&& "${BUILDKITE_BRANCH:-}" == "${ROCM_BASE_STABLE_BRANCH:-main}" ]]
}
setup_builder() {
echo "--- :buildkite: Setting up buildx builder for ROCm base"
if docker buildx inspect "${BUILDER_NAME}" >/dev/null 2>&1; then
docker buildx use "${BUILDER_NAME}"
else
docker buildx create --name "${BUILDER_NAME}" --driver docker-container --use
fi
docker buildx inspect --bootstrap
}
compute_base_content_hash() {
local use_sccache="$1"
local base_image_digest="$2"
local content_files="${ROCM_BASE_CONTENT_FILES:-${DEFAULT_ROCM_BASE_CONTENT_FILES}}"
local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
local -a content_paths=()
local -a content_arg_names=()
read -r -a content_paths <<< "${content_files}"
read -r -a content_arg_names <<< "${content_args}"
{
printf 'content-files-hash:%s\n' "$(compute_content_hash "${content_paths[@]}")"
printf 'dockerfile:%s\n' "${DOCKERFILE}"
printf 'resolved-build-args:\n'
hash_rocm_base_arg_values \
"${use_sccache}" "${base_image_digest}" "${content_arg_names[@]}"
} | sha256sum | cut -d' ' -f1
}
build_base_image() {
local use_sccache="${ROCM_BASE_USE_SCCACHE:-${USE_SCCACHE:-0}}"
local base_hash=""
local build_date=""
local build_suffix=""
local base_image_arg=""
local base_image_digest=""
local rocm_version=""
local triton_arg=""
local pytorch_arg=""
local pytorch_vision_arg=""
local pytorch_audio_arg=""
local fa_arg=""
local aiter_arg=""
local mori_arg=""
local python_version_arg=""
local pytorch_rocm_arch_arg=""
local pytorch_branch=""
local aiter_branch=""
local dependency_summary=""
local descriptor=""
local ci_descriptor=""
local descriptive_tag=""
local stable_tag="${BASE_REPO}:base"
local ci_descriptive_tag=""
local content_files="${ROCM_BASE_CONTENT_FILES:-${DEFAULT_ROCM_BASE_CONTENT_FILES}}"
local content_args="${ROCM_BASE_CONTENT_ARGS:-${DEFAULT_ROCM_BASE_CONTENT_ARGS}}"
local content_files_hash=""
local metadata_version="${ROCM_BASE_METADATA_VERSION:-${DEFAULT_ROCM_BASE_METADATA_VERSION}}"
local -a tags=()
local -a no_cache_args=()
local -a sccache_args=()
local -a content_paths=()
if [[ ! -f "${DOCKERFILE}" ]]; then
echo "Error: ROCm base Dockerfile not found: ${DOCKERFILE}" >&2
exit 1
fi
build_date="${ROCM_BASE_TAG_DATE:-$(date -u +%Y%m%d)}"
if [[ -n "${BUILDKITE_BUILD_NUMBER:-}" ]]; then
build_suffix="_bk_${BUILDKITE_BUILD_NUMBER}"
fi
base_image_arg="$(extract_arg_default BASE_IMAGE)"
base_image_digest="$(resolve_image_digest "${base_image_arg}")"
read -r -a content_paths <<< "${content_files}"
content_files_hash="$(compute_content_hash "${content_paths[@]}")"
base_hash=$(compute_base_content_hash "${use_sccache}" "${base_image_digest}")
rocm_version="$(rocm_version_from_base_image "${base_image_arg}")"
triton_arg="$(extract_arg_default TRITON_BRANCH)"
pytorch_arg="$(extract_arg_default PYTORCH_BRANCH)"
pytorch_vision_arg="$(extract_arg_default PYTORCH_VISION_BRANCH)"
pytorch_audio_arg="$(extract_arg_default PYTORCH_AUDIO_BRANCH)"
fa_arg="$(extract_arg_default FA_BRANCH)"
aiter_arg="$(extract_arg_default AITER_BRANCH)"
mori_arg="$(extract_arg_default MORI_BRANCH)"
python_version_arg="$(extract_arg_default PYTHON_VERSION)"
pytorch_rocm_arch_arg="$(extract_arg_default PYTORCH_ROCM_ARCH)"
pytorch_branch="$(tag_component "${pytorch_arg}" 16)"
aiter_branch="$(tag_component "${aiter_arg}" 24)"
dependency_summary="base=${base_image_arg},rocm=${rocm_version},python=${python_version_arg},pytorch=${pytorch_arg},torchvision=${pytorch_vision_arg},torchaudio=${pytorch_audio_arg},triton=${triton_arg},flash-attn=${fa_arg},aiter=${aiter_arg},mori=${mori_arg},pytorch-rocm-arch=${pytorch_rocm_arch_arg}"
descriptor="$(clean_docker_tag "base_custom_aiter_${aiter_branch}_torch_${pytorch_branch}_${build_date}${build_suffix}")"
ci_descriptor="$(clean_docker_tag "ci_custom_aiter_${aiter_branch}_torch_${pytorch_branch}_${build_date}${build_suffix}")"
descriptive_tag="${BASE_REPO}:${descriptor}"
ci_descriptive_tag="${CI_IMAGE_REPO}:${ci_descriptor}"
tags=(-t "${descriptive_tag}")
if should_push_stable_tag; then
tags+=(-t "${stable_tag}")
metadata_set "rocm-base-push-stable-tag" "1"
else
metadata_set "rocm-base-push-stable-tag" "0"
fi
if [[ "${ROCM_BASE_NO_CACHE:-1}" == "1" ]]; then
no_cache_args=(--no-cache)
fi
for env_name in \
SCCACHE_DOWNLOAD_URL \
SCCACHE_ENDPOINT \
SCCACHE_BUCKET_NAME \
SCCACHE_REGION_NAME \
SCCACHE_S3_NO_CREDENTIALS; do
if [[ -n "${!env_name:-}" ]]; then
sccache_args+=(--build-arg "${env_name}=${!env_name}")
fi
done
echo "--- :docker: Building ROCm base image"
echo "Dockerfile: ${DOCKERFILE}"
echo "Descriptive tag: ${descriptive_tag}"
echo "Stable tag: ${stable_tag} ($(should_push_stable_tag && echo enabled || echo disabled))"
echo "Content hash: ${base_hash}"
echo "Dependency summary: ${dependency_summary}"
echo "USE_SCCACHE: ${use_sccache}"
docker buildx build \
"${no_cache_args[@]}" \
--pull \
--progress "${BUILDKIT_PROGRESS:-plain}" \
--file "${DOCKERFILE}" \
--build-arg "USE_SCCACHE=${use_sccache}" \
"${sccache_args[@]}" \
--label "org.opencontainers.image.source=https://github.com/vllm-project/vllm" \
--label "org.opencontainers.image.vendor=vLLM" \
--label "org.opencontainers.image.title=vLLM ROCm base" \
--label "org.opencontainers.image.revision=${BUILDKITE_COMMIT:-}" \
--label "vllm.rocm_base.metadata_version=${metadata_version}" \
--label "vllm.rocm_base.content_hash=${base_hash}" \
--label "vllm.rocm_base.content_files_hash=${content_files_hash}" \
--label "vllm.rocm_base.dockerfile=${DOCKERFILE}" \
--label "vllm.rocm_base.image.descriptive=${descriptive_tag}" \
--label "vllm.rocm_base.image.stable=${stable_tag}" \
--label "vllm.rocm_base.git_commit=${BUILDKITE_COMMIT:-}" \
--label "vllm.rocm_base.stable_branch=${ROCM_BASE_STABLE_BRANCH:-main}" \
--label "vllm.rocm_base.descriptor=${descriptor}" \
--label "vllm.rocm_base.dependency_summary=${dependency_summary}" \
--label "vllm.rocm_base.base_image=${base_image_arg}" \
--label "vllm.rocm_base.base_image_digest=${base_image_digest}" \
--label "vllm.rocm_base.dependency.rocm=${rocm_version}" \
--label "vllm.rocm_base.dependency.python=${python_version_arg}" \
--label "vllm.rocm_base.dependency.pytorch=${pytorch_arg}" \
--label "vllm.rocm_base.dependency.torchvision=${pytorch_vision_arg}" \
--label "vllm.rocm_base.dependency.torchaudio=${pytorch_audio_arg}" \
--label "vllm.rocm_base.dependency.triton=${triton_arg}" \
--label "vllm.rocm_base.dependency.flash_attention=${fa_arg}" \
--label "vllm.rocm_base.dependency.aiter=${aiter_arg}" \
--label "vllm.rocm_base.dependency.mori=${mori_arg}" \
--label "vllm.rocm_base.pytorch_rocm_arch=${pytorch_rocm_arch_arg}" \
"${tags[@]}" \
--push \
.
docker buildx imagetools inspect "${descriptive_tag}" >/dev/null
metadata_set "rocm-base-refresh" "1"
metadata_set "rocm-base-image" "${descriptive_tag}"
metadata_set "rocm-base-image-descriptive" "${descriptive_tag}"
metadata_set "rocm-base-image-stable" "${stable_tag}"
metadata_set "rocm-base-image-ci-descriptive" "${ci_descriptive_tag}"
metadata_set "rocm-base-metadata-version" "${metadata_version}"
metadata_set "rocm-base-content-hash" "${base_hash}"
metadata_set "rocm-base-content-files-hash" "${content_files_hash}"
metadata_set "rocm-base-content-files" "${content_files}"
metadata_set "rocm-base-content-args" "${content_args}"
metadata_set "rocm-base-base-image-digest" "${base_image_digest}"
metadata_set "rocm-base-dockerfile" "${DOCKERFILE}"
metadata_set "rocm-base-descriptor" "${descriptor}"
metadata_set "rocm-base-dependency-summary" "${dependency_summary}"
metadata_set "rocm-base-dependency-rocm" "${rocm_version}"
metadata_set "rocm-base-dependency-python" "${python_version_arg}"
metadata_set "rocm-base-dependency-pytorch" "${pytorch_arg}"
metadata_set "rocm-base-dependency-torchvision" "${pytorch_vision_arg}"
metadata_set "rocm-base-dependency-torchaudio" "${pytorch_audio_arg}"
metadata_set "rocm-base-dependency-triton" "${triton_arg}"
metadata_set "rocm-base-dependency-flash-attention" "${fa_arg}"
metadata_set "rocm-base-dependency-aiter" "${aiter_arg}"
metadata_set "rocm-base-dependency-mori" "${mori_arg}"
metadata_set "rocm-base-pytorch-rocm-arch" "${pytorch_rocm_arch_arg}"
metadata_set "rocm-ci-image-descriptive" "${ci_descriptive_tag}"
echo "--- :white_check_mark: ROCm base image published"
echo "Use BASE_IMAGE=${descriptive_tag} for downstream ROCm CI builds"
}
main() {
metadata_set "rocm-base-refresh" "0"
if ! rocm_base_changed; then
echo "ROCm base Dockerfile did not change; skipping base image refresh"
return 0
fi
setup_builder
build_base_image
}
main "$@"
@@ -1,32 +0,0 @@
#!/usr/bin/env bash
# Fast structural smoke test for the full ROCm CI image.
set -euo pipefail
image_ref="${VLLM_CI_SMOKE_IMAGE:-rocm/vllm-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT is required}}"
docker run --rm --network=none --entrypoint /bin/bash "${image_ref}" -ec '
if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
command -v python3
command -v uv
command -v pytest
if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
echo No ROCm CLI found in image >&2
exit 1
fi
python3 - <<PY
import torch
import vllm
print(torch.__version__)
print(vllm.__version__)
PY
echo AMD image smoke OK
'
+1 -3
View File
@@ -109,9 +109,7 @@ run_nodes() {
if [ "$node" -ne 0 ]; then
docker exec -d "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
else
# Allocate a TTY (-t -i) for the foreground head node so its output
# keeps ANSI color in the Buildkite log (see run-amd-test.sh).
docker exec -t -i "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
docker exec "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
fi
done
}
@@ -8,12 +8,7 @@ if [[ "$MODE" != "style-clippy" && "$MODE" != "test" ]]; then
exit 2
fi
if ROOT_DIR="$(git rev-parse --show-toplevel 2>/dev/null)"; then
:
else
SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd -P)"
ROOT_DIR="$(cd -- "${SCRIPT_DIR}/../.." && pwd -P)"
fi
ROOT_DIR="$(git rev-parse --show-toplevel)"
cd "$ROOT_DIR"
export CARGO_TERM_COLOR="${CARGO_TERM_COLOR:-always}"
@@ -21,20 +16,16 @@ export CARGO_HOME="${CARGO_HOME:-$HOME/.cargo}"
export RUSTUP_HOME="${RUSTUP_HOME:-$HOME/.rustup}"
export PATH="$CARGO_HOME/bin:$PATH"
PROTOC_VERSION="${PROTOC_VERSION:-31.1}"
CARGO_BINSTALL_VERSION="${CARGO_BINSTALL_VERSION:-1.20.1}"
UV_VERSION="${UV_VERSION:-0.11.28}"
PYO3_PYTHON_VERSION="${PYO3_PYTHON_VERSION:-3.12}"
CARGO_SORT_VERSION_REQ="${CARGO_SORT_VERSION_REQ:-2}"
CARGO_DENY_VERSION_REQ="${CARGO_DENY_VERSION_REQ:-0.20}"
CARGO_NEXTEST_VERSION_REQ="${CARGO_NEXTEST_VERSION_REQ:-0.9}"
log_section() {
echo "--- $*"
}
install_protoc() {
if command -v protoc >/dev/null 2>&1; then
return
fi
local version="${PROTOC_VERSION:-31.1}"
local arch
case "$(uname -m)" in
x86_64)
@@ -49,17 +40,16 @@ install_protoc() {
;;
esac
local url="https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-${arch}.zip"
local url="https://github.com/protocolbuffers/protobuf/releases/download/v${version}/protoc-${version}-linux-${arch}.zip"
local tmp_dir
tmp_dir="$(mktemp -d)"
log_section "Installing protoc ${PROTOC_VERSION}"
log_section "Installing protoc ${version}"
curl -L --proto '=https' --tlsv1.2 -sSf "$url" -o "$tmp_dir/protoc.zip"
mkdir -p "$CARGO_HOME/bin"
unzip -q "$tmp_dir/protoc.zip" bin/protoc 'include/*' -d "$CARGO_HOME"
chmod +x "$CARGO_HOME/bin/protoc"
rm -rf "$tmp_dir"
protoc --version
}
rust_toolchain() {
@@ -80,48 +70,66 @@ install_rust_toolchain() {
}
install_cargo_binstall() {
log_section "Installing cargo-binstall ${CARGO_BINSTALL_VERSION}"
if command -v cargo-binstall >/dev/null 2>&1; then
return
fi
log_section "Installing cargo-binstall"
curl -L --proto '=https' --tlsv1.2 -sSf \
"https://raw.githubusercontent.com/cargo-bins/cargo-binstall/v${CARGO_BINSTALL_VERSION}/install-from-binstall-release.sh" \
| env BINSTALL_VERSION="$CARGO_BINSTALL_VERSION" bash
cargo-binstall -V
https://raw.githubusercontent.com/cargo-bins/cargo-binstall/main/install-from-binstall-release.sh \
| bash
}
install_cargo_sort() {
log_section "Installing cargo-sort ${CARGO_SORT_VERSION_REQ}"
cargo binstall --no-confirm --force "cargo-sort@${CARGO_SORT_VERSION_REQ}"
if command -v cargo-sort >/dev/null 2>&1; then
return
fi
log_section "Installing cargo-sort"
install_cargo_binstall
cargo binstall --no-confirm cargo-sort
}
install_cargo_deny() {
log_section "Installing cargo-deny ${CARGO_DENY_VERSION_REQ}"
cargo binstall --no-confirm --force "cargo-deny@${CARGO_DENY_VERSION_REQ}"
if command -v cargo-deny >/dev/null 2>&1; then
return
fi
log_section "Installing cargo-deny"
install_cargo_binstall
cargo binstall --no-confirm cargo-deny
}
install_cargo_nextest() {
log_section "Installing cargo-nextest ${CARGO_NEXTEST_VERSION_REQ}"
cargo binstall \
--no-confirm \
--force \
--secure \
"cargo-nextest@${CARGO_NEXTEST_VERSION_REQ}"
if command -v cargo-nextest >/dev/null 2>&1; then
return
fi
log_section "Installing cargo-nextest"
install_cargo_binstall
cargo binstall --no-confirm --secure cargo-nextest
}
install_uv() {
log_section "Installing uv ${UV_VERSION}"
curl -L --proto '=https' --tlsv1.2 -sSf \
"https://github.com/astral-sh/uv/releases/download/${UV_VERSION}/uv-installer.sh" \
if command -v uv >/dev/null 2>&1; then
return
fi
log_section "Installing uv"
curl -LsSf --proto '=https' --tlsv1.2 https://astral.sh/uv/install.sh \
| env UV_INSTALL_DIR="$CARGO_HOME/bin" sh
uv --version
}
setup_pyo3_python() {
log_section "Installing Python ${PYO3_PYTHON_VERSION} for PyO3 tests"
uv python install "$PYO3_PYTHON_VERSION"
local python_version="${PYO3_PYTHON_VERSION:-3.12}"
log_section "Installing Python ${python_version} for PyO3 tests"
uv python install "$python_version"
PYO3_PYTHON="$(uv python find \
--managed-python \
--no-project \
--resolve-links \
"$PYO3_PYTHON_VERSION")"
"$python_version")"
export PYO3_PYTHON
local python_libdir
@@ -143,7 +151,6 @@ PY
}
run_style_clippy() {
install_cargo_binstall
install_cargo_sort
install_cargo_deny
@@ -156,8 +163,8 @@ run_style_clippy() {
log_section "Checking Rust dependency bans"
cargo deny \
--manifest-path rust/Cargo.toml \
--config rust/deny.toml \
check \
--config rust/deny.toml \
bans
log_section "Running clippy"
@@ -174,7 +181,6 @@ run_style_clippy() {
run_tests() {
install_uv
setup_pyo3_python
install_cargo_binstall
install_cargo_nextest
log_section "Running cargo nextest"
@@ -18,10 +18,6 @@ wait_for_server() {
MODEL="Qwen/Qwen3-30B-A3B-FP8"
BACK="allgather_reducescatter"
if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
# Disable MOE padding for ROCm since it is causing eplb to fail.
export VLLM_ROCM_MOE_PADDING=0
fi
cleanup() {
if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+11 -32
View File
@@ -23,7 +23,6 @@ NC='\033[0m' # No Color
# Default configuration
PIPELINE="ci"
DRY_RUN=true
TORCH_NIGHTLY=false
usage() {
cat <<EOF
@@ -35,14 +34,12 @@ Sets RUN_ALL=1 and NIGHTLY=1 environment variables.
SAFETY: Dry-run by default. Use --execute to actually trigger a build.
Options:
--execute Actually trigger the build (default: dry-run)
--pipeline Buildkite pipeline slug (default: ${PIPELINE})
--commit Override commit SHA (default: current HEAD)
--branch Override branch name (default: current branch)
--message Custom build message (default: auto-generated)
--torch-nightly Also build and run the full suite against torch nightly
(sets TORCH_NIGHTLY=1)
--help Show this help message
--execute Actually trigger the build (default: dry-run)
--pipeline Buildkite pipeline slug (default: ${PIPELINE})
--commit Override commit SHA (default: current HEAD)
--branch Override branch name (default: current branch)
--message Custom build message (default: auto-generated)
--help Show this help message
Prerequisites:
- bk CLI installed: brew tap buildkite/buildkite && brew install buildkite/buildkite/bk
@@ -52,7 +49,6 @@ Examples:
$(basename "$0") # Dry-run, show what would happen
$(basename "$0") --execute # Actually trigger the build
$(basename "$0") --pipeline ci-shadow # Dry-run with different pipeline
$(basename "$0") --torch-nightly # Dry-run a full torch-nightly run
EOF
exit 1
}
@@ -100,10 +96,6 @@ while [[ $# -gt 0 ]]; do
MESSAGE="$2"
shift 2
;;
--torch-nightly)
TORCH_NIGHTLY=true
shift
;;
--help|-h)
usage
;;
@@ -179,17 +171,11 @@ if [[ $(echo "$REMOTE_BRANCHES" | wc -l) -gt 5 ]]; then
fi
echo ""
# Environment variables passed to the build.
BUILD_ENV=("RUN_ALL=1" "NIGHTLY=1")
if [[ "$TORCH_NIGHTLY" == true ]]; then
BUILD_ENV+=("TORCH_NIGHTLY=1")
fi
log_info "Pipeline: ${PIPELINE}"
log_info "Branch: ${BRANCH}"
log_info "Commit: ${COMMIT}"
log_info "Message: ${MESSAGE}"
log_info "Environment: ${BUILD_ENV[*]}"
log_info "Environment: RUN_ALL=1, NIGHTLY=1"
echo ""
# Build the command
@@ -201,10 +187,9 @@ CMD=(bk build create
--commit "${COMMIT}"
--branch "${BRANCH}"
--message "${MESSAGE}"
--env "RUN_ALL=1"
--env "NIGHTLY=1"
)
for env_var in "${BUILD_ENV[@]}"; do
CMD+=(--env "${env_var}")
done
if [[ "$DRY_RUN" == true ]]; then
echo "=========================================="
@@ -225,14 +210,8 @@ if [[ "$DRY_RUN" == true ]]; then
echo " --commit '$(escape_for_shell "${COMMIT}")' \\"
echo " --branch '$(escape_for_shell "${BRANCH}")' \\"
echo " --message '$(escape_for_shell "${MESSAGE}")' \\"
last_idx=$(( ${#BUILD_ENV[@]} - 1 ))
for i in "${!BUILD_ENV[@]}"; do
if [[ $i -eq $last_idx ]]; then
echo " --env '$(escape_for_shell "${BUILD_ENV[$i]}")'"
else
echo " --env '$(escape_for_shell "${BUILD_ENV[$i]}")' \\"
fi
done
echo " --env 'RUN_ALL=1' \\"
echo " --env 'NIGHTLY=1'"
echo ""
echo "=========================================="
echo -e "${YELLOW}To actually trigger this build, run:${NC}"
@@ -1,13 +0,0 @@
#!/bin/bash
set -euo pipefail
REGISTRY="public.ecr.aws/q9t5s3a7"
REPO="vllm-release-repo"
ARCH_TAG="${BUILDKITE_COMMIT}-$(uname -m)-xpu"
PLATFORM_TAG="${BUILDKITE_COMMIT}-xpu"
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin ${REGISTRY}
docker manifest rm ${REGISTRY}/${REPO}:${PLATFORM_TAG} || true
docker manifest create ${REGISTRY}/${REPO}:${PLATFORM_TAG} ${REGISTRY}/${REPO}:${ARCH_TAG} --amend
docker manifest push ${REGISTRY}/${REPO}:${PLATFORM_TAG}
@@ -1,21 +0,0 @@
#!/bin/bash
set -ex
ORIG_TAG_NAME="$BUILDKITE_COMMIT"
REPO="vllm/vllm-openai-xpu"
echo "Pushing original XPU tag ${ORIG_TAG_NAME}-xpu to nightly tags in ${REPO}"
aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu
docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu ${REPO}:nightly-x86_64
docker push ${REPO}:nightly-x86_64
docker manifest rm ${REPO}:nightly || true
docker manifest rm ${REPO}:nightly-"$BUILDKITE_COMMIT" || true
docker manifest create ${REPO}:nightly ${REPO}:nightly-x86_64 --amend
docker manifest create ${REPO}:nightly-"$BUILDKITE_COMMIT" ${REPO}:nightly-x86_64 --amend
docker manifest push ${REPO}:nightly
docker manifest push ${REPO}:nightly-"$BUILDKITE_COMMIT"
+34 -479
View File
@@ -275,7 +275,7 @@ steps:
- pytest -v -s v1/cudagraph/test_cudagraph_mode.py
- label: e2e Core (1 GPU) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 35
mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
agent_pool: mi250_1
optional: true
@@ -380,7 +380,6 @@ steps:
- tests/test_outputs.py
- tests/test_pooling_params.py
- tests/test_ray_env.py
- tests/test_sampling_params.py
- tests/multimodal
- tests/renderers
- tests/standalone_tests/lazy_imports.py
@@ -396,7 +395,6 @@ steps:
- pytest -v -s test_outputs.py
- pytest -v -s test_pooling_params.py
- pytest -v -s test_ray_env.py
- pytest -v -s test_sampling_params.py
- pytest -v -s -m 'cpu_test' multimodal
- pytest -v -s renderers
- pytest -v -s tokenizers_
@@ -406,58 +404,6 @@ steps:
- pytest -v -s transformers_utils
- pytest -v -s config
#------------------------------------------------------------ mi250 · rust -----------------------------------------------------------#
- label: Rust Frontend Cargo Style + Clippy # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
agent_pool: mi250_1
no_gpu: true
working_dir: "/vllm-workspace"
source_file_dependencies:
- rust/
- rust-toolchain.toml
- .buildkite/test_areas/rust_frontend_cargo.yaml
- .buildkite/scripts/run-rust-frontend-cargo-ci.sh
commands:
- bash .buildkite/scripts/run-rust-frontend-cargo-ci.sh style-clippy
- label: Rust Frontend Cargo Tests # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
agent_pool: mi250_1
no_gpu: true
working_dir: "/vllm-workspace"
source_file_dependencies:
- rust/
- rust-toolchain.toml
- .buildkite/test_areas/rust_frontend_cargo.yaml
- .buildkite/scripts/run-rust-frontend-cargo-ci.sh
commands:
- bash .buildkite/scripts/run-rust-frontend-cargo-ci.sh test
#----------------------------------------------------------- mi250 · docker ----------------------------------------------------------#
- label: Docker Build Metadata (ROCm) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
agent_pool: mi250_1
no_gpu: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- .buildkite/scripts/docker-build-metadata-args.sh
- .buildkite/scripts/ci-bake-rocm.sh
- docker/Dockerfile
- docker/Dockerfile.cpu
- docker/Dockerfile.rocm
- docker/Dockerfile.rocm_base
- docker/ci-rocm.hcl
- docker/docker-bake.hcl
- docker/docker-bake-rocm.hcl
- tests/tools/test_docker_build_metadata_args.py
commands:
- pytest -v -s tools/test_docker_build_metadata_args.py
#########################################################################################################################################
# #
# MI300 (gfx942) tests #
@@ -467,7 +413,7 @@ steps:
#----------------------------------------------------- mi300 · basic_correctness -----------------------------------------------------#
- label: Basic Correctness # TBD
timeout_in_minutes: 180
timeout_in_minutes: 95
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
fast_check: true
@@ -484,7 +430,7 @@ steps:
- pytest -v -s basic_correctness/test_cpu_offload.py
- label: Distributed Model Tests (2 GPUs) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 110
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
@@ -504,7 +450,7 @@ steps:
commands:
- TARGET_TEST_SUITE=MI300 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
- CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
- pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)'
- pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
- pytest models/language -v -s -m 'distributed(num_gpus=2)'
- pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
- pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
@@ -724,7 +670,7 @@ steps:
- pytest -v -s distributed/test_eplb_spec_decode.py
- label: Distributed Tests (2xH100-2xMI300) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 75
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
@@ -1057,23 +1003,6 @@ steps:
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
- label: MRCR Eval Small Models # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/model_executor/models/
- vllm/model_executor/model_loader/
- vllm/v1/attention/backends/
- vllm/v1/attention/selector.py
- vllm/_aiter_ops.py
- vllm/platforms/rocm.py
- tests/evals/mrcr/
commands:
- pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt
- label: LM Eval Small Models (MI300) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1208,27 +1137,6 @@ steps:
commands:
- bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
- label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy (4xH100-4xMI300) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_4
num_gpus: 4
optional: true
working_dir: "/vllm-workspace"
source_file_dependencies:
- vllm/model_executor/models/
- vllm/model_executor/model_loader/
- vllm/model_executor/layers/quantization/
- vllm/distributed/eplb
- vllm/model_executor/layers/fused_moe/
- vllm/v1/attention/backends/
- vllm/v1/attention/selector.py
- .buildkite/scripts/scheduled_integration_test/
- vllm/_aiter_ops.py
- vllm/platforms/rocm.py
commands:
- bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1297,7 +1205,7 @@ steps:
#--------------------------------------------------------- mi300 · examples ----------------------------------------------------------#
- label: Examples # TBD
timeout_in_minutes: 180
timeout_in_minutes: 90
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -1332,23 +1240,8 @@ steps:
#---------------------------------------------------------- mi300 · kernels ----------------------------------------------------------#
- label: vLLM IR Tests # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/"
source_file_dependencies:
- vllm/ir
- vllm/kernels
- vllm/_aiter_ops.py
- vllm/platforms/rocm.py
commands:
- pytest -v -s tests/ir
- pytest -v -s tests/kernels/ir
- label: Kernels Attention Test %N # TBD
timeout_in_minutes: 180
timeout_in_minutes: 100
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -1381,23 +1274,8 @@ steps:
commands:
- pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py
- label: Kernels KDA Test # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/model_executor/layers/fla/ops/kda.py
- vllm/model_executor/layers/fla/ops/chunk_delta_h.py
- vllm/model_executor/layers/fla/ops/l2norm.py
- tests/kernels/test_kda.py
- vllm/platforms/rocm.py
commands:
- pytest -v -s kernels/test_kda.py
- label: Kernels MoE Test %N # TBD
timeout_in_minutes: 180
timeout_in_minutes: 95
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -1508,135 +1386,13 @@ steps:
- pytest -v -s model_executor -m '(not slow_test)'
- pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
#---------------------------------------------------- mi300 · model_runner_v2 -------------------------------------------------------#
- label: Model Runner V2 Core Tests # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/worker/gpu_worker.py
- vllm/v1/core/sched/
- vllm/v1/attention/
- tests/v1/engine/test_llm_engine.py
- tests/v1/e2e/
- tests/entrypoints/llm/test_struct_output_generate.py
- vllm/platforms/rocm.py
commands:
- set -x
- export VLLM_USE_V2_MODEL_RUNNER=1
- pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
- ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
- pytest -v -s v1/e2e/general/test_context_length.py
- pytest -v -s v1/e2e/general/test_min_tokens.py
- pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
- label: Model Runner V2 Examples # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/examples"
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/core/sched/
- vllm/v1/worker/gpu_worker.py
- examples/basic/offline_inference/
- examples/generate/multimodal/
- examples/features/
- examples/pooling/embed/vision_embedding_offline.py
- examples/features/tensorize_vllm_model.py
- examples/deployment/llm_engine_example.py
- vllm/platforms/rocm.py
commands:
- set -x
- export VLLM_USE_V2_MODEL_RUNNER=1
- pip install tensorizer
- python3 basic/offline_inference/chat.py
- python3 basic/offline_inference/generate.py --model facebook/opt-125m
- python3 generate/multimodal/audio_language_offline.py --seed 0
- python3 generate/multimodal/vision_language_offline.py --seed 0
- python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
- python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
- python3 pooling/embed/vision_embedding_offline.py --seed 0
- python3 features/automatic_prefix_caching/prefix_caching_offline.py
- python3 deployment/llm_engine_example.py
- python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
- python3 features/speculative_decoding/spec_decode_offline.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 features/speculative_decoding/spec_decode_offline.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: Model Runner V2 Distributed (2 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/worker/gpu_worker.py
- tests/basic_correctness/test_basic_correctness.py
- tests/v1/distributed/test_async_llm_dp.py
- tests/v1/distributed/test_eagle_dp.py
- vllm/platforms/rocm.py
commands:
- set -x
- export VLLM_USE_V2_MODEL_RUNNER=1
- TARGET_TEST_SUITE=MI300 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
- TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
- TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
- label: Model Runner V2 Pipeline Parallelism (4 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_4
num_gpus: 4
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/worker/gpu_worker.py
- tests/distributed/test_pipeline_parallel.py
- tests/distributed/test_pp_cudagraph.py
- vllm/platforms/rocm.py
commands:
- set -x
- export VLLM_USE_V2_MODEL_RUNNER=1
- pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
- pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
- label: Model Runner V2 Spec Decode # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/worker/gpu_worker.py
- tests/v1/spec_decode/test_max_len.py
- tests/v1/spec_decode/test_rejection_sampler_utils.py
- tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
- tests/v1/e2e/spec_decode/test_spec_decode.py
- vllm/platforms/rocm.py
commands:
- set -x
- export VLLM_USE_V2_MODEL_RUNNER=1
- pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
- pytest -v -s v1/spec_decode/test_rejection_sampler_utils.py
- pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
- pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"
#------------------------------------------------------ mi300 · models / basic -------------------------------------------------------#
- label: Basic Models Tests (Extra Initialization) %N # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
parallelism: 6
parallelism: 2
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/model_executor/models/
@@ -1661,7 +1417,7 @@ steps:
- pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
- label: Basic Models Tests (Other) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 90
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -1669,10 +1425,10 @@ steps:
source_file_dependencies:
- vllm/
- tests/models/test_terratorch.py
- tests/models/transformers/test_backend.py
- tests/models/test_transformers.py
- tests/models/test_registry.py
commands:
- pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
- pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
#----------------------------------------------------- mi300 · models / language -----------------------------------------------------#
@@ -1806,7 +1562,7 @@ steps:
- cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
- label: Multi-Modal Processor # 1h 42m
timeout_in_minutes: 180
timeout_in_minutes: 138
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -1892,7 +1648,7 @@ steps:
- examples/
commands:
- pip install --upgrade git+https://github.com/huggingface/transformers
- pytest -v -s tests/models/transformers/test_backend.py
- pytest -v -s tests/models/test_transformers.py
- pytest -v -s tests/models/multimodal/test_mapping.py
- python3 examples/basic/offline_inference/chat.py
- python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
@@ -1938,11 +1694,6 @@ steps:
- pytest -v -s plugins_tests/test_stats_logger_plugins.py
- pip uninstall dummy_stat_logger -y
# END: `stat_logger` plugins test
# BEGIN: `endpoint` plugins test
- pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
- pytest -v -s plugins_tests/test_endpoint_plugins.py
- pip uninstall vllm_add_dummy_endpoint_plugin -y
# END: `endpoint` plugins test
# BEGIN: other tests
- pytest -v -s plugins_tests/test_scheduler_plugins.py
- pip install -e ./plugins/vllm_add_dummy_model
@@ -1951,144 +1702,6 @@ steps:
- pytest -v -s plugins_tests/test_oot_registration_offline.py # it needs a clean process
- pytest -v -s plugins_tests/lora_resolvers # unit tests for in-tree lora resolver plugins
- label: GGUF Plugin # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
soft_fail: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/model_executor/layers/quantization
- tests/plugins_tests/test_gguf_plugin.py
- tests/plugins_tests/gguf
- vllm/platforms/rocm.py
commands:
- pip install "vllm-gguf-plugin >= 0.0.2"
- pytest -v -s plugins_tests/gguf
#------------------------------------------------------- mi300 · rust_frontend -------------------------------------------------------#
- label: Rust Frontend OpenAI Coverage # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
- vllm/benchmarks/
- vllm/entrypoints/openai/
- vllm/entrypoints/serve/
- vllm/v1/sample/
- tests/utils.py
- tests/benchmarks/test_serve_cli.py
- tests/entrypoints/openai/chat_completion/test_chat_completion.py
- tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
- tests/entrypoints/openai/completion/test_shutdown.py
- tests/entrypoints/openai/test_return_token_ids.py
- tests/entrypoints/openai/test_uds.py
- tests/v1/sample/test_logprobs_e2e.py
- vllm/platforms/rocm.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
- pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py -k "not test_invalid_json_schema and not test_invalid_regex"
- pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not multiple"
- pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
- pytest -v -s entrypoints/openai/test_return_token_ids.py -k "not test_comparison"
- pytest -v -s entrypoints/openai/test_uds.py
- pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
- label: Rust Frontend Serve Admin Coverage # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
- vllm/entrypoints/openai/
- vllm/entrypoints/serve/
- vllm/v1/engine/
- tests/utils.py
- tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
- tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
- tests/entrypoints/serve/instrumentator/test_basic.py
- tests/entrypoints/serve/instrumentator/test_metrics.py
- tests/entrypoints/serve/tokenize/test_tokenization.py
- vllm/platforms/rocm.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
- pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
- pytest -v -s entrypoints/scale_out/token_in_token_out/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
- pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
- pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"
- label: Rust Frontend Core Correctness # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
- vllm/entrypoints/openai/
- tests/utils.py
- tests/entrypoints/openai/correctness/test_lmeval.py
- vllm/platforms/rocm.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
- label: Rust Frontend Tool Use # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
- vllm/entrypoints/openai/
- vllm/tool_parsers/
- tests/utils.py
- tests/tool_use/
- vllm/platforms/rocm.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
- label: Rust Frontend Distributed # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_4
num_gpus: 4
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
- vllm/distributed/
- vllm/engine/
- vllm/executor/
- vllm/v1/engine/
- vllm/v1/worker/
- tests/utils.py
- tests/v1/distributed/test_external_lb_dp.py
- tests/v1/distributed/test_hybrid_lb_dp.py
- tests/v1/distributed/test_internal_lb_dp.py
- vllm/platforms/rocm.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"
- TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py -k "not 4 and not server_info"
- TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py -k "not 4 and not server_info"
#------------------------------------------------------- mi300 · quantization --------------------------------------------------------#
- label: Quantization # TBD
@@ -2176,7 +1789,7 @@ steps:
- vllm/
- tests/test_regression
commands:
- pip install 'modelscope<1.38'
- pip install modelscope
- pytest -v -s test_regression.py
#--------------------------------------------------------- mi300 · ray_compat ---------------------------------------------------------#
@@ -2255,7 +1868,7 @@ steps:
- pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
- label: Spec Decode Eagle # TBD
timeout_in_minutes: 180
timeout_in_minutes: 90
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_1
optional: true
@@ -2340,8 +1953,7 @@ steps:
- label: Extract Hidden States Integration # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
agent_pool: mi300_1
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/config/speculative.py
@@ -2407,7 +2019,7 @@ steps:
- pytest -v -s v1/worker
- pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
- pytest -v -s -m 'not cpu_test' v1/metrics
- pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
- pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
# - export HSA_NO_SCRATCH_RECLAIM=1
- pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
@@ -2472,7 +2084,7 @@ steps:
- DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
- label: Metrics, Tracing (2 GPUs) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 65
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
optional: true
@@ -2598,59 +2210,6 @@ steps:
- uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
- HYBRID_SSM=1 ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
- label: Hybrid SSM NixlConnector PD prefix cache test (2 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/nixl/
- vllm/v1/core/sched/
- vllm/v1/core/kv_cache_coordinator.py
- tests/v1/kv_connector/nixl_integration/
- vllm/platforms/rocm.py
commands:
- uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
- ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_mamba_prefix_cache_test.sh
- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/nixl/
- vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
- vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
- vllm/distributed/kv_transfer/kv_connector/v1/offloading/
- tests/v1/kv_connector/nixl_integration/
- vllm/platforms/rocm.py
commands:
- uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
- ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/nixl/
- vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
- vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
- vllm/distributed/kv_transfer/kv_connector/v1/offloading/
- tests/v1/kv_connector/nixl_integration/
- vllm/platforms/rocm.py
commands:
- uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
- ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
- label: V1 e2e (4 GPUs) # TBD
timeout_in_minutes: 180
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -2678,7 +2237,7 @@ steps:
#------------------------------------------------------ mi300 · weight_loading -------------------------------------------------------#
- label: Weight Loading Multiple GPU # TBD
timeout_in_minutes: 180
timeout_in_minutes: 75
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
@@ -2690,7 +2249,7 @@ steps:
- bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
- label: Weight Loading Multiple GPU - Large Models # TBD
timeout_in_minutes: 180
timeout_in_minutes: 75
mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
agent_pool: mi300_2
num_gpus: 2
@@ -2941,19 +2500,15 @@ steps:
- vllm/envs.py
- examples/offline_inference/data_parallel.py
- tests/distributed/test_context_parallel.py
- tests/distributed/test_rocm_aiter_custom_ar.py
- tests/distributed/test_rocm_quick_reduce.py
- tests/distributed/test_quick_all_reduce.py
- tests/v1/e2e/general/test_rocm_aiter_custom_ar.py
- tests/v1/distributed/test_dbo.py
- tests/utils.py
commands:
- pytest -v -s tests/distributed/test_context_parallel.py
- pytest -v -s tests/distributed/test_rocm_aiter_custom_ar.py
- pytest -v -s tests/v1/e2e/general/test_rocm_aiter_custom_ar.py
- pytest -v -s tests/v1/distributed/test_dbo.py
- pytest -v -s tests/distributed/test_rocm_quick_reduce.py
- pytest -v -s tests/distributed/test_quick_all_reduce.py
- pytest -v -s tests/v1/distributed/test_dbo.py
#-------------------------------------------------------- mi355 · entrypoints --------------------------------------------------------#
@@ -3089,7 +2644,7 @@ steps:
- pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx950.txt
- label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 120
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_2
num_gpus: 2
@@ -3171,7 +2726,7 @@ steps:
#--------------------------------------------------------- mi355 · examples ----------------------------------------------------------#
- label: Examples # TBD
timeout_in_minutes: 180
timeout_in_minutes: 45
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
working_dir: "/vllm-workspace/examples"
@@ -3206,7 +2761,7 @@ steps:
#---------------------------------------------------------- mi355 · kernels ----------------------------------------------------------#
- label: Kernels (B200-MI355) # TBD
timeout_in_minutes: 180
timeout_in_minutes: 15
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
working_dir: "/vllm-workspace/"
@@ -3231,7 +2786,7 @@ steps:
- pytest -v -s tests/kernels/attention/test_rocm_aiter_mla_decode_metadata.py
- label: Kernels Attention Test %N # TBD
timeout_in_minutes: 180
timeout_in_minutes: 100
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
parallelism: 2
@@ -3248,7 +2803,7 @@ steps:
- pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
- label: Kernels MoE Test %N # TBD
timeout_in_minutes: 180
timeout_in_minutes: 50
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
parallelism: 5
@@ -3512,7 +3067,7 @@ steps:
- pytest -v -s v1/attention
- label: V1 Core + KV + Metrics # TBD
timeout_in_minutes: 180
timeout_in_minutes: 60
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
optional: true
@@ -3534,11 +3089,11 @@ steps:
- pytest -v -s v1/worker
- pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
- pytest -v -s -m 'not cpu_test' v1/metrics
- pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
- pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
- pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
- label: V1 Sample + Logits # TBD
timeout_in_minutes: 180
timeout_in_minutes: 60
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
optional: true
@@ -3558,7 +3113,7 @@ steps:
- pytest -v -s v1/test_outputs.py
- label: V1 Spec Decode # TBD
timeout_in_minutes: 180
timeout_in_minutes: 60
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_1
working_dir: "/vllm-workspace/tests"
@@ -3571,7 +3126,7 @@ steps:
#------------------------------------------------------ mi355 · weight_loading -------------------------------------------------------#
- label: Weight Loading Multiple GPU # TBD
timeout_in_minutes: 180
timeout_in_minutes: 75
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_2
num_gpus: 2
@@ -3583,7 +3138,7 @@ steps:
- bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
- label: Weight Loading Multiple GPU - Large Models # TBD
timeout_in_minutes: 180
timeout_in_minutes: 75
mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
agent_pool: mi355_2
working_dir: "/vllm-workspace/tests"
@@ -3607,5 +3162,5 @@ steps:
- vllm/
- tests/test_regression
commands:
- pip install 'modelscope<1.38'
- pip install modelscope
- pytest -v -s test_regression.py
+5 -7
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: V1 attention (H100-MI300)
key: v1-attention-h100-mi300
timeout_in_minutes: 85
timeout_in_minutes: 30
device: h100
source_file_dependencies:
- vllm/config/attention.py
@@ -12,12 +12,11 @@ steps:
- vllm/v1/attention
- tests/v1/attention
commands:
- pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
parallelism: 2
- pytest -v -s v1/attention
mirror:
amd:
device: mi325_1
timeout_in_minutes: 95
timeout_in_minutes: 70
depends_on:
- image-build-amd
source_file_dependencies:
@@ -31,7 +30,7 @@ steps:
- label: V1 attention (B200)
key: v1-attention-b200
timeout_in_minutes: 80
timeout_in_minutes: 30
device: b200-k8s
source_file_dependencies:
- vllm/config/attention.py
@@ -39,5 +38,4 @@ steps:
- vllm/v1/attention
- tests/v1/attention
commands:
- pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
parallelism: 2
- pytest -v -s v1/attention
+2 -2
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Basic Correctness
key: basic-correctness
timeout_in_minutes: 45
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/
@@ -19,6 +19,6 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 70
timeout_in_minutes: 50
depends_on:
- image-build-amd
+2 -2
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Benchmarks CLI Test
key: benchmarks-cli-test
timeout_in_minutes: 30
timeout_in_minutes: 20
device: h200_18gb
source_file_dependencies:
- vllm/
@@ -23,7 +23,7 @@ steps:
num_gpus: 2
optional: true
working_dir: "/vllm-workspace/"
timeout_in_minutes: 20
timeout_in_minutes: 10
source_file_dependencies:
- benchmarks/attention_benchmarks/
- vllm/v1/attention/
+11 -11
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Sequence Parallel Correctness Tests (2 GPUs)
key: sequence-parallel-correctness-tests-2-gpus
timeout_in_minutes: 80
timeout_in_minutes: 50
working_dir: "/vllm-workspace/"
num_devices: 2
source_file_dependencies:
@@ -19,7 +19,7 @@ steps:
- label: Sequence Parallel Correctness Tests (2xH100)
key: sequence-parallel-correctness-tests-2xh100
timeout_in_minutes: 75
timeout_in_minutes: 50
working_dir: "/vllm-workspace/"
device: h100
optional: true
@@ -30,7 +30,7 @@ steps:
- label: AsyncTP Correctness Tests (2xH100)
key: asynctp-correctness-tests-2xh100
timeout_in_minutes: 30
timeout_in_minutes: 50
working_dir: "/vllm-workspace/"
device: h100
optional: true
@@ -41,7 +41,7 @@ steps:
- label: AsyncTP Correctness Tests (B200)
key: asynctp-correctness-tests-b200
timeout_in_minutes: 30
timeout_in_minutes: 50
working_dir: "/vllm-workspace/"
device: b200-k8s
optional: true
@@ -52,7 +52,7 @@ steps:
- label: Distributed Compile Unit Tests (2xH100)
key: distributed-compile-unit-tests-2xh100
timeout_in_minutes: 45
timeout_in_minutes: 20
working_dir: "/vllm-workspace/"
device: h100
num_devices: 2
@@ -66,7 +66,7 @@ steps:
- label: Fusion and Compile Unit Tests (2xB200)
key: fusion-and-compile-unit-tests-2xb200
timeout_in_minutes: 30
timeout_in_minutes: 20
working_dir: "/vllm-workspace/"
device: b200-k8s
source_file_dependencies:
@@ -96,7 +96,7 @@ steps:
- label: Fusion E2E Quick (H100)
key: fusion-e2e-quick-h100
timeout_in_minutes: 25
timeout_in_minutes: 15
working_dir: "/vllm-workspace/"
device: h100
num_devices: 1
@@ -115,7 +115,7 @@ steps:
- label: Fusion E2E Config Sweep (H100)
key: fusion-e2e-config-sweep-h100
timeout_in_minutes: 25
timeout_in_minutes: 30
working_dir: "/vllm-workspace/"
device: h100
num_devices: 1
@@ -149,7 +149,7 @@ steps:
- label: Fusion E2E TP2 Quick (H100)
key: fusion-e2e-tp2-quick-h100
timeout_in_minutes: 35
timeout_in_minutes: 20
working_dir: "/vllm-workspace/"
device: h100
num_devices: 2
@@ -167,7 +167,7 @@ steps:
- label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
key: fusion-e2e-tp2-ar-rms-config-sweep-h100
timeout_in_minutes: 30
timeout_in_minutes: 40
working_dir: "/vllm-workspace/"
device: h100
num_devices: 2
@@ -207,7 +207,7 @@ steps:
- label: Fusion E2E TP2 (B200)
key: fusion-e2e-tp2-b200
timeout_in_minutes: 45
timeout_in_minutes: 20
working_dir: "/vllm-workspace/"
device: b200-k8s
num_devices: 2
+2 -2
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Platform Tests
key: platform-tests
timeout_in_minutes: 20
timeout_in_minutes: 15
device: h200_18gb
source_file_dependencies:
- vllm/envs.py
@@ -19,7 +19,7 @@ steps:
- label: Cudagraph
key: cudagraph
timeout_in_minutes: 30
timeout_in_minutes: 20
source_file_dependencies:
- tests/v1/cudagraph
- vllm/v1/cudagraph_dispatcher.py
+12 -25
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Distributed NixlConnector PD accuracy (4 GPUs)
key: distributed-nixlconnector-pd-accuracy-4-gpus
timeout_in_minutes: 55
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -16,7 +16,7 @@ steps:
mirror:
amd:
device: mi300_4
timeout_in_minutes: 85
timeout_in_minutes: 110
depends_on:
- image-build-amd
source_file_dependencies:
@@ -29,7 +29,7 @@ steps:
- label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
timeout_in_minutes: 55
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -39,19 +39,6 @@ steps:
- bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
- FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
- label: Push NixlConnector PP prefill PD accuracy (4 GPUs)
key: push-nixlconnector-pp-prefill-pd-accuracy-4-gpus
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/nixl/
- tests/v1/kv_connector/nixl_integration/
- tests/v1/kv_connector/nixl_push_integration/
commands:
- bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
- bash v1/kv_connector/nixl_push_integration/config_sweep_accuracy_test.sh
- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
timeout_in_minutes: 30
@@ -66,7 +53,7 @@ steps:
mirror:
amd:
device: mi300_4
timeout_in_minutes: 60
timeout_in_minutes: 50
depends_on:
- image-build-amd
source_file_dependencies:
@@ -79,7 +66,7 @@ steps:
- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
timeout_in_minutes: 55
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -91,7 +78,7 @@ steps:
mirror:
amd:
device: mi300_4
timeout_in_minutes: 85
timeout_in_minutes: 110
depends_on:
- image-build-amd
source_file_dependencies:
@@ -104,7 +91,7 @@ steps:
- label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
timeout_in_minutes: 60
timeout_in_minutes: 25
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -116,7 +103,7 @@ steps:
mirror:
amd:
device: mi300_4
timeout_in_minutes: 80
timeout_in_minutes: 60
depends_on:
- image-build-amd
source_file_dependencies:
@@ -143,7 +130,7 @@ steps:
- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
timeout_in_minutes: 40
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -158,7 +145,7 @@ steps:
- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
key: nixlconnector-pd-spec-decode-acceptance-2-gpus
timeout_in_minutes: 45
timeout_in_minutes: 30
device: a100
working_dir: "/vllm-workspace/tests"
num_devices: 2
@@ -172,7 +159,7 @@ steps:
mirror:
amd:
device: mi300_2
timeout_in_minutes: 70
timeout_in_minutes: 60
depends_on:
- image-build-amd
source_file_dependencies:
@@ -186,7 +173,7 @@ steps:
- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
timeout_in_minutes: 25
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
+10 -10
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Distributed Comm Ops
key: distributed-comm-ops
timeout_in_minutes: 25
timeout_in_minutes: 20
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -18,7 +18,7 @@ steps:
- label: Distributed DP Tests (2 GPUs)
key: distributed-dp-tests-2-gpus
timeout_in_minutes: 35
timeout_in_minutes: 20
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -55,7 +55,7 @@ steps:
- label: Distributed Compile + RPC Tests (2 GPUs)
key: distributed-compile-rpc-tests-2-gpus
timeout_in_minutes: 65
timeout_in_minutes: 20
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -78,7 +78,7 @@ steps:
- label: Distributed Torchrun + Shutdown Tests (2 GPUs)
key: distributed-torchrun-shutdown-tests-2-gpus
timeout_in_minutes: 30
timeout_in_minutes: 20
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -133,7 +133,7 @@ steps:
- label: Distributed DP Tests (4 GPUs)
key: distributed-dp-tests-4-gpus
timeout_in_minutes: 45
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -154,7 +154,7 @@ steps:
- label: Distributed Compile + Comm (4 GPUs)
key: distributed-compile-comm-4-gpus
timeout_in_minutes: 70
timeout_in_minutes: 30
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -176,7 +176,7 @@ steps:
- label: Distributed Tests (8xH100)
key: distributed-tests-8xh100
timeout_in_minutes: 20
timeout_in_minutes: 10
device: h100
num_devices: 8
working_dir: "/vllm-workspace/tests"
@@ -212,7 +212,7 @@ steps:
- label: Distributed Tests (2xH100-2xMI300)
key: distributed-tests-2xh100-2xmi300
timeout_in_minutes: 30
timeout_in_minutes: 15
device: h100
optional: true
working_dir: "/vllm-workspace/"
@@ -259,7 +259,7 @@ steps:
- label: Pipeline + Context Parallelism (4 GPUs)
key: pipeline-context-parallelism-4-gpus
timeout_in_minutes: 55
timeout_in_minutes: 60
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -274,7 +274,7 @@ steps:
- label: RayExecutorV2 (4 GPUs)
key: rayexecutorv2-4-gpus
timeout_in_minutes: 45
timeout_in_minutes: 60
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
+1 -1
View File
@@ -3,7 +3,7 @@ depends_on:
- image-build-cpu
steps:
- label: Docker Build Metadata
timeout_in_minutes: 20
timeout_in_minutes: 10
device: cpu-small
source_file_dependencies:
- .buildkite/release-pipeline.yaml
+5 -5
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: DeepSeek V2-Lite Sync EPLB Accuracy (4xH100)
key: deepseek-v2-lite-sync-eplb-accuracy-4xh100
timeout_in_minutes: 25
timeout_in_minutes: 60
device: h100
optional: true
num_devices: 4
@@ -14,7 +14,7 @@ steps:
- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (4xH100)
key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-4xh100
timeout_in_minutes: 25
timeout_in_minutes: 60
device: h100
optional: true
num_devices: 4
@@ -24,7 +24,7 @@ steps:
- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (2xB200)
key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-2xb200
timeout_in_minutes: 20
timeout_in_minutes: 60
device: b200-k8s
optional: true
num_devices: 2
@@ -34,7 +34,7 @@ steps:
- label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
timeout_in_minutes: 25
timeout_in_minutes: 60
device: h100
optional: true
num_devices: 4
@@ -44,7 +44,7 @@ steps:
- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
key: deepseek-v2-lite-prefetch-offload-accuracy-h100
timeout_in_minutes: 20
timeout_in_minutes: 60
device: h100
optional: true
num_devices: 1
+13 -13
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Engine
key: engine
timeout_in_minutes: 30
timeout_in_minutes: 15
device: h200_18gb
source_file_dependencies:
- vllm/compilation/
@@ -29,13 +29,13 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 50
timeout_in_minutes: 60
depends_on:
- image-build-amd
- label: Engine (1 GPU)
key: engine-1-gpu
timeout_in_minutes: 45
timeout_in_minutes: 30
source_file_dependencies:
- vllm/v1/engine/
- tests/v1/engine/
@@ -45,13 +45,13 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 55
timeout_in_minutes: 40
depends_on:
- image-build-amd
- label: e2e Scheduling (1 GPU)
key: e2e-scheduling-1-gpu
timeout_in_minutes: 35
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/v1/
@@ -60,15 +60,15 @@ steps:
- pytest -v -s v1/e2e/general/test_async_scheduling.py
mirror:
amd:
device: mi325_1
timeout_in_minutes: 70
device: mi250_1
timeout_in_minutes: 60
depends_on:
- image-build-amd
- label: e2e Core (1 GPU)
device: h200_35gb
key: e2e-core-1-gpu
timeout_in_minutes: 40
timeout_in_minutes: 30
source_file_dependencies:
- vllm/v1/
- tests/v1/e2e/general/
@@ -76,8 +76,8 @@ steps:
- pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
mirror:
amd:
device: mi325_1
timeout_in_minutes: 60
device: mi250_1
timeout_in_minutes: 35
depends_on:
- image-build-amd
source_file_dependencies:
@@ -87,7 +87,7 @@ steps:
- label: V1 e2e (2 GPUs)
key: v1-e2e-2-gpus
timeout_in_minutes: 25 # TODO: Fix timeout after we have more confidence in the test stability
timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
optional: true
num_devices: 2
source_file_dependencies:
@@ -120,7 +120,7 @@ steps:
- label: V1 e2e (4 GPUs)
key: v1-e2e-4-gpus
timeout_in_minutes: 20 # TODO: Fix timeout after we have more confidence in the test stability
timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
optional: true
num_devices: 4
source_file_dependencies:
@@ -148,7 +148,7 @@ steps:
- label: V1 e2e (4xH100)
key: v1-e2e-4xh100
timeout_in_minutes: 35
timeout_in_minutes: 60
device: h100
num_devices: 4
optional: true
+10 -10
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Entrypoints Unit Tests
key: entrypoints-unit-tests
timeout_in_minutes: 25
timeout_in_minutes: 10
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/entrypoints
@@ -16,7 +16,7 @@ steps:
- label: Entrypoints Integration (LLM)
key: entrypoints-integration-llm
timeout_in_minutes: 60
timeout_in_minutes: 40
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -37,7 +37,7 @@ steps:
- label: Entrypoints Integration (API Server)
key: entrypoints-integration-api-server
device: h200_35gb
timeout_in_minutes: 50
timeout_in_minutes: 130
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -56,7 +56,7 @@ steps:
- label: Entrypoints Integration (API Server OpenAI - Part 1)
key: entrypoints-integration-api-server-openai-part-1
timeout_in_minutes: 45
timeout_in_minutes: 50
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -68,13 +68,13 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 65
timeout_in_minutes: 80
depends_on:
- image-build-amd
- label: Entrypoints Integration (API Server OpenAI - Part 2)
key: entrypoints-integration-api-server-openai-part-2
timeout_in_minutes: 45
timeout_in_minutes: 50
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -109,7 +109,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 65
timeout_in_minutes: 60
depends_on:
- image-build-amd
@@ -126,7 +126,7 @@ steps:
- label: Entrypoints Integration (Speech to Text)
device: h200_35gb
key: entrypoints-integration-speech_to_text
timeout_in_minutes: 45
timeout_in_minutes: 50
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -138,7 +138,7 @@ steps:
- label: Entrypoints Integration (Multimodal)
device: h200_35gb
key: entrypoints-integration-multimodal
timeout_in_minutes: 45
timeout_in_minutes: 50
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/
@@ -160,7 +160,7 @@ steps:
- label: OpenAI API Correctness
key: openai-api-correctness
timeout_in_minutes: 20
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- csrc/
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: EPLB Algorithm
key: eplb-algorithm
timeout_in_minutes: 20
timeout_in_minutes: 15
device: h200_18gb
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
@@ -27,7 +27,7 @@ steps:
- label: EPLB Execution # 17min
key: eplb-execution
timeout_in_minutes: 25
timeout_in_minutes: 27
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
@@ -39,7 +39,7 @@ steps:
- label: Elastic EP Scaling Test
key: elastic-ep-scaling-test
timeout_in_minutes: 30
timeout_in_minutes: 20
device: h100
working_dir: "/vllm-workspace/tests"
num_devices: 4
+21 -23
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: vLLM IR Tests
key: vllm-ir-tests
timeout_in_minutes: 35
timeout_in_minutes: 10
device: h200_18gb
working_dir: "/vllm-workspace/"
source_file_dependencies:
@@ -16,19 +16,18 @@ steps:
- label: Kernels Core Operation Test
key: kernels-core-operation-test
timeout_in_minutes: 120
timeout_in_minutes: 75
source_file_dependencies:
- csrc/
- tests/kernels/core
- tests/kernels/test_concat_mla_q.py
- tests/kernels/test_fused_qk_norm_rope_gate.py
commands:
- pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
parallelism: 3
- pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py
- label: Kernels MiniMax Reduce RMS Test (2 GPUs)
key: kernels-minimax-reduce-rms-test-2-gpus
timeout_in_minutes: 20
timeout_in_minutes: 15
num_devices: 2
device: h100
source_file_dependencies:
@@ -42,7 +41,7 @@ steps:
- label: Deepseek V4 Kernel Test (H100)
key: deepseek-v4-kernel-test-h100
timeout_in_minutes: 30
timeout_in_minutes: 15
device: h100
source_file_dependencies:
- csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -55,7 +54,7 @@ steps:
- label: Deepseek V4 Kernel Test (B200)
key: deepseek-v4-kernel-test-b200
timeout_in_minutes: 20
timeout_in_minutes: 15
device: b200-k8s
source_file_dependencies:
- csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -66,7 +65,7 @@ steps:
- label: Kernels Attention Test %N
key: kernels-attention-test
timeout_in_minutes: 65
timeout_in_minutes: 35
source_file_dependencies:
- csrc/attention/
- vllm/v1/attention
@@ -80,7 +79,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 90
timeout_in_minutes: 55
depends_on:
- image-build-amd
source_file_dependencies:
@@ -107,7 +106,7 @@ steps:
- label: Kernels Quantization Test %N
key: kernels-quantization-test
timeout_in_minutes: 60
timeout_in_minutes: 90
source_file_dependencies:
- csrc/quantization/
- vllm/model_executor/layers/quantization
@@ -132,7 +131,7 @@ steps:
- label: Kernels MoE Test %N
key: kernels-moe-test
timeout_in_minutes: 50
timeout_in_minutes: 25
source_file_dependencies:
- csrc/quantization/cutlass_w8a8/moe/
- csrc/moe/
@@ -148,7 +147,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 65
timeout_in_minutes: 50
source_file_dependencies:
- csrc/quantization/cutlass_w8a8/moe/
- csrc/moe/
@@ -164,7 +163,7 @@ steps:
- label: Kernels Mamba Test
key: kernels-mamba-test
timeout_in_minutes: 40
timeout_in_minutes: 45
source_file_dependencies:
- csrc/mamba/
- tests/kernels/mamba
@@ -173,7 +172,7 @@ steps:
- pytest -v -s kernels/mamba
- label: Kernels KDA Test
timeout_in_minutes: 25
timeout_in_minutes: 20
device: h200_18gb
source_file_dependencies:
- vllm/model_executor/layers/fla/ops/kda.py
@@ -185,7 +184,7 @@ steps:
- label: Kernels DeepGEMM Test (H100)
key: kernels-deepgemm-test-h100
timeout_in_minutes: 35
timeout_in_minutes: 45
device: h100
num_devices: 1
source_file_dependencies:
@@ -212,7 +211,7 @@ steps:
- label: Kernels (B200)
key: kernels-b200
timeout_in_minutes: 80
timeout_in_minutes: 30
working_dir: "/vllm-workspace/"
device: b200-k8s
# optional: true
@@ -265,20 +264,19 @@ steps:
- label: Kernels Helion Test
key: kernels-helion-test
timeout_in_minutes: 115
timeout_in_minutes: 30
device: h100
source_file_dependencies:
- vllm/utils/import_utils.py
- tests/kernels/helion/
commands:
- pip install helion==1.1.0
- pytest -v -s kernels/helion/ --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
parallelism: 2
- pytest -v -s kernels/helion/
- label: Kernels FP8 MoE Test (1xH100)
key: kernels-fp8-moe-test-1xh100
timeout_in_minutes: 40
timeout_in_minutes: 90
device: h100
num_devices: 1
optional: true
@@ -295,7 +293,7 @@ steps:
- label: Kernels FP8 MoE Test (2xH100)
key: kernels-fp8-moe-test-2xh100
timeout_in_minutes: 45
timeout_in_minutes: 90
device: h100
num_devices: 2
optional: true
@@ -305,7 +303,7 @@ steps:
- label: Kernels Fp4 MoE Test (B200)
key: kernels-fp4-moe-test-b200
timeout_in_minutes: 25
timeout_in_minutes: 60
device: b200-k8s
num_devices: 1
optional: true
@@ -318,7 +316,7 @@ steps:
- label: Kernels FusedMoE Layer Test (2 H100s)
key: kernels-fusedmoe-layer-test-2-h100s
timeout_in_minutes: 30
timeout_in_minutes: 90
device: h100
num_devices: 2
source_file_dependencies:
+23 -112
View File
@@ -5,7 +5,7 @@ steps:
- label: LM Eval Small Models
device: h200_35gb
key: lm-eval-small-models
timeout_in_minutes: 45
timeout_in_minutes: 75
source_file_dependencies:
- csrc/
- vllm/model_executor/layers/quantization
@@ -56,7 +56,7 @@ steps:
- label: LM Eval Small Models (1xB200)
key: lm-eval-small-models-1xb200
timeout_in_minutes: 50
timeout_in_minutes: 120
device: b200-k8s
optional: true
source_file_dependencies:
@@ -80,7 +80,7 @@ steps:
- label: LM Eval Large Models EP (2xB200)
key: lm-eval-large-models-ep-2xb200
timeout_in_minutes: 60
timeout_in_minutes: 120
device: b200-k8s
optional: true
num_devices: 2
@@ -92,7 +92,7 @@ steps:
- label: LM Eval Qwen3.5 Models (2xB200)
key: lm-eval-qwen3-5-models-2xb200
timeout_in_minutes: 45
timeout_in_minutes: 120
device: b200-k8s
optional: true
num_devices: 2
@@ -109,7 +109,7 @@ steps:
- label: LM Eval Large Models (8xH200)
key: lm-eval-large-models-8xh200
timeout_in_minutes: 50
timeout_in_minutes: 60
device: h200
optional: true
num_devices: 8
@@ -118,7 +118,7 @@ steps:
mirror:
amd:
device: mi300_8
timeout_in_minutes: 60
timeout_in_minutes: 180
depends_on:
- image-build-amd
commands:
@@ -150,9 +150,9 @@ steps:
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
- label: LM Eval Humming f16 (A100 - TEMPORARY)
key: lm-eval-humming-f16-a100
timeout_in_minutes: 75
- label: LM Eval Humming (A100 - TEMPORARY)
key: lm-eval-humming-a100
timeout_in_minutes: 30
device: a100
optional: true
num_devices: 1
@@ -160,29 +160,13 @@ steps:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
- vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt
- label: LM Eval Humming Act int8 (A100 - TEMPORARY)
key: lm-eval-humming-act-a100
timeout_in_minutes: 45
device: a100
optional: true
num_devices: 1
source_file_dependencies:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt
- label: LM Eval Humming f16 (H100 - TEMPORARY)
key: lm-eval-humming-f16-h100
timeout_in_minutes: 70
- label: LM Eval Humming (H100 - TEMPORARY)
key: lm-eval-humming-h100
timeout_in_minutes: 30
device: h100
optional: true
num_devices: 1
@@ -190,30 +174,14 @@ steps:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
- vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt
- label: LM Eval Humming Act fp8/int8 (H100 - TEMPORARY)
key: lm-eval-humming-act-h100
timeout_in_minutes: 70
device: h100
optional: true
num_devices: 1
source_file_dependencies:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-fp8.txt
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt
- label: LM Eval Humming f16 (B200 - TEMPORARY)
key: lm-eval-humming-f16-b200
timeout_in_minutes: 50
- label: LM Eval Humming (B200 - TEMPORARY)
key: lm-eval-humming-b200
timeout_in_minutes: 30
device: b200-k8s
optional: true
num_devices: 1
@@ -221,30 +189,14 @@ steps:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
- vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config.txt
- label: LM Eval Humming Act fp8/int8 (B200 - TEMPORARY)
key: lm-eval-humming-act-b200
timeout_in_minutes: 50
device: b200-k8s
optional: true
num_devices: 1
source_file_dependencies:
- vllm/model_executor/layers/quantization/humming.py
- vllm/model_executor/layers/quantization/utils/humming_utils.py
- vllm/model_executor/layers/fused_moe/experts/fused_humming_moe.py
- vllm/model_executor/layers/fused_moe/oracle/
- vllm/model_executor/kernels/linear/
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-fp8.txt
- pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/humming/config-act-int8.txt
- label: LM Eval TurboQuant KV Cache
key: lm-eval-turboquant-kv-cache
timeout_in_minutes: 55
timeout_in_minutes: 75
device: h200_18gb
source_file_dependencies:
- vllm/model_executor/layers/quantization/turboquant/
@@ -256,7 +208,7 @@ steps:
- label: GPQA Eval (GPT-OSS) (2xH100)
key: gpqa-eval-gpt-oss-2xh100
timeout_in_minutes: 35
timeout_in_minutes: 120
device: h100
optional: true
num_devices: 2
@@ -270,7 +222,7 @@ steps:
- label: GPQA Eval (GPT-OSS) (2xB200)
key: gpqa-eval-gpt-oss-2xb200
timeout_in_minutes: 30
timeout_in_minutes: 120
device: b200-k8s
optional: true
num_devices: 2
@@ -284,7 +236,7 @@ steps:
- label: GPQA Eval (GPT-OSS) (DGX Spark)
key: gpqa-eval-gpt-oss-spark
timeout_in_minutes: 35
timeout_in_minutes: 120
device: dgx-spark
optional: true
num_devices: 1
@@ -298,50 +250,9 @@ steps:
- uv pip install --system 'gpt-oss[eval]==0.0.5'
- pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-spark.txt
- label: LM Eval KV-Offload (1xH200)
key: kv-offload-small
timeout_in_minutes: 30
device: h200_35gb
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/offloading/
- vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
- vllm/v1/kv_offload/
- vllm/v1/simple_kv_offload/
- tests/evals/gsm8k/test_gsm8k_offloading.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "nemotron-h-8b or gemma-4-e4b-it"
- label: LM Eval KV-Offload (2xH100)
key: kv-offload-medium
timeout_in_minutes: 30
device: h100
num_devices: 2
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/offloading/
- vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
- vllm/v1/kv_offload/
- vllm/v1/simple_kv_offload/
- tests/evals/gsm8k/test_gsm8k_offloading.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "qwen3.5-35b"
- label: LM Eval KV-Offload (4xH100)
key: kv-offload-large
timeout_in_minutes: 40
device: h100
num_devices: 4
source_file_dependencies:
- vllm/distributed/kv_transfer/kv_connector/v1/offloading/
- vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
- vllm/v1/kv_offload/
- vllm/v1/simple_kv_offload/
- tests/evals/gsm8k/test_gsm8k_offloading.py
commands:
- pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "deepseek-v4-flash"
- label: MRCR Eval Small Models
device: h200_35gb
timeout_in_minutes: 25
timeout_in_minutes: 30
source_file_dependencies:
- tests/evals/mrcr/
commands:
+3 -3
View File
@@ -5,7 +5,7 @@ steps:
- label: LoRA %N
device: h200_35gb
key: lora
timeout_in_minutes: 40
timeout_in_minutes: 30
source_file_dependencies:
- vllm/lora
- tests/lora
@@ -16,7 +16,7 @@ steps:
amd:
device: mi325_1
working_dir: "/vllm-workspace/tests"
timeout_in_minutes: 65
timeout_in_minutes: 60
source_file_dependencies:
- vllm/lora
- tests/lora
@@ -27,7 +27,7 @@ steps:
- label: LoRA TP (Distributed)
key: lora-tp-distributed
timeout_in_minutes: 60
timeout_in_minutes: 30
num_devices: 4
source_file_dependencies:
- vllm/lora
+17 -21
View File
@@ -5,7 +5,7 @@ steps:
- label: V1 Spec Decode
device: h200_35gb
key: v1-spec-decode
timeout_in_minutes: 40
timeout_in_minutes: 30
source_file_dependencies:
- vllm/config/
- vllm/distributed/
@@ -24,13 +24,13 @@ steps:
mirror:
amd:
device: mi300_1
timeout_in_minutes: 75
timeout_in_minutes: 65
depends_on:
- image-build-amd
- label: V1 Sample + Logits
key: v1-sample-logits
timeout_in_minutes: 45
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/config/
@@ -64,7 +64,7 @@ steps:
- label: V1 Core + KV + Metrics
key: v1-core-kv-metrics
timeout_in_minutes: 60
timeout_in_minutes: 30
source_file_dependencies:
- vllm/config/
- vllm/distributed/
@@ -89,7 +89,6 @@ steps:
- tests/v1/simple_kv_offload
- tests/v1/worker
- tests/v1/kv_connector/unit
- tests/v1/ec_connector/unit
- tests/v1/metrics
- tests/entrypoints/openai/correctness/test_lmeval.py
commands:
@@ -102,15 +101,14 @@ steps:
- pytest -v -s v1/simple_kv_offload
- pytest -v -s v1/worker
- pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
- pytest -v -s -m 'not cpu_test' v1/ec_connector/unit
- pytest -v -s -m 'not cpu_test' v1/metrics
# Integration test for streaming correctness (requires special branch).
- pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
- pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
- pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
mirror:
amd:
device: mi325_1
timeout_in_minutes: 75
timeout_in_minutes: 60
depends_on:
- image-build-amd
@@ -174,7 +172,7 @@ steps:
- label: Regression
key: regression
timeout_in_minutes: 30
timeout_in_minutes: 20
device: h200_18gb
source_file_dependencies:
- vllm/config/
@@ -190,14 +188,14 @@ steps:
- vllm/v1/
- tests/test_regression
commands:
- pip install 'modelscope<1.38'
- pip install modelscope
- pytest -v -s test_regression.py
working_dir: "/vllm-workspace/tests" # optional
- label: Examples
device: h200_35gb
key: examples
timeout_in_minutes: 40
timeout_in_minutes: 45
working_dir: "/vllm-workspace/examples"
source_file_dependencies:
- vllm/entrypoints
@@ -239,7 +237,7 @@ steps:
- label: Metrics, Tracing (2 GPUs)
key: metrics-tracing-2-gpus
timeout_in_minutes: 25
timeout_in_minutes: 20
num_devices: 2
source_file_dependencies:
- vllm/config/
@@ -283,7 +281,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 45
timeout_in_minutes: 20
depends_on:
- image-build-amd
source_file_dependencies:
@@ -294,7 +292,7 @@ steps:
- label: Async Engine, Inputs, Utils, Worker
device: h200_35gb
key: async-engine-inputs-utils-worker
timeout_in_minutes: 25
timeout_in_minutes: 50
source_file_dependencies:
- vllm/assets/
- vllm/config/
@@ -321,7 +319,7 @@ steps:
key: async-engine-inputs-utils-worker-config-cpu
depends_on:
- image-build-cpu
timeout_in_minutes: 65
timeout_in_minutes: 30
source_file_dependencies:
- vllm/assets/
- vllm/config/
@@ -353,7 +351,6 @@ steps:
- tests/test_outputs.py
- tests/test_pooling_params.py
- tests/test_ray_env.py
- tests/test_sampling_params.py
- tests/multimodal
- tests/renderers
- tests/standalone_tests/lazy_imports.py
@@ -371,7 +368,6 @@ steps:
- pytest -v -s test_outputs.py
- pytest -v -s test_pooling_params.py
- pytest -v -s test_ray_env.py
- pytest -v -s test_sampling_params.py
- pytest -v -s -m 'cpu_test' multimodal
- pytest -v -s renderers
- pytest -v -s reasoning
@@ -383,7 +379,7 @@ steps:
- label: Batch Invariance (A100)
key: batch-invariance-a100
timeout_in_minutes: 40
timeout_in_minutes: 30
device: a100
source_file_dependencies:
- vllm/v1/attention
@@ -397,7 +393,7 @@ steps:
- label: Batch Invariance (H100)
key: batch-invariance-h100
timeout_in_minutes: 40
timeout_in_minutes: 30
device: h100
source_file_dependencies:
- vllm/v1/attention
@@ -413,7 +409,7 @@ steps:
- label: Batch Invariance (B200)
key: batch-invariance-b200
timeout_in_minutes: 35
timeout_in_minutes: 30
device: b200-k8s
source_file_dependencies:
- vllm/v1/attention
@@ -432,7 +428,7 @@ steps:
- label: Acceptance Length Test (Large Models) # optional
device: h200_35gb
key: acceptance-length-test-large-models
timeout_in_minutes: 20
timeout_in_minutes: 25
gpu: h100
optional: true
num_gpus: 1
+1 -1
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Model Executor
key: model-executor
timeout_in_minutes: 45
timeout_in_minutes: 35
source_file_dependencies:
- vllm/engine/arg_utils.py
- vllm/config/model.py
+4 -4
View File
@@ -5,7 +5,7 @@ steps:
- label: Model Runner V2 Core Tests
device: h200_35gb
key: model-runner-v2-core-tests
timeout_in_minutes: 35
timeout_in_minutes: 45
source_file_dependencies:
- vllm/v1/worker/gpu/
- vllm/v1/worker/gpu_worker.py
@@ -27,7 +27,7 @@ steps:
- label: Model Runner V2 Examples
device: h200_35gb
key: model-runner-v2-examples
timeout_in_minutes: 35
timeout_in_minutes: 45
working_dir: "/vllm-workspace/examples"
source_file_dependencies:
- vllm/v1/worker/gpu/
@@ -63,7 +63,7 @@ steps:
- label: Model Runner V2 Distributed (2 GPUs)
key: model-runner-v2-distributed-2-gpus
timeout_in_minutes: 30
timeout_in_minutes: 45
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -84,7 +84,7 @@ steps:
- label: Model Runner V2 Pipeline Parallelism (4 GPUs)
key: model-runner-v2-pipeline-parallelism-4-gpus
timeout_in_minutes: 50
timeout_in_minutes: 60
working_dir: "/vllm-workspace/tests"
num_devices: 4
source_file_dependencies:
+8 -9
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Basic Models Tests (Initialization)
key: basic-models-tests-initialization
timeout_in_minutes: 25
timeout_in_minutes: 45
device: h200_18gb
source_file_dependencies:
- vllm/
@@ -17,7 +17,7 @@ steps:
- label: Basic Models Tests (Extra Initialization) %N
device: h200_35gb
key: basic-models-tests-extra-initialization
timeout_in_minutes: 100
timeout_in_minutes: 45
source_file_dependencies:
- vllm/model_executor/models/
- tests/models/test_initialization.py
@@ -27,19 +27,19 @@ steps:
# subset of supported models (the complement of the small subset in the above
# test.) Also run if model initialization test file is modified
- pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
parallelism: 4
parallelism: 2
- label: Basic Models Tests (Other)
device: h200_35gb
key: basic-models-tests-other
timeout_in_minutes: 35
timeout_in_minutes: 45
source_file_dependencies:
- vllm/
- tests/models/test_terratorch.py
- tests/models/transformers/test_backend.py
- tests/models/test_transformers.py
- tests/models/test_registry.py
commands:
- pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
- pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
mirror:
amd:
device: mi325_1
@@ -50,12 +50,11 @@ steps:
key: basic-models-test-other-cpu
depends_on:
- image-build-cpu
timeout_in_minutes: 20
timeout_in_minutes: 10
source_file_dependencies:
- vllm/
- tests/models/test_utils.py
- tests/models/test_vision.py
- tests/models/transformers/fusers/
device: cpu-small
commands:
- pytest -v -s models/test_utils.py models/test_vision.py models/transformers/fusers/
- pytest -v -s models/test_utils.py models/test_vision.py
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Distributed Model Tests (2 GPUs)
key: distributed-model-tests-2-gpus
timeout_in_minutes: 60
timeout_in_minutes: 50
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -17,7 +17,7 @@ steps:
- TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
- CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
# Avoid importing model tests that cause CUDA reinitialization error
- pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)'
- pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
- pytest models/language -v -s -m 'distributed(num_gpus=2)'
- pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
- pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
+8 -8
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Language Models Tests (Standard)
key: language-models-tests-standard
timeout_in_minutes: 30
timeout_in_minutes: 25
device: h200_18gb
source_file_dependencies:
- vllm/
@@ -21,7 +21,7 @@ steps:
- label: Language Models Tests (Extra Standard) %N
key: language-models-tests-extra-standard
timeout_in_minutes: 40
timeout_in_minutes: 45
source_file_dependencies:
- vllm/model_executor/models/
- tests/models/language/pooling/test_embedding.py
@@ -52,7 +52,7 @@ steps:
- label: Language Models Tests (Hybrid) %N
key: language-models-tests-hybrid
timeout_in_minutes: 65
timeout_in_minutes: 75
source_file_dependencies:
- vllm/
- tests/models/language/generation
@@ -67,7 +67,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 70
timeout_in_minutes: 90
depends_on:
- image-build-amd
commands:
@@ -78,7 +78,7 @@ steps:
- label: Language Models Test (Extended Generation) # 80min
device: h200_35gb
key: language-models-test-extended-generation
timeout_in_minutes: 65
timeout_in_minutes: 110
optional: true
source_file_dependencies:
- vllm/
@@ -92,7 +92,7 @@ steps:
- label: Language Models Test (PPL)
key: language-models-test-ppl
timeout_in_minutes: 30
timeout_in_minutes: 110
device: h200_18gb
optional: true
source_file_dependencies:
@@ -104,7 +104,7 @@ steps:
- label: Language Models Test (Extended Pooling) # 36min
device: h200_35gb
key: language-models-test-extended-pooling
timeout_in_minutes: 70
timeout_in_minutes: 50
optional: true
source_file_dependencies:
- vllm/
@@ -120,7 +120,7 @@ steps:
- label: Language Models Test (MTEB)
key: language-models-test-mteb
timeout_in_minutes: 45
timeout_in_minutes: 110
device: h200_18gb
optional: true
source_file_dependencies:
+10 -12
View File
@@ -20,14 +20,13 @@ steps:
- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
key: multi-modal-models-standard-2-qwen3-gemma
timeout_in_minutes: 50
timeout_in_minutes: 45
device: h200_18gb
source_file_dependencies:
- vllm/
- tests/models/multimodal
commands:
- pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
- pytest -v -s models/multimodal/generation/test_mm_prefix_lm.py -m core_model
- pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
mirror:
amd:
@@ -38,7 +37,7 @@ steps:
- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
device: h200_35gb
key: multi-modal-models-standard-3-llava-qwen2-vl
timeout_in_minutes: 40
timeout_in_minutes: 45
source_file_dependencies:
- vllm/
- tests/models/multimodal
@@ -54,12 +53,12 @@ steps:
- label: "Multi-Modal Models (Standard) 4: other + whisper"
device: h200_35gb
key: multi-modal-models-standard-4-other-whisper
timeout_in_minutes: 50
timeout_in_minutes: 45
source_file_dependencies:
- vllm/
- tests/models/multimodal
commands:
- pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_mm_prefix_lm.py --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/generation/test_vit_cudagraph.py --ignore models/multimodal/processing
- pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/generation/test_vit_cudagraph.py --ignore models/multimodal/processing
- pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
- pytest models/multimodal/generation/test_memory_leak.py -m core_model
- cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model # Otherwise, mp_method="spawn" doesn't work
@@ -69,23 +68,22 @@ steps:
depends_on:
- image-build-amd
- label: Multi-Modal Processor (CPU) %N
- label: Multi-Modal Processor (CPU)
key: multi-modal-processor-cpu
depends_on:
- image-build-cpu
timeout_in_minutes: 125
timeout_in_minutes: 60
source_file_dependencies:
- vllm/
- tests/models/multimodal
- tests/models/registry.py
device: cpu-medium
commands:
- pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
parallelism: 4
- pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
- label: Multi-Modal Processor # 44min
key: multi-modal-processor
timeout_in_minutes: 65
timeout_in_minutes: 60
device: h200_18gb
source_file_dependencies:
- vllm/
@@ -97,7 +95,7 @@ steps:
- label: Multi-Modal Accuracy Eval (Small Models) # 50min
device: h200_35gb
key: multi-modal-accuracy-eval-small-models
timeout_in_minutes: 30
timeout_in_minutes: 70
working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
source_file_dependencies:
- vllm/multimodal/
@@ -165,7 +163,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 75
timeout_in_minutes: 60
depends_on:
- image-build-amd
source_file_dependencies:
+1 -6
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Plugin Tests (2 GPUs)
key: plugin-tests-2-gpus
timeout_in_minutes: 35
timeout_in_minutes: 60
working_dir: "/vllm-workspace/tests"
num_devices: 2
source_file_dependencies:
@@ -37,11 +37,6 @@ steps:
- pytest -v -s plugins_tests/test_stats_logger_plugins.py
- pip uninstall dummy_stat_logger -y
# end stat_logger plugins test
# begin endpoint plugins test
- pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
- pytest -v -s plugins_tests/test_endpoint_plugins.py
- pip uninstall vllm_add_dummy_endpoint_plugin -y
# end endpoint plugins test
# other tests continue here:
- pytest -v -s plugins_tests/test_scheduler_plugins.py
- pip install -e ./plugins/vllm_add_dummy_model
+5 -5
View File
@@ -5,7 +5,7 @@ steps:
- label: PyTorch Compilation Unit Tests
device: h200_35gb
key: pytorch-compilation-unit-tests
timeout_in_minutes: 90
timeout_in_minutes: 10
source_file_dependencies:
- vllm/__init__.py
- vllm/_aiter_ops.py
@@ -78,7 +78,7 @@ steps:
- label: PyTorch Compilation Passes Unit Tests
key: pytorch-compilation-passes-unit-tests
timeout_in_minutes: 45
timeout_in_minutes: 20
source_file_dependencies:
- vllm/__init__.py
- vllm/_aiter_ops.py
@@ -110,13 +110,13 @@ steps:
mirror:
amd:
device: mi300_1
timeout_in_minutes: 65
timeout_in_minutes: 180
depends_on:
- image-build-amd
- label: PyTorch Fullgraph Smoke Test
key: pytorch-fullgraph-smoke-test
timeout_in_minutes: 60
timeout_in_minutes: 35
source_file_dependencies:
- vllm/__init__.py
- vllm/_aiter_ops.py
@@ -152,7 +152,7 @@ steps:
- label: PyTorch Fullgraph
key: pytorch-fullgraph
timeout_in_minutes: 40
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/__init__.py
+4 -4
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Quantization
key: quantization
timeout_in_minutes: 60
timeout_in_minutes: 90
source_file_dependencies:
- csrc/
- vllm/model_executor/layers/quantization
@@ -23,7 +23,7 @@ steps:
- label: Quantized Fusions
key: quantized-fusions
timeout_in_minutes: 20
timeout_in_minutes: 30
source_file_dependencies:
- tests/fusion
- vllm/model_executor/layers/fusion
@@ -35,7 +35,7 @@ steps:
- label: Quantized MoE Test (B200)
key: quantized-moe-test-b200
timeout_in_minutes: 120
timeout_in_minutes: 60
working_dir: "/vllm-workspace/"
device: b200-k8s
source_file_dependencies:
@@ -53,7 +53,7 @@ steps:
- label: Quantized Models Test
key: quantized-models-test
timeout_in_minutes: 50
timeout_in_minutes: 60
source_file_dependencies:
- vllm/model_executor/layers/quantization
- tests/models/quantization
+13 -20
View File
@@ -3,7 +3,7 @@ depends_on:
- image-build
steps:
- label: Rust Frontend OpenAI Coverage
timeout_in_minutes: 30
timeout_in_minutes: 90
device: h200_18gb
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
@@ -15,30 +15,28 @@ steps:
- tests/utils.py
- tests/benchmarks/test_serve_cli.py
- tests/entrypoints/openai/chat_completion/test_chat_completion.py
- tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
# - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
# - tests/entrypoints/openai/completion/test_prompt_validation.py
- tests/entrypoints/openai/completion/test_shutdown.py
- tests/entrypoints/openai/test_return_token_ids.py
- tests/entrypoints/openai/test_uds.py
# - tests/entrypoints/openai/test_return_token_ids.py
# - tests/entrypoints/openai/test_uds.py
- tests/v1/sample/test_logprobs_e2e.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
- pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py -k "not test_invalid_json_schema and not test_invalid_regex"
- pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not multiple"
# - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"
# - pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
- pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
# test_comparison streams differently: Rust emits a separate first (prompt_token_ids) chunk and
# finish chunk without logprobs, while the test reads `logprobs.tokens` on every chunk.
- pytest -v -s entrypoints/openai/test_return_token_ids.py -k "not test_comparison"
- pytest -v -s entrypoints/openai/test_uds.py
# - pytest -v -s entrypoints/openai/test_return_token_ids.py
# - pytest -v -s entrypoints/openai/test_uds.py
- pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
- label: Rust Frontend Serve/Admin Coverage
timeout_in_minutes: 25
timeout_in_minutes: 60
device: h200_18gb
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
@@ -47,27 +45,22 @@ steps:
- vllm/entrypoints/serve/
- vllm/v1/engine/
- tests/utils.py
- tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
# - tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
- tests/entrypoints/scale_out/token_in_token_out/test_serving_tokens.py
- tests/entrypoints/serve/instrumentator/test_basic.py
- tests/entrypoints/serve/instrumentator/test_metrics.py
# - tests/entrypoints/serve/dev/test_sleep.py
- tests/entrypoints/serve/tokenize/test_tokenization.py
commands:
- export VLLM_USE_RUST_FRONTEND=1
- export VLLM_WORKER_MULTIPROC_METHOD=spawn
- PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
# server_load can be flaky under the Rust frontend; keep it excluded for now.
# - pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
- pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
# test_generate_logprobs expects Python-style top_logprobs truncation (dedup sampled + cap at max(k, 1)).
- pytest -v -s entrypoints/scale_out/token_in_token_out/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
- pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
# - pytest -v -s entrypoints/serve/dev/test_sleep.py
# /tokenizer_info is not implemented in the Rust frontend (the CLI flag is accepted as a no-op).
- pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"
- label: Rust Frontend Core Correctness
timeout_in_minutes: 20
timeout_in_minutes: 30
device: h200_18gb
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
@@ -81,7 +74,7 @@ steps:
- pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
- label: Rust Frontend Tool Use
timeout_in_minutes: 25
timeout_in_minutes: 60
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- rust/
@@ -95,7 +88,7 @@ steps:
- pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
- label: Rust Frontend Distributed
timeout_in_minutes: 25
timeout_in_minutes: 30
num_devices: 4
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
@@ -4,7 +4,7 @@ steps:
- label: Rust Frontend Cargo Style + Clippy
key: rust-frontend-cargo-style-clippy
depends_on: []
timeout_in_minutes: 20
timeout_in_minutes: 30
device: cpu-medium
no_plugin: true
source_file_dependencies:
@@ -18,7 +18,7 @@ steps:
- label: Rust Frontend Cargo Tests
key: rust-frontend-cargo-tests
depends_on: []
timeout_in_minutes: 20
timeout_in_minutes: 30
device: cpu-medium
no_plugin: true
source_file_dependencies:
+2 -2
View File
@@ -5,7 +5,7 @@ steps:
- label: Samplers Test
device: h200_35gb
key: samplers-test
timeout_in_minutes: 40
timeout_in_minutes: 75
source_file_dependencies:
- vllm/model_executor/layers
- vllm/sampling_metadata.py
@@ -19,7 +19,7 @@ steps:
- VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
mirror:
amd:
device: mi325_1
device: mi250_1
depends_on:
- image-build-amd
commands:
+11 -11
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Spec Decode Eagle
key: spec-decode-eagle
timeout_in_minutes: 25
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/v1/spec_decode/
@@ -15,7 +15,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 60
timeout_in_minutes: 45
depends_on:
- image-build-amd
source_file_dependencies:
@@ -29,7 +29,7 @@ steps:
- label: Spec Decode Eagle Nightly B200
key: spec-decode-eagle-nightly-b200
timeout_in_minutes: 25
timeout_in_minutes: 30
device: b200-k8s
optional: true
source_file_dependencies:
@@ -41,7 +41,7 @@ steps:
- label: Spec Decode Speculators + MTP
key: spec-decode-speculators-mtp
timeout_in_minutes: 20
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/v1/spec_decode/
@@ -82,7 +82,7 @@ steps:
- label: Spec Decode Ngram + Suffix
key: spec-decode-ngram-suffix
timeout_in_minutes: 20
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/v1/spec_decode/
@@ -93,7 +93,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 55
timeout_in_minutes: 65
# TODO(akaratza): Test after Torch >= 2.12 bump
soft_fail: true
depends_on:
@@ -109,7 +109,7 @@ steps:
- label: Spec Decode Draft Model
key: spec-decode-draft-model
timeout_in_minutes: 45
timeout_in_minutes: 30
device: h200_18gb
source_file_dependencies:
- vllm/v1/spec_decode/
@@ -120,7 +120,7 @@ steps:
mirror:
amd:
device: mi325_1
timeout_in_minutes: 55
timeout_in_minutes: 50
depends_on:
- image-build-amd
source_file_dependencies:
@@ -134,7 +134,7 @@ steps:
- label: Spec Decode Draft Model Nightly B200
key: spec-decode-draft-model-nightly-b200
timeout_in_minutes: 40
timeout_in_minutes: 30
device: b200-k8s
optional: true
source_file_dependencies:
@@ -146,7 +146,7 @@ steps:
- label: Speculators Correctness
key: speculators-correctness
timeout_in_minutes: 30
timeout_in_minutes: 60
device: h100
optional: true
num_devices: 1
@@ -159,7 +159,7 @@ steps:
- pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
- label: Spec Decode MTP hybrid (B200)
timeout_in_minutes: 20
timeout_in_minutes: 30
device: b200-k8s
optional: true
source_file_dependencies:
+1 -1
View File
@@ -4,7 +4,7 @@ depends_on:
steps:
- label: Weight Loading Multiple GPU # 33min
key: weight-loading-multiple-gpu
timeout_in_minutes: 50
timeout_in_minutes: 45
working_dir: "/vllm-workspace/tests"
num_devices: 2
optional: true
+1 -1
View File
@@ -119,7 +119,7 @@
# Transformers modeling backend
/vllm/model_executor/models/transformers @hmellor
/tests/models/transformers @hmellor
/tests/models/test_transformers.py @hmellor
# Docs
/docs/mkdocs @hmellor
+2 -3
View File
@@ -28,8 +28,7 @@ jobs:
pull_number: context.payload.pull_request.number,
});
const readyLabels = ['ready', 'ready-run-all-tests'];
const hasReadyLabel = pr.labels.some(l => readyLabels.includes(l.name));
const hasReadyLabel = pr.labels.some(l => l.name === 'ready');
const hasVerifiedLabel = pr.labels.some(l => l.name === 'verified');
const { data: mergedPRs } = await github.rest.search.issuesAndPullRequests({
@@ -41,7 +40,7 @@ jobs:
if (hasReadyLabel || hasVerifiedLabel || mergedCount >= 4) {
core.info(`Check passed: verified label=${hasVerifiedLabel}, ready label=${hasReadyLabel}, 4+ merged PRs=${mergedCount >= 4}`);
} else {
core.setFailed(`PR must have the 'verified', 'ready', or 'ready-run-all-tests' label (the ready labels also trigger tests) or the author must have at least 4 merged PRs (found ${mergedCount}).`);
core.setFailed(`PR must have the 'verified' or 'ready' (which also triggers tests) label or the author must have at least 4 merged PRs (found ${mergedCount}).`);
}
pre-commit:
+18 -29
View File
@@ -29,7 +29,6 @@ Do not open one-off PRs for tiny edits (single typo, isolated style change, one
- PR descriptions for AI-assisted work **must** include:
- Why this is not duplicating an existing PR.
- Test commands run and results.
- Model evaluation results when the change affects output, accuracy, or serving.
- Clear statement that AI assistance was used.
### Fail-closed behavior
@@ -67,38 +66,23 @@ VLLM_USE_PRECOMPILED=1 uv pip install -e . --torch-backend=auto
uv pip install -e . --torch-backend=auto
```
### Tests
### Running tests
> Requires [Environment setup](#environment-setup) and [Installing dependencies](#installing-dependencies).
```bash
# Install test dependencies (use cuda.in on non-x86_64):
uv pip install -r requirements/test/cuda.in
# Install test dependencies.
# requirements/test/cuda.txt is pinned to x86_64; on other platforms, use the
# unpinned source file instead:
uv pip install -r requirements/test/cuda.in # resolves for current platform
# Or on x86_64:
uv pip install -r requirements/test/cuda.txt
# Run a specific test file:
# Run a specific test file (use .venv/bin/python directly;
# `source activate` does not persist in non-interactive shells):
.venv/bin/python -m pytest tests/path/to/test_file.py -v
```
When adding tests:
- **Design before you write.** Answer four questions first: what is the module
for, what is its I/O contract, what failure am I guarding against, and what is
the cheapest level that catches it (unit over integration over e2e)?
- **Reuse before create.** Extend existing test files, `conftest.py` fixtures, and
helpers; add a new file only when no nearby suite fits.
- **Test behavior with intent.** Assert observable outcomes through public APIs;
state why in the name or docstring. Skip trivial wiring; flaky tests are worse
than no tests.
- **Keep it minimal.** One behavior per test and the smallest setup that
triggers it; if the test diff dwarfs the code change, cut scope.
- **No one-off kernel benchmarks in `tests/`.** Put kernel perf work in
`benchmarks/kernels/`; prove correctness in existing pytest suites.
- **Run model evals for model-affecting changes.** Search `tests/evals/` or use
`vllm bench` and include results in the PR — do not wait for reviewers to ask.
For model-specific requirements, see
[`docs/contributing/model/tests.md`](docs/contributing/model/tests.md).
### Running linters
> Requires [Environment setup](#environment-setup).
@@ -123,18 +107,23 @@ Use [Google-style docstrings](https://google.github.io/styleguide/pyguide.html#3
### Coding style guidelines
- Match existing code style
- Minimize use of comments. Eliminate comments which are redundant, preferring legible and self-documenting code. When used, keep docstrings and comments brief and direct.
Follow these rules for all code changes in this repository:
- Try to match existing code style.
- Code should be self-documenting and self-explanatory.
- Keep comments and docstrings minimal and concise.
- Assume the reader is familiar with vLLM.
### Commit messages
Add attribution using commit trailers such as `Co-authored-by:` (other projects use `Assisted-by:` or `Generated-by:`):
Add attribution using commit trailers such as `Co-authored-by:` (other projects use `Assisted-by:` or `Generated-by:`). For example:
```text
Your commit message here
Co-authored-by: Agent Name Here
Co-authored-by: GitHub Copilot
Co-authored-by: Claude
Co-authored-by: gemini-code-assist
Signed-off-by: Your Name <your.email@example.com>
```
+5 -12
View File
@@ -70,15 +70,6 @@ endif()
#
set(TORCH_SUPPORTED_VERSION_CUDA "2.11.0")
set(TORCH_SUPPORTED_VERSION_ROCM "2.11.0")
# TORCH_NIGHTLY=1 builds run against unpinned nightly wheels, so the supported-
# version check would always warn. Only treat it as a nightly build when the
# value is exactly "1" (the bootstrap exports TORCH_NIGHTLY=0 by default, which
# must NOT suppress the warning for normal builds).
if (DEFINED ENV{TORCH_NIGHTLY} AND "$ENV{TORCH_NIGHTLY}" STREQUAL "1")
set(TORCH_NIGHTLY_BUILD TRUE)
else()
set(TORCH_NIGHTLY_BUILD FALSE)
endif()
#
# Try to find python package with an executable that exactly matches
@@ -184,7 +175,7 @@ endif()
if (NOT HIP_FOUND AND NOT PYTORCH_FOUND_HIP AND CUDA_FOUND)
set(VLLM_GPU_LANG "CUDA")
if (NOT TORCH_NIGHTLY_BUILD AND NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
if (NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
message(WARNING "Pytorch version ${TORCH_SUPPORTED_VERSION_CUDA} "
"expected for CUDA build, saw ${Torch_VERSION} instead.")
endif()
@@ -197,7 +188,7 @@ elseif(HIP_FOUND OR PYTORCH_FOUND_HIP)
enable_language(HIP)
# ROCm 5.X and 6.X
if (NOT TORCH_NIGHTLY_BUILD AND ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
if (ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
Torch_VERSION VERSION_LESS ${TORCH_SUPPORTED_VERSION_ROCM})
message(WARNING "Pytorch version >= ${TORCH_SUPPORTED_VERSION_ROCM} "
"expected for ROCm build, saw ${Torch_VERSION} instead.")
@@ -390,7 +381,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
"csrc/libtorch_stable/cuda_view.cu"
"csrc/libtorch_stable/cuda_utils_kernels.cu"
"csrc/libtorch_stable/activation_kernels.cu"
"csrc/libtorch_stable/ngram_embedding_kernels.cu"
"csrc/libtorch_stable/quantization/activation_kernels.cu"
"csrc/libtorch_stable/quantization/w8a8/int8/scaled_quant.cu"
"csrc/libtorch_stable/quantization/w8a8/fp8/common.cu"
@@ -409,6 +399,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
"csrc/libtorch_stable/sampler.cu"
"csrc/libtorch_stable/topk.cu"
"csrc/libtorch_stable/mamba/selective_scan_fwd.cu"
"csrc/libtorch_stable/attention/paged_attention_v1.cu"
"csrc/libtorch_stable/attention/paged_attention_v2.cu"
"csrc/libtorch_stable/cache_kernels.cu"
"csrc/libtorch_stable/cache_kernels.cu"
"csrc/libtorch_stable/cache_kernels_fused.cu"
"csrc/libtorch_stable/custom_all_reduce.cu"
+2 -2
View File
@@ -12,7 +12,7 @@ from dataclasses import dataclass, field
import aiohttp
import huggingface_hub.constants
from tqdm.asyncio import tqdm
from transformers import AutoTokenizer, PythonBackend, TokenizersBackend
from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
# NOTE(simon): do not import vLLM here so the benchmark script
# can run without vLLM installed.
@@ -609,7 +609,7 @@ def get_tokenizer(
tokenizer_mode: str = "auto",
trust_remote_code: bool = False,
**kwargs,
) -> PythonBackend | TokenizersBackend:
) -> PreTrainedTokenizer | PreTrainedTokenizerFast:
if pretrained_model_name_or_path is not None and not os.path.exists(
pretrained_model_name_or_path
):
+2 -4
View File
@@ -132,10 +132,8 @@ def benchmark_function(
reset_memory_stats()
# Benchmark
start_events = [
torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)
]
end_events = [torch.cuda.Event(enable_timing=True) for _ in range(benchmark_iters)]
start_events = [torch.Event(enable_timing=True) for _ in range(benchmark_iters)]
end_events = [torch.Event(enable_timing=True) for _ in range(benchmark_iters)]
for i in range(benchmark_iters):
logits_copy = logits.clone()
@@ -17,7 +17,7 @@ from vllm.model_executor.layers.fused_moe.fused_flydsl_moe import fused_flydsl_m
from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import ( # noqa: E501
compressed_tensors_moe_w4a16_flydsl,
)
from vllm.utils.platform_utils import get_device_name_as_file_name
from vllm.platforms import current_platform
RoutingBuffers = tuple[
torch.Tensor, # sorted_token_ids
@@ -259,7 +259,7 @@ def tune_flydsl_moe_w4a16(
)
us_best = us
tuned_config[str(num_tokens)] = tile_config
device_name = get_device_name_as_file_name()
device_name = current_platform.get_device_name().replace(" ", "_")
tuned_config_file_name = (
f"E={num_experts},N={inter_dim},device_name={device_name},"
f"dtype=int4_w4a16,backend=flydsl.json"
+2 -2
View File
@@ -134,8 +134,8 @@ def benchmark_config(
torch.accelerator.synchronize()
# Benchmark
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start = torch.Event(enable_timing=True)
end = torch.Event(enable_timing=True)
start.record()
for _ in range(num_iters):
with override_config(config):
+93 -38
View File
@@ -19,11 +19,13 @@ from vllm.utils.torch_utils import (
logger = init_logger(__name__)
NUM_BLOCKS = 128 * 1024
PARTITION_SIZE = 512
PARTITION_SIZE_ROCM = 256
@torch.inference_mode()
def main(
version: str,
num_seqs: int,
seq_len: int,
num_query_heads: int,
@@ -80,20 +82,27 @@ def main(
# Prepare for the paged attention kernel.
output = torch.empty_like(query)
num_partitions = (max_seq_len + PARTITION_SIZE_ROCM - 1) // PARTITION_SIZE_ROCM
tmp_output = torch.empty(
size=(num_seqs, num_query_heads, num_partitions, head_size),
dtype=output.dtype,
device=output.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_query_heads, num_partitions),
dtype=torch.float32,
device=output.device,
)
max_logits = torch.empty_like(exp_sums)
if version == "v2":
if current_platform.is_rocm():
global PARTITION_SIZE
if not args.custom_paged_attn and not current_platform.is_navi():
PARTITION_SIZE = 1024
else:
PARTITION_SIZE = PARTITION_SIZE_ROCM
num_partitions = (max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE
tmp_output = torch.empty(
size=(num_seqs, num_query_heads, num_partitions, head_size),
dtype=output.dtype,
device=output.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_query_heads, num_partitions),
dtype=torch.float32,
device=output.device,
)
max_logits = torch.empty_like(exp_sums)
def run_benchmark(num_iters: int, profile: bool = False) -> float:
def run_cuda_benchmark(num_iters: int, profile: bool = False) -> float:
torch.accelerator.synchronize()
if profile:
torch.cuda.cudart().cudaProfilerStart()
@@ -103,26 +112,67 @@ def main(
k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32, device=device)
for _ in range(num_iters):
ops.paged_attention_rocm(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
None,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
k_scale,
v_scale,
)
if version == "v1":
ops.paged_attention_v1(
output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
k_scale,
v_scale,
)
elif version == "v2":
if not args.custom_paged_attn:
ops.paged_attention_v2(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
k_scale,
v_scale,
)
else:
ops.paged_attention_rocm(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
scale,
block_tables,
seq_lens,
None,
block_size,
max_seq_len,
alibi_slopes,
kv_cache_dtype,
k_scale,
v_scale,
)
else:
raise ValueError(f"Invalid version: {version}")
torch.accelerator.synchronize()
end_time = time.perf_counter()
@@ -132,6 +182,7 @@ def main(
# Warmup.
print("Warming up...")
run_benchmark = run_cuda_benchmark
run_benchmark(num_iters=3, profile=False)
# Benchmark.
@@ -144,13 +195,12 @@ def main(
if __name__ == "__main__":
logger.warning(
"This script benchmarks the ROCm paged attention kernel. "
"This script benchmarks the paged attention kernel. "
"By default this is no longer used in vLLM inference."
)
if not current_platform.is_rocm():
raise RuntimeError("This benchmark requires the ROCm platform.")
parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
parser.add_argument("--version", type=str, choices=["v1", "v2"], default="v2")
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--seq-len", type=int, default=4096)
parser.add_argument("--num-query-heads", type=int, default=64)
@@ -158,7 +208,7 @@ if __name__ == "__main__":
parser.add_argument(
"--head-size",
type=int,
choices=[64, 128],
choices=[64, 80, 96, 112, 120, 128, 192, 256],
default=128,
)
parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
@@ -174,7 +224,11 @@ if __name__ == "__main__":
choices=["auto", "fp8", "fp8_e5m2", "fp8_e4m3"],
default="auto",
help="Data type for kv cache storage. If 'auto', will use model "
"data type. ROCm (AMD GPU) supports fp8 (=fp8_e4m3)",
"data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. "
"ROCm (AMD GPU) supports fp8 (=fp8_e4m3)",
)
parser.add_argument(
"--custom-paged-attn", action="store_true", help="Use custom paged attention"
)
args = parser.parse_args()
print(args)
@@ -182,6 +236,7 @@ if __name__ == "__main__":
if args.num_query_heads % args.num_kv_heads != 0:
raise ValueError("num_query_heads must be divisible by num_kv_heads")
main(
version=args.version,
num_seqs=args.batch_size,
seq_len=args.seq_len,
num_query_heads=args.num_query_heads,
@@ -1,108 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Benchmark ReLUSquaredActivation: custom CUDA kernel vs forward_native, both
# eager and under torch.compile (Inductor fuses relu+square into one kernel).
import itertools
import torch
import torch.nn.functional as F
import vllm.model_executor.layers.activation # noqa: F401
from vllm.benchmarks.lib.utils import default_vllm_config
from vllm.triton_utils import triton
from vllm.utils.argparse_utils import FlexibleArgumentParser
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
# Capped so the largest tensor stays under 2**31 elements: the shared activation
# kernel computes the per-token pointer offset (blockIdx.x * d) in 32-bit, which
# overflows for tensors with >2**32 elements. Realistic token counts are well
# below this; the kernel-vs-native gap is already clear at these sizes.
batch_size_range = [1, 16, 128]
seq_len_range = [1, 16, 64, 1024]
intermediate_size = [3072, 9728, 12288]
configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
@default_vllm_config()
def benchmark_relu_squared(
batch_size: int,
seq_len: int,
intermediate_size: int,
provider: str,
dtype: torch.dtype,
):
device = "cuda"
num_tokens = batch_size * seq_len
set_random_seed(42)
torch.set_default_device(device)
x = torch.randn(num_tokens, intermediate_size, dtype=dtype, device=device)
out = torch.empty_like(x)
def native(x: torch.Tensor) -> torch.Tensor:
return torch.square(F.relu(x))
# Verify the custom kernel matches the native implementation before timing.
ref = native(x)
torch.ops._C.relu_squared(out, x)
torch.testing.assert_close(out, ref)
if provider == "custom":
# Custom CUDA kernel — single fused kernel.
fn = lambda: torch.ops._C.relu_squared(out, x)
elif provider == "native":
# forward_native, eager — relu and square as separate ops.
fn = lambda: native(x)
elif provider == "native_compiled":
# forward_native under torch.compile — Inductor fuses relu+square.
# This is the real production baseline (custom ops are off when
# Inductor is enabled), so it is the comparison reviewers care about.
compiled = torch.compile(native)
compiled(x) # warm up / trigger compilation before timing
fn = lambda: compiled(x)
ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
fn, quantiles=[0.5, 0.2, 0.8]
)
return ms, max_ms, min_ms
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark ReLUSquaredActivation: custom kernel vs native."
)
parser.add_argument(
"--dtype",
type=str,
choices=["half", "bfloat16", "float"],
default="bfloat16",
)
args = parser.parse_args()
dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
perf_report = triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["batch_size", "seq_len", "intermediate_size"],
x_vals=configs,
line_arg="provider",
line_vals=["custom", "native_compiled", "native"],
line_names=[
"Custom Kernel",
"Native (torch.compile)",
"Native (eager)",
],
styles=[("blue", "-"), ("green", "-"), ("red", "-")],
ylabel="ms",
plot_name="relu_squared-eager-performance",
args={},
)
)
perf_report(
lambda batch_size, seq_len, intermediate_size, provider: benchmark_relu_squared(
batch_size, seq_len, intermediate_size, provider, dtype
)
).run(print_data=True)
@@ -170,8 +170,8 @@ def benchmark_config(
graph.replay()
torch.accelerator.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start = torch.Event(enable_timing=True)
end = torch.Event(enable_timing=True)
latencies: list[float] = []
for _ in range(num_iters):
start.record()
@@ -19,7 +19,6 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
from vllm.platforms import current_platform
from vllm.triton_utils import triton
from vllm.utils.argparse_utils import FlexibleArgumentParser
from vllm.utils.platform_utils import get_device_name_as_file_name
mp.set_start_method("spawn", force=True)
@@ -265,7 +264,7 @@ def save_configs(
input_type="fp8",
) -> None:
os.makedirs(save_path, exist_ok=True)
device_name = get_device_name_as_file_name()
device_name = current_platform.get_device_name().replace(" ", "_")
json_file_name = (
f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
f"block_shape=[{block_n},{block_k}].json"
-241
View File
@@ -1,241 +0,0 @@
#!/bin/bash
set -eoux pipefail
########################################
# Resolve repo root (IMPORTANT)
########################################
REPO_ROOT="$(pwd)"
cd "$REPO_ROOT"
########################################
# DevPI configuration
########################################
IBM_DEVPI_URL=${IBM_DEVPI_URL:-"https://wheels.developerfirst.ibm.com/ppc64le/linux/+simple/"}
RHOAI_INDEX_URL=${RHOAI_INDEX_URL:-"https://console.redhat.com/api/pypi/public-rhai/rhoai/3.4/cpu-ubi9/simple/"}
########################################
# wheel dir
########################################
WHEEL_DIR=${WHEEL_DIR:-"/tmp/wheels"}
mkdir -p "$WHEEL_DIR"
########################################
# Helpers
########################################
try_install_from_devpi() {
local pkg=$1
uv pip install \
--extra-index-url "${IBM_DEVPI_URL}" \
--index-strategy unsafe-best-match \
--no-build-isolation \
"${pkg}"
}
########################################
# Package Versions
########################################
cd "$REPO_ROOT"
TORCH_VERSION=${TORCH_VERSION:-$(grep -E '^torch==.+==\s*"ppc64le"' requirements/cpu.txt | grep -Eo '\b[0-9\.]+\b' || true)}
TORCH_VERSION=${TORCH_VERSION:-2.11.0}
TORCHVISION_VERSION=${TORCHVISION_VERSION:-0.26.0}
TORCHAUDIO_VERSION=${TORCHAUDIO_VERSION:-${TORCH_VERSION}}
export TORCH_VERSION
export TORCHVISION_VERSION
export TORCHAUDIO_VERSION
export OPENCV_VERSION=${OPENCV_VERSION:-4.13.0.92}
export XGRAMMAR_VERSION=${XGRAMMAR_VERSION:-0.2.1}
########################################
# install system dependencies
########################################
rpm -ivh https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm || true
microdnf install -y \
python3.12 python3.12-devel python3.12-pip gcc \
git jq gcc-toolset-14 gcc-toolset-14-libatomic-devel \
automake libtool clang-devel openssl-devel \
harfbuzz-devel kmod lcms2-devel libimagequant-devel libjpeg-turbo-devel \
llvm15-devel libraqm-devel libtiff-devel libwebp-devel libxcb-devel \
ninja-build openjpeg2-devel pkgconfig \
tcl-devel tk-devel xsimd-devel zeromq-devel zlib-devel patchelf file openblas openblas-devel protobuf numactl numactl-devel openmpi openmpi-devel
rpm -ivh --nodeps \
https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-lite-devel-3.14.0-17.el9.ppc64le.rpm
rpm -ivh --nodeps \
https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-devel-3.14.0-17.el9.ppc64le.rpm
rpm -ivh --nodeps \
https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-compiler-3.14.0-17.el9.ppc64le.rpm
########################################
# Python 3.12 virtual environment
########################################
python3.12 -m venv /opt/vllm
source /opt/vllm/bin/activate
export PATH=/opt/vllm/bin:$PATH
python --version
########################################
# install build tools (stable uv)
########################################
pip install -U pip setuptools-rust
pip install uv
pip install "setuptools<70" build wheel cmake auditwheel
uv pip install "setuptools<70" cython meson-python pybind11 "sympy>=1.13.3" --no-build-isolation
########################################
# Rust
########################################
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
source /root/.cargo/env
########################################
# Compiler env
########################################
source /opt/rh/gcc-toolset-14/enable
export PATH=/usr/lib64/llvm15/bin:$PATH
export LLVM_CONFIG=/usr/lib64/llvm15/bin/llvm-config
export CMAKE_ARGS="-DPython3_EXECUTABLE=python"
export MAX_JOBS=${MAX_JOBS:-$(nproc)}
export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
########################################
# Install Packages From Devpi
########################################
uv pip install numpy==2.3.5 pillow==12.2.0 --extra-index-url "$IBM_DEVPI_URL"
try_install_from_devpi "opencv-python-headless==${OPENCV_VERSION}"
try_install_from_devpi "torch==${TORCH_VERSION}"
try_install_from_devpi "torchvision==${TORCHVISION_VERSION}"
########################################
# torch audio
########################################
TEMP_BUILD_DIR=$(mktemp -d)
cd "${TEMP_BUILD_DIR}"
export BUILD_SOX=1 BUILD_KALDI=1 BUILD_RNNT=1 USE_FFMPEG=0 USE_ROCM=0 USE_CUDA=0
export TORCHAUDIO_TEST_ALLOW_SKIP_IF_NO_FFMPEG=1
git clone --recursive https://github.com/pytorch/audio.git -b v${TORCHAUDIO_VERSION}
cd audio
#patching
sed -i '
s|_CSRC_DIR / "_torchaudio.cpp"|str(_CSRC_DIR / "_torchaudio.cpp")|;
s|_CSRC_DIR / "utils.cpp"|str(_CSRC_DIR / "utils.cpp")|;
s|sources=\[_CSRC_DIR / s for s in sources\]|sources=[str(_CSRC_DIR / s) for s in sources]|;
' tools/setup_helpers/extension.py
MAX_JOBS=${MAX_JOBS:-$(nproc)} \
BUILD_VERSION=${TORCHAUDIO_VERSION} \
uv build --wheel --out-dir "${WHEEL_DIR}" --no-build-isolation
uv pip install "${WHEEL_DIR}"/torchaudio*.whl
cd "${REPO_ROOT}"
rm -rf "${TEMP_BUILD_DIR}"
########################################
# Xgrammar
########################################
uv pip install \
"scikit-build-core==0.11.6" \
"pyproject-metadata<0.8" \
pathspec \
packaging \
distro \
"setuptools<70" \
setuptools_scm \
cmake \
ninja \
pybind11 \
nanobind
uv pip install apache-tvm-ffi==0.1.12 \
--no-build-isolation \
--no-cache
TEMP_BUILD_DIR=$(mktemp -d)
pushd "${TEMP_BUILD_DIR}"
export CFLAGS="-fno-lto -mcpu=power9"
export CXXFLAGS="-fno-lto -mcpu=power9"
export LDFLAGS="-fno-lto"
export PATH=/opt/vllm/bin:$PATH
export Python_EXECUTABLE=/opt/vllm/bin/python3
export Python3_EXECUTABLE=/opt/vllm/bin/python3
export PYTHON_EXECUTABLE=/opt/vllm/bin/python3
export Python_ROOT_DIR=/opt/vllm
export Python3_ROOT_DIR=/opt/vllm
git clone \
--recursive \
https://github.com/mlc-ai/xgrammar \
-b "v${XGRAMMAR_VERSION}"
cd xgrammar
cp cmake/config.cmake .
export PYTHONPATH=/opt/vllm/lib64/python3.12/site-packages:/opt/vllm/lib/python3.12/site-packages:${PYTHONPATH:-}
uv build \
--wheel \
--out-dir "${WHEEL_DIR}" \
--no-build-isolation
uv pip install "${WHEEL_DIR}"/xgrammar*.whl -v
popd
rm -rf "${TEMP_BUILD_DIR}"
cd "${REPO_ROOT}"
########################################
# RHOAI Binary Downloads
########################################
pip download \
--index-url "${RHOAI_INDEX_URL}" \
--only-binary=:all: \
--no-deps \
llvmlite==0.47.0 \
-d "${WHEEL_DIR}"
pip download \
--index-url "${RHOAI_INDEX_URL}" \
--only-binary=:all: \
--no-deps \
Numba==0.65.0 \
-d "${WHEEL_DIR}"
########################################
# install built wheels
########################################
uv pip install setuptools_scm maturin setuptools-rust ninja scikit-build-core pybind11 nanobind \
--no-build-isolation
uv pip install "${WHEEL_DIR}"/*.whl
########################################
# install remaining deps
########################################
sed -i.bak -e 's/.*torch.*//g' pyproject.toml requirements/*.txt
uv pip install "setuptools>=78.1.1" --no-build-isolation
export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/local/lib64/pkgconfig:/usr/lib64/pkgconfig
uv pip install -r requirements/common.txt \
-r requirements/cpu.txt \
-r requirements/build/cpu.txt --index-strategy unsafe-best-match
+6 -24
View File
@@ -15,7 +15,6 @@ endif()
#
set(ENABLE_X86_ISA $ENV{VLLM_CPU_X86})
set(ENABLE_ARM_BF16 $ENV{VLLM_CPU_ARM_BF16})
set(ENABLE_RVV_BF16 $ENV{VLLM_CPU_RVV_BF16})
include_directories("${CMAKE_SOURCE_DIR}/csrc")
@@ -111,13 +110,6 @@ else()
set(ARM_BF16_FOUND ON)
message(STATUS "ARM BF16 support enabled via VLLM_CPU_ARM_BF16 environment variable")
endif()
# Some kernels (e.g. Bianbu on Spacemit X100) do not report zvfbfmin
# in /proc/cpuinfo despite hardware support. VLLM_CPU_RVV_BF16=1
# overrides the detection result.
if (ENABLE_RVV_BF16)
set(RVV_BF16_FOUND ON)
message(STATUS "RVV BF16 support enabled via VLLM_CPU_RVV_BF16 environment variable")
endif()
endif()
if (CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64|amd64" OR ENABLE_X86_ISA)
@@ -186,10 +178,7 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
# Override with -DVLLM_RVV_VLEN=128 or -DVLLM_RVV_VLEN=256 for RVV.
if(NOT DEFINED VLLM_RVV_VLEN)
# Auto-detect: find the largest zvl<N>b in /proc/cpuinfo isa line.
# Skip when cross-compiling /proc/cpuinfo describes the build host.
if(CMAKE_CROSSCOMPILING)
message(STATUS "Cross-compiling: skipping VLEN auto-detection from /proc/cpuinfo")
elseif(EXISTS /proc/cpuinfo)
if(EXISTS /proc/cpuinfo)
file(READ /proc/cpuinfo _cpuinfo)
set(_best 0)
foreach(_n IN ITEMS 128 256 512 1024)
@@ -197,13 +186,6 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
set(_best ${_n})
endif()
endforeach()
# Only VLEN=128 and VLEN=256 are supported by the RVV kernels.
if(_best GREATER 256)
message(WARNING
"Detected VLEN=${_best} but only 128/256 are supported; "
"clamping to 256")
set(_best 256)
endif()
if(_best GREATER 0)
set(VLLM_RVV_VLEN ${_best})
endif()
@@ -213,9 +195,9 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
if(NOT DEFINED VLLM_RVV_VLEN AND (RVV_FP16_FOUND OR RVV_BF16_FOUND))
message(FATAL_ERROR
"RISC-V RVV is available but VLEN could not be auto-detected. "
"Please specify VLEN explicitly via CMAKE_ARGS:\n"
" CMAKE_ARGS='-DVLLM_RVV_VLEN=128' (for VLEN=128 hardware)\n"
" CMAKE_ARGS='-DVLLM_RVV_VLEN=256' (for VLEN=256 hardware, e.g. Spacemit X100)")
"Please specify VLEN explicitly:\n"
" -DVLLM_RVV_VLEN=128 (for VLEN=128 hardware)\n"
" -DVLLM_RVV_VLEN=256 (for VLEN=256 hardware, e.g. Spacemit X100)")
endif()
endif()
if(VLLM_RVV_VLEN AND VLLM_RVV_VLEN GREATER 0)
@@ -227,7 +209,7 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
message(STATUS "BF16 extension detected")
set(MARCH_FLAGS -march=rv64gcv_zvfh_zfbfmin_zvfbfmin_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
elseif(RVV_FP16_FOUND)
message(WARNING "BF16 functionality is not available.")
message(WARNING "BF16 functionality is not available")
set(MARCH_FLAGS -march=rv64gcv_zvfh_zvl${VLLM_RVV_VLEN}b -mrvv-vector-bits=zvl -mabi=lp64d)
else()
message(STATUS "compile riscv with scalar (no FP16/BF16)")
@@ -347,7 +329,7 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
set(ONEDNN_BUILD_GRAPH "OFF")
set(ONEDNN_ENABLE_JIT_PROFILING "ON")
set(ONEDNN_ENABLE_ITT_TASKS "ON")
set(ONEDNN_ENABLE_ITT_TASKS "OFF")
set(ONEDNN_ENABLE_MAX_CPU_ISA "ON")
set(ONEDNN_ENABLE_CPU_ISA_HINTS "ON")
set(ONEDNN_VERBOSE "ON")
+1 -1
View File
@@ -19,7 +19,7 @@ else()
FetchContent_Declare(
flashmla
GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
GIT_TAG b70aff3d110a2b1a037e62eac295166b5143643a
GIT_TAG a6ec2ba7bd0a7dff98b3f4d3e6b52b159c48d78b
GIT_PROGRESS TRUE
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
+1 -1
View File
@@ -17,7 +17,7 @@ else()
FetchContent_Declare(
fmha_sm100
GIT_REPOSITORY https://github.com/vllm-project/MSA.git
GIT_TAG 2e63ec37a0fc29bc20f39cd1a52e0f5affc33a73
GIT_TAG fee783153f3efe57e3e933c5cb7e267a7cebcfb5
GIT_PROGRESS TRUE
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
@@ -39,7 +39,7 @@ else()
FetchContent_Declare(
vllm-flash-attn
GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
GIT_TAG bb9a72e7dde0dc614ffc663e052cd6a19ce73a42
GIT_TAG 2c839c33742309ec41e620bf837495ec9926c56e
GIT_PROGRESS TRUE
# Don't share the vllm-flash-attn build between build types
BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
+1 -2
View File
@@ -13,8 +13,7 @@ static inline cpu_attention::Fp8KVCacheDataType parse_fp8_kv_dtype(
bool cpu_attn_has_isa(const std::string& isa) {
if (isa == "rvv") {
#if defined(__riscv) && defined(__riscv_v_min_vlen) && \
(__riscv_v_min_vlen == 128 || __riscv_v_min_vlen == 256)
#if defined(__riscv) && defined(__riscv_v_min_vlen) && __riscv_v_min_vlen == 128
return true;
#else
return false;
+14 -56
View File
@@ -417,10 +417,8 @@ class AttentionScheduler {
has_decode_request = has_decode_request || (q_token_num == 1);
decode_only_batch = decode_only_batch && (q_token_num == 1);
}
const int32_t original_q_head_per_kv =
input.num_heads_q / input.num_heads_kv;
int32_t q_head_per_kv = original_q_head_per_kv;
const bool supports_gqa = original_q_head_per_kv <= max_num_q_per_iter;
int32_t q_head_per_kv = input.num_heads_q / input.num_heads_kv;
const bool supports_gqa = q_head_per_kv <= max_num_q_per_iter;
const bool use_gqa_fast_path = supports_gqa && decode_only_batch;
const bool use_gqa_scratchpad = supports_gqa && has_decode_request;
if (!use_gqa_scratchpad) {
@@ -673,62 +671,22 @@ class AttentionScheduler {
metadata_ptr->effective_thread_num = effective_thread_num;
{
// when q_tile_size = max_num_q_per_iter, requires max
// attention_scratchpad_size
AttentionScratchPad sc(0, *metadata_ptr, 0x0);
int64_t max_attention_scratchpad_size = 0;
for (const AttentionWorkItemGroup& item : workitems) {
const bool curr_use_gqa =
use_gqa_fast_path || (supports_gqa && item.q_token_num == 1);
const int32_t curr_q_heads_per_kv =
curr_use_gqa ? original_q_head_per_kv : 1;
const int32_t curr_default_q_tile_token_num =
default_tile_size / curr_q_heads_per_kv;
for (int32_t q_token_offset = 0; q_token_offset < item.q_token_num;
q_token_offset += curr_default_q_tile_token_num) {
const int32_t actual_q_token_num = std::min(
curr_default_q_tile_token_num, item.q_token_num - q_token_offset);
const int32_t q_head_tile_size =
actual_q_token_num * curr_q_heads_per_kv;
const int32_t rounded_q_head_tile_size =
((q_head_tile_size + max_num_q_per_iter - 1) /
max_num_q_per_iter) *
max_num_q_per_iter;
const int64_t n = AttentionScheduler::calcu_tile_size_with_constant_q(
cache_size, input.head_dim, input.elem_size,
input.q_buffer_elem_size, input.logits_buffer_elem_size,
input.output_buffer_elem_size, max_num_q_per_iter,
kv_len_alignment, rounded_q_head_tile_size,
rounded_q_head_tile_size <= max_num_q_per_iter);
sc.update(input.head_dim, input.q_buffer_elem_size,
input.logits_buffer_elem_size,
input.output_buffer_elem_size, max_num_q_per_iter,
rounded_q_head_tile_size, n);
max_attention_scratchpad_size = std::max(
max_attention_scratchpad_size, sc.get_thread_scratchpad_size());
}
}
int64_t n = AttentionScheduler::calcu_tile_size_with_constant_q(
cache_size, input.head_dim, input.elem_size, input.q_buffer_elem_size,
input.logits_buffer_elem_size, input.output_buffer_elem_size,
max_num_q_per_iter, kv_len_alignment, max_num_q_per_iter, true);
sc.update(input.head_dim, input.q_buffer_elem_size,
input.logits_buffer_elem_size, input.output_buffer_elem_size,
max_num_q_per_iter, max_num_q_per_iter, n);
metadata_ptr->attention_scratchpad_size_per_thread =
((max_attention_scratchpad_size + 63) / 64) * 64;
int32_t max_reduction_q_head_tile_size = 0;
for (const ReductionWorkItemGroup& item : reduce_workitems) {
const bool curr_use_gqa =
use_gqa_fast_path || (supports_gqa && item.q_token_id_num == 1);
const int32_t curr_q_heads_per_kv =
curr_use_gqa ? original_q_head_per_kv : 1;
max_reduction_q_head_tile_size =
std::max(max_reduction_q_head_tile_size,
item.q_token_id_num * curr_q_heads_per_kv);
}
((sc.get_thread_scratchpad_size() + 63) / 64) * 64;
sc.update(0, metadata_ptr->reduction_split_num, input.head_dim,
max_reduction_q_head_tile_size, input.output_buffer_elem_size);
q_head_per_kv * split_kv_q_token_num_threshold,
input.output_buffer_elem_size);
metadata_ptr->reduction_scratchpad_size_per_kv_head =
((sc.get_reduction_scratchpad_size() + 63) / 64) * 64;
}
+2
View File
@@ -323,6 +323,8 @@ class AttentionImpl<ISA::VSX, scalar_t, head_dim> {
const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
const int64_t block_size, const int64_t block_size_stride,
const float k_inv = 0.0f, const float v_inv = 0.0f) {
// k_inv and v_inv are unused on VSX: FP8 KV cache is not supported on
// PowerPC. The parameters are present to match the common interface.
#pragma omp parallel for collapse(2)
for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
+2 -2
View File
@@ -4,7 +4,7 @@
#if defined(__x86_64__)
// x86 implementation
#include "cpu_types_x86.hpp"
#elif defined(__powerpc__)
#elif defined(__POWER9_VECTOR__)
// ppc implementation
#include "cpu_types_vsx.hpp"
#elif defined(__s390x__)
@@ -41,4 +41,4 @@ inline int get_max_threads() {
}
} // namespace cpu_utils
#endif
#endif
+16 -35
View File
@@ -214,18 +214,11 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
explicit BF16Vec32(const BF16Vec8& v) {
fixed_u16x8_t u16_val = bf16_to_u16(v.reg);
// Widen LMUL_128 → LMUL_256 so vslideup operands share a type.
// At VLEN=256 this is mf2→m1 (both integer); at VLEN=128 it is m1→m2.
fixed_u16x16_t ext =
RVVI4(__riscv_vlmul_ext_v_u16, LMUL_128, _u16, LMUL_256)(u16_val);
// Build 16-element half: place the 8 elements at offsets 0 and 8.
fixed_u16x16_t half = RVVI(__riscv_vmv_v_x_u16, LMUL_256)(0, 16);
half = RVVI(__riscv_vslideup_vx_u16, LMUL_256)(half, ext, 0, 8);
half = RVVI(__riscv_vslideup_vx_u16, LMUL_256)(half, ext, 8, 16);
// Double to LMUL_512 (m1→m2 at VLEN=256, m2→m4 at VLEN=128).
fixed_u16x32_t dst =
RVVI4(__riscv_vcreate_v_u16, LMUL_256, _u16, LMUL_512)(half, half);
reg = RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16, LMUL_512)(dst);
fixed_u16x32_t u16_combined =
RVVI4(__riscv_vcreate_v_u16, LMUL_128, _u16, LMUL_512)(
u16_val, u16_val, u16_val, u16_val);
reg = RVVI4(__riscv_vreinterpret_v_u16, LMUL_512, _bf16,
LMUL_512)(u16_combined);
};
void save(void* ptr) const {
@@ -630,29 +623,17 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
data.reg, data.reg)) {};
explicit FP32Vec16(const FP32Vec16& data) : reg(data.reg) {};
explicit FP32Vec16(int64_t value, const FP32Vec16& lut) {
// Split into two 32-bit halves to avoid u64 @ LMUL_1024 (m8 on
// VLEN=128 / m4 on VLEN=256), which causes heavy register spilling.
constexpr int HALF = VEC_ELEM_NUM / 2;
const auto q = static_cast<uint64_t>(value);
const uint32_t lo = static_cast<uint32_t>(q);
const uint32_t hi = static_cast<uint32_t>(q >> 32);
auto lane_ids = RVVI(__riscv_vid_v_u32, LMUL_256)(HALF);
auto shifts = RVVI(__riscv_vsll_vx_u32, LMUL_256)(lane_ids, 2, HALF);
auto packed_lo = RVVI(__riscv_vmv_v_x_u32, LMUL_256)(lo, HALF);
auto idx_lo = RVVI(__riscv_vand_vx_u32, LMUL_256)(
RVVI(__riscv_vsrl_vv_u32, LMUL_256)(packed_lo, shifts, HALF), 0xF,
HALF);
auto packed_hi = RVVI(__riscv_vmv_v_x_u32, LMUL_256)(hi, HALF);
auto idx_hi = RVVI(__riscv_vand_vx_u32, LMUL_256)(
RVVI(__riscv_vsrl_vv_u32, LMUL_256)(packed_hi, shifts, HALF), 0xF,
HALF);
auto idx =
RVVI4(__riscv_vcreate_v_u32, LMUL_256, _u32, LMUL_512)(idx_lo, idx_hi);
reg = RVVI(__riscv_vrgather_vv_f32, LMUL_512)(lut.reg, idx, VEC_ELEM_NUM);
const uint64_t q_values = static_cast<uint64_t>(value);
auto packed = RVVI(__riscv_vmv_v_x_u64, LMUL_1024)(q_values, VEC_ELEM_NUM);
auto lane_ids = RVVI(__riscv_vid_v_u64, LMUL_1024)(VEC_ELEM_NUM);
auto shifts =
RVVI(__riscv_vsll_vx_u64, LMUL_1024)(lane_ids, 2, VEC_ELEM_NUM);
auto shifted =
RVVI(__riscv_vsrl_vv_u64, LMUL_1024)(packed, shifts, VEC_ELEM_NUM);
auto idx64 =
RVVI(__riscv_vand_vx_u64, LMUL_1024)(shifted, 0xF, VEC_ELEM_NUM);
auto idx32 = RVVI(__riscv_vnsrl_wx_u32, LMUL_512)(idx64, 0, VEC_ELEM_NUM);
reg = RVVI(__riscv_vrgather_vv_f32, LMUL_512)(lut.reg, idx32, VEC_ELEM_NUM);
}
explicit FP32Vec16(const FP16Vec16& v);
+40 -120
View File
@@ -344,133 +344,53 @@ struct FP32Vec8 : public Vec<FP32Vec8> {
return result;
}
FP32Vec8 exp() const {
f32x4x2_t out;
const __vector float log2e = vec_splats(1.44269504088896341f);
const __vector float one = vec_splats(1.0f);
const __vector float min_x = vec_splats(-87.3f);
const __vector float max_x = vec_splats(88.7f);
// 5th-degree minimax polynomial for 2^r (r in [0,1))
const __vector float c1 = vec_splats(0.6931471805599453f);
const __vector float c2 = vec_splats(0.240226506959101f);
const __vector float c3 = vec_splats(0.05550410866482158f);
const __vector float c4 = vec_splats(0.009618129107628477f);
const __vector float c5 = vec_splats(0.0013333558146428443f);
for (int i = 0; i < 2; i++) {
__vector float x = reg.val[i];
x = vec_max(x, min_x);
x = vec_min(x, max_x);
__vector float y = vec_mul(x, log2e);
__vector float kf = vec_floor(y);
__vector float r = vec_sub(y, kf);
// Convert float to signed integer. Use vec_cts for PowerPC AltiVec
// compatibility.
__vector signed int k = vec_cts(kf, 0);
const __vector signed int min_k = vec_splats((signed int)-126);
const __vector signed int max_k = vec_splats((signed int)127);
k = vec_min(vec_max(k, min_k), max_k);
// Build 2^k from exponent bits
__vector signed int exp_int = vec_add(k, vec_splats((signed int)127));
__vector unsigned int bits = (__vector unsigned int)exp_int;
bits = vec_sl(bits, vec_splats((unsigned int)23));
__vector float pow2k = (__vector float)bits;
// Improved minimax polynomial
__vector float poly = vec_madd(c5, r, c4);
poly = vec_madd(poly, r, c3);
poly = vec_madd(poly, r, c2);
poly = vec_madd(poly, r, c1);
poly = vec_madd(poly, r, one);
out.val[i] = vec_mul(pow2k, poly);
}
return FP32Vec8(out);
// TODO: Vectorize this
AliasReg ar;
ar.reg = reg;
f32x4x4_t ret;
ret.val[0][0] = std::exp(ar.values[0]);
ret.val[0][1] = std::exp(ar.values[1]);
ret.val[0][2] = std::exp(ar.values[2]);
ret.val[0][3] = std::exp(ar.values[3]);
ret.val[1][0] = std::exp(ar.values[4]);
ret.val[1][1] = std::exp(ar.values[5]);
ret.val[1][2] = std::exp(ar.values[6]);
ret.val[1][3] = std::exp(ar.values[7]);
return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
}
FP32Vec8 tanh() const {
const __vector float one = vec_splats(1.0f);
const __vector float two = vec_splats(2.0f);
const __vector float zero = vec_splats(0.0f);
const __vector float sat = vec_splats(9.0f);
f32x4x2_t out;
for (int i = 0; i < 2; i++) {
__vector float x = reg.val[i];
__vector float ax = vec_abs(x);
__vector bool int mask = vec_cmpge(x, zero);
__vector float sign = vec_sel(vec_splats(-1.0f), one, mask);
__vector bool int saturated = vec_cmpge(ax, sat);
__vector float two_x = vec_mul(x, two);
f32x4x2_t tmp;
tmp.val[0] = two_x;
tmp.val[1] = two_x;
FP32Vec8 temp_vec(tmp);
vector float e = temp_vec.exp().reg.val[0];
vector float num = vec_sub(e, one);
vector float den = vec_add(e, one);
vector float t = vec_div(num, den);
out.val[i] = vec_sel(t, sign, saturated);
}
return FP32Vec8(out);
// TODO: Vectorize this
AliasReg ar;
ar.reg = reg;
f32x4x4_t ret;
ret.val[0][0] = std::tanh(ar.values[0]);
ret.val[0][1] = std::tanh(ar.values[1]);
ret.val[0][2] = std::tanh(ar.values[2]);
ret.val[0][3] = std::tanh(ar.values[3]);
ret.val[1][0] = std::tanh(ar.values[4]);
ret.val[1][1] = std::tanh(ar.values[5]);
ret.val[1][2] = std::tanh(ar.values[6]);
ret.val[1][3] = std::tanh(ar.values[7]);
return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
}
FP32Vec8 er() const {
const vector float a1 = vec_splats(0.254829592f);
const vector float a2 = vec_splats(-0.284496736f);
const vector float a3 = vec_splats(1.421413741f);
const vector float a4 = vec_splats(-1.453152027f);
const vector float a5 = vec_splats(1.061405429f);
const vector float p = vec_splats(0.3275911f);
const vector float one = vec_splats(1.0f);
const vector float zero = vec_splats(0.0f);
const vector float sat = vec_splats(6.0f);
f32x4x2_t ret;
for (int i = 0; i < 2; i++) {
vector float x = reg.val[i];
vector float ax = vec_abs(x);
vector bool int mask = vec_cmpge(x, zero);
vector float sign = vec_sel(vec_splats(-1.0f), one, mask);
vector bool int saturated = vec_cmpge(ax, sat);
vector float t = vec_div(one, vec_madd(p, ax, one));
vector float poly = a5;
poly = vec_madd(poly, t, a4);
poly = vec_madd(poly, t, a3);
poly = vec_madd(poly, t, a2);
poly = vec_madd(poly, t, a1);
poly = vec_mul(poly, t);
vector float x_squared = vec_mul(x, x);
vector float neg_x_squared = vec_mul(vec_splats(-1.0f), x_squared);
f32x4x2_t tmp;
tmp.val[0] = neg_x_squared;
tmp.val[1] = neg_x_squared;
FP32Vec8 exp_input(tmp);
vector float exp_term = exp_input.exp().reg.val[0];
vector float y = vec_nmsub(poly, exp_term, one);
vector float erf_val = vec_mul(sign, y);
ret.val[i] = vec_sel(erf_val, sign, saturated);
}
return FP32Vec8(ret);
// TODO: Vectorize this
AliasReg ar;
ar.reg = reg;
f32x4x4_t ret;
ret.val[0][0] = std::erf(ar.values[0]);
ret.val[0][1] = std::erf(ar.values[1]);
ret.val[0][2] = std::erf(ar.values[2]);
ret.val[0][3] = std::erf(ar.values[3]);
ret.val[1][0] = std::erf(ar.values[4]);
ret.val[1][1] = std::erf(ar.values[5]);
ret.val[1][2] = std::erf(ar.values[6]);
ret.val[1][3] = std::erf(ar.values[7]);
return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
}
FP32Vec8 operator*(const FP32Vec8& b) const {
+1 -2
View File
@@ -278,8 +278,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.def(
"dynamic_4bit_int_moe("
"Tensor x, Tensor topk_ids, Tensor topk_weights,"
"Tensor w13_packed, Tensor w2_packed,"
"int hidden_size, int intermediate_size,"
"Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
"int group_size, bool apply_router_weight_on_input, int activation_kind"
") -> Tensor");
+2 -2
View File
@@ -76,14 +76,14 @@ inline int64_t get_available_l2_size() {
if (l2_cache_size == 0) {
l2_cache_size = 256 * 1024;
}
return static_cast<int64_t>(l2_cache_size) >> 1;
return static_cast<int64_t>(l2_cache_size) >> 1; // use 50% of L2 cache
}();
return size;
#else
static int64_t size = []() {
auto caps = at::cpu::get_cpu_capabilities();
const uint32_t l2_cache_size = caps.at("l2_cache_size").toInt();
return l2_cache_size >> 1;
return l2_cache_size >> 1; // use 50% of L2 cache
}();
return size;
#endif
@@ -669,14 +669,6 @@ __device__ __forceinline__ T gelu_quick_kernel(const T& x) {
return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
}
template <typename T>
__device__ __forceinline__ T relu_squared_kernel(const T& x) {
// relu(x)^2 — introduced in https://arxiv.org/abs/2109.08668v2
const float f = (float)x;
const float val = f > 0.0f ? f : 0.0f;
return (T)(val * val);
}
} // namespace vllm
void gelu_new(torch::stable::Tensor& out, // [..., d]
@@ -696,9 +688,3 @@ void gelu_quick(torch::stable::Tensor& out, // [..., d]
{
LAUNCH_ACTIVATION_KERNEL(vllm::gelu_quick_kernel);
}
void relu_squared(torch::stable::Tensor& out, // [..., d]
torch::stable::Tensor& input) // [..., d]
{
LAUNCH_ACTIVATION_KERNEL(vllm::relu_squared_kernel);
}
@@ -0,0 +1,667 @@
/*
* Adapted from
* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
* Copyright (c) 2023, The vLLM team.
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <algorithm>
#include "../../attention/attention_dtypes.h"
#include "attention_utils.cuh"
#include "../../cuda_compat.h"
#ifdef USE_ROCM
#include <hip/hip_bf16.h>
#include "../../quantization/w8a8/fp8/amd/quant_utils.cuh"
typedef __hip_bfloat16 __nv_bfloat16;
#else
#include "../../quantization/w8a8/fp8/nvidia/quant_utils.cuh"
#endif
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define DIVIDE_ROUND_UP(a, b) (((a) + (b) - 1) / (b))
namespace vllm {
// Utility function for attention softmax.
template <int NUM_WARPS>
inline __device__ float block_sum(float* red_smem, float sum) {
// Decompose the thread index into warp / lane.
int warp = threadIdx.x / WARP_SIZE;
int lane = threadIdx.x % WARP_SIZE;
// Compute the sum per warp.
#pragma unroll
for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
sum += VLLM_SHFL_XOR_SYNC(sum, mask);
}
// Warp leaders store the data to shared memory.
if (lane == 0) {
red_smem[warp] = sum;
}
// Make sure the data is in shared memory.
__syncthreads();
// The warps compute the final sums.
if (lane < NUM_WARPS) {
sum = red_smem[lane];
}
// Parallel reduction inside the warp.
#pragma unroll
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
sum += VLLM_SHFL_XOR_SYNC(sum, mask);
}
// Broadcast to other threads.
return VLLM_SHFL_SYNC(sum, 0);
}
// TODO(woosuk): Merge the last two dimensions of the grid.
// Grid: (num_heads, num_seqs, max_num_partitions).
template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
int NUM_THREADS, vllm::Fp8KVCacheDataType KV_DTYPE,
bool IS_BLOCK_SPARSE,
int PARTITION_SIZE = 0> // Zero means no partitioning.
__device__ void paged_attention_kernel(
float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
float* __restrict__ max_logits, // [num_seqs, num_heads,
// max_num_partitions]
scalar_t* __restrict__ out, // [num_seqs, num_heads, max_num_partitions,
// head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
// head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
// head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ seq_lens, // [num_seqs]
const int max_num_blocks_per_seq,
const float* __restrict__ alibi_slopes, // [num_heads]
const int q_stride, const int kv_block_stride, const int kv_head_stride,
const float* k_scale, const float* v_scale, const int tp_rank,
const int blocksparse_local_blocks, const int blocksparse_vert_stride,
const int blocksparse_block_size, const int blocksparse_head_sliding_step) {
const int seq_idx = blockIdx.y;
const int partition_idx = blockIdx.z;
const int max_num_partitions = gridDim.z;
constexpr bool USE_PARTITIONING = PARTITION_SIZE > 0;
const int seq_len = seq_lens[seq_idx];
if (USE_PARTITIONING && partition_idx * PARTITION_SIZE >= seq_len) {
// No work to do. Terminate the thread block.
return;
}
const int num_seq_blocks = DIVIDE_ROUND_UP(seq_len, BLOCK_SIZE);
const int num_blocks_per_partition =
USE_PARTITIONING ? PARTITION_SIZE / BLOCK_SIZE : num_seq_blocks;
// [start_block_idx, end_block_idx) is the range of blocks to process.
const int start_block_idx =
USE_PARTITIONING ? partition_idx * num_blocks_per_partition : 0;
const int end_block_idx =
MIN(start_block_idx + num_blocks_per_partition, num_seq_blocks);
const int num_blocks = end_block_idx - start_block_idx;
// [start_token_idx, end_token_idx) is the range of tokens to process.
const int start_token_idx = start_block_idx * BLOCK_SIZE;
const int end_token_idx =
MIN(start_token_idx + num_blocks * BLOCK_SIZE, seq_len);
const int num_tokens = end_token_idx - start_token_idx;
constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
constexpr int NUM_THREAD_GROUPS =
NUM_THREADS / THREAD_GROUP_SIZE; // Note: This assumes THREAD_GROUP_SIZE
// divides NUM_THREADS
assert(NUM_THREADS % THREAD_GROUP_SIZE == 0);
constexpr int NUM_TOKENS_PER_THREAD_GROUP =
DIVIDE_ROUND_UP(BLOCK_SIZE, WARP_SIZE);
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
const int thread_idx = threadIdx.x;
const int warp_idx = thread_idx / WARP_SIZE;
const int lane = thread_idx % WARP_SIZE;
const int head_idx = blockIdx.x;
const int num_heads = gridDim.x;
const int num_queries_per_kv = num_heads / num_kv_heads;
const int kv_head_idx = head_idx / num_queries_per_kv;
const float alibi_slope =
alibi_slopes == nullptr ? 0.f : alibi_slopes[head_idx];
// A vector type to store a part of a key or a query.
// The vector size is configured in such a way that the threads in a thread
// group fetch or compute 16 bytes at a time. For example, if the size of a
// thread group is 4 and the data type is half, then the vector size is 16 /
// (4 * sizeof(half)) == 2.
constexpr int VEC_SIZE = MAX(16 / (THREAD_GROUP_SIZE * sizeof(scalar_t)), 1);
using K_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
using Q_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
using Quant_vec = typename Vec<cache_t, VEC_SIZE>::Type;
constexpr int NUM_ELEMS_PER_THREAD = HEAD_SIZE / THREAD_GROUP_SIZE;
constexpr int NUM_VECS_PER_THREAD = NUM_ELEMS_PER_THREAD / VEC_SIZE;
const int thread_group_idx = thread_idx / THREAD_GROUP_SIZE;
const int thread_group_offset = thread_idx % THREAD_GROUP_SIZE;
// Load the query to registers.
// Each thread in a thread group has a different part of the query.
// For example, if the thread group size is 4, then the first thread in
// the group has 0, 4, 8, ... th vectors of the query, and the second thread
// has 1, 5, 9, ... th vectors of the query, and so on. NOTE(woosuk): Because
// q is split from a qkv tensor, it may not be contiguous.
const scalar_t* q_ptr = q + seq_idx * q_stride + head_idx * HEAD_SIZE;
__shared__ Q_vec q_vecs[THREAD_GROUP_SIZE][NUM_VECS_PER_THREAD];
#pragma unroll
for (int i = thread_group_idx; i < NUM_VECS_PER_THREAD;
i += NUM_THREAD_GROUPS) {
const int vec_idx = thread_group_offset + i * THREAD_GROUP_SIZE;
q_vecs[thread_group_offset][i] =
*reinterpret_cast<const Q_vec*>(q_ptr + vec_idx * VEC_SIZE);
}
__syncthreads(); // TODO(naed90): possible speedup if this is replaced with a
// memory wall right before we use q_vecs
// Memory planning.
extern __shared__ char shared_mem[];
// NOTE(woosuk): We use FP32 for the softmax logits for better accuracy.
float* logits = reinterpret_cast<float*>(shared_mem);
// Workspace for reduction.
__shared__ float red_smem[2 * NUM_WARPS];
// x == THREAD_GROUP_SIZE * VEC_SIZE
// Each thread group fetches x elements from the key at a time.
constexpr int x = 16 / sizeof(cache_t);
float qk_max = -FLT_MAX;
// Iterate over the key blocks.
// Each warp fetches a block of keys for each iteration.
// Each thread group in a warp fetches a key from the block, and computes
// dot product with the query.
const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
// blocksparse specific vars
int bs_block_offset;
int q_bs_block_id;
if constexpr (IS_BLOCK_SPARSE) {
// const int num_blocksparse_blocks = DIVIDE_ROUND_UP(seq_len,
// blocksparse_block_size);
q_bs_block_id = (seq_len - 1) / blocksparse_block_size;
if (blocksparse_head_sliding_step >= 0)
// sliding on q heads
bs_block_offset =
(tp_rank * num_heads + head_idx) * blocksparse_head_sliding_step + 1;
else
// sliding on kv heads
bs_block_offset = (tp_rank * num_kv_heads + kv_head_idx) *
(-blocksparse_head_sliding_step) +
1;
}
for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx;
block_idx += NUM_WARPS) {
// NOTE(woosuk): The block number is stored in int32. However, we cast it to
// int64 because int32 can lead to overflow when this variable is multiplied
// by large numbers (e.g., kv_block_stride).
// For blocksparse attention: skip computation on blocks that are not
// attended
if constexpr (IS_BLOCK_SPARSE) {
const int k_bs_block_id = block_idx * BLOCK_SIZE / blocksparse_block_size;
const bool is_remote =
((k_bs_block_id + bs_block_offset) % blocksparse_vert_stride == 0);
const bool is_local =
(k_bs_block_id > q_bs_block_id - blocksparse_local_blocks);
if (!is_remote && !is_local) {
for (int i = 0; i < NUM_TOKENS_PER_THREAD_GROUP; i++) {
const int physical_block_offset =
(thread_group_idx + i * WARP_SIZE) % BLOCK_SIZE;
const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
if (thread_group_offset == 0) {
// NOTE(linxihui): assign very large number to skipped tokens to
// avoid contribution to the sumexp softmax normalizer. This will
// not be used at computing sum(softmax*v) as the blocks will be
// skipped.
logits[token_idx - start_token_idx] = -FLT_MAX;
}
}
continue;
}
}
const int64_t physical_block_number =
static_cast<int64_t>(block_table[block_idx]);
// Load a key to registers.
// Each thread in a thread group has a different part of the key.
// For example, if the thread group size is 4, then the first thread in
// the group has 0, 4, 8, ... th vectors of the key, and the second thread
// has 1, 5, 9, ... th vectors of the key, and so on.
for (int i = 0; i < NUM_TOKENS_PER_THREAD_GROUP; i++) {
const int physical_block_offset =
(thread_group_idx + i * WARP_SIZE) % BLOCK_SIZE;
const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
K_vec k_vecs[NUM_VECS_PER_THREAD];
#pragma unroll
for (int j = 0; j < NUM_VECS_PER_THREAD; j++) {
const cache_t* k_ptr =
k_cache + physical_block_number * kv_block_stride +
kv_head_idx * kv_head_stride + physical_block_offset * x;
const int vec_idx = thread_group_offset + j * THREAD_GROUP_SIZE;
const int offset1 = (vec_idx * VEC_SIZE) / x;
const int offset2 = (vec_idx * VEC_SIZE) % x;
if constexpr (KV_DTYPE == Fp8KVCacheDataType::kAuto) {
k_vecs[j] = *reinterpret_cast<const K_vec*>(
k_ptr + offset1 * BLOCK_SIZE * x + offset2);
} else {
// Vector conversion from Quant_vec to K_vec.
Quant_vec k_vec_quant = *reinterpret_cast<const Quant_vec*>(
k_ptr + offset1 * BLOCK_SIZE * x + offset2);
k_vecs[j] = fp8::scaled_convert<K_vec, Quant_vec, KV_DTYPE>(
k_vec_quant, *k_scale);
}
}
// Compute dot product.
// This includes a reduction across the threads in the same thread group.
float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(
q_vecs[thread_group_offset], k_vecs);
// Add the ALiBi bias if slopes are given.
qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
if (thread_group_offset == 0) {
// Store the partial reductions to shared memory.
// NOTE(woosuk): It is required to zero out the masked logits.
const bool mask = token_idx >= seq_len;
logits[token_idx - start_token_idx] = mask ? 0.f : qk;
// Update the max value.
qk_max = mask ? qk_max : fmaxf(qk_max, qk);
}
}
}
// Perform reduction across the threads in the same warp to get the
// max qk value for each "warp" (not across the thread block yet).
// The 0-th thread of each thread group already has its max qk value.
#pragma unroll
for (int mask = WARP_SIZE / 2; mask >= THREAD_GROUP_SIZE; mask /= 2) {
qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
}
if (lane == 0) {
red_smem[warp_idx] = qk_max;
}
__syncthreads();
// TODO(woosuk): Refactor this part.
// Get the max qk value for the sequence.
qk_max = lane < NUM_WARPS ? red_smem[lane] : -FLT_MAX;
#pragma unroll
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
qk_max = fmaxf(qk_max, VLLM_SHFL_XOR_SYNC(qk_max, mask));
}
// Broadcast the max qk value to all threads.
qk_max = VLLM_SHFL_SYNC(qk_max, 0);
// Get the sum of the exp values.
float exp_sum = 0.f;
for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
float val = __expf(logits[i] - qk_max);
logits[i] = val;
exp_sum += val;
}
exp_sum = block_sum<NUM_WARPS>(&red_smem[NUM_WARPS], exp_sum);
// Compute softmax.
const float inv_sum = __fdividef(1.f, exp_sum + 1e-6f);
for (int i = thread_idx; i < num_tokens; i += NUM_THREADS) {
logits[i] *= inv_sum;
}
__syncthreads();
// If partitioning is enabled, store the max logit and exp_sum.
if (USE_PARTITIONING && thread_idx == 0) {
float* max_logits_ptr = max_logits +
seq_idx * num_heads * max_num_partitions +
head_idx * max_num_partitions + partition_idx;
*max_logits_ptr = qk_max;
float* exp_sums_ptr = exp_sums + seq_idx * num_heads * max_num_partitions +
head_idx * max_num_partitions + partition_idx;
*exp_sums_ptr = exp_sum;
}
// Each thread will fetch 16 bytes from the value cache at a time.
constexpr int V_VEC_SIZE = MIN(16 / sizeof(scalar_t), BLOCK_SIZE);
using V_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
using L_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
using V_quant_vec = typename Vec<cache_t, V_VEC_SIZE>::Type;
using Float_L_vec = typename FloatVec<L_vec>::Type;
constexpr int NUM_V_VECS_PER_ROW = BLOCK_SIZE / V_VEC_SIZE;
constexpr int NUM_ROWS_PER_ITER = WARP_SIZE / NUM_V_VECS_PER_ROW;
constexpr int NUM_ROWS_PER_THREAD =
DIVIDE_ROUND_UP(HEAD_SIZE, NUM_ROWS_PER_ITER);
// NOTE(woosuk): We use FP32 for the accumulator for better accuracy.
float accs[NUM_ROWS_PER_THREAD];
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
accs[i] = 0.f;
}
scalar_t zero_value;
zero(zero_value);
for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx;
block_idx += NUM_WARPS) {
// NOTE(woosuk): The block number is stored in int32. However, we cast it to
// int64 because int32 can lead to overflow when this variable is multiplied
// by large numbers (e.g., kv_block_stride).
// For blocksparse attention: skip computation on blocks that are not
// attended
if constexpr (IS_BLOCK_SPARSE) {
int v_bs_block_id = block_idx * BLOCK_SIZE / blocksparse_block_size;
if (!((v_bs_block_id + bs_block_offset) % blocksparse_vert_stride == 0) &&
!((v_bs_block_id > q_bs_block_id - blocksparse_local_blocks))) {
continue;
}
}
const int64_t physical_block_number =
static_cast<int64_t>(block_table[block_idx]);
const int physical_block_offset = (lane % NUM_V_VECS_PER_ROW) * V_VEC_SIZE;
const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
L_vec logits_vec;
from_float(logits_vec, *reinterpret_cast<Float_L_vec*>(logits + token_idx -
start_token_idx));
const cache_t* v_ptr = v_cache + physical_block_number * kv_block_stride +
kv_head_idx * kv_head_stride;
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
if (row_idx < HEAD_SIZE) {
const int offset = row_idx * BLOCK_SIZE + physical_block_offset;
V_vec v_vec;
if constexpr (KV_DTYPE == Fp8KVCacheDataType::kAuto) {
v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
} else {
V_quant_vec v_quant_vec =
*reinterpret_cast<const V_quant_vec*>(v_ptr + offset);
// Vector conversion from V_quant_vec to V_vec.
v_vec = fp8::scaled_convert<V_vec, V_quant_vec, KV_DTYPE>(v_quant_vec,
*v_scale);
}
if (block_idx == num_seq_blocks - 1) {
// NOTE(woosuk): When v_vec contains the tokens that are out of the
// context, we should explicitly zero out the values since they may
// contain NaNs. See
// https://github.com/vllm-project/vllm/issues/641#issuecomment-1682544472
scalar_t* v_vec_ptr = reinterpret_cast<scalar_t*>(&v_vec);
#pragma unroll
for (int j = 0; j < V_VEC_SIZE; j++) {
v_vec_ptr[j] = token_idx + j < seq_len ? v_vec_ptr[j] : zero_value;
}
}
accs[i] += dot(logits_vec, v_vec);
}
}
}
// Perform reduction within each warp.
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
float acc = accs[i];
#pragma unroll
for (int mask = NUM_V_VECS_PER_ROW / 2; mask >= 1; mask /= 2) {
acc += VLLM_SHFL_XOR_SYNC(acc, mask);
}
accs[i] = acc;
}
// NOTE(woosuk): A barrier is required because the shared memory space for
// logits is reused for the output.
__syncthreads();
// Perform reduction across warps.
float* out_smem = reinterpret_cast<float*>(shared_mem);
#pragma unroll
for (int i = NUM_WARPS; i > 1; i /= 2) {
int mid = i / 2;
// Upper warps write to shared memory.
if (warp_idx >= mid && warp_idx < i) {
float* dst = &out_smem[(warp_idx - mid) * HEAD_SIZE];
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
dst[row_idx] = accs[i];
}
}
}
__syncthreads();
// Lower warps update the output.
if (warp_idx < mid) {
const float* src = &out_smem[warp_idx * HEAD_SIZE];
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
accs[i] += src[row_idx];
}
}
}
__syncthreads();
}
// Write the final output.
if (warp_idx == 0) {
scalar_t* out_ptr =
out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
head_idx * max_num_partitions * HEAD_SIZE + partition_idx * HEAD_SIZE;
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
if (row_idx < HEAD_SIZE && lane % NUM_V_VECS_PER_ROW == 0) {
from_float(*(out_ptr + row_idx), accs[i]);
}
}
}
}
// Grid: (num_heads, num_seqs, 1).
template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
int NUM_THREADS, vllm::Fp8KVCacheDataType KV_DTYPE,
bool IS_BLOCK_SPARSE>
__global__ void paged_attention_v1_kernel(
scalar_t* __restrict__ out, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
// head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
// head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ seq_lens, // [num_seqs]
const int max_num_blocks_per_seq,
const float* __restrict__ alibi_slopes, // [num_heads]
const int q_stride, const int kv_block_stride, const int kv_head_stride,
const float* k_scale, const float* v_scale, const int tp_rank,
const int blocksparse_local_blocks, const int blocksparse_vert_stride,
const int blocksparse_block_size, const int blocksparse_head_sliding_step) {
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS,
KV_DTYPE, IS_BLOCK_SPARSE>(
/* exp_sums */ nullptr, /* max_logits */ nullptr, out, q, k_cache,
v_cache, num_kv_heads, scale, block_tables, seq_lens,
max_num_blocks_per_seq, alibi_slopes, q_stride, kv_block_stride,
kv_head_stride, k_scale, v_scale, tp_rank, blocksparse_local_blocks,
blocksparse_vert_stride, blocksparse_block_size,
blocksparse_head_sliding_step);
}
// Grid: (num_heads, num_seqs, max_num_partitions).
template <typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE,
int NUM_THREADS, vllm::Fp8KVCacheDataType KV_DTYPE,
bool IS_BLOCK_SPARSE,
int PARTITION_SIZE>
__global__ void paged_attention_v2_kernel(
float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
float* __restrict__ max_logits, // [num_seqs, num_heads,
// max_num_partitions]
scalar_t* __restrict__ tmp_out, // [num_seqs, num_heads,
// max_num_partitions, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads,
// head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads,
// head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
const int* __restrict__ seq_lens, // [num_seqs]
const int max_num_blocks_per_seq,
const float* __restrict__ alibi_slopes, // [num_heads]
const int q_stride, const int kv_block_stride, const int kv_head_stride,
const float* k_scale, const float* v_scale, const int tp_rank,
const int blocksparse_local_blocks, const int blocksparse_vert_stride,
const int blocksparse_block_size, const int blocksparse_head_sliding_step) {
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS,
KV_DTYPE, IS_BLOCK_SPARSE, PARTITION_SIZE>(
exp_sums, max_logits, tmp_out, q, k_cache, v_cache, num_kv_heads, scale,
block_tables, seq_lens, max_num_blocks_per_seq, alibi_slopes, q_stride,
kv_block_stride, kv_head_stride, k_scale, v_scale, tp_rank,
blocksparse_local_blocks, blocksparse_vert_stride, blocksparse_block_size,
blocksparse_head_sliding_step);
}
// Grid: (num_heads, num_seqs).
template <typename scalar_t, int HEAD_SIZE, int NUM_THREADS,
int PARTITION_SIZE>
__global__ void paged_attention_v2_reduce_kernel(
scalar_t* __restrict__ out, // [num_seqs, num_heads, head_size]
const float* __restrict__ exp_sums, // [num_seqs, num_heads,
// max_num_partitions]
const float* __restrict__ max_logits, // [num_seqs, num_heads,
// max_num_partitions]
const scalar_t* __restrict__ tmp_out, // [num_seqs, num_heads,
// max_num_partitions, head_size]
const int* __restrict__ seq_lens, // [num_seqs]
const int max_num_partitions) {
const int num_heads = gridDim.x;
const int head_idx = blockIdx.x;
const int seq_idx = blockIdx.y;
const int seq_len = seq_lens[seq_idx];
const int num_partitions = DIVIDE_ROUND_UP(seq_len, PARTITION_SIZE);
if (num_partitions == 1) {
// No need to reduce. Only copy tmp_out to out.
scalar_t* out_ptr =
out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE;
const scalar_t* tmp_out_ptr =
tmp_out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
head_idx * max_num_partitions * HEAD_SIZE;
for (int i = threadIdx.x; i < HEAD_SIZE; i += blockDim.x) {
out_ptr[i] = tmp_out_ptr[i];
}
// Terminate the thread block.
return;
}
constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
const int warp_idx = threadIdx.x / WARP_SIZE;
const int lane = threadIdx.x % WARP_SIZE;
// Size: 2 * num_partitions.
extern __shared__ char shared_mem[];
// Workspace for reduction.
__shared__ float red_smem[2 * NUM_WARPS];
// Load max logits to shared memory.
float* shared_max_logits = reinterpret_cast<float*>(shared_mem);
const float* max_logits_ptr = max_logits +
seq_idx * num_heads * max_num_partitions +
head_idx * max_num_partitions;
float max_logit = -FLT_MAX;
for (int i = threadIdx.x; i < num_partitions; i += blockDim.x) {
const float l = max_logits_ptr[i];
shared_max_logits[i] = l;
max_logit = fmaxf(max_logit, l);
}
__syncthreads();
// Get the global max logit.
// Reduce within the warp.
#pragma unroll
for (int mask = WARP_SIZE / 2; mask >= 1; mask /= 2) {
max_logit = fmaxf(max_logit, VLLM_SHFL_XOR_SYNC(max_logit, mask));
}
if (lane == 0) {
red_smem[warp_idx] = max_logit;
}
__syncthreads();
// Reduce across warps.
max_logit = lane < NUM_WARPS ? red_smem[lane] : -FLT_MAX;
#pragma unroll
for (int mask = NUM_WARPS / 2; mask >= 1; mask /= 2) {
max_logit = fmaxf(max_logit, VLLM_SHFL_XOR_SYNC(max_logit, mask));
}
// Broadcast the max value to all threads.
max_logit = VLLM_SHFL_SYNC(max_logit, 0);
// Load rescaled exp sums to shared memory.
float* shared_exp_sums =
reinterpret_cast<float*>(shared_mem + sizeof(float) * num_partitions);
const float* exp_sums_ptr = exp_sums +
seq_idx * num_heads * max_num_partitions +
head_idx * max_num_partitions;
float global_exp_sum = 0.0f;
for (int i = threadIdx.x; i < num_partitions; i += blockDim.x) {
float l = shared_max_logits[i];
float rescaled_exp_sum = exp_sums_ptr[i] * expf(l - max_logit);
global_exp_sum += rescaled_exp_sum;
shared_exp_sums[i] = rescaled_exp_sum;
}
__syncthreads();
global_exp_sum = block_sum<NUM_WARPS>(&red_smem[NUM_WARPS], global_exp_sum);
const float inv_global_exp_sum = __fdividef(1.0f, global_exp_sum + 1e-6f);
// Aggregate tmp_out to out.
const scalar_t* tmp_out_ptr =
tmp_out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
head_idx * max_num_partitions * HEAD_SIZE;
scalar_t* out_ptr =
out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE;
#pragma unroll
for (int i = threadIdx.x; i < HEAD_SIZE; i += NUM_THREADS) {
float acc = 0.0f;
for (int j = 0; j < num_partitions; ++j) {
acc += to_float(tmp_out_ptr[j * HEAD_SIZE + i]) * shared_exp_sums[j] *
inv_global_exp_sum;
}
from_float(out_ptr[i], acc);
}
}
} // namespace vllm
#undef MAX
#undef MIN
#undef DIVIDE_ROUND_UP

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