[Build/CI] Revert back to Ubuntu 20.04, install python 3.12 with uv (#26103)

Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>
Co-authored-by: Simon Mo <simon.mo@hey.com>
Signed-off-by: simon-mo <simon.mo@hey.com>

.buildkite/check-wheel-size.py

@@ -5,11 +5,11 @@ import os
 import sys
 import zipfile
 
-# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 500 MiB
+# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 450 MiB
 # Note that we have 800 MiB quota, please use it wisely.
 # See https://github.com/pypi/support/issues/6326 .
 # Please also sync the value with the one in Dockerfile.
-VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 500))
+VLLM_MAX_SIZE_MB = int(os.environ.get("VLLM_MAX_SIZE_MB", 450))
 
 
 def print_top_10_largest_files(zip_file):

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml

@@ -1,12 +0,0 @@
-# For vllm script, with -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
-model_name: "HandH1998/QQQ-Llama-3-8b-g128"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.419
-  - name: "exact_match,flexible-extract"
-    value: 0.416
-limit: 1000
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml

@@ -1,12 +0,0 @@
-# For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
-model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-backend: "vllm-vlm"
-tasks:
-- name: "chartqa"
-  metrics:
-  - name: "relaxed_accuracy,none"
-    # TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
-    value: 0.80
-limit: 100
-num_fewshot: 0

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -1,10 +0,0 @@
-# For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
-model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-tasks:
-- name: "mmlu_pro"
-  metrics:
-  - name: "exact_match,custom-extract"
-    value: 0.80
-limit: 250 # will run on 250 * 14 subjects = 3500 samples
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -1,5 +1,4 @@
-# For vllm script, with -t option (tensor parallel size)
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -b auto -l 1319 -f 5 -t 1
 model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
 tasks:
 - name: "gsm8k"

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-7B-Instruct.yaml

@@ -1,12 +0,0 @@
-# For vllm script, with -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m Qwen/Qwen2.5-VL-7B-Instruct -l 2500 -t 1
-
-model_name: "Qwen/Qwen2.5-VL-7B-Instruct"
-backend: "vllm-vlm"
-tasks:
-- name: "chartqa"
-  metrics:
-  - name: "relaxed_accuracy,none"
-    value: 0.855
-limit: 2500
-num_fewshot: 0

.buildkite/lm-eval-harness/configs/models-large-h100.txt

@@ -1 +0,0 @@
-Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

.buildkite/lm-eval-harness/configs/models-mm-large-h100.txt

@@ -1 +0,0 @@
-Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml

.buildkite/lm-eval-harness/configs/models-mm-small.txt

@@ -1 +0,0 @@
-Qwen2.5-VL-7B-Instruct.yaml

.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-# We can use this script to compute baseline accuracy on chartqa for vllm.
-#
-# Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.9
-
-usage() {
-    echo``
-    echo "Runs lm eval harness on ChartQA using multimodal vllm."
-    echo "This pathway is intended to be used to create baselines for "
-    echo "our correctness tests in vllm's CI."
-    echo
-    echo "usage: ${0} <options>"
-    echo
-    echo "  -m    - huggingface stub or local directory of the model"
-    echo "  -l    - limit number of samples to run"
-    echo "  -t    - tensor parallel size to run at"
-    echo
-}
-
-while getopts "m:l:t:" OPT; do
-  case ${OPT} in
-    m ) 
-        MODEL="$OPTARG"
-        ;;
-    l ) 
-        LIMIT="$OPTARG"
-        ;;
-    t ) 
-        TP_SIZE="$OPTARG"
-        ;;
-    \? ) 
-        usage
-        exit 1
-        ;;
-  esac
-done
-
-lm_eval --model vllm-vlm \
-  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE" \
-  --tasks chartqa \
-  --batch_size auto \
-  --apply_chat_template \
-  --limit $LIMIT

.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh

@@ -1,50 +0,0 @@
-#!/bin/bash
-# We can use this script to compute baseline accuracy on MMLUPRO for vllm.
-# We use this for fp8, which HF does not support.
-#
-# Make sure you have lm-eval-harness installed:
-#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
-
-usage() {
-    echo``
-    echo "Runs lm eval harness on MMLU Pro using huggingface transformers."
-    echo "This pathway is intended to be used to create baselines for "
-    echo "our automated nm-test-accuracy workflow"
-    echo
-    echo "usage: ${0} <options>"
-    echo
-    echo "  -m    - huggingface stub or local directory of the model"
-    echo "  -l    - limit number of samples to run"
-    echo "  -f    - number of fewshot samples to use"
-    echo "  -t    - tensor parallel size to run at"
-    echo
-}
-
-while getopts "m:b:l:f:t:" OPT; do
-  case ${OPT} in
-    m )
-        MODEL="$OPTARG"
-        ;;
-    b )
-        BATCH_SIZE="$OPTARG"
-        ;;
-    l )
-        LIMIT="$OPTARG"
-        ;;
-    f )
-        FEWSHOT="$OPTARG"
-        ;;
-    t )
-        TP_SIZE="$OPTARG"
-        ;;
-    \? )
-        usage
-        exit 1
-        ;;
-  esac
-done
-
-lm_eval --model vllm \
-  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true,trust_remote_code=true,max_model_len=4096" \
-  --tasks mmlu_pro --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
-  --batch_size auto

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -19,27 +19,21 @@ RTOL = 0.08
 def launch_lm_eval(eval_config, tp_size):
     trust_remote_code = eval_config.get("trust_remote_code", False)
     max_model_len = eval_config.get("max_model_len", 4096)
-    batch_size = eval_config.get("batch_size", "auto")
-    backend = eval_config.get("backend", "vllm")
     model_args = (
         f"pretrained={eval_config['model_name']},"
         f"tensor_parallel_size={tp_size},"
         f"enforce_eager=true,"
         f"add_bos_token=true,"
         f"trust_remote_code={trust_remote_code},"
-        f"max_model_len={max_model_len},"
+        f"max_model_len={max_model_len}"
     )
     results = lm_eval.simple_evaluate(
-        model=backend,
+        model="vllm",
         model_args=model_args,
         tasks=[task["name"] for task in eval_config["tasks"]],
         num_fewshot=eval_config["num_fewshot"],
         limit=eval_config["limit"],
-        # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
-        # text models. however, this is regressing measured strict-match for
-        # existing text models in CI, so only apply it for mm.
-        apply_chat_template=backend == "vllm-vlm",
-        batch_size=batch_size,
+        batch_size="auto",
     )
     return results
 

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -368,7 +368,7 @@ if __name__ == "__main__":
         # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
         # we want to turn it into "8xGPUTYPE"
         df["GPU"] = df["GPU"].apply(
-            lambda x: f"{len(x.splitlines())}x{x.splitlines()[0]}"
+            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}"
         )
 
     # get markdown tables

.buildkite/nightly-benchmarks/scripts/launch-server.sh

@@ -181,14 +181,18 @@ launch_vllm_server() {
   if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
     echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
     model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
-    server_command="vllm serve $model \
+    server_command="python3 \
+        -m vllm.entrypoints.openai.api_server \
         -tp $tp \
+        --model $model \
         --port $port \
         $server_args"
   else
     echo "Key 'fp8' does not exist in common params."
-    server_command="vllm serve $model \
+    server_command="python3 \
+        -m vllm.entrypoints.openai.api_server \
         -tp $tp \
+        --model $model \
         --port $port \
         $server_args"
   fi

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -365,7 +365,8 @@ run_serving_tests() {
       continue
     fi
 
-    server_command="$server_envs vllm serve \
+    server_command="$server_envs python3 \
+      -m vllm.entrypoints.openai.api_server \
       $server_args"
 
     # run the server
@@ -454,6 +455,11 @@ main() {
   fi
   check_hf_token
 
+  # Set to v1 to run v1 benchmark
+  if [[ "${ENGINE_VERSION:-v0}" == "v1" ]]; then
+    export VLLM_USE_V1=1
+  fi
+
   # dependencies
   (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
   (which jq) || (apt-get update && apt-get -y install jq)

.buildkite/pyproject.toml

@@ -0,0 +1,46 @@
+# This local pyproject file is part of the migration from yapf to ruff format.
+# It uses the same core rules as the main pyproject.toml file, but with the
+# following differences:
+# - ruff line length is overridden to 88
+# - deprecated typing ignores (UP006, UP035) have been removed
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint.per-file-ignores]
+"vllm/third_party/**" = ["ALL"]
+"vllm/version.py" = ["F401"]
+"vllm/_version.py" = ["ALL"]
+
+[tool.ruff.lint]
+select = [
+    # pycodestyle
+    "E",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # flake8-logging-format
+    "G",
+]
+ignore = [
+    # star imports
+    "F405", "F403",
+    # lambda expression assignment
+    "E731",
+    # Loop control variable not used within loop body
+    "B007",
+    # f-string format
+    "UP032",
+    # Can remove once 3.10+ is the minimum Python version
+    "UP007",
+]
+
+[tool.ruff.format]
+docstring-code-format = true

.buildkite/release-pipeline.yaml

@@ -8,7 +8,7 @@ steps:
     commands:
       # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
       # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg VLLM_MAIN_CUDA_VERSION=12.9 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg VLLM_MAIN_CUDA_VERSION=12.9 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
       - "bash .buildkite/scripts/upload-wheels.sh"
@@ -48,7 +48,7 @@ steps:
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
       - "bash .buildkite/scripts/upload-wheels.sh"
@@ -76,7 +76,7 @@ steps:
       queue: arm64_cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
 
   # Add job to create multi-arch manifest
@@ -150,16 +150,11 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "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:$BUILDKITE_COMMIT-x86_64"
-      - "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64"
-      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 vllm/vllm-openai:nightly-x86_64"
-      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 vllm/vllm-openai:nightly-aarch64"
-      - "docker push vllm/vllm-openai:nightly-x86_64"
-      - "docker push vllm/vllm-openai:nightly-aarch64"
-      - "docker manifest create vllm/vllm-openai:nightly vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
-      - "docker manifest create vllm/vllm-openai:nightly-$BUILDKITE_COMMIT vllm/vllm-openai:nightly-x86_64 vllm/vllm-openai:nightly-aarch64 --amend"
-      - "docker manifest push vllm/vllm-openai:nightly"
-      - "docker manifest push vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
+      - "docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT vllm/vllm-openai:nightly"
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
+      - "docker push vllm/vllm-openai:nightly"
+      - "docker push vllm/vllm-openai:nightly-$BUILDKITE_COMMIT"
       # Clean up old nightly builds (keep only last 14)
       - "bash .buildkite/scripts/cleanup-nightly-builds.sh"
     plugins:
@@ -168,4 +163,3 @@ steps:
           password-env: DOCKERHUB_TOKEN
     env:
       DOCKER_BUILDKIT: "1"
-      DOCKERHUB_USERNAME: "vllmbot"

.buildkite/scripts/cleanup-nightly-builds.sh

@@ -8,41 +8,20 @@ set -ex
 # DockerHub API endpoint for vllm/vllm-openai repository
 REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
 
-# Get DockerHub credentials from environment
+# Get DockerHub token from environment
 if [ -z "$DOCKERHUB_TOKEN" ]; then
     echo "Error: DOCKERHUB_TOKEN environment variable is not set"
     exit 1
 fi
 
-if [ -z "$DOCKERHUB_USERNAME" ]; then
-    echo "Error: DOCKERHUB_USERNAME environment variable is not set"
-    exit 1
-fi
-
-# Get DockerHub bearer token
-echo "Getting DockerHub bearer token..."
-set +x
-BEARER_TOKEN=$(curl -s -X POST \
-    -H "Content-Type: application/json" \
-    -d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \
-    "https://hub.docker.com/v2/users/login" | jq -r '.token')
-set -x
-
-if [ -z "$BEARER_TOKEN" ] || [ "$BEARER_TOKEN" = "null" ]; then
-    echo "Error: Failed to get DockerHub bearer token"
-    exit 1
-fi
-
 # Function to get all tags from DockerHub
 get_all_tags() {
     local page=1
     local all_tags=""
     
     while true; do
-        set +x
-        local response=$(curl -s -H "Authorization: Bearer $BEARER_TOKEN" \
+        local response=$(curl -s -H "Authorization: Bearer $DOCKERHUB_TOKEN" \
             "$REPO_API_URL?page=$page&page_size=100")
-        set -x
         
         # Get both last_updated timestamp and tag name, separated by |
         local tags=$(echo "$response" | jq -r '.results[] | select(.name | startswith("nightly-")) | "\(.last_updated)|\(.name)"')
@@ -64,9 +43,7 @@ delete_tag() {
     echo "Deleting tag: $tag_name"
     
     local delete_url="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags/$tag_name"
-    set +x
-    local response=$(curl -s -X DELETE -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
-    set -x
+    local response=$(curl -s -X DELETE -H "Authorization: Bearer $DOCKERHUB_TOKEN" "$delete_url")
     
     if echo "$response" | jq -e '.detail' > /dev/null 2>&1; then
         echo "Warning: Failed to delete tag $tag_name: $(echo "$response" | jq -r '.detail')"

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -25,28 +25,25 @@ function cpu_tests() {
 
   # offline inference
   podman exec -it "$container_id" bash -c "
-    set -xve
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log
+    set -e
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
 
   # Run basic model test
   podman exec -it "$container_id" bash -c "
-    set -evx
+    set -e
     pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
     pip install sentence-transformers datamodel_code_generator
-
-    # Note: disable Bart until supports V1
-    # pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
+    pytest -v -s tests/models/language/generation/test_bart.py -m cpu_model
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-openai-community/gpt2]
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
     pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    # TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
-    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
+    pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model"
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.
 
 export container_id
 export -f cpu_tests
-timeout 120m bash -c cpu_tests
+timeout 40m bash -c cpu_tests
 

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -70,7 +70,7 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -x -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
 
   # Note: disable it until supports V1
   # Run AWQ test

.buildkite/scripts/hardware_ci/run-npu-test.sh

@@ -1,191 +0,0 @@
-#!/bin/bash
-
-# This script build the Ascend NPU docker image and run the offline inference inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -ex
-
-# Base ubuntu image with basic ascend development libraries and python installed
-VLLM_ASCEND_REPO="https://github.com/vllm-project/vllm-ascend.git"
-CONFIG_FILE_REMOTE_PATH="tests/e2e/vllm_interface/vllm_test.cfg"
-TEST_RUN_CONFIG_FILE="vllm_test.cfg"
-VLLM_ASCEND_TMP_DIR=
-# Get the test run configuration file from the vllm-ascend repository
-fetch_vllm_test_cfg() {
-    VLLM_ASCEND_TMP_DIR=$(mktemp -d)
-    # Ensure that the temporary directory is cleaned up when an exception occurs during configuration file retrieval
-    cleanup() {
-        rm -rf "${VLLM_ASCEND_TMP_DIR}"
-    }
-    trap cleanup EXIT
-
-    GIT_TRACE=1 git clone -v --depth 1 "${VLLM_ASCEND_REPO}" "${VLLM_ASCEND_TMP_DIR}"
-    if [ ! -f "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" ]; then
-        echo "Error: file '${CONFIG_FILE_REMOTE_PATH}' does not exist in the warehouse" >&2
-        exit 1
-    fi
-
-    # If the file already exists locally, just overwrite it
-    cp "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" "${TEST_RUN_CONFIG_FILE}"
-    echo "Copied ${CONFIG_FILE_REMOTE_PATH} to ${TEST_RUN_CONFIG_FILE}"
-
-    # Since the trap will be overwritten later, and when it is executed here, the task of cleaning up resources
-    # when the trap is abnormal has been completed, so the temporary resources are manually deleted here.
-    rm -rf "${VLLM_ASCEND_TMP_DIR}"
-    trap - EXIT
-}
-
-# Downloads test run configuration file from a remote URL.
-# Loads the configuration into the current script environment.
-get_config() {
-    if [ ! -f "${TEST_RUN_CONFIG_FILE}" ]; then
-        echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2
-        exit 1
-    fi
-    source "${TEST_RUN_CONFIG_FILE}"
-    echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}"
-    return 0
-}
-
-# get test running configuration.
-fetch_vllm_test_cfg
-get_config
-# Check if the function call was successful. If not, exit the script.
-if [ $? -ne 0 ]; then
-  exit 1
-fi
-
-image_name="npu/vllm-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
-container_name="npu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-
-# BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards
-agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
-echo "agent_idx: ${agent_idx}"
-builder_name="cachebuilder${agent_idx}"
-builder_cache_dir="/mnt/docker-cache${agent_idx}"
-mkdir -p ${builder_cache_dir}
-
-# Try building the docker image
-cat <<EOF | DOCKER_BUILDKIT=1 docker build \
-    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_HOST} \
-    --builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
-                           --cache-to type=local,dest=${builder_cache_dir},mode=max \
-    --progress=plain --load -t ${image_name} -f - .
-FROM ${BASE_IMAGE_NAME}
-
-# Define environments
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN pip config set global.index-url http://cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_PORT}/pypi/simple && \
-    pip config set global.trusted-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local && \
-    apt-get update -y && \
-    apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev && \
-    rm -rf /var/cache/apt/* && \
-    rm -rf /var/lib/apt/lists/*
-
-# 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
-
-WORKDIR /workspace/vllm
-
-# Install vLLM dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid.
-COPY requirements/common.txt /workspace/vllm/requirements/common.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements/common.txt
-
-COPY . .
-
-# Install vLLM
-RUN --mount=type=cache,target=/root/.cache/pip \
-    VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /workspace/vllm/ --extra-index https://download.pytorch.org/whl/cpu/ && \
-    python3 -m pip uninstall -y triton
-
-# Install vllm-ascend
-WORKDIR /workspace
-ARG VLLM_ASCEND_REPO=https://github.com/vllm-project/vllm-ascend.git
-ARG VLLM_ASCEND_TAG=main
-RUN git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/" && \
-    git clone --depth 1 \$VLLM_ASCEND_REPO --branch \$VLLM_ASCEND_TAG /workspace/vllm-ascend
-
-# Install vllm dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid.
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r /workspace/vllm-ascend/requirements.txt
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
-    source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
-    source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
-    python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-ENV VLLM_USE_MODELSCOPE=True
-
-WORKDIR /workspace/vllm-ascend
-
-CMD ["/bin/bash"]
-
-EOF
-
-# Setup cleanup
-remove_docker_container() {
-  docker rm -f "${container_name}" || true;
-  docker image rm -f "${image_name}" || true;
-  docker system prune -f || true;
-}
-trap remove_docker_container EXIT
-
-# Generate corresponding --device args based on BUILDKITE_AGENT_NAME
-# Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
-#   e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
-#   returns --device /dev/davinci0 --device /dev/davinci1
-parse_and_gen_devices() {
-    local input="$1"
-    local index cards_num
-    if [[ "$input" =~ ([0-9]+)-([0-9]+)cards$ ]]; then
-        index="${BASH_REMATCH[1]}"
-        cards_num="${BASH_REMATCH[2]}"
-    else
-        echo "parse error" >&2
-        return 1
-    fi
-
-    local devices=""
-    local i=0
-    while (( i < cards_num )); do
-        local dev_idx=$(((index - 1)*cards_num + i ))
-        devices="$devices --device /dev/davinci${dev_idx}"
-        ((i++))
-    done
-
-    # trim leading space
-    devices="${devices#"${devices%%[![:space:]]*}"}"
-    # Output devices: assigned to the caller variable
-    printf '%s' "$devices"
-}
-
-devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
-
-# Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
-# This test checks whether the OOT platform interface is functioning properly in conjunction with
-# the hardware plugin vllm-ascend.
-model_cache_dir=/mnt/modelscope${agent_idx}
-mkdir -p ${model_cache_dir}
-docker run \
-    ${devices} \
-    --device /dev/davinci_manager \
-    --device /dev/devmm_svm \
-    --device /dev/hisi_hdc \
-    -v /usr/local/dcmi:/usr/local/dcmi \
-    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-    -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v ${model_cache_dir}:/root/.cache/modelscope \
-    --entrypoint="" \
-    --name "${container_name}" \
-    "${image_name}" \
-    bash -c '
-    set -e
-    pytest -v -s tests/e2e/vllm_interface/
-'

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -64,9 +64,10 @@ python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git
     && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
-
+export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1
 export VLLM_XLA_CACHE_PATH=
+echo "Using VLLM V1"
 
 echo "--- Hardware Information ---"
 # tpu-info

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -64,9 +64,10 @@ python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git
     && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
-
+export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1
 export VLLM_XLA_CACHE_PATH=
+echo "Using VLLM V1"
 
 echo "--- Hardware Information ---"
 # tpu-info

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -42,7 +42,9 @@ docker run \
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
     pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
     pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py --ignore=v1/spec_decode/test_tree_attention.py
     pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py
     pytest -v -s v1/test_serial_utils.py
+    pytest -v -s v1/test_utils.py
+    pytest -v -s v1/test_metrics_reader.py
 '

.buildkite/scripts/run-benchmarks.sh

@@ -18,7 +18,7 @@ vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
-vllm serve meta-llama/Llama-2-7b-chat-hf &
+python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf &
 server_pid=$!
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 

.buildkite/scripts/tpu/quantized_v6e_1.env

@@ -9,6 +9,6 @@ MAX_NUM_BATCHED_TOKENS=1024
 TENSOR_PARALLEL_SIZE=1
 MAX_MODEL_LEN=2048
 DOWNLOAD_DIR=/mnt/disks/persist
-EXPECTED_THROUGHPUT=8.7
+EXPECTED_THROUGHPUT=10.0
 INPUT_LEN=1800
 OUTPUT_LEN=128

.buildkite/scripts/tpu/run_bm.sh

@@ -42,7 +42,7 @@ echo "lanching vllm..."
 echo "logging to $VLLM_LOG"
 echo
 
-vllm serve $MODEL \
+VLLM_USE_V1=1 vllm serve $MODEL \
  --seed 42 \
  --max-num-seqs $MAX_NUM_SEQS \
  --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \

.buildkite/test-pipeline.yaml

@@ -50,28 +50,19 @@ steps:
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
-  - tests/multimodal
-  - tests/utils_
-  commands:
-  - pytest -v -s -m 'not cpu_test' multimodal
-  - pytest -v -s utils_
-
-- label: Async Engine, Inputs, Utils, Worker Test (CPU) # 4 mins
-  timeout_in_minutes: 10
-  source_file_dependencies:
-  - vllm/
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/multimodal
+  - tests/utils_
   - tests/standalone_tests/lazy_imports.py
   - tests/transformers_utils
-  no_gpu: true
   commands:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
-  - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s transformers_utils
+  - pytest -v -s multimodal
+  - pytest -v -s utils_ # Utils
+  - pytest -v -s transformers_utils # transformers_utils
 
 - label: Python-only Installation Test # 10min
   timeout_in_minutes: 20
@@ -168,7 +159,10 @@ steps:
   - examples/offline_inference/rlhf.py
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
-  - tests/v1/distributed
+  - tests/v1/test_async_llm_dp.py
+  - tests/v1/test_external_lb_dp.py
+  - tests/v1/test_internal_lb_dp.py
+  - tests/v1/test_hybrid_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
   - tests/distributed/test_symm_mem_allreduce.py
   commands:
@@ -186,10 +180,10 @@ steps:
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_hybrid_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
@@ -296,35 +290,26 @@ steps:
     - tests/v1
   commands:
     # split the test to avoid interference
-    - pytest -v -s -m 'not cpu_test' v1/core
+    - pytest -v -s v1/core
     - pytest -v -s v1/executor
     - pytest -v -s v1/kv_offload
     - pytest -v -s v1/sample
     - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
+    - pytest -v -s v1/structured_output
     - pytest -v -s v1/spec_decode
-    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'not cpu_test' v1/metrics
+    - pytest -v -s v1/kv_connector/unit
+    - pytest -v -s v1/metrics
+    - pytest -v -s v1/test_kv_sharing.py
+    - pytest -v -s v1/test_metrics_reader.py
     - pytest -v -s v1/test_oracle.py
     - pytest -v -s v1/test_request.py
+    - pytest -v -s v1/test_serial_utils.py
+    - pytest -v -s v1/test_utils.py
     # Integration test for streaming correctness (requires special branch).
     - 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 Test others (CPU) # 5 mins
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  no_gpu: true
-  commands:
-    # split the test to avoid interference
-    - pytest -v -s -m 'cpu_test' v1/core
-    - pytest -v -s v1/structured_output
-    - pytest -v -s v1/test_serial_utils.py
-    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
-    - pytest -v -s -m 'cpu_test' v1/metrics
-
-
 - label: Examples Test # 30min
   timeout_in_minutes: 45
   mirror_hardwares: [amdexperimental]
@@ -398,12 +383,12 @@ steps:
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py
     - pytest -v -s compile/test_fusion_attn.py
-    - pytest -v -s compile/test_functionalization.py
     - pytest -v -s compile/test_silu_mul_quant_fusion.py
+    - pytest -v -s compile/test_sequence_parallelism.py
+    - pytest -v -s compile/test_async_tp.py
     - pytest -v -s compile/test_fusion_all_reduce.py
     - pytest -v -s compile/test_decorator.py
     - pytest -v -s compile/test_noop_elimination.py
-    - pytest -v -s compile/test_aot_compile.py
 
 - label: PyTorch Fullgraph Smoke Test # 15min
   timeout_in_minutes: 30
@@ -416,8 +401,8 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s compile/piecewise/
 
-- label: PyTorch Fullgraph Test # 22min
-  timeout_in_minutes: 35
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
@@ -425,7 +410,6 @@ steps:
   - tests/compile
   commands:
   - pytest -v -s compile/test_full_graph.py
-  - pytest -v -s compile/test_fusions_e2e.py
 
 - label: Kernels Core Operation Test # 48min
   timeout_in_minutes: 75
@@ -433,9 +417,8 @@ steps:
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
   commands:
-    - pytest -v -s kernels/core kernels/test_top_k_per_row.py
+    - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N # 23min
   timeout_in_minutes: 35
@@ -479,23 +462,33 @@ steps:
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
-  - vllm/model_executor/layers/mamba/ops
   commands:
     - pytest -v -s kernels/mamba
 
-- label: Model Executor Test # 23min
-  timeout_in_minutes: 35
+- label: Tensorizer Test # 14min
+  timeout_in_minutes: 25
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
-  - vllm/model_executor
-  - tests/model_executor
+  - vllm/model_executor/model_loader
+  - tests/tensorizer_loader
   - tests/entrypoints/openai/test_tensorizer_entrypoint.py
   commands:
     - apt-get update && apt-get install -y curl libsodium23
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
+    - pytest -v -s tensorizer_loader
     - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
 
+- label: Model Executor Test # 7min
+  timeout_in_minutes: 20
+  mirror_hardwares: [amdexperimental]
+  source_file_dependencies:
+  - vllm/model_executor
+  - tests/model_executor
+  commands:
+    - apt-get update && apt-get install -y curl libsodium23
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -v -s model_executor
+
 - label: Benchmarks # 11min
   timeout_in_minutes: 20
   mirror_hardwares: [amdexperimental]
@@ -528,9 +521,8 @@ steps:
   # since torchao nightly is only compatible with torch nightly currently
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
-  # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.13.0
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
+  - pip install --pre torchao==0.13.0.dev20250814 --index-url https://download.pytorch.org/whl/nightly/cu128
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
 
 - label: LM Eval Small Models # 53min
   timeout_in_minutes: 75
@@ -558,17 +550,10 @@ steps:
   source_file_dependencies:
     - vllm/
     - tests/tool_use
+    - tests/mistral_tool_use
   commands:
-    - pytest -v -s -m 'not cpu_test' tool_use
-
-- label: OpenAI-Compatible Tool Use (CPU) # 5 mins
-  timeout_in_minutes: 10
-  source_file_dependencies:
-    - vllm/
-    - tests/tool_use
-  no_gpu: true
-  commands:
-    - pytest -v -s -m 'cpu_test' tool_use
+    - pytest -v -s tool_use
+    - pytest -v -s mistral_tool_use
 
 #####  models test  #####
 
@@ -608,19 +593,13 @@ steps:
   - vllm/
   - tests/models/test_transformers.py
   - tests/models/test_registry.py
-  commands:
-    - pytest -v -s models/test_transformers.py models/test_registry.py
-
-- label: Basic Models Test (Other CPU) # 5min
-  timeout_in_minutes: 10
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
   - tests/models/test_utils.py
   - tests/models/test_vision.py
-  no_gpu: true
   commands:
-    - pytest -v -s models/test_utils.py models/test_vision.py
+    - pytest -v -s models/test_transformers.py \
+                   models/test_registry.py \
+                   models/test_utils.py \
+                   models/test_vision.py
 
 - label: Language Models Tests (Standard)
   timeout_in_minutes: 25
@@ -735,16 +714,6 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
     - 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
 
-- label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  timeout_in_minutes: 70
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
-  commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
-
 - label: Multi-Modal Models Test (Extended) 1
   mirror_hardwares: [amdexperimental]
   optional: true
@@ -800,7 +769,6 @@ steps:
   commands:
     - pip install --upgrade git+https://github.com/huggingface/transformers
     - pytest -v -s tests/models/test_initialization.py
-    - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/offline_inference/basic/chat.py
@@ -808,8 +776,8 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
-  timeout_in_minutes: 30
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
   # optional: true
@@ -822,6 +790,8 @@ steps:
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -838,38 +808,19 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
-    - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-
-- label: Blackwell Fusion Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
+    - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py
+    # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
 
-- label: Blackwell GPT-OSS Eval
+- label: GPT-OSS Eval (Blackwell)
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
-  optional: true # run on nightlies
+  optional: true # disable while debugging
   source_file_dependencies:
   - tests/evals/gpt_oss
   - vllm/model_executor/models/gpt_oss.py
@@ -877,34 +828,7 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   commands:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
-
-- label: Blackwell Quantized MoE Test
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - tests/quantization/test_blackwell_moe.py
-  - vllm/model_executor/models/deepseek_v2.py
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/models/llama4.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization/compressed_tensors
-  - vllm/model_executor/layers/quantization/modelopt.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/flashinfer.py
-  commands:
-    - pytest -s -v tests/quantization/test_blackwell_moe.py
-
-- label: Blackwell LM Eval Small Models
-  timeout_in_minutes: 120
-  gpu: b200
-  optional: true # run on nightlies
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1
+    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58 --server-args '--tensor-parallel-size 2'
 
 #####  1 GPU test  #####
 #####  multi gpus test  #####
@@ -965,13 +889,14 @@ steps:
   - tests/compile/test_wrapper.py
   - tests/distributed/
   - tests/entrypoints/llm/test_collective_rpc.py
-  - tests/v1/distributed
+  - tests/v1/test_async_llm_dp.py
+  - tests/v1/test_external_lb_dp.py
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py
@@ -1020,11 +945,6 @@ steps:
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
   # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -1089,17 +1009,6 @@ steps:
   - tests/weight_loading
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
-  
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
 
 
 ##### multi gpus test #####
@@ -1132,16 +1041,12 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 ##### H200 test #####
-- label: Distributed Tests (H200) # optional
+- label: Distrubted Tests (H200) # optional
   gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
   num_gpus: 2
   commands:
-    - pytest -v -s tests/compile/test_async_tp.py
-    - pytest -v -s tests/compile/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
 

.coveragerc

@@ -1,10 +1,5 @@
 [run]
-# Track the installed vllm package (this is what actually gets imported during tests)
-# Use wildcard pattern to match the installed location
-source =
-    vllm
-    */dist-packages/vllm
-    */site-packages/vllm
+source = vllm
 omit =
     */tests/*
     */test_*
@@ -17,16 +12,6 @@ omit =
     */benchmarks/*
     */docs/*
 
-[paths]
-# Map all possible vllm locations to a canonical "vllm" path
-# This ensures coverage.combine properly merges data from different test runs
-source =
-    vllm
-    /vllm-workspace/src/vllm
-    /vllm-workspace/vllm
-    */site-packages/vllm
-    */dist-packages/vllm
-
 [report]
 exclude_lines =
     pragma: no cover

.git-blame-ignore-revs

@@ -1,4 +0,0 @@
-# Migrate from `yapf` & `isort` to `ruff`
-d6953beb91da4e9c99be4c0a1304a2d24189535c
-# Convert `Optional[x]` to `x | None` and `Union[x, y]` to `x | y`
-8fcaaf6a165e661f63fc51be906bc05b0767332f

.github/CODEOWNERS

@@ -5,11 +5,15 @@
 /vllm/attention @LucasWilkinson
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
+/vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
 /vllm/model_executor/layers/fused_moe @mgoin
+/vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @NickLucche
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
 /vllm/model_executor/layers/mamba @tdoublep
 /vllm/model_executor/model_loader @22quinn
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche
+/vllm/v1/attention @LucasWilkinson
+/vllm/v1/sample @22quinn @houseroad
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm @chaunceyjiang
@@ -21,16 +25,14 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
 /vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
-/vllm/config/cache.py @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg @heheda12345
 
 # vLLM V1
-/vllm/v1/attention @LucasWilkinson
+/vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
+/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
+/vllm/v1/spec_decode @benchislett @luccafong
 /vllm/v1/attention/backends/flashinfer.py @mgoin
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
 /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC
-/vllm/v1/sample @22quinn @houseroad @njhill
-/vllm/v1/spec_decode @benchislett @luccafong
-/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
 /vllm/v1/kv_cache_interface.py @heheda12345
 /vllm/v1/offloading @ApostaC
 
@@ -52,12 +54,12 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/weight_loading @mgoin @youkaichao @yewentao256
 /tests/lora @jeejeelee
 /tests/models/language/generation/test_hybrid.py @tdoublep
-/tests/v1/kv_connector/nixl_integration @NickLucche
+/tests/v1/kv_connector/nixl_integration @NickLucche 
 /tests/v1/kv_connector @ApostaC
 /tests/v1/offloading @ApostaC
 
 # Transformers backend
-/vllm/model_executor/models/transformers @hmellor
+/vllm/model_executor/models/transformers.py @hmellor
 /tests/models/test_transformers.py @hmellor
 
 # Docs
@@ -118,11 +120,3 @@ mkdocs.yaml @hmellor
 
 # KVConnector installation files
 /requirements/kv_connectors.txt @NickLucche
-
-# Pooling models
-/examples/*/pooling/ @noooop
-/tests/models/*/pooling* @noooop
-/tests/entrypoints/pooling @noooop
-/vllm/config/pooler.py @noooop
-/vllm/pooling_params.py @noooop
-/vllm/model_executor/layers/pooler.py @noooop

.github/mergify.yml

@@ -2,7 +2,6 @@ pull_request_rules:
 - name: label-documentation
   description: Automatically apply documentation label
   conditions:
-    - label != stale
     - or:
       - files~=^[^/]+\.md$
       - files~=^docs/
@@ -11,13 +10,10 @@ pull_request_rules:
     label:
       add:
         - documentation
-    comment:
-      message: "Documentation preview: https://vllm--{{number}}.org.readthedocs.build/en/{{number}}/"
 
 - name: label-ci-build
   description: Automatically apply ci/build label
   conditions:
-    - label != stale
     - or:
       - files~=^\.github/
       - files~=\.buildkite/
@@ -34,7 +30,6 @@ pull_request_rules:
 - name: label-deepseek
   description: Automatically apply deepseek label
   conditions:
-    - label != stale
     - or:
       - files~=^examples/.*deepseek.*\.py
       - files~=^tests/.*deepseek.*\.py
@@ -51,7 +46,6 @@ pull_request_rules:
 - name: label-frontend
   description: Automatically apply frontend label
   conditions:
-    - label != stale
     - files~=^vllm/entrypoints/
   actions:
     label:
@@ -61,7 +55,6 @@ pull_request_rules:
 - name: label-llama
   description: Automatically apply llama label
   conditions:
-    - label != stale
     - or:
       - files~=^examples/.*llama.*\.py
       - files~=^tests/.*llama.*\.py
@@ -77,7 +70,6 @@ pull_request_rules:
 - name: label-multi-modality
   description: Automatically apply multi-modality label
   conditions:
-    - label != stale
     - or:
       - files~=^vllm/multimodal/
       - files~=^tests/multimodal/
@@ -91,7 +83,6 @@ pull_request_rules:
 - name: label-new-model
   description: Automatically apply new-model label
   conditions:
-    - label != stale
     - and:
       - files~=^vllm/model_executor/models/
       - files=vllm/model_executor/models/registry.py
@@ -103,7 +94,6 @@ pull_request_rules:
 - name: label-performance
   description: Automatically apply performance label
   conditions:
-    - label != stale
     - or:
       - files~=^benchmarks/
       - files~=^vllm/benchmarks/
@@ -117,7 +107,6 @@ pull_request_rules:
 - name: label-qwen
   description: Automatically apply qwen label
   conditions:
-    - label != stale
     - or:
       - files~=^examples/.*qwen.*\.py
       - files~=^tests/.*qwen.*\.py
@@ -132,7 +121,6 @@ pull_request_rules:
 - name: label-gpt-oss
   description: Automatically apply gpt-oss label
   conditions:
-    - label != stale
     - or:
       - files~=^examples/.*gpt[-_]?oss.*\.py
       - files~=^tests/.*gpt[-_]?oss.*\.py
@@ -154,7 +142,6 @@ pull_request_rules:
 - name: label-rocm
   description: Automatically apply rocm label
   conditions:
-    - label != stale
     - or:
       - files~=^csrc/rocm/
       - files~=^docker/Dockerfile.rocm
@@ -175,7 +162,6 @@ pull_request_rules:
 - name: label-structured-output
   description: Automatically apply structured-output label
   conditions:
-    - label != stale
     - or:
       - files~=^benchmarks/structured_schemas/
       - files=benchmarks/benchmark_serving_structured_output.py
@@ -195,7 +181,6 @@ pull_request_rules:
 - name: label-speculative-decoding
   description: Automatically apply speculative-decoding label
   conditions:
-    - label != stale
     - or:
       - files~=^vllm/v1/spec_decode/
       - files~=^tests/v1/spec_decode/
@@ -211,7 +196,6 @@ pull_request_rules:
 - name: label-v1
   description: Automatically apply v1 label
   conditions:
-    - label != stale
     - or:
       - files~=^vllm/v1/
       - files~=^tests/v1/
@@ -224,7 +208,6 @@ pull_request_rules:
   description: Automatically apply tpu label
   # Keep this list in sync with `label-tpu-remove` conditions
   conditions:
-    - label != stale
     - or:
       - files~=tpu.py
       - files~=_tpu
@@ -240,7 +223,6 @@ pull_request_rules:
   description: Automatically remove tpu label
   # Keep this list in sync with `label-tpu` conditions
   conditions:
-    - label != stale
     - and:
       - -files~=tpu.py
       - -files~=_tpu
@@ -255,9 +237,9 @@ pull_request_rules:
 - name: label-tool-calling
   description: Automatically add tool-calling label
   conditions:
-    - label != stale
     - or:
       - files~=^tests/tool_use/
+      - files~=^tests/mistral_tool_use/
       - files~=^tests/entrypoints/openai/tool_parsers/
       - files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py
       - files~=^vllm/entrypoints/openai/tool_parsers/
@@ -274,9 +256,8 @@ pull_request_rules:
 
 - name: ping author on conflicts and add 'needs-rebase' label
   conditions:
-    - label != stale
-    - conflict
-    - -closed
+      - conflict
+      - -closed
   actions:
     label:
       add:
@@ -290,12 +271,10 @@ pull_request_rules:
 
 - name: assign reviewer for tensorizer changes
   conditions:
-    - label != stale
-    - or:
       - files~=^vllm/model_executor/model_loader/tensorizer.py
       - files~=^vllm/model_executor/model_loader/tensorizer_loader.py
       - files~=^tests/entrypoints/openai/test_tensorizer_entrypoint.py
-      - files~=^tests/model_executor/model_loader/tensorizer_loader/
+      - files~=^tests/tensorizer_loader/
   actions:
     assign:
       users:
@@ -303,7 +282,6 @@ pull_request_rules:
 
 - name: assign reviewer for modelopt changes
   conditions:
-    - label != stale
     - or:
         - files~=^vllm/model_executor/layers/quantization/modelopt\.py$
         - files~=^vllm/model_executor/layers/quantization/__init__\.py$
@@ -318,8 +296,8 @@ pull_request_rules:
 
 - name: remove 'needs-rebase' label when conflict is resolved
   conditions:
-    - -conflict
-    - -closed
+      - -conflict
+      - -closed
   actions:
     label:
       remove:
@@ -328,7 +306,6 @@ pull_request_rules:
 - name: label-kv-connector
   description: Automatically apply kv-connector label
   conditions:
-    - label != stale
     - or:
       - files~=^examples/online_serving/disaggregated[^/]*/.*
       - files~=^examples/offline_inference/disaggregated[^/]*/.*

.github/workflows/issue_autolabel.yml

@@ -13,7 +13,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Label issues based on keywords
-        id: label-step
         uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
         with:
           script: |
@@ -43,6 +42,7 @@ jobs:
                     searchIn: "body"
                   },
                 ],
+                
                 // Substring search - matches anywhere in text (partial matches)
                 substrings: [
                   {
@@ -89,12 +89,14 @@ jobs:
                     term: "hip_",
                     searchIn: "both"
                   },
+                  
                   // ROCm tools and libraries
                   {
                     term: "hipify",
                     searchIn: "both"
                   },
                 ],
+                
                 // Regex patterns - for complex pattern matching
                 regexPatterns: [
                   {
@@ -105,17 +107,13 @@ jobs:
                   }
                 ],
               },
-              // Add more label configurations here as needed
-              // example: {
-              //   keywords: [...],
-              //   substrings: [...],
-              //   regexPatterns: [...]
-              // },
             };
+            
             // Helper function to create regex based on search type
             function createSearchRegex(term, type) {
               // Escape special regex characters in the term
               const escapedTerm = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
+              
               switch (type) {
                 case 'keyword':
                   // Word boundary search - matches whole words only
@@ -127,13 +125,16 @@ jobs:
                   throw new Error(`Unknown search type: ${type}`);
               }
             }
+            
             // Helper function to find matching terms in text with line information
             function findMatchingTermsWithLines(text, searchTerms = [], searchType = 'keyword', searchLocation = '') {
               const matches = [];
               const lines = text.split('\n');
+              
               for (const termConfig of searchTerms) {
                 let regex;
                 let term, searchIn, pattern, description, flags;
+                
                 // Handle different input formats (string or object)
                 if (typeof termConfig === 'string') {
                   term = termConfig;
@@ -145,17 +146,21 @@ jobs:
                   description = termConfig.description;
                   flags = termConfig.flags;
                 }
+                
                 // Skip if this term shouldn't be searched in the current location
                 if (searchIn !== 'both' && searchIn !== searchLocation) {
                   continue;
                 }
+                
                 // Create appropriate regex
                 if (searchType === 'regex') {
                   regex = new RegExp(pattern, flags || "gi");
                 } else {
                   regex = createSearchRegex(term, searchType);
                 }
+                
                 const termMatches = [];
+                
                 // Check each line for matches
                 lines.forEach((line, lineIndex) => {
                   const lineMatches = line.match(regex);
@@ -170,14 +175,15 @@ jobs:
                         originalTerm: term || pattern,
                         description: description,
                         // Show context around the match in the line
-                        context: line.length > 100 ?
-                          line.substring(Math.max(0, line.toLowerCase().indexOf(match.toLowerCase()) - 30),
-                                       line.toLowerCase().indexOf(match.toLowerCase()) + match.length + 30) + '...'
+                        context: line.length > 100 ? 
+                          line.substring(Math.max(0, line.toLowerCase().indexOf(match.toLowerCase()) - 30), 
+                                       line.toLowerCase().indexOf(match.toLowerCase()) + match.length + 30) + '...' 
                           : line.trim()
                       });
                     });
                   }
                 });
+                
                 if (termMatches.length > 0) {
                   matches.push({
                     term: term || (description || pattern),
@@ -190,48 +196,64 @@ jobs:
                   });
                 }
               }
+              
               return matches;
             }
+            
             // Helper function to check if label should be added
             async function processLabel(labelName, config) {
               const body = context.payload.issue.body || "";
               const title = context.payload.issue.title || "";
+              
               core.notice(`Processing label: ${labelName}`);
               core.notice(`Issue Title: "${title}"`);
               core.notice(`Issue Body length: ${body.length} characters`);
+              
               let shouldAddLabel = false;
               let allMatches = [];
               let reason = '';
+              
               const keywords = config.keywords || [];
               const substrings = config.substrings || [];
               const regexPatterns = config.regexPatterns || [];
+              
               core.notice(`Searching with ${keywords.length} keywords, ${substrings.length} substrings, and ${regexPatterns.length} regex patterns`);
+              
               // Search in title
               if (title.trim()) {
                 core.notice(`Searching in title: "${title}"`);
+                
                 const titleKeywordMatches = findMatchingTermsWithLines(title, keywords, 'keyword', 'title');
                 const titleSubstringMatches = findMatchingTermsWithLines(title, substrings, 'substring', 'title');
                 const titleRegexMatches = findMatchingTermsWithLines(title, regexPatterns, 'regex', 'title');
+                
                 allMatches.push(...titleKeywordMatches, ...titleSubstringMatches, ...titleRegexMatches);
               }
+              
               // Search in body
               if (body.trim()) {
                 core.notice(`Searching in body (${body.length} characters)`);
+                
                 const bodyKeywordMatches = findMatchingTermsWithLines(body, keywords, 'keyword', 'body');
                 const bodySubstringMatches = findMatchingTermsWithLines(body, substrings, 'substring', 'body');
                 const bodyRegexMatches = findMatchingTermsWithLines(body, regexPatterns, 'regex', 'body');
+                
                 allMatches.push(...bodyKeywordMatches, ...bodySubstringMatches, ...bodyRegexMatches);
               }
+              
               if (allMatches.length > 0) {
                 core.notice(`Found ${allMatches.length} matching term(s):`);
+                
                 for (const termMatch of allMatches) {
                   const locationText = termMatch.searchLocation === 'title' ? 'title' : 'body';
                   const searchInText = termMatch.searchIn === 'both' ? 'both' : termMatch.searchIn;
+                  
                   if (termMatch.searchType === 'regex') {
                     core.notice(`  📍 Regex: "${termMatch.term}" (pattern: ${termMatch.pattern}) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
                   } else {
                     core.notice(`  📍 Term: "${termMatch.term}" (${termMatch.searchType} search) found ${termMatch.count} time(s) in ${locationText} (configured to search in: ${searchInText}):`);
                   }
+                  
                   // Show details for each match
                   termMatch.matches.forEach((match, index) => {
                     core.notice(`    ${index + 1}. Line ${match.lineNumber} in ${match.searchLocation}: "${match.match}" [${match.searchType}]`);
@@ -244,6 +266,7 @@ jobs:
                     }
                   });
                 }
+                
                 shouldAddLabel = true;
                 const totalMatches = allMatches.reduce((sum, t) => sum + t.count, 0);
                 const titleMatches = allMatches.filter(t => t.searchLocation === 'title').reduce((sum, t) => sum + t.count, 0);
@@ -251,10 +274,13 @@ jobs:
                 const keywordMatches = allMatches.filter(t => t.searchType === 'keyword').reduce((sum, t) => sum + t.count, 0);
                 const substringMatches = allMatches.filter(t => t.searchType === 'substring').reduce((sum, t) => sum + t.count, 0);
                 const regexMatches = allMatches.filter(t => t.searchType === 'regex').reduce((sum, t) => sum + t.count, 0);
+                
                 reason = `Found ${totalMatches} total matches (${titleMatches} in title, ${bodyMatches} in body) - ${keywordMatches} keyword matches, ${substringMatches} substring matches, ${regexMatches} regex matches`;
               }
+              
               core.notice(`Final decision: ${shouldAddLabel ? 'ADD LABEL' : 'DO NOT ADD LABEL'}`);
               core.notice(`Reason: ${reason || 'No matching terms found'}`);
+              
               if (shouldAddLabel) {
                 const existingLabels = context.payload.issue.labels.map(l => l.name);
                 if (!existingLabels.includes(labelName)) {
@@ -270,92 +296,14 @@ jobs:
                 core.notice(`Label "${labelName}" already present.`);
                 return false;
               }
+              
               core.notice(`No matching terms found for label "${labelName}".`);
               return false;
             }
+            
             // Process all configured labels
-            const labelsAddedResults = await Promise.all(
-              Object.entries(labelConfig).map(([labelName, config]) => 
-                processLabel(labelName, config).then(added => ({ labelName, added }))
-              )
-            );
-            
-            const numLabelsAdded = labelsAddedResults.filter(r => r.added).length;
-            core.notice(`Processing complete. ${numLabelsAdded} label(s) added.`);
-            
-            // Return which labels were added for the next step
-            const addedLabels = labelsAddedResults.filter(r => r.added).map(r => r.labelName);
-            core.setOutput('labels_added', JSON.stringify(addedLabels));
-            return addedLabels;
-
-      - name: CC users for labeled issues
-        if: steps.label-step.outputs.labels_added != '[]'
-        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
-        with:
-          script: |
-            // Configuration: Map labels to GitHub users to CC
-            // You can add multiple users per label, and multiple label configurations
-            const ccConfig = {
-              rocm: {
-                users: ['hongxiayang', 'tjtanaa', 'vllmellm'],  // Add more users as needed: ['user1', 'user2', 'user3']
-                message: 'CC {users} for ROCm-related issue'  // {users} will be replaced with @mentions
-              },
-              // Add more label -> user mappings here
-              // Example:
-              // cuda: {
-              //   users: ['user1', 'user2'],
-              //   message: 'CC {users} for CUDA-related issue'
-              // },
-              // performance: {
-              //   users: ['perfexpert'],
-              //   message: 'CC {users} for performance issue'
-              // },
-            };
-            
-            const labelsAdded = JSON.parse('${{ steps.label-step.outputs.labels_added }}');
-            core.notice(`Labels added: ${labelsAdded.join(', ')}`);
-            
-            // Get existing comments to check for already mentioned users
-            const comments = await github.rest.issues.listComments({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-            });
-            
-            const issueBody = context.payload.issue.body || '';
-            const allExistingText = issueBody + '\n' + comments.data.map(c => c.body).join('\n');
-            
-            // Process each label that was added
-            for (const label of labelsAdded) {
-              if (ccConfig[label]) {
-                const config = ccConfig[label];
-                const usersToMention = [];
-                
-                // Check which users haven't been mentioned yet
-                for (const user of config.users) {
-                  const mentionPattern = new RegExp(`@${user}\\b`, 'i');
-                  if (!mentionPattern.test(allExistingText)) {
-                    usersToMention.push(user);
-                  } else {
-                    core.notice(`@${user} already mentioned for label "${label}", skipping`);
-                  }
-                }
-                
-                // Post comment if there are users to mention
-                if (usersToMention.length > 0) {
-                  const mentions = usersToMention.map(u => `@${u}`).join(' ');
-                  const message = config.message.replace('{users}', mentions);
-                  
-                  await github.rest.issues.createComment({
-                    owner: context.repo.owner,
-                    repo: context.repo.repo,
-                    issue_number: context.issue.number,
-                    body: message
-                  });
-                  
-                  core.notice(`CC comment added for label "${label}": ${mentions}`);
-                } else {
-                  core.notice(`All users for label "${label}" already mentioned, skipping comment`);
-                }
-              }
-            }
+            const processLabels = Object.entries(labelConfig)
+              .map(([labelName, config]) => processLabel(labelName, config));
+            const labelsAdded = await Promise.all(processLabels);
+            const numLabelsAdded = labelsAdded.reduce((x, y) => x + y, 0);
+            core.notice(`Processing complete. ${numLabelsAdded} label(s) added.`);

.github/workflows/stale.yml

@@ -13,7 +13,7 @@ jobs:
       actions: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10.1.0
+      - uses: actions/stale@3a9db7e6a41a89f618792c92c0e97cc736e1b13f # v10.0.0
         with:
           # Increasing this value ensures that changes to this workflow
           # propagate to all issues and PRs in days rather than months

.markdownlint.yaml

@@ -4,6 +4,7 @@ MD013: false
 MD024:
   siblings_only: true
 MD033: false
+MD042: false
 MD045: false
 MD046: false
 MD051: false

.pre-commit-config.yaml

@@ -6,19 +6,30 @@ default_stages:
   - manual # Run in CI
 exclude: 'vllm/third_party/.*'
 repos:
-- repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.14.0
+- repo: https://github.com/google/yapf
+  rev: v0.43.0
   hooks:
-  - id: ruff-check
+  - id: yapf
+    args: [--in-place, --verbose]
+    # Keep the same list from yapfignore here to avoid yapf failing without any inputs
+    exclude: '(.buildkite|benchmarks|build|examples)/.*'
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.11.7
+  hooks:
+  - id: ruff
     args: [--output-format, github, --fix]
   - id: ruff-format
+    files: ^(.buildkite|benchmarks|examples)/.*
 - repo: https://github.com/crate-ci/typos
-  rev: v1.38.1
+  rev: v1.35.5
   hooks:
   - id: typos
-    args: [--force-exclude]
+- repo: https://github.com/PyCQA/isort
+  rev: 6.0.1
+  hooks:
+  - id: isort
 - repo: https://github.com/pre-commit/mirrors-clang-format
-  rev: v21.1.2
+  rev: v20.1.3
   hooks:
   - id: clang-format
     exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
@@ -35,7 +46,7 @@ repos:
   hooks:
   - id: actionlint
 - repo: https://github.com/astral-sh/uv-pre-commit
-  rev: 0.9.1
+  rev: 0.6.17
   hooks:
     - id: pip-compile
       args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128, --python-platform, x86_64-manylinux_2_28]
@@ -56,6 +67,11 @@ repos:
       types_or: [python, pyi]
       require_serial: true
       additional_dependencies: [mypy==1.11.1, regex, types-cachetools, types-setuptools, types-PyYAML, types-requests, types-torch, pydantic]
+  - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
+    name: Run mypy for Python 3.9
+    entry: python tools/pre_commit/mypy.py 1 "3.9"
+    <<: *mypy_common
+    stages: [manual] # Only run in CI
   - id: mypy-3.10 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.10
     entry: python tools/pre_commit/mypy.py 1 "3.10"
@@ -71,11 +87,6 @@ repos:
     entry: python tools/pre_commit/mypy.py 1 "3.12"
     <<: *mypy_common
     stages: [manual] # Only run in CI
-  - id: mypy-3.13 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
-    name: Run mypy for Python 3.13
-    entry: python tools/pre_commit/mypy.py 1 "3.13"
-    <<: *mypy_common
-    stages: [manual] # Only run in CI
   - id: shellcheck
     name: Lint shell scripts
     entry: tools/shellcheck.sh

CMakeLists.txt

@@ -34,10 +34,10 @@ install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
-set(PYTHON_SUPPORTED_VERSIONS "3.10" "3.11" "3.12" "3.13")
+set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12" "3.13")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
 
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -86,9 +86,6 @@ find_package(Torch REQUIRED)
 # Supported NVIDIA architectures.
 # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
 if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
-   CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
-  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0")
-elseif(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8)
   set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
 else()
@@ -178,15 +175,6 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
 endif()
 
-#
-# Set compression mode for CUDA >=13.x.
-#
-if(VLLM_GPU_LANG STREQUAL "CUDA" AND
-   DEFINED CMAKE_CUDA_COMPILER_VERSION AND
-   CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
-  list(APPEND VLLM_GPU_FLAGS "--compress-mode=size")
-endif()
-
 #
 # Set CUDA include flags for CXX compiler.
 #
@@ -269,8 +257,8 @@ set(VLLM_EXT_SRC
   "csrc/sampler.cu"
   "csrc/cuda_view.cu"
   "csrc/quantization/gptq/q_gemm.cu"
-  "csrc/quantization/w8a8/int8/scaled_quant.cu"
-  "csrc/quantization/w8a8/fp8/common.cu"
+  "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
+  "csrc/quantization/fp8/common.cu"
   "csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
   "csrc/quantization/gguf/gguf_kernel.cu"
   "csrc/quantization/activation_kernels.cu"
@@ -282,7 +270,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
   # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
-  set(CUTLASS_REVISION "v4.2.1" CACHE STRING "CUTLASS revision to use")
+  set(CUTLASS_REVISION "v4.0.0" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -314,13 +302,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_EXT_SRC
     "csrc/quantization/awq/gemm_kernels.cu"
     "csrc/permute_cols.cu"
-    "csrc/quantization/w8a8/cutlass/scaled_mm_entry.cu"
+    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
     "csrc/quantization/fp4/nvfp4_quant_entry.cu"
     "csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
+    "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
     "csrc/cutlass_extensions/common.cpp"
-    "csrc/quantization/w8a8/fp8/per_token_group_quant.cu"
-    "csrc/quantization/w8a8/int8/per_token_group_quant.cu")
+    "csrc/quantization/fp8/per_token_group_quant.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -424,11 +412,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
     set(SRCS
-       "csrc/quantization/w8a8/cutlass/scaled_mm_c3x_sm90.cu"
-       "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_sm90_fp8.cu"
-       "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_sm90_int8.cu"
-       "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_azp_sm90_int8.cu"
-       "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm90_fp8.cu")
+       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
+       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
+       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8.cu"
+       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_azp_sm90_int8.cu"
+       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -452,16 +440,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # The cutlass_scaled_mm kernels for Geforce Blackwell SM120 (c3x, i.e. CUTLASS 3.x) require
   # CUDA 12.8 or later
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
     set(SRCS
-      "csrc/quantization/w8a8/cutlass/scaled_mm_c3x_sm120.cu"
-      "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_sm120_fp8.cu"
-      "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm120_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
     )
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -486,16 +470,12 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x)
   # require CUDA 12.8 or later
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
     set(SRCS
-      "csrc/quantization/w8a8/cutlass/scaled_mm_c3x_sm100.cu"
-      "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8.cu"
-      "csrc/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8.cu"
     )
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -526,7 +506,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # subtract out the archs that are already built for 3x
   list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
   if (SCALED_MM_2X_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/scaled_mm_c2x.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_2X_ARCHS}")
@@ -570,11 +550,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require
   # CUDA 12.8 or later
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(FP4_ARCHS "12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(FP4_ARCHS "12.0a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
@@ -593,11 +569,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 
   # FP4 Archs and flags
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(FP4_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(FP4_ARCHS "10.0a;10.1a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
@@ -619,11 +591,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 
   # CUTLASS MLA Archs and flags
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(MLA_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(MLA_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
     set(SRCS
       "csrc/attention/mla/sm100_cutlass_mla_kernel.cu")
@@ -649,7 +617,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # if it's possible to compile MoE kernels that use its output.
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/moe/grouped_mm_c3x_sm90.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -667,13 +635,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/moe/grouped_mm_c3x_sm100.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -692,13 +656,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 
   # moe_data.cu is used by all CUTLASS MoE kernels.
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/moe/moe_data.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${CUTLASS_MOE_DATA_ARCHS}")
@@ -715,13 +675,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
-  endif()
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/w8a8/cutlass/moe/blockwise_scaled_group_mm_sm100.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -1007,7 +963,6 @@ endif()
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
     include(cmake/external_projects/flashmla.cmake)
-    include(cmake/external_projects/qutlass.cmake)
 
     # vllm-flash-attn should be last as it overwrites some CMake functions
     include(cmake/external_projects/vllm_flash_attn.cmake)

README.md

@@ -21,7 +21,6 @@ Join us at the [PyTorch Conference, October 22-23](https://events.linuxfoundatio
 
 *Latest News* 🔥
 
-- [2025/09] We hosted [vLLM Toronto Meetup](https://luma.com/e80e0ymm) focused on tackling inference at scale and speculative decoding with speakers from NVIDIA and Red Hat! Please find the meetup slides [here](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing).
 - [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA).
 - [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing).
 - [2025/08] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg) focusing on building, developing, and integrating with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH).
@@ -149,7 +148,6 @@ Compute Resources:
 - Trainy
 - UC Berkeley
 - UC San Diego
-- Volcengine
 
 Slack Sponsor: Anyscale
 

benchmarks/auto_tune/auto_tune.sh

@@ -74,7 +74,7 @@ start_server() {
     local vllm_log=$4
     local profile_dir=$5
 
-    pkill -if "vllm serve" || true
+    pkill -if vllm
 
     # Define the common arguments as a bash array.
     # Each argument and its value are separate elements.
@@ -96,11 +96,11 @@ start_server() {
     # This correctly passes each element as a separate argument.
     if [[ -n "$profile_dir" ]]; then
         # Start server with profiling enabled
-        VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
+        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
             vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
     else
         # Start server without profiling
-        VLLM_SERVER_DEV_MODE=1 \
+        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 \
             vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
     fi
     local server_pid=$!
@@ -139,7 +139,7 @@ run_benchmark() {
     echo "vllm_log: $vllm_log"
     echo
     rm -f $vllm_log
-    pkill -if "vllm serve" || true
+    pkill -if vllm
 
     echo "starting server..."
     # Call start_server without a profile_dir to avoid profiling overhead
@@ -232,7 +232,7 @@ run_benchmark() {
 
     echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
 
-    pkill -if "vllm serve" || true
+    pkill -if vllm
     sleep 10
     echo "===================="
     return 0
@@ -308,6 +308,6 @@ if (( $(echo "$best_throughput > 0" | bc -l) )); then
 else
     echo "No configuration met the latency requirements. Skipping final profiling run."
 fi
-pkill -if "vllm serve" || true
+pkill -if vllm
 echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH"
 echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"

benchmarks/backend_request_func.py

@@ -8,6 +8,7 @@ import sys
 import time
 import traceback
 from dataclasses import dataclass, field
+from typing import Optional, Union
 
 import aiohttp
 import huggingface_hub.constants
@@ -27,13 +28,13 @@ class RequestFuncInput:
     prompt_len: int
     output_len: int
     model: str
-    model_name: str | None = None
-    logprobs: int | None = None
-    extra_body: dict | None = None
-    multi_modal_content: dict | list[dict] | None = None
+    model_name: Optional[str] = None
+    logprobs: Optional[int] = None
+    extra_body: Optional[dict] = None
+    multi_modal_content: Optional[dict | list[dict]] = None
     ignore_eos: bool = False
-    language: str | None = None
-    request_id: str | None = None
+    language: Optional[str] = None
+    request_id: Optional[str] = None
 
 
 @dataclass
@@ -51,7 +52,7 @@ class RequestFuncOutput:
 
 async def async_request_tgi(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith("generate_stream")
@@ -132,7 +133,7 @@ async def async_request_tgi(
 
 async def async_request_trt_llm(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith("generate_stream")
@@ -203,7 +204,7 @@ async def async_request_trt_llm(
 
 async def async_request_deepspeed_mii(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(("completions", "profile")), (
@@ -266,7 +267,7 @@ async def async_request_deepspeed_mii(
 
 async def async_request_openai_completions(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(("completions", "profile")), (
@@ -366,7 +367,7 @@ async def async_request_openai_completions(
 
 async def async_request_openai_chat_completions(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(("chat/completions", "profile")), (
@@ -475,7 +476,7 @@ async def async_request_openai_chat_completions(
 
 async def async_request_openai_audio(
     request_func_input: RequestFuncInput,
-    pbar: tqdm | None = None,
+    pbar: Optional[tqdm] = None,
 ) -> RequestFuncOutput:
     # Lazy import without PlaceholderModule to avoid vllm dep.
     import soundfile
@@ -609,7 +610,7 @@ def get_tokenizer(
     tokenizer_mode: str = "auto",
     trust_remote_code: bool = False,
     **kwargs,
-) -> PreTrainedTokenizer | PreTrainedTokenizerFast:
+) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
     if pretrained_model_name_or_path is not None and not os.path.exists(
         pretrained_model_name_or_path
     ):

benchmarks/benchmark_block_pool.py

@@ -2,9 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import gc
 
-from benchmark_utils import TimeCollector
 from tabulate import tabulate
 
+from benchmark_utils import TimeCollector
 from vllm.utils import FlexibleArgumentParser
 from vllm.v1.core.block_pool import BlockPool
 

benchmarks/benchmark_ngram_proposer.py

@@ -5,9 +5,9 @@ import time
 from unittest import mock
 
 import numpy as np
-from benchmark_utils import TimeCollector
 from tabulate import tabulate
 
+from benchmark_utils import TimeCollector
 from vllm.config import (
     CacheConfig,
     DeviceConfig,
@@ -164,7 +164,7 @@ def invoke_main() -> None:
     )
     parser.add_argument(
         "--batched", action="store_true", help="consider time to prepare batch"
-    )
+    )  # noqa: E501
     parser.add_argument(
         "--num-iteration",
         type=int,

benchmarks/benchmark_prefix_caching.py

@@ -32,6 +32,7 @@ import dataclasses
 import json
 import random
 import time
+from typing import Optional
 
 from transformers import PreTrainedTokenizerBase
 
@@ -79,7 +80,7 @@ def sample_requests_from_dataset(
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
     input_length_range: tuple[int, int],
-    fixed_output_len: int | None,
+    fixed_output_len: Optional[int],
 ) -> list[Request]:
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")
@@ -127,7 +128,7 @@ def sample_requests_from_random(
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
     input_length_range: tuple[int, int],
-    fixed_output_len: int | None,
+    fixed_output_len: Optional[int],
     prefix_len: int,
 ) -> list[Request]:
     requests = []

benchmarks/benchmark_prioritization.py

@@ -7,6 +7,7 @@ import dataclasses
 import json
 import random
 import time
+from typing import Optional
 
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 
@@ -23,7 +24,7 @@ def sample_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: int | None,
+    fixed_output_len: Optional[int],
 ) -> list[tuple[str, int, int, int]]:
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")

benchmarks/benchmark_serving_structured_output.py

@@ -31,19 +31,20 @@ import time
 import uuid
 import warnings
 from collections.abc import AsyncGenerator
-from contextlib import nullcontext
 from dataclasses import dataclass
+from typing import Optional
 
 import datasets
 import numpy as np
 import pandas as pd
+from tqdm.asyncio import tqdm
+from transformers import PreTrainedTokenizerBase
+
 from backend_request_func import (
     ASYNC_REQUEST_FUNCS,
     RequestFuncInput,
     RequestFuncOutput,
 )
-from tqdm.asyncio import tqdm
-from transformers import PreTrainedTokenizerBase
 
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer
@@ -316,7 +317,7 @@ def calculate_metrics(
     tokenizer: PreTrainedTokenizerBase,
     selected_percentile_metrics: list[str],
     selected_percentiles: list[float],
-    goodput_config_dict: dict[str, float] | None = None,
+    goodput_config_dict: Optional[dict[str, float]] = None,
 ) -> tuple[BenchmarkMetrics, list[int]]:
     actual_output_lens: list[int] = []
     total_input = 0
@@ -436,9 +437,9 @@ async def benchmark(
     selected_percentile_metrics: list[str],
     selected_percentiles: list[str],
     ignore_eos: bool,
-    max_concurrency: int | None,
+    max_concurrency: Optional[int],
     structured_output_ratio: float,
-    goodput_config_dict: dict[str, float] | None = None,
+    goodput_config_dict: Optional[dict[str, float]] = None,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -502,9 +503,15 @@ async def benchmark(
 
     pbar = None if disable_tqdm else tqdm(total=len(input_requests))
 
-    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else nullcontext()
+    # This can be used once the minimum Python version is 3.10 or higher,
+    # and it will simplify the code in limited_request_func.
+    #    semaphore = (asyncio.Semaphore(max_concurrency)
+    #                 if max_concurrency else contextlib.nullcontext())
+    semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None
 
     async def limited_request_func(request_func_input, pbar):
+        if semaphore is None:
+            return await request_func(request_func_input=request_func_input, pbar=pbar)
         async with semaphore:
             return await request_func(request_func_input=request_func_input, pbar=pbar)
 
@@ -903,13 +910,13 @@ def create_argument_parser():
     parser.add_argument(
         "--tokenizer",
         type=str,
-        help="Name or path of the tokenizer, if not using the default tokenizer.",
+        help="Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
     parser.add_argument(
         "--tokenizer-mode",
         type=str,
         default="auto",
-        help="Name or path of the tokenizer, if not using the default tokenizer.",
+        help="Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
     parser.add_argument(
         "--num-prompts",

benchmarks/benchmark_utils.py

@@ -6,7 +6,7 @@ import math
 import os
 import time
 from types import TracebackType
-from typing import Any
+from typing import Any, Optional, Union
 
 
 def convert_to_pytorch_benchmark_format(
@@ -92,7 +92,7 @@ class TimeCollector:
     def __init__(self, scale: int) -> None:
         self.cnt: int = 0
         self._sum: int = 0
-        self._max: int | None = None
+        self._max: Optional[int] = None
         self.scale = scale
         self.start_time: int = time.monotonic_ns()
 
@@ -104,13 +104,13 @@ class TimeCollector:
         else:
             self._max = max(self._max, v)
 
-    def avg(self) -> float | str:
+    def avg(self) -> Union[float, str]:
         return self._sum * 1.0 / self.cnt / self.scale if self.cnt > 0 else "N/A"
 
-    def max(self) -> float | str:
+    def max(self) -> Union[float, str]:
         return self._max / self.scale if self._max else "N/A"
 
-    def dump_avg_max(self) -> list[float | str]:
+    def dump_avg_max(self) -> list[Union[float, str]]:
         return [self.avg(), self.max()]
 
     def __enter__(self) -> None:
@@ -118,8 +118,8 @@ class TimeCollector:
 
     def __exit__(
         self,
-        exc_type: type[BaseException] | None,
-        exc_value: BaseException | None,
-        exc_traceback: TracebackType | None,
+        exc_type: Optional[type[BaseException]],
+        exc_value: Optional[BaseException],
+        exc_traceback: Optional[TracebackType],
     ) -> None:
         self.collect(time.monotonic_ns() - self.start_time)

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -6,7 +6,8 @@ import copy
 import itertools
 import pickle as pkl
 import time
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
+from typing import Callable
 
 import torch
 import torch.utils.benchmark as TBenchmark

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -6,7 +6,8 @@ import copy
 import itertools
 import pickle as pkl
 import time
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
+from typing import Callable, Optional
 
 import torch
 import torch.utils.benchmark as TBenchmark
@@ -16,7 +17,7 @@ from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    w8a8_triton_block_scaled_mm,
+    w8a8_block_fp8_matmul,
 )
 from vllm.utils import FlexibleArgumentParser, cdiv
 
@@ -52,7 +53,7 @@ def bench_int8(
     n: int,
     label: str,
     sub_label: str,
-    bench_kernels: list[str] | None = None,
+    bench_kernels: Optional[list[str]] = None,
 ) -> Iterable[TMeasurement]:
     """Benchmark INT8-based kernels."""
     assert dtype == torch.int8
@@ -107,7 +108,7 @@ def bench_fp8(
     n: int,
     label: str,
     sub_label: str,
-    bench_kernels: list[str] | None = None,
+    bench_kernels: Optional[list[str]] = None,
 ) -> Iterable[TMeasurement]:
     """Benchmark FP8-based kernels."""
     assert dtype == torch.float8_e4m3fn
@@ -157,7 +158,7 @@ def bench_fp8(
         "cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
             a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
         ),
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_triton_block_scaled_mm(
+        "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
             a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
         ),
         "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(
@@ -182,7 +183,7 @@ def bench(
     n: int,
     label: str,
     sub_label: str,
-    bench_kernels: list[str] | None = None,
+    bench_kernels: Optional[list[str]] = None,
 ) -> Iterable[TMeasurement]:
     if dtype == torch.int8:
         return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
@@ -200,7 +201,7 @@ def print_timers(timers: Iterable[TMeasurement]):
 def run(
     dtype: torch.dtype,
     MKNs: Iterable[tuple[int, int, int]],
-    bench_kernels: list[str] | None = None,
+    bench_kernels: Optional[list[str]] = None,
 ) -> Iterable[TMeasurement]:
     results = []
     for m, k, n in MKNs:

benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh

@@ -55,7 +55,9 @@ benchmark() {
   output_len=$2
 
 
-  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=0 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8100 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \
@@ -63,7 +65,9 @@ benchmark() {
     '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
 
 
-  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=1 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8200 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \

benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh

@@ -38,12 +38,16 @@ wait_for_server() {
 launch_chunked_prefill() {
   model="meta-llama/Meta-Llama-3.1-8B-Instruct"
   # disagg prefill
-  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=0 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8100 \
     --max-model-len 10000 \
     --enable-chunked-prefill \
     --gpu-memory-utilization 0.6 &
-  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=1 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8200 \
     --max-model-len 10000 \
     --enable-chunked-prefill \
@@ -58,14 +62,18 @@ launch_chunked_prefill() {
 launch_disagg_prefill() {
   model="meta-llama/Meta-Llama-3.1-8B-Instruct"
   # disagg prefill
-  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=0 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8100 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \
     --kv-transfer-config \
     '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
 
-  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
+  CUDA_VISIBLE_DEVICES=1 python3 \
+    -m vllm.entrypoints.openai.api_server \
+    --model $model \
     --port 8200 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -3,9 +3,10 @@
 
 import pickle as pkl
 import time
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 from dataclasses import dataclass
 from itertools import product
+from typing import Callable, Optional
 
 import torch
 import torch.utils.benchmark as TBenchmark
@@ -50,7 +51,7 @@ def get_bench_params() -> list[bench_params_t]:
 def unfused_int8_impl(
     rms_norm_layer: RMSNorm,
     x: torch.Tensor,
-    residual: torch.Tensor | None,
+    residual: Optional[torch.Tensor],
     quant_dtype: torch.dtype,
 ):
     # Norm
@@ -67,7 +68,7 @@ def unfused_int8_impl(
 def unfused_fp8_impl(
     rms_norm_layer: RMSNorm,
     x: torch.Tensor,
-    residual: torch.Tensor | None,
+    residual: Optional[torch.Tensor],
     quant_dtype: torch.dtype,
 ):
     # Norm
@@ -84,7 +85,7 @@ def unfused_fp8_impl(
 def fused_impl(
     rms_norm_layer: RMSNorm,  # this stores the weights
     x: torch.Tensor,
-    residual: torch.Tensor | None,
+    residual: Optional[torch.Tensor],
     quant_dtype: torch.dtype,
 ):
     out, _ = ops.rms_norm_dynamic_per_token_quant(

benchmarks/kernels/bench_mxfp4_qutlass.py

@@ -1,191 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Copyright (C) 2025 Roberto L. Castro (Roberto.LopezCastro@ist.ac.at).
-# 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.
-#
-
-import argparse
-import copy
-import itertools
-
-import torch
-from compressed_tensors.transform.utils.hadamard import deterministic_hadamard_matrix
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm._custom_ops import fusedQuantizeMx, matmul_mxf4_bf16_tn
-from vllm.model_executor.layers.quantization.qutlass_utils import to_blocked
-from vllm.triton_utils import triton
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "mxfp4": dict(no_a_quant=False, enabled=True),
-    "mxfp4-noquant": dict(no_a_quant=True, enabled=True),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def get_hadamard_matrix(group_size: int, dtype: torch.dtype, device: torch.device):
-    return (
-        deterministic_hadamard_matrix(group_size, dtype=dtype, device=device)
-        * group_size**-0.5
-    )
-
-
-def _quant_weight_mxfp4(
-    b: torch.Tensor, forward_hadamard_matrix: torch.Tensor, device: str
-):
-    weight_hf_e2m1, weight_hf_e8m0 = fusedQuantizeMx(
-        b, forward_hadamard_matrix, method="abs_max"
-    )
-    weight_hf_scale_block = to_blocked(weight_hf_e8m0, backend="triton")
-    return weight_hf_e2m1, weight_hf_scale_block
-
-
-def build_mxfp4_runner(cfg, a, b, forward_hadamard_matrix, dtype, device):
-    weight_hf_e2m1, weight_hf_scale_block = _quant_weight_mxfp4(
-        b, forward_hadamard_matrix, device
-    )
-    alpha = torch.tensor([1.0], device="cuda")
-
-    if cfg["no_a_quant"]:
-        # Pre-quantize activation
-        input_hf_e2m1, input_hf_e8m0 = fusedQuantizeMx(
-            a, forward_hadamard_matrix, method="abs_max"
-        )
-        input_hf_scale_block = to_blocked(input_hf_e8m0, backend="triton")
-
-        def run():
-            return matmul_mxf4_bf16_tn(
-                input_hf_e2m1,
-                weight_hf_e2m1,
-                input_hf_scale_block,
-                weight_hf_scale_block,
-                alpha,
-            )
-
-        return run
-
-    # Quantize activation on-the-fly
-    def run():
-        input_hf_e2m1, input_hf_e8m0 = fusedQuantizeMx(
-            a, forward_hadamard_matrix, method="abs_max"
-        )
-        input_hf_scale_block = to_blocked(input_hf_e8m0, backend="triton")
-        return matmul_mxf4_bf16_tn(
-            input_hf_e2m1,
-            weight_hf_e2m1,
-            input_hf_scale_block,
-            weight_hf_scale_block,
-            alpha,
-        )
-
-    return run
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[
-            1,
-            4,
-            8,
-            16,
-            32,
-            64,
-            128,
-            256,
-            512,
-            1024,
-            2048,
-            4096,
-            8192,
-            16384,
-            24576,
-            32768,
-        ],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs MXFP4 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K, had_size):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-    forward_hadamard_matrix = get_hadamard_matrix(had_size, dtype, device)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), rep=200, quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_mxfp4_runner(
-            cfg, a, b, forward_hadamard_matrix, dtype, device
-        )
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), rep=200, quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.3-70B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        for had_size in [32, 64, 128]:
-            print(f"{model}, N={N} K={K}, HAD={had_size}, BF16 vs MXFP4 GEMMs TFLOP/s:")
-            benchmark.run(
-                print_data=True,
-                show_plots=True,
-                save_path=f"bench_mxfp4_res_n{N}_k{K}",
-                N=N,
-                K=K,
-                had_size=had_size,
-            )
-
-    print("Benchmark finished!")

benchmarks/kernels/bench_nvfp4_qutlass.py

@@ -1,207 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Copyright (C) 2025 Roberto L. Castro (Roberto.LopezCastro@ist.ac.at).
-# 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.
-#
-
-import argparse
-import copy
-import itertools
-
-import torch
-from compressed_tensors.transform.utils.hadamard import deterministic_hadamard_matrix
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm import _custom_ops as ops  # use existing nvfp4 gemm in vllm
-from vllm._custom_ops import fusedQuantizeNv
-from vllm.model_executor.layers.quantization.qutlass_utils import to_blocked
-from vllm.triton_utils import triton
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "nvfp4": dict(no_a_quant=False, enabled=True),
-    "nvfp4-noquant": dict(no_a_quant=True, enabled=True),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def get_hadamard_matrix(group_size: int, dtype: torch.dtype, device: torch.device):
-    return (
-        deterministic_hadamard_matrix(group_size, dtype=dtype, device=device)
-        * group_size**-0.5
-    )
-
-
-def _quant_weight_nvfp4(
-    b: torch.Tensor,
-    forward_hadamard_matrix: torch.Tensor,
-    global_scale: torch.Tensor,
-    device: str,
-    M: int,
-    N: int,
-    K: int,
-):
-    weight_hf_e2m1, weight_hf_e8m0 = fusedQuantizeNv(
-        b, forward_hadamard_matrix, global_scale
-    )
-    weight_hf_scale_block = to_blocked(weight_hf_e8m0, backend="triton").view(
-        -1, K // 16
-    )
-    return weight_hf_e2m1, weight_hf_scale_block
-
-
-def build_nvfp4_runner(cfg, a, b, forward_hadamard_matrix, dtype, device, M, N, K):
-    alpha = torch.tensor([1.0], device="cuda")
-    global_scale = torch.tensor([1.0], device="cuda")
-    weight_hf_e2m1, weight_hf_scale_block = _quant_weight_nvfp4(
-        b, forward_hadamard_matrix, global_scale, device, M, N, K
-    )
-
-    if cfg["no_a_quant"]:
-        # Pre-quantize activation
-        input_hf_e2m1, input_hf_e8m0 = fusedQuantizeNv(
-            a, forward_hadamard_matrix, global_scale
-        )
-        input_hf_scale_block = to_blocked(input_hf_e8m0, backend="triton").view(
-            -1, K // 16
-        )
-
-        def run():
-            return ops.cutlass_scaled_fp4_mm(
-                input_hf_e2m1,
-                weight_hf_e2m1,
-                input_hf_scale_block,
-                weight_hf_scale_block,
-                alpha,
-                torch.bfloat16,
-            )
-
-        return run
-
-    # Quantize activation on-the-fly
-    def run():
-        input_hf_e2m1, input_hf_e8m0 = fusedQuantizeNv(
-            a, forward_hadamard_matrix, global_scale
-        )
-        input_hf_scale_block = to_blocked(input_hf_e8m0, backend="triton").view(
-            -1, K // 16
-        )
-        return ops.cutlass_scaled_fp4_mm(
-            input_hf_e2m1,
-            weight_hf_e2m1,
-            input_hf_scale_block,
-            weight_hf_scale_block,
-            alpha,
-            torch.bfloat16,
-        )
-
-    return run
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[
-            1,
-            4,
-            8,
-            16,
-            32,
-            64,
-            128,
-            256,
-            512,
-            1024,
-            2048,
-            4096,
-            8192,
-            16384,
-            24576,
-            32768,
-        ],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs NVFP4 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K, had_size):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-    forward_hadamard_matrix = get_hadamard_matrix(had_size, dtype, device)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), rep=200, quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_nvfp4_runner(
-            cfg, a, b, forward_hadamard_matrix, dtype, device, M, N, K
-        )
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), rep=200, quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.3-70B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        for had_size in [16, 32, 64, 128]:
-            print(f"{model}, N={N} K={K}, HAD={had_size}, BF16 vs NVFP4 GEMMs TFLOP/s:")
-            benchmark.run(
-                print_data=True,
-                show_plots=True,
-                save_path=f"bench_nvfp4_res_n{N}_k{K}",
-                N=N,
-                K=K,
-                had_size=had_size,
-            )
-
-    print("Benchmark finished!")

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import itertools
-from collections.abc import Callable
+from typing import Callable
 from unittest.mock import patch
 
 import pandas as pd
@@ -10,8 +10,7 @@ import torch
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 def with_triton_mode(fn):

benchmarks/kernels/benchmark_activation.py

@@ -10,8 +10,7 @@ import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 batch_size_range = [1, 16, 32, 64, 128]
 seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]

benchmarks/kernels/benchmark_device_communicators.py

@@ -22,8 +22,8 @@ Example:
 import json
 import os
 import time
-from collections.abc import Callable
 from contextlib import nullcontext
+from typing import Callable, Optional
 
 import torch
 import torch.distributed as dist
@@ -264,12 +264,12 @@ class CommunicatorBenchmark:
     def benchmark_allreduce_single(
         self,
         sequence_length: int,
-        allreduce_fn: Callable[[torch.Tensor], torch.Tensor | None],
+        allreduce_fn: Callable[[torch.Tensor], Optional[torch.Tensor]],
         should_use_fn: Callable[[torch.Tensor], bool],
         context,
         num_warmup: int,
         num_trials: int,
-    ) -> float | None:
+    ) -> Optional[float]:
         """Benchmark method with CUDA graph optimization."""
         try:
             # Create test tensor (2D: sequence_length x hidden_size)

benchmarks/kernels/benchmark_layernorm.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_lora.py

@@ -6,12 +6,11 @@ import copy
 import json
 import pickle
 import time
-from collections.abc import Callable
 from dataclasses import dataclass
 from enum import Enum, auto
 from itertools import product
 from pathlib import Path
-from typing import Any
+from typing import Any, Callable, Optional
 
 import torch
 import torch.utils.benchmark as TBenchmark
@@ -159,7 +158,7 @@ def ref_group_gemm(
     seq_lens_cpu: torch.Tensor,
     prompt_lora_mapping_cpu: torch.Tensor,
     scaling: float,
-    add_inputs: bool | None,
+    add_inputs: Optional[bool],
 ):
     """
     Torch group gemm reference implementation to test correctness of
@@ -317,8 +316,8 @@ class BenchmarkContext:
     lora_rank: int
     sort_by_lora_id: bool
     dtype: torch.dtype
-    seq_length: int | None = None
-    num_slices: int | None = None  # num_slices for slice based ops
+    seq_length: Optional[int] = None
+    num_slices: Optional[int] = None  # num_slices for slice based ops
 
     def with_seq_length(self, seq_length: int) -> "BenchmarkContext":
         ctx = copy.copy(self)
@@ -562,7 +561,7 @@ class BenchmarkTensors:
         }
 
     def bench_fn_kwargs(
-        self, op_type: OpType, add_inputs: bool | None = None
+        self, op_type: OpType, add_inputs: Optional[bool] = None
     ) -> dict[str, Any]:
         if op_type.is_shrink_fn():
             assert add_inputs is None
@@ -576,7 +575,7 @@ class BenchmarkTensors:
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(
-        self, op_type: OpType, expand_fn_add_inputs: bool | None
+        self, op_type: OpType, expand_fn_add_inputs: Optional[bool]
     ) -> bool:
         """
         Test correctness of op_type implementation against a grouped gemm
@@ -612,8 +611,8 @@ def bench_optype(
     ctx: BenchmarkContext,
     arg_pool_size: int,
     op_type: OpType,
-    cuda_graph_nops: int | None = None,
-    expand_fn_add_inputs: bool | None = None,
+    cuda_graph_nops: Optional[int] = None,
+    expand_fn_add_inputs: Optional[bool] = None,
     test_correctness: bool = False,
 ) -> TMeasurement:
     assert arg_pool_size >= 1
@@ -680,7 +679,7 @@ def bench_torch_mm(
     ctx: BenchmarkContext,
     arg_pool_size: int,
     op_type: OpType,
-    cuda_graph_nops: int | None = None,
+    cuda_graph_nops: Optional[int] = None,
 ) -> TMeasurement:
     """
     Benchmark basic torch.mm as a roofline.
@@ -745,7 +744,7 @@ def use_cuda_graph_recommendation() -> str:
             """
 
 
-def print_timers(timers: list[TMeasurement], args: argparse.Namespace | None = None):
+def print_timers(timers: list[TMeasurement], args: Optional[argparse.Namespace] = None):
     compare = TBenchmark.Compare(timers)
     compare.print()
 

benchmarks/kernels/benchmark_machete.py

@@ -8,9 +8,10 @@ import math
 import os
 import pickle as pkl
 import time
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 from dataclasses import dataclass
 from itertools import product
+from typing import Callable, Optional
 
 import pandas as pd
 import torch
@@ -62,23 +63,23 @@ class BenchmarkTensors:
     a: torch.Tensor
 
     w_q: torch.Tensor
-    group_size: int | None
+    group_size: Optional[int]
     wtype: ScalarType
     w_g_s: torch.Tensor
-    w_g_zp: torch.Tensor | None
-    w_ch_s: torch.Tensor | None
-    w_tok_s: torch.Tensor | None
+    w_g_zp: Optional[torch.Tensor]
+    w_ch_s: Optional[torch.Tensor]
+    w_tok_s: Optional[torch.Tensor]
 
 
 @dataclass
 class TypeConfig:
     act_type: torch.dtype
     weight_type: ScalarType
-    output_type: torch.dtype | None
-    group_scale_type: torch.dtype | None
-    group_zero_type: torch.dtype | None
-    channel_scale_type: torch.dtype | None
-    token_scale_type: torch.dtype | None
+    output_type: Optional[torch.dtype]
+    group_scale_type: Optional[torch.dtype]
+    group_zero_type: Optional[torch.dtype]
+    channel_scale_type: Optional[torch.dtype]
+    token_scale_type: Optional[torch.dtype]
 
 
 def rand_data(shape, dtype=torch.float16, scale=1):
@@ -92,8 +93,8 @@ def quantize_and_pack(
     atype: torch.dtype,
     w: torch.Tensor,
     wtype: ScalarType,
-    stype: torch.dtype | None,
-    group_size: int | None,
+    stype: Optional[torch.dtype],
+    group_size: Optional[int],
     zero_points: bool = False,
 ):
     assert wtype.is_integer(), "TODO: support floating point weights"
@@ -112,7 +113,7 @@ def quantize_and_pack(
 
 
 def create_bench_tensors(
-    shape: tuple[int, int, int], types: TypeConfig, group_size: int | None
+    shape: tuple[int, int, int], types: TypeConfig, group_size: Optional[int]
 ) -> list[BenchmarkTensors]:
     m, n, k = shape
 
@@ -330,8 +331,8 @@ def bench_fns(label: str, sub_label: str, description: str, fns: list[Callable])
     return res
 
 
-_SWEEP_SCHEDULES_RESULTS: pd.DataFrame | None = None
-_SWEEP_SCHEDULES_RESULTS_CSV: str | None = None
+_SWEEP_SCHEDULES_RESULTS: Optional[pd.DataFrame] = None
+_SWEEP_SCHEDULES_RESULTS_CSV: Optional[str] = None
 
 
 def bench(

benchmarks/kernels/benchmark_moe.py

@@ -579,12 +579,10 @@ def main(args: argparse.Namespace):
         E = config.ffn_config.moe_num_experts
         topk = config.ffn_config.moe_top_k
         intermediate_size = config.ffn_config.ffn_hidden_size
-        hidden_size = config.hidden_size
     elif config.architectures[0] == "JambaForCausalLM":
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
-        hidden_size = config.hidden_size
     elif config.architectures[0] in (
         "DeepseekV2ForCausalLM",
         "DeepseekV3ForCausalLM",
@@ -594,7 +592,6 @@ def main(args: argparse.Namespace):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
-        hidden_size = config.hidden_size
     elif config.architectures[0] in (
         "Qwen2MoeForCausalLM",
         "Qwen3MoeForCausalLM",
@@ -603,18 +600,10 @@ def main(args: argparse.Namespace):
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
-        hidden_size = config.hidden_size
-    elif config.architectures[0] == "Qwen3VLMoeForConditionalGeneration":
-        text_config = config.get_text_config()
-        E = text_config.num_experts
-        topk = text_config.num_experts_per_tok
-        intermediate_size = text_config.moe_intermediate_size
-        hidden_size = text_config.hidden_size
     elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
         E = config.num_experts
         topk = config.moe_topk[0]
         intermediate_size = config.moe_intermediate_size[0]
-        hidden_size = config.hidden_size
     else:
         # Support for llama4
         config = config.get_text_config()
@@ -622,7 +611,6 @@ def main(args: argparse.Namespace):
         E = config.num_local_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
-        hidden_size = config.hidden_size
     enable_ep = bool(args.enable_expert_parallel)
     if enable_ep:
         ensure_divisibility(E, args.tp_size, "Number of experts")
@@ -631,7 +619,8 @@ def main(args: argparse.Namespace):
     else:
         ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
-    dtype = torch.float16 if current_platform.is_rocm() else config.dtype
+    hidden_size = config.hidden_size
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
     use_fp8_w8a8 = args.dtype == "fp8_w8a8"
     use_int8_w8a16 = args.dtype == "int8_w8a16"
     block_quant_shape = get_weight_block_size_safety(config)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -344,7 +344,7 @@ def main(args: argparse.Namespace):
         topk = config.num_experts_per_tok
 
     hidden_size = config.hidden_size
-    dtype = torch.float16 if current_platform.is_rocm() else config.dtype
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
     use_fp8_w8a8 = args.dtype == "fp8_w8a8"
     use_int8_w8a16 = args.dtype == "int8_w8a16"
     use_customized_permute = args.use_customized_permute

benchmarks/kernels/benchmark_paged_attention.py

@@ -3,15 +3,16 @@
 
 import random
 import time
+from typing import Optional
 
 import torch
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 
@@ -36,7 +37,7 @@ def main(
     seed: int,
     do_profile: bool,
     device: str = "cuda",
-    kv_cache_dtype: str | None = None,
+    kv_cache_dtype: Optional[str] = None,
 ) -> None:
     current_platform.seed_everything(seed)
 

benchmarks/kernels/benchmark_per_token_group_quant.py

@@ -3,8 +3,8 @@
 
 import argparse
 import math
-from collections.abc import Callable
 from contextlib import contextmanager
+from typing import Callable
 from unittest.mock import patch
 
 import torch

benchmarks/kernels/benchmark_polynorm.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import itertools
+
+import torch
+
+from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
+
+
+def polynorm_naive(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    def norm(x, eps: float):
+        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + eps)
+
+    x = x.float()
+    return (
+        (
+            weight[0] * norm(x**3, eps)
+            + weight[1] * norm(x**2, eps)
+            + weight[2] * norm(x, eps)
+            + bias
+        )
+        .to(weight.dtype)
+        .view(orig_shape)
+    )
+
+
+def polynorm_vllm(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    out = torch.empty_like(x)
+    vllm_ops.poly_norm(out, x, weight, bias, eps)
+    output = out
+
+    output = output.view(orig_shape)
+    return output
+
+
+def calculate_diff(batch_size, seq_len, hidden_dim):
+    dtype = torch.bfloat16
+    x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+    weight = torch.ones(3, dtype=dtype, device="cuda")
+    bias = torch.ones(1, dtype=dtype, device="cuda")
+
+    output_naive = polynorm_naive(x, weight, bias)
+    output_vllm = polynorm_vllm(x, weight, bias)
+
+    if torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [2**i for i in range(0, 7, 2)]
+seq_length_range = [2**i for i in range(6, 11, 1)]
+dim_range = [2048, 4096]
+configs = list(itertools.product(dim_range, batch_size_range, seq_length_range))
+
+
+def get_benchmark():
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["dim", "batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["naive", "vllm"],
+            line_names=["Naive", "vLLM"],
+            styles=[("blue", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name="polynorm-perf",
+            args={},
+        )
+    )
+    def benchmark(dim, batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        hidden_dim = dim * 4
+
+        x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+        weight = torch.ones(3, dtype=dtype, device="cuda")
+        bias = torch.ones(1, dtype=dtype, device="cuda")
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "naive":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_naive(x, weight, bias),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_vllm(x, weight, bias),
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=4,
+        help="Batch size",
+    )
+    parser.add_argument(
+        "--seq-len",
+        type=int,
+        default=128,
+        help="Sequence length",
+    )
+    parser.add_argument(
+        "--hidden-dim",
+        type=int,
+        default=8192,
+        help="Intermediate size of MLP",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/polnorm/",
+        help="Path to save polnorm benchmark results",
+    )
+
+    args = parser.parse_args()
+
+    # Run correctness test
+    calculate_diff(
+        batch_size=args.batch_size,
+        seq_len=args.seq_len,
+        hidden_dim=args.hidden_dim,
+    )
+
+    benchmark = get_benchmark()
+    # Run performance benchmark
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmarks/kernels/benchmark_quant.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -1,172 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import random
-import time
-
-import torch
-from tabulate import tabulate
-
-from vllm import _custom_ops as ops
-from vllm.logger import init_logger
-from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
-    STR_DTYPE_TO_TORCH_DTYPE,
-    create_kv_caches_with_random,
-)
-
-logger = init_logger(__name__)
-
-
-@torch.inference_mode()
-def run_benchmark(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-    kv_cache_dtype: str,
-    num_iters: int,
-    benchmark_mode: str,
-    device: str = "cuda",
-) -> float:
-    """Return latency (seconds) for given num_tokens."""
-
-    if kv_cache_dtype == "fp8" and head_size % 16:
-        raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
-
-    current_platform.seed_everything(42)
-    torch.set_default_device(device)
-
-    # create random key / value tensors [T, H, D].
-    key = torch.randn(num_tokens, num_heads, head_size, dtype=dtype, device=device)
-    value = torch.randn_like(key)
-
-    # prepare the slot mapping.
-    # each token is assigned a unique slot in the KV-cache.
-    num_slots = block_size * num_blocks
-    if num_tokens > num_slots:
-        raise ValueError("num_tokens cannot exceed the total number of cache slots")
-    slot_mapping_lst = random.sample(range(num_slots), num_tokens)
-    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)
-
-    key_caches, value_caches = create_kv_caches_with_random(
-        num_blocks,
-        block_size,
-        1,  # num_layers
-        num_heads,
-        head_size,
-        kv_cache_dtype,
-        dtype,
-        device=device,
-    )
-    key_cache, value_cache = key_caches[0], value_caches[0]
-    # to free unused memory
-    del key_caches, value_caches
-
-    # compute per-kernel scaling factors for fp8 conversion (if used).
-    k_scale = (key.amax() / 64.0).to(torch.float32)
-    v_scale = (value.amax() / 64.0).to(torch.float32)
-
-    function_under_test = lambda: ops.reshape_and_cache(
-        key,  # noqa: F821
-        value,  # noqa: F821
-        key_cache,  # noqa: F821
-        value_cache,  # noqa: F821
-        slot_mapping,  # noqa: F821
-        kv_cache_dtype,
-        k_scale,
-        v_scale,
-    )
-
-    if benchmark_mode == "cudagraph":
-        g = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(g):
-            function_under_test()
-        torch.cuda.synchronize()
-        function_under_test = lambda: g.replay()
-
-    def run_cuda_benchmark(n_iters: int) -> float:
-        nonlocal key, value, key_cache, value_cache, slot_mapping
-        torch.cuda.synchronize()
-        start = time.perf_counter()
-        for _ in range(n_iters):
-            function_under_test()
-            torch.cuda.synchronize()
-        end = time.perf_counter()
-        return (end - start) / n_iters
-
-    # warm-up
-    run_cuda_benchmark(3)
-
-    lat = run_cuda_benchmark(num_iters)
-
-    # free tensors to mitigate OOM when sweeping
-    del key, value, key_cache, value_cache, slot_mapping
-    torch.cuda.empty_cache()
-
-    return lat
-
-
-def main(args):
-    rows = []
-    for exp in range(1, 17):
-        n_tok = 2**exp
-        lat = run_benchmark(
-            num_tokens=n_tok,
-            num_heads=args.num_heads,
-            head_size=args.head_size,
-            block_size=args.block_size,
-            num_blocks=args.num_blocks,
-            dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-            kv_cache_dtype=args.kv_cache_dtype,
-            num_iters=args.iters,
-            benchmark_mode=args.mode,
-            device="cuda",
-        )
-        rows.append([n_tok, lat * 1e6])  # convert to microseconds
-
-    print(f"Benchmark results for implementation cuda (measuring with {args.mode}):")
-    print(tabulate(rows, headers=["num_tokens", "latency (µs)"], floatfmt=".3f"))
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser()
-
-    parser.add_argument("--num-heads", type=int, default=128)
-    parser.add_argument(
-        "--head-size",
-        type=int,
-        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-        default=128,
-    )
-    parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
-    parser.add_argument("--num-blocks", type=int, default=128 * 128)
-
-    parser.add_argument(
-        "--dtype",
-        type=str,
-        choices=["half", "bfloat16", "float"],
-        default="bfloat16",
-    )
-
-    parser.add_argument(
-        "--kv-cache-dtype",
-        type=str,
-        choices=["auto", "fp8"],
-        default="auto",
-    )
-
-    parser.add_argument("--iters", type=int, default=200)
-
-    parser.add_argument(
-        "--mode",
-        type=str,
-        choices=["cudagraph", "no_graph"],
-        default="cudagraph",
-    )
-
-    args = parser.parse_args()
-
-    main(args)

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
 import random
 import time
 
@@ -12,9 +14,9 @@ from vllm.attention.ops.triton_reshape_and_cache_flash import (
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random_flash,
 )
 

benchmarks/kernels/benchmark_rmsnorm.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import itertools
+from typing import Optional, Union
 
 import torch
 from flashinfer.norm import fused_add_rmsnorm, rmsnorm
@@ -20,8 +21,8 @@ class HuggingFaceRMSNorm(nn.Module):
     def forward(
         self,
         x: torch.Tensor,
-        residual: torch.Tensor | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
         orig_dtype = x.dtype
         x = x.to(torch.float32)
         if residual is not None:
@@ -40,7 +41,7 @@ class HuggingFaceRMSNorm(nn.Module):
 def rmsnorm_naive(
     x: torch.Tensor,
     weight: torch.Tensor,
-    residual: torch.Tensor | None = None,
+    residual: Optional[torch.Tensor] = None,
     eps: float = 1e-6,
 ):
     naive_norm = HuggingFaceRMSNorm(x.shape[-1], eps=eps)
@@ -64,7 +65,7 @@ def rmsnorm_naive(
 def rmsnorm_flashinfer(
     x: torch.Tensor,
     weight: torch.Tensor,
-    residual: torch.Tensor | None = None,
+    residual: Optional[torch.Tensor] = None,
     eps: float = 1e-6,
 ):
     orig_shape = x.shape
@@ -88,7 +89,7 @@ def rmsnorm_flashinfer(
 def rmsnorm_vllm(
     x: torch.Tensor,
     weight: torch.Tensor,
-    residual: torch.Tensor | None = None,
+    residual: Optional[torch.Tensor] = None,
     eps: float = 1e-6,
 ):
     orig_shape = x.shape

benchmarks/kernels/benchmark_rope.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from itertools import accumulate
+from typing import Optional
 
 import nvtx
 import torch
@@ -17,7 +18,7 @@ def benchmark_rope_kernels_multi_lora(
     seq_len: int,
     num_heads: int,
     head_size: int,
-    rotary_dim: int | None,
+    rotary_dim: Optional[int],
     dtype: torch.dtype,
     seed: int,
     device: str,

benchmarks/kernels/benchmark_silu_mul_fp8_quant.py

@@ -1,19 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""
-Comprehensive 3-way SiLU Benchmark Suite
-
-This benchmark compares three SiLU implementations:
-1. SiLU V2 (CUDA) - Optimized CUDA kernel implementation
-2. Triton Kernel - Triton-based implementation
-
-The suite generates detailed performance comparisons including:
-- Memory bandwidth utilization
-- Speedup ratios (baseline vs optimized implementations)
-- Performance across different expert configurations and token distributions
-"""
-
 from collections.abc import Callable
 
 import matplotlib.pyplot as plt
@@ -21,7 +7,7 @@ import numpy as np
 import torch
 
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
-    persistent_masked_m_silu_mul_quant,
+    silu_mul_fp8_quant_deep_gemm_cuda,
 )
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
@@ -108,7 +94,6 @@ def silu_mul_fp8_quant_deep_gemm_triton(
     num_parallel_tokens,
     group_size: int = 128,
     eps: float = 1e-10,
-    expert_offsets: torch.Tensor = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """Quantize silu(y[..., :H]) * y[..., H:] to FP8 with group per-token scales
 
@@ -189,7 +174,7 @@ def silu_mul_fp8_quant_deep_gemm_triton(
 
 
 # Parse generation strategies
-strategies = ["random_imbalanced", "uniform", "max_t"]
+strategies = ["uniform", "max_t", "first_t"]
 
 
 def benchmark(
@@ -210,27 +195,15 @@ def benchmark(
         current_platform.seed_everything(42 + seed_offset)
         y = torch.rand((E, T, 2 * H), dtype=torch.bfloat16, device="cuda").contiguous()
 
-        if gen_strategy == "random_imbalanced":
-
-            def generate_expert_loads(n_e, total_tokens, ratio, device="cuda"):
-                mean = total_tokens // n_e
-                min_max = mean // ratio
-                e = torch.ones(size=(E,), dtype=torch.int64, device=device) * mean
-                e[0] = min_max
-                r = torch.rand(size=(E - 1,))
-                r /= r.sum()
-                r *= total_tokens - min_max
-                r = r.round().long()
-                e[1:] = r.to(device=device)
-                return e
-
-            tokens_per_expert = generate_expert_loads(E, total_tokens, 0.7, "cuda")
-        elif gen_strategy == "uniform":
-            r = torch.rand(size=(E,))
+        if gen_strategy == "uniform":
+            r = torch.rand(size=(E,), device="cuda")
             r /= r.sum()
             r *= total_tokens
-            r = r.round().long()
-            tokens_per_expert = r
+            tokens_per_expert = r.int()
+            tokens_per_expert = torch.minimum(
+                tokens_per_expert,
+                torch.ones((E,), device=r.device, dtype=torch.int) * T,
+            )
         elif gen_strategy == "max_t":
             tokens_per_expert = torch.empty(size=(E,), dtype=torch.int32, device="cuda")
             tokens_per_expert.fill_(total_tokens / E)
@@ -308,34 +281,40 @@ def benchmark(
 
 
 def create_comparison_plot(
-    ratios, silu_v2_times, triton_times, config_labels, strategy_name, id
+    ratio, cuda_times, baseline_times, config_labels, strategy_name, id
 ):
-    fig, ax = plt.subplots(1, 1, figsize=(18, 6))
+    """Create a comparison plot for a specific generation strategy"""
+    fig, ax = plt.subplots(1, 1, figsize=(16, 6))
 
     # Configure x-axis positions
     x = np.arange(len(config_labels))
-    width = 0.25
+    width = 0.35
 
     # Execution Time plot (lower is better)
-    ax.bar(x, silu_v2_times, width, label="SiLU V2 (CUDA)", alpha=0.8, color="blue")
     ax.bar(
-        x + width, triton_times, width, label="Triton Kernel", alpha=0.8, color="green"
+        x - width / 2, cuda_times, width, label="CUDA Kernel", alpha=0.8, color="blue"
+    )
+    ax.bar(
+        x + width / 2,
+        baseline_times,
+        width,
+        label="Baseline",
+        alpha=0.8,
+        color="orange",
     )
 
-    # Add speedup labels over each bar trio
+    # Add speedup labels over each bar pair
     for i in range(len(x)):
-        triton_v2_speedup = ratios[i][1]  # triton/v2
-        max_height = max(silu_v2_times[i], triton_times[i])
-
-        # Triton/V2 speedup
+        speedup = ratio[i]
+        max_height = max(cuda_times[i], baseline_times[i])
         ax.text(
-            x[i] + width / 2,
+            x[i],
             max_height + max_height * 0.02,
-            f"{triton_v2_speedup:.2f}x",
+            f"{speedup:.2f}x",
             ha="center",
             va="bottom",
             fontweight="bold",
-            fontsize=8,
+            fontsize=9,
         )
 
     ax.set_xlabel("Configuration")
@@ -353,75 +332,56 @@ def create_comparison_plot(
 
 
 def create_combined_plot(all_results):
+    """Create a combined plot with all strategies in one PNG"""
     num_strategies = len(all_results)
-    fig, axes = plt.subplots(num_strategies, 1, figsize=(22, 7 * num_strategies))
+    fig, axes = plt.subplots(num_strategies, 1, figsize=(20, 6 * num_strategies))
 
     if num_strategies == 1:
         axes = [axes]
 
     for idx, (
         strategy_name,
-        all_ratios,
-        all_silu_v2_results,
-        all_triton_results,
+        ratio,
+        cuda_times,
+        baseline_times,
         config_labels,
-        config_x_axis,
     ) in enumerate(all_results):
         ax = axes[idx]
 
-        # Flatten the nested results to get bandwidth percentages for plotting
-        silu_v2_bandwidths = []
-        triton_bandwidths = []
-        flat_ratios = []
-
-        for config_results in all_silu_v2_results:
-            for result in config_results:
-                silu_v2_bandwidths.append(result[3])  # bandwidth percentage
-
-        for config_results in all_triton_results:
-            for result in config_results:
-                triton_bandwidths.append(result[3])  # bandwidth percentage
-
-        for config_ratios in all_ratios:
-            for ratio in config_ratios:
-                flat_ratios.append(ratio)
-
         # Configure x-axis positions
         x = np.arange(len(config_labels))
-        width = 0.25
+        width = 0.35
 
-        # Bandwidth utilization plot (higher is better)
+        # Execution Time plot (lower is better)
         ax.bar(
-            x,
-            silu_v2_bandwidths,
+            x - width / 2,
+            cuda_times,
             width,
-            label="SiLU V2 (CUDA)",
+            label="CUDA Kernel",
             alpha=0.8,
             color="blue",
         )
         ax.bar(
-            x + width,
-            triton_bandwidths,
+            x + width / 2,
+            baseline_times,
             width,
-            label="Triton Kernel",
+            label="Baseline",
             alpha=0.8,
-            color="green",
+            color="orange",
         )
 
-        # Add speedup labels over each bar trio
+        # Add speedup labels over each bar pair
         for i in range(len(x)):
-            triton_v2_speedup = flat_ratios[i]  # triton/v2
-            max_height = max(silu_v2_bandwidths[i], triton_bandwidths[i])
-
-            # Triton/V2 speedup
+            speedup = ratio[i]
+            max_height = max(cuda_times[i], baseline_times[i])
             ax.text(
-                x[i] + width / 2,
+                x[i],
                 max_height + max_height * 0.02,
-                f"{triton_v2_speedup:.2f}x",
+                f"{speedup:.2f}x",
                 ha="center",
                 va="bottom",
                 fontweight="bold",
-                fontsize=8,
+                fontsize=9,
             )
 
         ax.set_xlabel("Configuration")
@@ -435,7 +395,7 @@ def create_combined_plot(all_results):
         ax.grid(True, alpha=0.3)
 
     plt.tight_layout()
-    filename = "silu_benchmark_combined_3way.png"
+    filename = "../../silu_bench/silu_benchmark_combined.png"
     plt.savefig(filename, dpi=300, bbox_inches="tight")
     plt.show()
 
@@ -445,9 +405,7 @@ def create_combined_plot(all_results):
 outer_dim = 7168
 configs = [
     # DeepSeekV3 Configs
-    # (1, 56, 7168),
     (8, 1024, 7168),
-    # (32, 56, 7168),
     # DeepSeekV3 Configs
     (32, 1024, 7168),
     # DeepSeekV3 Configs
@@ -459,7 +417,6 @@ num_warmups = 20
 
 strategy_descriptions = {
     "uniform": "Uniform Random",
-    "random_imbalanced": "Imbalanced Random",
     "max_t": "Even Assignment",
     "first_t": "experts[0] = T, experts[1:] = 0",
 }
@@ -476,31 +433,28 @@ for id, strategy in enumerate(strategies):
     print(f"Testing strategy: {strategy_descriptions[strategy]}")
     print(f"{'=' * 60}")
 
-    # Collect benchmark data for all three algorithms
+    # Collect benchmark data for both algorithms
     config_labels = []
     config_x_axis = []
-    all_silu_v2_results = []
-    all_triton_results = []
+    all_cuda_results = []
+    all_baseline_results = []
     all_ratios = []
 
     for E, T, H in configs:
-        total_tokens_config = []
-        for i in [8, 16, 32, 64, 128, 256, 512]:
-            if i <= T:
-                total_tokens_config.append(i * E)
+        total_tokens_config = [8 * E, 16 * E, 32 * E, 64 * E, 128 * E, 256 * E]
         config_x_axis.append(total_tokens_config)
 
-        silu_v2_results = []
-        triton_results = []
+        cuda_results = []
+        baseline_results = []
         ratios = []
 
         for total_tokens in total_tokens_config:
             config_label = f"E={E},T={T},H={H},TT={total_tokens}"
             config_labels.append(config_label)
 
-            # SiLU V2 (CUDA kernel) results
-            time_ms_silu_v2, gflops, gbps, perc = benchmark(
-                persistent_masked_m_silu_mul_quant,
+            # CUDA kernel results
+            time_ms_cuda, gflops, gbps, perc = benchmark(
+                silu_mul_fp8_quant_deep_gemm_cuda,
                 E,
                 T,
                 H,
@@ -509,9 +463,9 @@ for id, strategy in enumerate(strategies):
                 num_warmups=num_warmups,
                 gen_strategy=strategy,
             )
-            silu_v2_results.append((time_ms_silu_v2, gflops, gbps, perc))
+            cuda_results.append((time_ms_cuda, gflops, gbps, perc))
 
-            # Triton kernel results
+            # Baseline results
             time_ms_triton, gflops, gbps, perc = benchmark(
                 silu_mul_fp8_quant_deep_gemm_triton,
                 E,
@@ -522,20 +476,12 @@ for id, strategy in enumerate(strategies):
                 num_warmups=num_warmups,
                 gen_strategy=strategy,
             )
-            triton_results.append((time_ms_triton, gflops, gbps, perc))
+            baseline_results.append((time_ms_triton, gflops, gbps, perc))
+            ratios.append(time_ms_triton / time_ms_cuda)
 
-            # Calculate speedup ratios (triton baseline / implementation)
-            triton_v2_ratio = time_ms_triton / time_ms_silu_v2
-            ratios.append(triton_v2_ratio)
-
-            print(
-                f"Completed: {config_label}:"
-                f" V2: {time_ms_silu_v2:.3f}ms,"
-                f" Triton: {time_ms_triton:.3f}ms"
-            )
-
-        all_silu_v2_results.append(silu_v2_results)
-        all_triton_results.append(triton_results)
+            print(f"Completed: {config_label}")
+        all_cuda_results.append(cuda_results)
+        all_baseline_results.append(baseline_results)
         all_ratios.append(ratios)
 
     # Store results for combined plotting
@@ -543,8 +489,8 @@ for id, strategy in enumerate(strategies):
         (
             strategy_descriptions[strategy],
             all_ratios,
-            all_silu_v2_results,
-            all_triton_results,
+            all_cuda_results,
+            all_baseline_results,
             config_labels,
             config_x_axis,
         )
@@ -552,18 +498,15 @@ for id, strategy in enumerate(strategies):
 
     # Print summary table for this strategy
     print(f"\nSummary Table - {strategy_descriptions[strategy]}:")
-    print(f" {'V2 Time(ms)':<12} {'Triton Time(ms)':<14} {'Triton/V2':<10}")
-    print("-" * 90)
+    print(f"{'Config':<20} {'CUDA Time(ms)':<12} {'Base Time(ms)':<12} {'Speedup':<8}")
+    print("-" * 60)
 
     for i, (E, T, H) in enumerate(configs):
-        # Get the first result for each config (simplifying for summary)
-        v2_time = silu_v2_results[i][0]
-        triton_time = triton_results[i][0]
-        triton_v2_speedup = triton_time / v2_time
+        speedup = baseline_results[i][0] / cuda_results[i][0]
         config_label = f"E={E:3d},T={T:4d},H={H:4d}"
         print(
-            f"{config_label:<20} {v2_time:8.5f} {triton_time:10.5f} "
-            f"{triton_v2_speedup:8.2f}x"
+            f"{config_label:<20} {cuda_results[i][0]:8.5f} "
+            f"{baseline_results[i][0]:8.5f} {speedup:6.2f}x"
         )
 
 
@@ -571,14 +514,15 @@ def create_total_tokens_plot(all_results):
     num_strategies = len(all_results)
     num_configs = len(configs)
 
+    # Create side-by-side subplots: 2 columns for speedup and bandwidth percentage
     fig, axs = plt.subplots(
-        num_strategies, num_configs * 2, figsize=(32, 8 * num_strategies)
+        num_strategies, num_configs * 2, figsize=(28, 6 * num_strategies)
     )
 
     # Add main title to the entire figure
     fig.suptitle(
-        "Performance Analysis: Speedup vs Bandwidth Utilization (SiLU V2, and Triton)",
-        fontsize=18,
+        "Performance Analysis: Speedup vs Bandwidth Utilization (Triton & CUDA)",
+        fontsize=16,
         fontweight="bold",
         y=0.98,
     )
@@ -595,8 +539,8 @@ def create_total_tokens_plot(all_results):
         (
             strategy_name,
             all_ratios,
-            all_silu_v2_results,
-            all_triton_results,
+            all_cuda_results,
+            all_baseline_results,
             config_labels,
             config_x_axis,
         ) = result
@@ -611,54 +555,42 @@ def create_total_tokens_plot(all_results):
             ratios = all_ratios[config_idx]
             total_tokens_values = config_x_axis[config_idx]
 
-            # Extract speedup ratios
-            triton_v2_ratios = [ratio for ratio in ratios]
-
-            # Extract bandwidth percentages for all implementations
-            v2_bandwidth_percentages = [
-                result[3] for result in all_silu_v2_results[config_idx]
+            # Extract CUDA and Triton bandwidth percentages
+            cuda_bandwidth_percentages = [
+                result[3] for result in all_cuda_results[config_idx]
             ]
             triton_bandwidth_percentages = [
-                result[3] for result in all_triton_results[config_idx]
+                result[3] for result in all_baseline_results[config_idx]
             ]
 
             # Plot speedup ratios vs total tokens (left plot)
             ax_speedup.plot(
-                total_tokens_values,
-                triton_v2_ratios,
-                "go-",
-                linewidth=3,
-                markersize=8,
-                label="Triton/V2 Speedup",
+                total_tokens_values, ratios, "bo-", linewidth=3, markersize=8
             )
             ax_speedup.set_title(
-                f"{strategy_name}\nSpeedup vs Baseline (Triton)\nE={E}, T={T}, H={H}",
+                f"{strategy_name}\nSpeedup (CUDA/Triton)\nE={E}, T={T}, H={H}",
                 fontsize=12,
                 fontweight="bold",
             )
             ax_speedup.set_xlabel("Total Tokens", fontweight="bold", fontsize=11)
             ax_speedup.set_ylabel("Speedup Ratio", fontweight="bold", fontsize=11)
-            ax_speedup.legend(prop={"weight": "bold"})
             ax_speedup.grid(True, alpha=0.3)
 
-            # Plot bandwidth utilization (right plot)
             ax_bandwidth.plot(
                 total_tokens_values,
-                v2_bandwidth_percentages,
-                "o-",
+                cuda_bandwidth_percentages,
+                "ro-",
                 linewidth=3,
                 markersize=8,
-                label="SiLU V2",
-                color="blue",
+                label="CUDA",
             )
             ax_bandwidth.plot(
                 total_tokens_values,
                 triton_bandwidth_percentages,
-                "o-",
+                "go-",
                 linewidth=3,
                 markersize=8,
                 label="Triton",
-                color="green",
             )
             ax_bandwidth.set_title(
                 f"{strategy_name}\nBandwidth Utilization (Hopper)\nE={E}, T={T}, H={H}",
@@ -686,12 +618,38 @@ def create_total_tokens_plot(all_results):
                 for label in ax.get_xticklabels() + ax.get_yticklabels():
                     label.set_fontweight("bold")
 
-            # Add value labels on Triton/V2 speedup points
-            for x, y in zip(total_tokens_values, triton_v2_ratios):
+            # Add value labels on speedup points
+            for x, y in zip(total_tokens_values, ratios):
                 ax_speedup.annotate(
                     f"{y:.2f}x",
                     (x, y),
                     textcoords="offset points",
+                    xytext=(0, 12),
+                    ha="center",
+                    fontsize=10,
+                    fontweight="bold",
+                    bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.7),
+                )
+
+            # Add value labels on CUDA bandwidth points
+            for x, y in zip(total_tokens_values, cuda_bandwidth_percentages):
+                ax_bandwidth.annotate(
+                    f"{y:.1f}%",
+                    (x, y),
+                    textcoords="offset points",
+                    xytext=(0, 12),
+                    ha="center",
+                    fontsize=9,
+                    fontweight="bold",
+                    bbox=dict(boxstyle="round,pad=0.2", facecolor="red", alpha=0.3),
+                )
+
+            # Add value labels on Triton bandwidth points
+            for x, y in zip(total_tokens_values, triton_bandwidth_percentages):
+                ax_bandwidth.annotate(
+                    f"{y:.1f}%",
+                    (x, y),
+                    textcoords="offset points",
                     xytext=(0, -15),
                     ha="center",
                     fontsize=9,
@@ -701,20 +659,17 @@ def create_total_tokens_plot(all_results):
 
     plt.tight_layout()
     plt.subplots_adjust(top=0.93)  # Make room for main title
-    filename = "silu_benchmark_total_tokens_3way.png"
+    filename = "silu_benchmark_total_tokens.png"
     plt.savefig(filename, dpi=300, bbox_inches="tight")
     plt.show()
 
     return filename
 
 
-# Create comprehensive 3-way comparison plots
-combined_plot_filename = create_combined_plot(all_results)
-total_tokens_plot_filename = create_total_tokens_plot(all_results)
+# Create combined plot with all strategies
+combined_plot_filename = create_total_tokens_plot(all_results)
 
-print(f"\n{'=' * 80}")
-print("3-Way Benchmark Suite Complete!")
-print(f"Generated combined comparison plot: {combined_plot_filename}")
-print(f"Generated total tokens analysis plot: {total_tokens_plot_filename}")
-print("Compared: SiLU V2 (CUDA), and Triton implementations")
-print(f"{'=' * 80}")
+print(f"\n{'=' * 60}")
+print("Benchmark Complete!")
+print(f"Generated combined plot: {combined_plot_filename}")
+print(f"{'=' * 60}")

benchmarks/kernels/benchmark_trtllm_decode_attention.py

@@ -4,6 +4,7 @@
 import csv
 import os
 from datetime import datetime
+from typing import Optional
 
 import flashinfer
 import torch
@@ -27,7 +28,9 @@ def to_float8(x, dtype=torch.float8_e4m3fn):
 @torch.no_grad()
 def benchmark_decode(
     dtype: torch.dtype,
-    quant_dtypes: tuple[torch.dtype | None, torch.dtype | None, torch.dtype | None],
+    quant_dtypes: tuple[
+        Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
+    ],
     batch_size: int,
     max_seq_len: int,
     num_heads: tuple[int, int] = (64, 8),

benchmarks/kernels/benchmark_trtllm_prefill_attention.py

@@ -4,6 +4,7 @@
 import csv
 import os
 from datetime import datetime
+from typing import Optional
 
 import flashinfer
 import torch
@@ -27,7 +28,9 @@ def to_float8(x, dtype=torch.float8_e4m3fn):
 @torch.no_grad()
 def benchmark_prefill(
     dtype: torch.dtype,
-    quant_dtypes: tuple[torch.dtype | None, torch.dtype | None, torch.dtype | None],
+    quant_dtypes: tuple[
+        Optional[torch.dtype], Optional[torch.dtype], Optional[torch.dtype]
+    ],
     batch_size: int,
     max_seq_len: int,
     num_heads: tuple[int, int] = (64, 8),

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -14,7 +14,7 @@ import torch
 from tqdm import tqdm
 
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    _w8a8_triton_block_scaled_mm,
+    _w8a8_block_fp8_matmul,
 )
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
@@ -83,7 +83,7 @@ def w8a8_block_matmul(
         )
 
     if A.dtype == torch.float8_e4m3fn:
-        kernel = _w8a8_triton_block_scaled_mm
+        kernel = _w8a8_block_fp8_matmul
     else:
         raise RuntimeError("Currently, only support tune w8a8 block fp8 kernel.")
 

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# fmt: off
 # ruff: noqa: E501
 import time
 
@@ -8,7 +9,7 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8,
-    w8a8_triton_block_scaled_mm,
+    w8a8_block_fp8_matmul,
 )
 from vllm.triton_utils import triton
 from vllm.utils.deep_gemm import (
@@ -19,21 +20,19 @@ from vllm.utils.deep_gemm import (
 )
 
 
-def benchmark_shape(
-    m: int,
-    n: int,
-    k: int,
-    warmup: int = 100,
-    repeat: int = 10000,
-    verbose: bool = False,
-) -> dict:
+def benchmark_shape(m: int,
+                    n: int,
+                    k: int,
+                    warmup: int = 100,
+                    repeat: int = 10000,
+                    verbose: bool = False) -> dict:
     """Benchmark all implementations for a specific (m, n, k) shape."""
     if verbose:
         print(f"\n=== Benchmarking shape: m={m}, n={n}, k={k} ===")
 
     # Create test tensors
-    A = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
-    B = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+    A = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
+    B = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
 
     # Reference result in BF16
     torch.cuda.synchronize()
@@ -50,39 +49,34 @@ def benchmark_shape(
     # Pre-quantize A for all implementations
     A_deepgemm, A_scale_deepgemm = per_token_group_quant_fp8(A, block_size[1])
     A_scale_deepgemm = get_col_major_tma_aligned_tensor(A_scale_deepgemm)
-    C_deepgemm = torch.empty((m, n), device="cuda", dtype=torch.bfloat16)
+    C_deepgemm = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
     A_vllm, A_scale_vllm = per_token_group_quant_fp8(A, block_size[1])
     A_vllm_cutlass, A_scale_vllm_cutlass = per_token_group_quant_fp8(
-        A, block_size[1], column_major_scales=True
-    )
+        A, block_size[1], column_major_scales=True)
 
     # === DeepGEMM Implementation ===
     def deepgemm_gemm():
-        fp8_gemm_nt(
-            (A_deepgemm, A_scale_deepgemm), (B_deepgemm, B_scale_deepgemm), C_deepgemm
-        )
+        fp8_gemm_nt((A_deepgemm, A_scale_deepgemm),
+                                       (B_deepgemm, B_scale_deepgemm),
+                                       C_deepgemm)
         return C_deepgemm
 
     # === vLLM Triton Implementation ===
     def vllm_triton_gemm():
-        return w8a8_triton_block_scaled_mm(
-            A_vllm,
-            B_vllm,
-            A_scale_vllm,
-            B_scale_vllm,
-            block_size,
-            output_dtype=torch.bfloat16,
-        )
+        return w8a8_block_fp8_matmul(A_vllm,
+                                     B_vllm,
+                                     A_scale_vllm,
+                                     B_scale_vllm,
+                                     block_size,
+                                     output_dtype=torch.bfloat16)
 
     # === vLLM CUTLASS Implementation ===
     def vllm_cutlass_gemm():
-        return ops.cutlass_scaled_mm(
-            A_vllm_cutlass,
-            B_vllm.T,
-            scale_a=A_scale_vllm_cutlass,
-            scale_b=B_scale_vllm.T,
-            out_dtype=torch.bfloat16,
-        )
+        return ops.cutlass_scaled_mm(A_vllm_cutlass,
+                                     B_vllm.T,
+                                     scale_a=A_scale_vllm_cutlass,
+                                     scale_b=B_scale_vllm.T,
+                                     out_dtype=torch.bfloat16)
 
     # Run correctness check first
     if verbose:
@@ -99,23 +93,26 @@ def benchmark_shape(
         print(f"DeepGEMM vs Reference difference: {deepgemm_diff:.6f}")
         print(f"vLLM Triton vs Reference difference: {vllm_triton_diff:.6f}")
         print(f"vLLM CUTLASS vs Reference difference: {vllm_cutlass_diff:.6f}")
-        print(
-            "vLLM Triton vs DeepGEMM difference: "
-            f"{calc_diff(C_vllm_triton, C_deepgemm):.6f}"
-        )
-        print(
-            "vLLM CUTLASS vs DeepGEMM difference: "
-            f"{calc_diff(C_vllm_cutlass, C_deepgemm):.6f}"
-        )
+        print("vLLM Triton vs DeepGEMM difference: "
+              f"{calc_diff(C_vllm_triton, C_deepgemm):.6f}")
+        print("vLLM CUTLASS vs DeepGEMM difference: "
+              f"{calc_diff(C_vllm_cutlass, C_deepgemm):.6f}")
 
     # Benchmark implementations
     implementations = {
         "DeepGEMM": deepgemm_gemm,
         "vLLM Triton": vllm_triton_gemm,
-        "vLLM CUTLASS": vllm_cutlass_gemm,
+        "vLLM CUTLASS": vllm_cutlass_gemm
     }
 
-    benchmark_results = {"shape": {"m": m, "n": n, "k": k}, "implementations": {}}
+    benchmark_results = {
+        "shape": {
+            "m": m,
+            "n": n,
+            "k": k
+        },
+        "implementations": {}
+    }
 
     for name, func in implementations.items():
         # Warmup
@@ -143,36 +140,38 @@ def benchmark_shape(
             "tflops": tflops,
             "gb_s": gb_s,
             "diff": {
-                "DeepGEMM": 0.0
-                if name == "DeepGEMM"
-                else calc_diff(func(), C_deepgemm),
-                "Reference": deepgemm_diff
-                if name == "DeepGEMM"
-                else (vllm_triton_diff if name == "vLLM Triton" else vllm_cutlass_diff),
-            },
+                "DeepGEMM":
+                0.0 if name == "DeepGEMM" else calc_diff(func(), C_deepgemm),
+                "Reference":
+                deepgemm_diff if name == "DeepGEMM" else
+                (vllm_triton_diff
+                 if name == "vLLM Triton" else vllm_cutlass_diff)
+            }
         }
 
         if verbose:
-            print(f"{name}: {avg_time_ms:.3f} ms, {tflops:.2f} TFLOPS, {gb_s:.2f} GB/s")
+            print(
+                f"{name}: {avg_time_ms:.3f} ms, {tflops:.2f} TFLOPS, {gb_s:.2f} GB/s"
+            )
 
     # Calculate speedups
     baseline = benchmark_results["implementations"]["DeepGEMM"]["time_ms"]
     for name, data in benchmark_results["implementations"].items():
         if name != "DeepGEMM":
             speedup = baseline / data["time_ms"]
-            benchmark_results["implementations"][name]["speedup_vs_deepgemm"] = speedup
+            benchmark_results["implementations"][name][
+                "speedup_vs_deepgemm"] = speedup
             if verbose:
-                print(
-                    f"DeepGEMM is {1 / speedup:.2f}x "
-                    f"{'faster' if 1 / speedup > 1 else 'slower'} than {name}"
-                )
+                print(f"DeepGEMM is {1/speedup:.2f}x "
+                      f"{'faster' if 1/speedup > 1 else 'slower'} than {name}")
 
-    vllm_triton_time = benchmark_results["implementations"]["vLLM Triton"]["time_ms"]
-    vllm_cutlass_time = benchmark_results["implementations"]["vLLM CUTLASS"]["time_ms"]
+    vllm_triton_time = benchmark_results["implementations"]["vLLM Triton"][
+        "time_ms"]
+    vllm_cutlass_time = benchmark_results["implementations"]["vLLM CUTLASS"][
+        "time_ms"]
     cutlass_vs_triton = vllm_triton_time / vllm_cutlass_time
-    benchmark_results["implementations"]["vLLM CUTLASS"]["speedup_vs_triton"] = (
-        cutlass_vs_triton
-    )
+    benchmark_results["implementations"]["vLLM CUTLASS"][
+        "speedup_vs_triton"] = cutlass_vs_triton
     if verbose:
         print(
             f"vLLM CUTLASS is {cutlass_vs_triton:.2f}x "
@@ -184,7 +183,8 @@ def benchmark_shape(
 
 def format_table_row(values, widths):
     """Format a row with specified column widths."""
-    return "| " + " | ".join(f"{val:{w}}" for val, w in zip(values, widths)) + " |"
+    return "| " + " | ".join(f"{val:{w}}"
+                             for val, w in zip(values, widths)) + " |"
 
 
 def print_table(headers, rows, title=None):
@@ -292,50 +292,38 @@ def run_benchmarks(verbose: bool = False):
     for result in all_results:
         shape = result["shape"]
         impl_data = result["implementations"]["DeepGEMM"]
-        deepgemm_rows.append(
-            [
-                shape["m"],
-                shape["n"],
-                shape["k"],
-                f"{impl_data['time_us']:.1f}",
-                f"{impl_data['tflops']:.1f}",
-                f"{impl_data['gb_s']:.1f}",
-            ]
-        )
+        deepgemm_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}"
+        ])
 
-    print_table(deepgemm_headers, deepgemm_rows, title="DeepGEMM Implementation:")
+    print_table(deepgemm_headers,
+                deepgemm_rows,
+                title="DeepGEMM Implementation:")
 
     # Print vLLM Triton table
-    triton_headers = ["m", "n", "k", "Time (μs)", "TFLOPS", "GB/s", "vs DeepGEMM"]
+    triton_headers = [
+        "m", "n", "k", "Time (μs)", "TFLOPS", "GB/s", "vs DeepGEMM"
+    ]
     triton_rows = []
     for result in all_results:
         shape = result["shape"]
         impl_data = result["implementations"]["vLLM Triton"]
         speedup = impl_data.get("speedup_vs_deepgemm", 1.0)
-        triton_rows.append(
-            [
-                shape["m"],
-                shape["n"],
-                shape["k"],
-                f"{impl_data['time_us']:.1f}",
-                f"{impl_data['tflops']:.1f}",
-                f"{impl_data['gb_s']:.1f}",
-                format_speedup(speedup),
-            ]
-        )
+        triton_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}",
+            format_speedup(speedup)
+        ])
 
-    print_table(triton_headers, triton_rows, title="vLLM Triton Implementation:")
+    print_table(triton_headers,
+                triton_rows,
+                title="vLLM Triton Implementation:")
 
     # Print vLLM CUTLASS table
     cutlass_headers = [
-        "m",
-        "n",
-        "k",
-        "Time (μs)",
-        "TFLOPS",
-        "GB/s",
-        "vs DeepGEMM",
-        "vs Triton",
+        "m", "n", "k", "Time (μs)", "TFLOPS", "GB/s", "vs DeepGEMM",
+        "vs Triton"
     ]
     cutlass_rows = []
     for result in all_results:
@@ -343,27 +331,28 @@ def run_benchmarks(verbose: bool = False):
         impl_data = result["implementations"]["vLLM CUTLASS"]
         vs_deepgemm = impl_data.get("speedup_vs_deepgemm", 1.0)
         vs_triton = impl_data.get("speedup_vs_triton", 1.0)
-        cutlass_rows.append(
-            [
-                shape["m"],
-                shape["n"],
-                shape["k"],
-                f"{impl_data['time_us']:.1f}",
-                f"{impl_data['tflops']:.1f}",
-                f"{impl_data['gb_s']:.1f}",
-                format_speedup(vs_deepgemm),
-                format_speedup(vs_triton),
-            ]
-        )
+        cutlass_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}",
+            format_speedup(vs_deepgemm),
+            format_speedup(vs_triton)
+        ])
 
-    print_table(cutlass_headers, cutlass_rows, title="vLLM CUTLASS Implementation:")
+    print_table(cutlass_headers,
+                cutlass_rows,
+                title="vLLM CUTLASS Implementation:")
 
     # Calculate and print averages
     print("\n===== AVERAGE PERFORMANCE =====")
 
     implementations = ["DeepGEMM", "vLLM Triton", "vLLM CUTLASS"]
     avg_metrics = {
-        impl: {"tflops": 0, "gb_s": 0, "time_ms": 0} for impl in implementations
+        impl: {
+            "tflops": 0,
+            "gb_s": 0,
+            "time_ms": 0
+        }
+        for impl in implementations
     }
 
     for result in all_results:
@@ -381,9 +370,9 @@ def run_benchmarks(verbose: bool = False):
         avg_tflops = avg_metrics[impl]["tflops"] / num_shapes
         avg_mem_bw = avg_metrics[impl]["gb_s"] / num_shapes
         avg_time = avg_metrics[impl]["time_ms"] / num_shapes
-        avg_rows.append(
-            [impl, f"{avg_tflops:.2f}", f"{avg_mem_bw:.2f}", f"{avg_time:.2f}"]
-        )
+        avg_rows.append([
+            impl, f"{avg_tflops:.2f}", f"{avg_mem_bw:.2f}", f"{avg_time:.2f}"
+        ])
 
     print_table(avg_headers, avg_rows)
 
@@ -391,19 +380,21 @@ def run_benchmarks(verbose: bool = False):
     avg_speedups = {
         "DeepGEMM vs vLLM Triton": 0,
         "DeepGEMM vs vLLM CUTLASS": 0,
-        "vLLM CUTLASS vs vLLM Triton": 0,
+        "vLLM CUTLASS vs vLLM Triton": 0
     }
 
     for result in all_results:
         deepgemm_time = result["implementations"]["DeepGEMM"]["time_ms"]
         vllm_triton_time = result["implementations"]["vLLM Triton"]["time_ms"]
-        vllm_cutlass_time = result["implementations"]["vLLM CUTLASS"]["time_ms"]
+        vllm_cutlass_time = result["implementations"]["vLLM CUTLASS"][
+            "time_ms"]
 
-        avg_speedups["DeepGEMM vs vLLM Triton"] += vllm_triton_time / deepgemm_time
-        avg_speedups["DeepGEMM vs vLLM CUTLASS"] += vllm_cutlass_time / deepgemm_time
-        avg_speedups["vLLM CUTLASS vs vLLM Triton"] += (
-            vllm_triton_time / vllm_cutlass_time
-        )
+        avg_speedups[
+            "DeepGEMM vs vLLM Triton"] += vllm_triton_time / deepgemm_time
+        avg_speedups[
+            "DeepGEMM vs vLLM CUTLASS"] += vllm_cutlass_time / deepgemm_time
+        avg_speedups[
+            "vLLM CUTLASS vs vLLM Triton"] += vllm_triton_time / vllm_cutlass_time
 
     print("\n===== AVERAGE SPEEDUPS =====")
     speedup_headers = ["Comparison", "Speedup"]
@@ -421,7 +412,8 @@ def run_benchmarks(verbose: bool = False):
 
     for result in all_results:
         for impl in implementations:
-            avg_diff[impl] += result["implementations"][impl]["diff"]["Reference"]
+            avg_diff[impl] += result["implementations"][impl]["diff"][
+                "Reference"]
 
     diff_headers = ["Implementation", "Avg Diff vs Reference"]
     diff_rows = []

benchmarks/kernels/utils.py

@@ -2,8 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import dataclasses
-from collections.abc import Callable, Iterable
-from typing import Any
+from collections.abc import Iterable
+from typing import Any, Callable, Optional
 
 import torch
 import torch.utils.benchmark as TBenchmark
@@ -55,7 +55,7 @@ class Bench:
 
     def __init__(
         self,
-        cuda_graph_params: CudaGraphBenchParams | None,
+        cuda_graph_params: Optional[CudaGraphBenchParams],
         label: str,
         sub_label: str,
         description: str,

benchmarks/multi_turn/bench_dataset.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from abc import ABC, abstractmethod
 from statistics import mean
-from typing import Any, NamedTuple
+from typing import Any, NamedTuple, Optional, Union
 
 import numpy as np  # type: ignore
 import pandas as pd  # type: ignore
@@ -35,8 +35,8 @@ class Distribution(ABC):
 class UniformDistribution(Distribution):
     def __init__(
         self,
-        min_val: int | float,
-        max_val: int | float,
+        min_val: Union[int, float],
+        max_val: Union[int, float],
         is_integer: bool = True,
     ) -> None:
         self.min_val = min_val
@@ -56,7 +56,7 @@ class UniformDistribution(Distribution):
 
 
 class ConstantDistribution(Distribution):
-    def __init__(self, value: int | float) -> None:
+    def __init__(self, value: Union[int, float]) -> None:
         self.value = value
         self.max_val = value
 
@@ -68,7 +68,7 @@ class ConstantDistribution(Distribution):
 
 
 class ZipfDistribution(Distribution):
-    def __init__(self, alpha: float, max_val: int | None = None) -> None:
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
         self.alpha = alpha
         self.max_val = max_val
 
@@ -83,7 +83,7 @@ class ZipfDistribution(Distribution):
 
 
 class PoissonDistribution(Distribution):
-    def __init__(self, alpha: float, max_val: int | None = None) -> None:
+    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
         self.alpha = alpha
         self.max_val = max_val
 
@@ -100,11 +100,11 @@ class PoissonDistribution(Distribution):
 class LognormalDistribution(Distribution):
     def __init__(
         self,
-        mean: float | None = None,
-        sigma: float | None = None,
-        average: int | None = None,
-        median_ratio: float | None = None,
-        max_val: int | None = None,
+        mean: Optional[float] = None,
+        sigma: Optional[float] = None,
+        average: Optional[int] = None,
+        median_ratio: Optional[float] = None,
+        max_val: Optional[int] = None,
     ) -> None:
         self.average = average
         self.median_ratio = median_ratio

benchmarks/multi_turn/benchmark_serving_multi_turn.py

@@ -13,7 +13,7 @@ from datetime import datetime
 from enum import Enum
 from http import HTTPStatus
 from statistics import mean
-from typing import NamedTuple
+from typing import NamedTuple, Optional, Union
 
 import aiohttp  # type: ignore
 import numpy as np  # type: ignore
@@ -46,9 +46,9 @@ class ConversationSampling(str, Enum):
 
 class ClientArgs(NamedTuple):
     seed: int
-    max_num_requests: int | None
+    max_num_requests: Optional[int]
     skip_first_turn: bool
-    max_turns: int | None
+    max_turns: Optional[int]
     max_active_conversations: int
     verbose: bool
     print_content: bool
@@ -109,9 +109,9 @@ class RequestStats(NamedTuple):
 
 class MetricStats:
     def __init__(self) -> None:
-        self.min: float | None = None
-        self.max: float | None = None
-        self.avg: float | None = None
+        self.min: Optional[float] = None
+        self.max: Optional[float] = None
+        self.avg: Optional[float] = None
         self.sum = 0.0
         self.count = 0
 
@@ -143,7 +143,7 @@ class MovingAverage:
         self.index = 0
         self.sum = 0.0
         self.count = 0
-        self.avg: float | None = None
+        self.avg: Optional[float] = None
 
     def update(self, new_value: float) -> None:
         if self.count < self.window_size:
@@ -169,7 +169,7 @@ class MovingAverage:
 class DebugStats:
     def __init__(self, logger: logging.Logger, window_size: int) -> None:
         self.logger = logger
-        self.metrics: dict[str, MovingAverage | MetricStats] = {
+        self.metrics: dict[str, Union[MovingAverage, MetricStats]] = {
             "moving_avg_ttft_ms": MovingAverage(window_size),
             "moving_avg_tpot_ms": MovingAverage(window_size),
             "ttft_ms": MetricStats(),
@@ -198,6 +198,14 @@ class DebugStats:
         self.logger.info("-" * 50)
 
 
+# Must support Python 3.8, we can't use str.removeprefix(prefix)
+# introduced in Python 3.9
+def remove_prefix(text: str, prefix: str) -> str:
+    if text.startswith(prefix):
+        return text[len(prefix) :]
+    return text
+
+
 def nanosec_to_millisec(value: float) -> float:
     return value / 1000000.0
 
@@ -212,8 +220,8 @@ async def send_request(
     chat_url: str,
     model: str,
     stream: bool = True,
-    min_tokens: int | None = None,
-    max_tokens: int | None = None,
+    min_tokens: Optional[int] = None,
+    max_tokens: Optional[int] = None,
 ) -> ServerResponse:
     payload = {
         "model": model,
@@ -242,9 +250,9 @@ async def send_request(
     timeout = aiohttp.ClientTimeout(total=timeout_sec)
 
     valid_response = True
-    ttft: float | None = None
+    ttft: Optional[float] = None
     chunk_delay: list[int] = []
-    latency: float | None = None
+    latency: Optional[float] = None
     first_chunk = ""
     generated_text = ""
 
@@ -261,7 +269,7 @@ async def send_request(
                 if not chunk_bytes:
                     continue
 
-                chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
+                chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
                 if chunk == "[DONE]":
                     # End of stream
                     latency = time.perf_counter_ns() - start_time
@@ -356,7 +364,7 @@ async def send_turn(
     req_args: RequestArgs,
     verbose: bool,
     verify_output: bool,
-) -> RequestStats | None:
+) -> Optional[RequestStats]:
     assert messages_to_use > 0
     assert messages_to_use <= len(conversation_messages)
 
@@ -636,7 +644,7 @@ async def client_main(
 
             if args.verbose:
                 curr_time_sec: float = time.perf_counter()
-                time_since_last_turn: str | float = "N/A"
+                time_since_last_turn: Union[str, float] = "N/A"
                 if conv_id in time_of_last_turn:
                     time_since_last_turn = round(
                         curr_time_sec - time_of_last_turn[conv_id], 3
@@ -761,7 +769,7 @@ def get_client_config(
             "Number of conversations must be equal or larger than the number of clients"
         )
 
-    max_req_per_client: int | None = None
+    max_req_per_client: Optional[int] = None
     if args.max_num_requests is not None:
         # Max number of requests per client
         req_per_client = args.max_num_requests // args.num_clients
@@ -928,13 +936,13 @@ async def main_mp(
                     f"{num_clients_finished} out of {bench_args.num_clients} clients finished, collected {len(client_metrics)} measurements, runtime {runtime_sec:.3f} sec{Color.RESET}"  # noqa: E501
                 )
 
-                rps: str | float = round(len(client_metrics) / runtime_sec, 3)
+                rps: Union[str, float] = round(len(client_metrics) / runtime_sec, 3)
                 if len(client_metrics) < (5 * bench_args.num_clients):
                     # Do not estimate the RPS if the number of samples is very low
                     # (threshold can be tuned if needed)
                     rps = "N/A"
 
-                runtime_left_sec: str | float = round(
+                runtime_left_sec: Union[str, float] = round(
                     (runtime_sec / finished_convs) * (total_convs - finished_convs), 3
                 )
                 if percent < 0.05:
@@ -1024,7 +1032,7 @@ def process_statistics(
     warmup_percentages: list[float],
     test_params: dict,
     verbose: bool,
-    gen_conv_args: GenConvArgs | None = None,
+    gen_conv_args: Optional[GenConvArgs] = None,
     excel_output: bool = False,
 ) -> None:
     if len(client_metrics) == 0:
@@ -1251,7 +1259,7 @@ async def main() -> None:
         default=None,
         help="The model name used in the API. "
         "If not specified, the model name will be the "
-        "same as the `--model` argument. ",
+        "same as the ``--model`` argument. ",
     )
 
     parser.add_argument(

benchmarks/multi_turn/convert_sharegpt_to_openai.py

@@ -13,7 +13,7 @@ import argparse
 import json
 import random
 from statistics import mean
-from typing import Any
+from typing import Any, Optional
 
 import pandas as pd  # type: ignore
 import tqdm  # type: ignore
@@ -25,7 +25,7 @@ def has_non_english_chars(text: str) -> bool:
 
 
 def content_is_valid(
-    content: str, min_content_len: int | None, max_content_len: int | None
+    content: str, min_content_len: Optional[int], max_content_len: Optional[int]
 ) -> bool:
     if min_content_len and len(content) < min_content_len:
         return False
@@ -37,7 +37,7 @@ def content_is_valid(
 
 
 def print_stats(
-    conversations: "list[dict[Any, Any]]", tokenizer: AutoTokenizer | None = None
+    conversations: "list[dict[Any, Any]]", tokenizer: Optional[AutoTokenizer] = None
 ) -> None:
     # Collect statistics
     stats = []
@@ -109,12 +109,12 @@ def convert_sharegpt_to_openai(
     seed: int,
     input_file: str,
     output_file: str,
-    max_items: int | None,
-    min_content_len: int | None = None,
-    max_content_len: int | None = None,
-    min_turns: int | None = None,
-    max_turns: int | None = None,
-    model: str | None = None,
+    max_items: Optional[int],
+    min_content_len: Optional[int] = None,
+    max_content_len: Optional[int] = None,
+    min_turns: Optional[int] = None,
+    max_turns: Optional[int] = None,
+    model: Optional[str] = None,
 ) -> None:
     if min_turns and max_turns:
         assert min_turns <= max_turns

benchmarks/pyproject.toml

@@ -0,0 +1,49 @@
+# This local pyproject file is part of the migration from yapf to ruff format.
+# It uses the same core rules as the main pyproject.toml file, but with the
+# following differences:
+# - ruff line length is overridden to 88
+# - deprecated typing ignores (UP006, UP035) have been removed
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint.per-file-ignores]
+"vllm/third_party/**" = ["ALL"]
+"vllm/version.py" = ["F401"]
+"vllm/_version.py" = ["ALL"]
+
+[tool.ruff.lint]
+select = [
+    # pycodestyle
+    "E",
+    # Pyflakes
+    "F",
+    # pyupgrade
+    "UP",
+    # flake8-bugbear
+    "B",
+    # flake8-simplify
+    "SIM",
+    # isort
+    "I",
+    # flake8-logging-format
+    "G",
+]
+ignore = [
+    # star imports
+    "F405", "F403",
+    # lambda expression assignment
+    "E731",
+    # Loop control variable not used within loop body
+    "B007",
+    # f-string format
+    "UP032",
+    # Can remove once 3.10+ is the minimum Python version
+    "UP007",
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["vllm"]
+
+[tool.ruff.format]
+docstring-code-format = true

cmake/cpu_extension.cmake

@@ -198,24 +198,13 @@ else()
 endif()
 
 if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
-    set(FETCHCONTENT_SOURCE_DIR_ONEDNN "$ENV{FETCHCONTENT_SOURCE_DIR_ONEDNN}" CACHE PATH "Path to a local oneDNN source directory.")
-
-    if(FETCHCONTENT_SOURCE_DIR_ONEDNN)
-        message(STATUS "Using oneDNN from specified source directory: ${FETCHCONTENT_SOURCE_DIR_ONEDNN}")
-        FetchContent_Declare(
-            oneDNN
-            SOURCE_DIR ${FETCHCONTENT_SOURCE_DIR_ONEDNN}
-        )
-    else()
-        message(STATUS "Downloading oneDNN from GitHub")
-        FetchContent_Declare(
-            oneDNN
-            GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-            GIT_TAG v3.9
-            GIT_PROGRESS TRUE
-            GIT_SHALLOW TRUE
-        )
-    endif()
+    FetchContent_Declare(
+        oneDNN
+        GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
+        GIT_TAG v3.9
+        GIT_PROGRESS TRUE
+        GIT_SHALLOW TRUE
+    )
 
     if(USE_ACL)
         find_library(ARM_COMPUTE_LIBRARY NAMES arm_compute PATHS $ENV{ACL_ROOT_DIR}/build/)
@@ -224,7 +213,6 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON
         endif()
         set(ONEDNN_AARCH64_USE_ACL "ON")
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
-        add_compile_definitions(VLLM_USE_ACL)
     endif()
 
     set(ONEDNN_LIBRARY_TYPE "STATIC")
@@ -320,4 +308,4 @@ define_gpu_extension_target(
     WITH_SOABI
 )
 
-message(STATUS "Enabling C extension.")
+message(STATUS "Enabling C extension.")

cmake/external_projects/qutlass.cmake

@@ -1,97 +0,0 @@
-include(FetchContent)
-
-set(CUTLASS_INCLUDE_DIR "${CUTLASS_INCLUDE_DIR}" CACHE PATH "Path to CUTLASS include/ directory")
-
-if(DEFINED ENV{QUTLASS_SRC_DIR})
-  set(QUTLASS_SRC_DIR $ENV{QUTLASS_SRC_DIR})
-endif()
-
-if(QUTLASS_SRC_DIR)
-  FetchContent_Declare(
-    qutlass
-    SOURCE_DIR ${QUTLASS_SRC_DIR}
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-  )
-else()
-  FetchContent_Declare(
-    qutlass
-    GIT_REPOSITORY https://github.com/IST-DASLab/qutlass.git
-    GIT_TAG 830d2c4537c7396e14a02a46fbddd18b5d107c65
-    GIT_PROGRESS TRUE
-    CONFIGURE_COMMAND ""
-    BUILD_COMMAND ""
-  )
-endif()
-
-FetchContent_Populate(qutlass)
-
-if(NOT qutlass_SOURCE_DIR)
-  message(FATAL_ERROR "[QUTLASS] source directory could not be resolved.")
-endif()
-message(STATUS "[QUTLASS] QuTLASS is available at ${qutlass_SOURCE_DIR}")
-
-cuda_archs_loose_intersection(QUTLASS_ARCHS "12.0a;10.0a" "${CUDA_ARCHS}")
-if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND QUTLASS_ARCHS)
-
-  if(QUTLASS_ARCHS MATCHES "10\\.0a")
-    set(QUTLASS_TARGET_CC 100)
-  elseif(QUTLASS_ARCHS MATCHES "12\\.0a")
-    set(QUTLASS_TARGET_CC 120)
-  else()
-    message(FATAL_ERROR "[QUTLASS] internal error parsing CUDA_ARCHS='${QUTLASS_ARCHS}'.")
-  endif()
-
-  set(QUTLASS_SOURCES
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/bindings.cpp
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/gemm.cu
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/gemm_ada.cu
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/fused_quantize_mx.cu
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/fused_quantize_nv.cu
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/fused_quantize_mx_sm100.cu
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/fused_quantize_nv_sm100.cu
-  )
-
-  set(QUTLASS_INCLUDES
-    ${qutlass_SOURCE_DIR}
-    ${qutlass_SOURCE_DIR}/qutlass
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/include
-    ${qutlass_SOURCE_DIR}/qutlass/csrc/include/cutlass_extensions
-  )
-
-  if(CUTLASS_INCLUDE_DIR AND EXISTS "${CUTLASS_INCLUDE_DIR}/cutlass/cutlass.h")
-    list(APPEND QUTLASS_INCLUDES "${CUTLASS_INCLUDE_DIR}")
-  elseif(EXISTS "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include/cutlass/cutlass.h")
-    list(APPEND QUTLASS_INCLUDES "${qutlass_SOURCE_DIR}/qutlass/third_party/cutlass/include")
-    message(STATUS "[QUTLASS] Using QuTLASS vendored CUTLASS headers (no vLLM CUTLASS detected).")
-  else()
-    message(FATAL_ERROR "[QUTLASS] CUTLASS headers not found. "
-                        "Set -DCUTLASS_INCLUDE_DIR=/path/to/cutlass/include")
-  endif()
-
-  set_gencode_flags_for_srcs(
-    SRCS "${QUTLASS_SOURCES}"
-    CUDA_ARCHS "${QUTLASS_ARCHS}"
-  )
-
-  target_sources(_C PRIVATE ${QUTLASS_SOURCES})
-  target_include_directories(_C PRIVATE ${QUTLASS_INCLUDES})
-  target_compile_definitions(_C PRIVATE
-    QUTLASS_DISABLE_PYBIND=1
-    TARGET_CUDA_ARCH=${QUTLASS_TARGET_CC}
-  )
-
-  set_property(SOURCE ${QUTLASS_SOURCES} APPEND PROPERTY COMPILE_OPTIONS
-    $<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr --use_fast_math -O3>
-  )
-
-else()
-  if("${CMAKE_CUDA_COMPILER_VERSION}" VERSION_LESS "12.8")
-    message(STATUS
-      "[QUTLASS] Skipping build: CUDA 12.8 or newer is required (found ${CMAKE_CUDA_COMPILER_VERSION}).")
-  else()
-    message(STATUS
-      "[QUTLASS] Skipping build: no supported arch (12.0a / 10.0a) found in "
-      "CUDA_ARCHS='${CUDA_ARCHS}'.")
-  endif()
-endif()

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG a893712401d70362fbb299cd9c4b3476e8e9ed54
+          GIT_TAG ee4d25bd84e0cbc7e0b9b9685085fd5db2dcb62a
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

cmake/hipify.py

@@ -16,7 +16,7 @@ import shutil
 
 from torch.utils.hipify.hipify_python import hipify
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     parser = argparse.ArgumentParser()
 
     # Project directory where all the source + include files live.
@@ -34,14 +34,15 @@ if __name__ == "__main__":
     )
 
     # Source files to convert.
-    parser.add_argument(
-        "sources", help="Source files to hipify.", nargs="*", default=[]
-    )
+    parser.add_argument("sources",
+                        help="Source files to hipify.",
+                        nargs="*",
+                        default=[])
 
     args = parser.parse_args()
 
     # Limit include scope to project_dir only
-    includes = [os.path.join(args.project_dir, "*")]
+    includes = [os.path.join(args.project_dir, '*')]
 
     # Get absolute path for all source files.
     extra_files = [os.path.abspath(s) for s in args.sources]
@@ -50,31 +51,25 @@ if __name__ == "__main__":
     # The directory might already exist to hold object files so we ignore that.
     shutil.copytree(args.project_dir, args.output_dir, dirs_exist_ok=True)
 
-    hipify_result = hipify(
-        project_directory=args.project_dir,
-        output_directory=args.output_dir,
-        header_include_dirs=[],
-        includes=includes,
-        extra_files=extra_files,
-        show_detailed=True,
-        is_pytorch_extension=True,
-        hipify_extra_files_only=True,
-    )
+    hipify_result = hipify(project_directory=args.project_dir,
+                           output_directory=args.output_dir,
+                           header_include_dirs=[],
+                           includes=includes,
+                           extra_files=extra_files,
+                           show_detailed=True,
+                           is_pytorch_extension=True,
+                           hipify_extra_files_only=True)
 
     hipified_sources = []
     for source in args.sources:
         s_abs = os.path.abspath(source)
-        hipified_s_abs = (
-            hipify_result[s_abs].hipified_path
-            if (
-                s_abs in hipify_result
-                and hipify_result[s_abs].hipified_path is not None
-            )
-            else s_abs
-        )
+        hipified_s_abs = (hipify_result[s_abs].hipified_path if
+                          (s_abs in hipify_result
+                           and hipify_result[s_abs].hipified_path is not None)
+                          else s_abs)
         hipified_sources.append(hipified_s_abs)
 
-    assert len(hipified_sources) == len(args.sources)
+    assert (len(hipified_sources) == len(args.sources))
 
     # Print hipified source files.
     print("\n".join(hipified_sources))

cmake/utils.cmake

@@ -310,13 +310,13 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
   list(REMOVE_DUPLICATES _PTX_ARCHS)
   list(REMOVE_DUPLICATES _SRC_CUDA_ARCHS)
 
-  # If x.0a or x.0f is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
-  # remove x.0a or x.0f from SRC_CUDA_ARCHS and add x.0a or x.0f to _CUDA_ARCHS
+  # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
+  # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS
   set(_CUDA_ARCHS)
   foreach(_arch ${_SRC_CUDA_ARCHS})
-    if(_arch MATCHES "[af]$")
+    if(_arch MATCHES "\\a$")
       list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
-      string(REGEX REPLACE "[af]$" "" _base "${_arch}")
+      string(REPLACE "a" "" _base "${_arch}")
       if ("${_base}" IN_LIST TGT_CUDA_ARCHS)
         list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}")
         list(APPEND _CUDA_ARCHS "${_arch}")

codecov.yml

@@ -1,12 +0,0 @@
-codecov:
-  require_ci_to_pass: false
-
-fixes:
-  # Map source code paths to repository root paths
-  # Wildcards match any Python version (python3.*)
-  - "/vllm-workspace/src/vllm/::vllm/"
-  - "/vllm-workspace/vllm/::vllm/"
-  - "/usr/local/lib/python3.*/dist-packages/vllm/::vllm/"
-  - "/usr/local/lib/python3.*/site-packages/vllm/::vllm/"
-  - "/usr/lib/python3.*/dist-packages/vllm/::vllm/"
-  - "/usr/lib/python3.*/site-packages/vllm/::vllm/"

csrc/attention/attention_kernels.cuh

@@ -28,10 +28,10 @@
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>
-  #include "../quantization/w8a8/fp8/amd/quant_utils.cuh"
+  #include "../quantization/fp8/amd/quant_utils.cuh"
 typedef __hip_bfloat16 __nv_bfloat16;
 #else
-  #include "../quantization/w8a8/fp8/nvidia/quant_utils.cuh"
+  #include "../quantization/fp8/nvidia/quant_utils.cuh"
 #endif
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))

csrc/attention/mla/cutlass_sm100_mla/device/sm100_mla.hpp

@@ -125,37 +125,32 @@ public:
   }
 
   static void set_split_kv (KernelArguments& args) {
+    // printf("set_split_kv start");
     if (args.split_kv >= 1) return;
     auto [H, K, D, B] = args.problem_shape;
+    // std::cout << H << " " << K << " " << D << " " << B << "\n";      
     int sm_count = args.hw_info.sm_count;
-    float seq_length_k = static_cast<float>(K) / 1024.0f;
-    int max_splits = 1;
+    // printf("    sm_count = %d\n", sm_count);
+    int max_splits = ceil_div(K, 128);
+    max_splits = min(16, max_splits);
 
-    if (B <= 4 && seq_length_k >= 16) {
-      max_splits = 16;
+    // TODO: This avoids a hang when the batch size larger than 1 and 
+    // there is more than 1 kv_splits. 
+    // Discuss with NVIDIA how this can be fixed.
+    if (B > 1) {
+      max_splits = min(1, max_splits);
     }
-    else if (B <= 8 && seq_length_k >= 4) {
-      max_splits = 8;
-    }
-    else if ((B <= 16 && seq_length_k >= 8) ||
-             (B == 48 && seq_length_k >= 32)) {
-      max_splits = 4;
-    }
-    else if ((B <= 32 && seq_length_k >= 16) ||
-             (B == 96 && seq_length_k >= 16)) {
-      max_splits = 2;
-    }
-    else {
-      max_splits = 1;
-    }
-
-    // Wave-aware scheduling: ensure integer number of waves in K dimension
+    
+    // printf("    max_splits = %d\n", max_splits);
     int sms_per_batch = max(1, sm_count / B);
+    // printf("    sms_per_batch = %d\n", sms_per_batch);
     int split_heur = min(max_splits, sms_per_batch);
     int waves = ceil_div(B * split_heur, sm_count);
     int k_waves = ceil_div(max_splits, split_heur);
     int split_wave_aware = ceil_div(max_splits, k_waves);
     args.split_kv = split_wave_aware;
+    // printf("    args.split_kv = %d\n", args.split_kv);
+
   }
 
   /// Determines whether the GEMM can execute the given problem.

csrc/cache.h

@@ -64,11 +64,3 @@ void indexer_k_quant_and_cache(
     torch::Tensor& slot_mapping,  // [num_tokens]
     int64_t quant_block_size,     // quantization block size
     const std::string& scale_fmt);
-
-// Extract function to gather quantized K cache
-void cp_gather_indexer_k_quant_cache(
-    const torch::Tensor& kv_cache,  // [num_blocks, block_size, cache_stride]
-    torch::Tensor& dst_k,           // [num_tokens, head_dim]
-    torch::Tensor& dst_scale,  // [num_tokens, head_dim / quant_block_size * 4]
-    const torch::Tensor& block_table,   // [batch_size, num_blocks]
-    const torch::Tensor& cu_seq_lens);  // [batch_size + 1]

csrc/cache_kernels.cu

@@ -9,14 +9,16 @@
 #include "quantization/vectorization_utils.cuh"
 
 #ifdef USE_ROCM
-  #include "quantization/w8a8/fp8/amd/quant_utils.cuh"
+  #include "quantization/fp8/amd/quant_utils.cuh"
 #else
-  #include "quantization/w8a8/fp8/nvidia/quant_utils.cuh"
+  #include "quantization/fp8/nvidia/quant_utils.cuh"
 #endif
 
 #include <algorithm>
 #include <cassert>
-#include <cfloat>
+#include <cfloat>  // FLT_MIN
+#include <map>
+#include <vector>
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>
@@ -208,20 +210,6 @@ void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
 
 namespace vllm {
 
-// Used to copy/convert one element
-template <typename OutT, typename InT, Fp8KVCacheDataType kv_dt>
-struct CopyWithScaleOp {
-  float scale;
-
-  __device__ __forceinline__ void operator()(OutT& dst, const InT src) const {
-    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
-      dst = static_cast<OutT>(src);
-    } else {
-      dst = fp8::scaled_convert<OutT, InT, kv_dt>(src, scale);
-    }
-  }
-};
-
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
 __global__ void reshape_and_cache_kernel(
     const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
@@ -237,51 +225,59 @@ __global__ void reshape_and_cache_kernel(
   const int64_t token_idx = blockIdx.x;
   const int64_t slot_idx = slot_mapping[token_idx];
   if (slot_idx < 0) {
+    // Padding token that should be ignored.
     return;
   }
 
   const int64_t block_idx = slot_idx / block_size;
   const int64_t block_offset = slot_idx % block_size;
-  const int h_block_count = head_size / x;  // head_size//x
 
-  const int h_block_idx = threadIdx.x;
-  if (h_block_idx >= num_heads * h_block_count) {
-    return;
-  }
+  const int n = num_heads * head_size;
+  for (int i = threadIdx.x; i < n; i += blockDim.x) {
+    const int64_t src_key_idx = token_idx * key_stride + i;
+    const int64_t src_value_idx = token_idx * value_stride + i;
 
-  const int head_idx = h_block_idx / h_block_count;
-  const int h_block = h_block_idx % h_block_count;
+    const int head_idx = i / head_size;
+    const int head_offset = i % head_size;
+    const int x_idx = head_offset / x;
+    const int x_offset = head_offset % x;
 
-  const scalar_t* __restrict__ key_src =
-      key + token_idx * key_stride + head_idx * head_size + h_block * x;
-  const int64_t src_value_start =
-      token_idx * value_stride + head_idx * head_size + h_block * x;
-
-  cache_t* __restrict__ key_dst =
-      key_cache + block_idx * num_heads * h_block_count * block_size * x +
-      head_idx * h_block_count * block_size * x + h_block * block_size * x +
-      block_offset * x;
-  const int64_t tgt_value_start =
-      block_idx * num_heads * h_block_count * x * block_size +
-      head_idx * h_block_count * x * block_size + h_block * x * block_size +
-      block_offset;
-
-  constexpr int VEC_SIZE = (sizeof(scalar_t) == 2) ? 8 : 4;
-  float k_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *k_scale;
-  CopyWithScaleOp<cache_t, scalar_t, kv_dt> k_op{k_scale_val};
-  float v_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *v_scale;
-  CopyWithScaleOp<cache_t, scalar_t, kv_dt> v_op{v_scale_val};
-
-  vectorize_with_alignment<VEC_SIZE>(key_src, key_dst, x, 0, 1, k_op);
-
-  const scalar_t* __restrict__ value_src = value + src_value_start;
-  cache_t* __restrict__ value_dst = value_cache + tgt_value_start;
-#pragma unroll
-  for (int i = 0; i < x; i++) {
-    v_op(value_dst[i * block_size], value_src[i]);
+    const int64_t tgt_key_idx =
+        block_idx * num_heads * (head_size / x) * block_size * x +
+        head_idx * (head_size / x) * block_size * x + x_idx * block_size * x +
+        block_offset * x + x_offset;
+    const int64_t tgt_value_idx =
+        block_idx * num_heads * head_size * block_size +
+        head_idx * head_size * block_size + head_offset * block_size +
+        block_offset;
+    scalar_t tgt_key = key[src_key_idx];
+    scalar_t tgt_value = value[src_value_idx];
+    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+      key_cache[tgt_key_idx] = tgt_key;
+      value_cache[tgt_value_idx] = tgt_value;
+    } else {
+      key_cache[tgt_key_idx] =
+          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, *k_scale);
+      value_cache[tgt_value_idx] =
+          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, *v_scale);
+    }
   }
 }
 
+// Used by vectorization_utils to copy/convert one element
+template <typename OutT, typename InT, Fp8KVCacheDataType kv_dt>
+struct CopyWithScaleOp {
+  float scale;
+
+  __device__ __forceinline__ void operator()(OutT& dst, const InT src) const {
+    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+      dst = static_cast<OutT>(src);
+    } else {
+      dst = fp8::scaled_convert<OutT, InT, kv_dt>(src, scale);
+    }
+  }
+};
+
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
 __global__ void reshape_and_cache_flash_kernel(
     const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
@@ -428,81 +424,84 @@ __global__ void concat_and_cache_ds_mla_kernel(
   const int64_t dst_idx_start =
       block_idx * block_stride + block_offset * entry_stride;
 
-  // For the NoPE part, each tile of 128 elements is handled by half of one warp
-  // (16 threads). There are 4 total tiles, so 2 warps (64 threads).
-  // Lanes 0 and 16 of each warp write the scale values for that warp's tiles.
-  // The RoPE part (last 64 elements) is handled by another 1 warp (32 threads).
-  // So in total, we use 3 warps (96 threads) per block.
+  // Create 4 tile scales in shared memory
+  __shared__ float smem[20];
+  float* shard_abs_max = smem;
+  float* tile_scales = smem + 16;
+
+  // For the NoPE part, each tile of 128 elements is handled by 4 warps
+  // (128 threads). There are 4 total tiles, so 16 warps (512 threads).
+  // The first thread of the first warp in each tile writes the scale
+  // value for the tile. The RoPE part (last 64 elements) is handled
+  // by another 2 warps (64 threads).
+  // So in total, we use 18 warps (576 threads) per block.
 
   // Cast kv_cache to 16_bit for RoPE values
   scalar_t* kv_cache_16bit =
       reinterpret_cast<scalar_t*>(&kv_cache[dst_idx_start]);
 
-  // The last warp handles the RoPE part
-  if (threadIdx.x >= 64) {
-    // Each thread handles two elements of RoPE
-    const int8_t pe_idx_start = (threadIdx.x - 64) * 2;
-    const int64_t src_idx = token_idx * k_pe_stride + pe_idx_start;
-    // Vectorized load of two 16-bit values, performed as one 32-bit load
-    const int32_t vals = *reinterpret_cast<const int32_t*>(&k_pe[src_idx]);
+  // The last 64 threads handle the RoPE part
+  if (threadIdx.x >= kv_lora_rank) {
+    const int8_t pe_idx = threadIdx.x - kv_lora_rank;
+    const int64_t src_idx = token_idx * k_pe_stride + pe_idx;
     // RoPE values start after the packed 8-bit NoPE values and the
     // 32-bit scales
-    const int64_t dst_idx = kv_lora_rank / 2 + 8 + pe_idx_start;
-    // Vectorized store of two 16-bit values, performed as one 32-bit store
-    *reinterpret_cast<int32_t*>(&kv_cache_16bit[dst_idx]) = vals;
+    const int64_t dst_idx = kv_lora_rank / 2 + 8 + pe_idx;
+    kv_cache_16bit[dst_idx] = k_pe[src_idx];
     return;
   }
 
-  // The first two warps handle the NoPE part
-  const int8_t warp_idx = threadIdx.x >> 5;
-  const int8_t lane_idx = threadIdx.x & 31;
-  const int8_t tile_idx = warp_idx * 2 + (lane_idx >> 4);
+  // Determine the scale for each chunk of NoPE
+  const int16_t tile_idx = threadIdx.x >> 7;
+  const int16_t warp_idx = (threadIdx.x & 127) >> 5;
+  const int16_t lane_idx = threadIdx.x & 31;
 
-  // Each thread handles 8 elements of NoPE
-  // Load the NoPE elements for this thread into registers
-  const int64_t src_idx_start = token_idx * kv_c_stride + (threadIdx.x * 8);
-  // Vectorized load of eight 16-bit values, performed as an int4 load
-  const int4 vals_i4 = *reinterpret_cast<const int4*>(&kv_c[src_idx_start]);
-  const scalar_t* vals = reinterpret_cast<const scalar_t*>(&vals_i4);
+  // Load the NoPE element for this thread into registers
+  const int64_t src_idx = token_idx * kv_c_stride + threadIdx.x;
+  const scalar_t src_val = kv_c[src_idx];
 
-  // Max absolute value of this thread's elements
-  float max_abs = fmaxf(fmaxf(fmaxf(fabsf(vals[0]), fabsf(vals[1])),
-                              fmaxf(fabsf(vals[2]), fabsf(vals[3]))),
-                        fmaxf(fmaxf(fabsf(vals[4]), fabsf(vals[5])),
-                              fmaxf(fabsf(vals[6]), fabsf(vals[7]))));
-
-  // Warp-level reduction to find the max absolute value in each half-warp
+  // Warp-level reduction to find the max absolute value in the warp
+  float max_abs = fabsf(src_val);
 #pragma unroll
-  for (int offset = 8; offset > 0; offset /= 2) {
-    max_abs = fmaxf(max_abs, VLLM_SHFL_XOR_SYNC_WIDTH(max_abs, offset, 16));
+  for (int offset = 16; offset > 0; offset /= 2) {
+#ifdef USE_ROCM
+    max_abs = fmaxf(max_abs, __shfl_down_sync(UINT64_MAX, max_abs, offset));
+#else
+    max_abs = fmaxf(max_abs, __shfl_down_sync(0xFFFFFFFF, max_abs, offset));
+#endif
   }
 
-  // Compute the scale for the tile
-  float tile_scale = max_abs / 448.f;
-  tile_scale = fmaxf(tile_scale, FLT_MIN);
+  // The first lane of each warp in each tile writes the max_abs of this part
+  // of the tile to shared memory
+  if (lane_idx == 0) {
+    shard_abs_max[tile_idx * 4 + warp_idx] = max_abs;
+  }
+  __syncthreads();
 
-  // The first lane of each half-warp writes the scale to kv_cache
-  if ((lane_idx == 0) || (lane_idx == 16)) {
+  // The first lane of the first warp in each tile computes the scale for the
+  // tile and writes it to shared memory and to kv_cache
+  if (warp_idx == 0 && lane_idx == 0) {
+    float4 shard_abs_max_vec =
+        reinterpret_cast<float4*>(shard_abs_max)[tile_idx];
+    float tile_scale = fmaxf(fmaxf(shard_abs_max_vec.x, shard_abs_max_vec.y),
+                             fmaxf(shard_abs_max_vec.z, shard_abs_max_vec.w)) /
+                       448.f;
+
+    // Avoid division by zero in `scaled_convert`
+    tile_scales[tile_idx] = fmaxf(tile_scale, FLT_MIN);
     float* kv_cache_32bit = reinterpret_cast<float*>(&kv_cache[dst_idx_start]);
     const uint64_t dst_idx = kv_lora_rank / 4 + tile_idx;
-    kv_cache_32bit[dst_idx] = tile_scale;
+    kv_cache_32bit[dst_idx] = tile_scales[tile_idx];
   }
 
-  // Now all threads in the block scale and write their elements
-  // NoPE data is packed in the first kv_lora_rank/2 bytes (first 256 bytes)
-  const int64_t dst_idx_base = dst_idx_start + (threadIdx.x * 8);
+  __syncthreads();
 
-  uint8_t result[8];
-#pragma unroll
-  for (int i = 0; i < 8; i++) {
-    result[i] =
-        fp8::scaled_convert<uint8_t, scalar_t, Fp8KVCacheDataType::kFp8E4M3>(
-            vals[i], tile_scale);
-  }
-
-  // Store as aligned 64-bit writes
-  *reinterpret_cast<uint64_t*>(&kv_cache[dst_idx_base]) =
-      *reinterpret_cast<const uint64_t*>(result);
+  // Now all threads in the block scale and write their element
+  const float scale_val = tile_scales[tile_idx];
+  const int64_t dst_idx = dst_idx_start + threadIdx.x;
+  kv_cache[dst_idx] =
+      fp8::scaled_convert<uint8_t, scalar_t, Fp8KVCacheDataType::kFp8E4M3>(
+          src_val, scale_val);
 }
 
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
@@ -572,70 +571,6 @@ __global__ void indexer_k_quant_and_cache_kernel(
   }
 }
 
-template <int BLOCK_Y_SIZE>
-__global__ void cp_gather_indexer_k_quant_cache_kernel(
-    const char* __restrict__ kv_cache,  // [num_blocks, block_size,
-                                        // cache_stride]
-    char* __restrict__ dst_k,           // [num_tokens, head_dim]
-    char* __restrict__ dst_scale,  // [num_tokens, head_dim / quant_block_size *
-                                   // 4]
-    const int* __restrict__ block_table,  // [batch_size, num_blocks]
-    const int* __restrict__ cu_seq_lens,  // [batch_size + 1]
-    const int batch_size,                 // batch size
-    const int64_t token_stride,           // stride for each token in dst_k
-    const int64_t head_dim,               // dimension of each head
-    const int64_t block_stride,           // stride for each block in kv_cache
-    const int64_t cache_token_stride,     // stride for each token in kv_cache
-    const int64_t cache_block_size,  // num_tokens for each block in kv_cache
-    const int num_blocks,            // number of blocks
-    const int num_tokens,            // number of tokens
-    const int quant_block_size       // quantization block size
-) {
-  constexpr int VEC_SIZE = sizeof(float4) / sizeof(char);
-  const int token_idx = blockIdx.x * blockDim.y + threadIdx.y;
-  const int head_idx = (blockIdx.y * blockDim.x + threadIdx.x) * VEC_SIZE;
-  // Find batch index within a block
-  __shared__ int batch_idx[BLOCK_Y_SIZE];
-  for (int iter = 0; iter < cuda_utils::ceil_div(batch_size, int(blockDim.x));
-       iter++) {
-    int tid = iter * blockDim.x + threadIdx.x;
-    if (tid < batch_size) {
-      const int seq_start = cu_seq_lens[tid];
-      const int seq_end = cu_seq_lens[tid + 1];
-      if (token_idx >= seq_start && token_idx < seq_end) {
-        batch_idx[threadIdx.y] = tid;
-      }
-    }
-  }
-
-#ifndef USE_ROCM
-  __syncwarp();
-#endif
-
-  if (head_idx >= head_dim || token_idx >= num_tokens) {
-    return;
-  }
-  const int inbatch_seq_idx = token_idx - cu_seq_lens[batch_idx[threadIdx.y]];
-  const int block_idx = block_table[batch_idx[threadIdx.y] * num_blocks +
-                                    inbatch_seq_idx / cache_block_size];
-  const int64_t src_block_offset = block_idx * block_stride;
-  const int64_t cache_inblock_offset =
-      (inbatch_seq_idx % cache_block_size) * head_dim + head_idx;
-  const int64_t src_inblock_offset = src_block_offset + cache_inblock_offset;
-  const int64_t dst_inblock_offset = token_idx * token_stride + head_idx;
-
-  reinterpret_cast<float4*>(dst_k)[dst_inblock_offset / VEC_SIZE] =
-      reinterpret_cast<const float4*>(kv_cache)[src_inblock_offset / VEC_SIZE];
-  ;
-  if (threadIdx.x == 0) {
-    const int64_t src_scale_offset =
-        src_block_offset + cache_block_size * head_dim +
-        cache_inblock_offset * 4 / quant_block_size;
-    reinterpret_cast<float*>(dst_scale)[dst_inblock_offset / quant_block_size] =
-        reinterpret_cast<const float*>(kv_cache)[src_scale_offset / 4];
-  }
-}
-
 }  // namespace vllm
 
 // KV_T is the data type of key and value tensors.
@@ -671,10 +606,9 @@ void reshape_and_cache(
 
   int key_stride = key.stride(0);
   int value_stride = value.stride(0);
-  int head_div_x = head_size / x;
 
   dim3 grid(num_tokens);
-  dim3 block(std::min(num_heads * head_div_x, 512));
+  dim3 block(std::min(num_heads * head_size, 512));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
@@ -807,12 +741,13 @@ void concat_and_cache_mla(
 
   if (kv_cache_dtype == "fp8_ds_mla") {
     dim3 grid(num_tokens);
-    // For the NoPE part, each tile of 128 elements is handled by half of one
-    // warp (16 threads). There are 4 total tiles, so 2 warps (64 threads).
-    // Lanes 0 and 16 of each warp write the scale values for that warp's tiles.
-    // The RoPE part (last 64 elements) is handled by another 1 warp (32
-    // threads). So in total, we use 3 warps (96 threads) per block.
-    dim3 block(96);
+    // For the NoPE part, each tile of 128 elements is handled by 4 warps
+    // (128 threads). There are 4 total tiles, so 16 warps (512 threads).
+    // The first thread of the first warp in each tile writes the scale
+    // value for the tile. The RoPE part (last 64 elements) is handled
+    // by another 2 warps (64 threads).
+    // So in total, we use 18 warps (576 threads) per block.
+    dim3 block(576);
     DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype,
                                CALL_CONCAT_AND_CACHE_DS_MLA);
   } else {
@@ -1237,59 +1172,3 @@ void indexer_k_quant_and_cache(
   DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), "fp8_e4m3",
                              CALL_INDEXER_K_QUANT_AND_CACHE);
 }
-
-// Macro to dispatch the kernel based on the data amount.
-#define CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(BLOCK_Y_SIZE)                  \
-  vllm::cp_gather_indexer_k_quant_cache_kernel<BLOCK_Y_SIZE>                \
-      <<<dim3((num_tokens + BLOCK_Y_SIZE - 1) / BLOCK_Y_SIZE,               \
-              (head_dim + 8 * vec_size - 1) / (8 * vec_size)),              \
-         dim3(8, BLOCK_Y_SIZE), 0, stream>>>(                               \
-          reinterpret_cast<char*>(kv_cache.data_ptr()),                     \
-          reinterpret_cast<char*>(dst_k.data_ptr()),                        \
-          reinterpret_cast<char*>(dst_scale.data_ptr()),                    \
-          block_table.data_ptr<int32_t>(), cu_seq_lens.data_ptr<int32_t>(), \
-          batch_size, dst_k.stride(0), dst_k.size(1), kv_cache.stride(0),   \
-          kv_cache.stride(1), kv_cache.size(1), block_table.size(1),        \
-          num_tokens, quant_block_size);
-
-void cp_gather_indexer_k_quant_cache(
-    const torch::Tensor& kv_cache,  // [num_blocks, block_size, cache_stride]
-    torch::Tensor& dst_k,           // [num_tokens, head_dim]
-    torch::Tensor& dst_scale,  // [num_tokens, head_dim / quant_block_size * 4]
-    const torch::Tensor& block_table,  // [batch_size, num_blocks]
-    const torch::Tensor& cu_seq_lens   // [batch_size + 1]
-) {
-  int batch_size = block_table.size(0);
-  int num_tokens = dst_k.size(0);
-  int head_dim = dst_k.size(1);
-  int quant_block_size = head_dim * 4 / dst_scale.size(1);
-
-  TORCH_CHECK(kv_cache.device() == dst_k.device(),
-              "kv_cache and dst_k must be on the same device");
-  TORCH_CHECK(kv_cache.device() == dst_scale.device(),
-              "kv_cache and dst_scale must be on the same device");
-  TORCH_CHECK(kv_cache.device() == block_table.device(),
-              "kv_cache and block_table must be on the same device");
-  TORCH_CHECK(kv_cache.device() == cu_seq_lens.device(),
-              "kv_cache and cu_seq_lens must be on the same device");
-  TORCH_CHECK(head_dim % quant_block_size == 0,
-              "head_dim must be divisible by quant_block_size");
-
-  constexpr int vec_size = 16;
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(kv_cache));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  if (num_tokens < 32) {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(1);
-  } else if (num_tokens < 64) {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(2);
-  } else if (num_tokens < 128) {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(4);
-  } else if (num_tokens < 256) {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(8);
-  } else if (num_tokens < 512) {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(16);
-  } else {
-    CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(32);
-  }
-}

csrc/core/batch_invariant.hpp

@@ -1,19 +0,0 @@
-#pragma once
-#include <cstdlib>
-#include <string>
-#include <cctype>
-
-namespace vllm {
-
-// vllm_is_batch_invariant(); returns true
-// if env VLLM_BATCH_INVARIANT=1
-inline bool vllm_is_batch_invariant() {
-  static bool cached = []() {
-    std::string env_key = "VLLM_BATCH_INVARIANT";
-    const char* val = std::getenv(env_key.c_str());
-    return (val && std::atoi(val) != 0) ? 1 : 0;
-  }();
-  return cached;
-}
-
-}  // namespace vllm

csrc/cpu/dnnl_helper.cpp

@@ -137,8 +137,9 @@ DNNLMatMulPrimitiveHandler::DNNLMatMulPrimitiveHandler(
 }
 
 void DNNLMatMulPrimitiveHandler::prepack_weight(
-    void* original_b_ptr, dnnl::memory::desc original_b_md,
-    dnnl::memory::desc b_target_mem_desc) {
+    void* original_b_ptr, dnnl::memory::desc b_target_mem_desc) {
+  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
+                                   {b_k_stride_, b_n_stride_});
   dnnl::memory original_weight(original_b_md, default_engine(), original_b_ptr);
   dnnl::memory packed_weight(b_target_mem_desc, default_engine());
   {
@@ -249,9 +250,7 @@ W8A8MatMulPrimitiveHandler::W8A8MatMulPrimitiveHandler(const Args& args)
   if (a_qs_ == QuantizationStrategy::PER_TOKEN) {
     assert(!use_azp_);
   };
-  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
-                                   {b_k_stride_, b_n_stride_});
-  prepack_weight(args.b_ptr, original_b_md,
+  prepack_weight(args.b_ptr,
                  create_primitive_desc(
                      MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
                                    .use_bias = false,
@@ -413,25 +412,12 @@ MatMulPrimitiveHandler::MatMulPrimitiveHandler(const Args& args)
   assert(ab_type_ == dnnl::memory::data_type::f32 ||
          ab_type_ == dnnl::memory::data_type::bf16 ||
          ab_type_ == dnnl::memory::data_type::f16);
-
-  dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
-                                   {b_k_stride_, b_n_stride_});
-
-  prepack_weight(args.b_ptr, original_b_md,
+  prepack_weight(args.b_ptr,
                  create_primitive_desc(
-                     MSizeCacheKey{
-#ifdef VLLM_USE_ACL
-                         // Arm Compute Library (ACL) backend for oneDNN does
-                         // not support runtime
-                         // dimensions, so we set M to a default value
-                         .a_m_size = 128,
-                         .a_m_stride = b_k_size_,
-#else
-                         .a_m_size = DNNL_RUNTIME_DIM_VAL,
-                         .a_m_stride = DNNL_RUNTIME_DIM_VAL,
-#endif
-                         .use_bias = false,
-                         .bias_type = dnnl::memory::data_type::undef},
+                     MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
+                                   .a_m_stride = DNNL_RUNTIME_DIM_VAL,
+                                   .use_bias = false,
+                                   .bias_type = dnnl::memory::data_type::undef},
                      true)
                      .weights_desc());
   init_runtime_memory_cache(args);
@@ -457,30 +443,12 @@ void MatMulPrimitiveHandler::execute(ExecArgs& args) {
   c_storage->set_data_handle((void*)args.c_ptr);
   c_mem_desc->dims[0] = args.a_m_size;
 
-#ifndef VLLM_USE_ACL
-  // We do not support in ACL backend of oneDNN, we handle bias by:
-  // 1. copying it into the result tensor
-  // 2. attaching a fused-sum post-op to the matmul primitive
   if (args.use_bias) {
     auto&& [bias_storage, bias_mem_desc] = get_runtime_memory_ptr(2);
     bias_storage->set_data_handle((void*)args.bias_ptr);
   }
-#endif
-  dnnl::matmul matmul = get_matmul_cache(args);
 
-// With ACL backend of oneDNN, the required memory format might change when the
-// source tensor dims change. This does not really happen in practice, so isn't
-// a performance hit, but we need to support it because the API allows for it.
-#ifdef VLLM_USE_ACL
-  auto new_expected_wei_desc =
-      dnnl::matmul::primitive_desc(
-          const_cast<dnnl_primitive_desc_t>(matmul.get_primitive_desc()))
-          .weights_desc();
-  if (new_expected_wei_desc != b_target_mem_desc_) {
-    prepack_weight(memory_cache_[DNNL_ARG_WEIGHTS].get_data_handle(),
-                   b_target_mem_desc_, new_expected_wei_desc);
-  }
-#endif
+  dnnl::matmul matmul = get_matmul_cache(args);
 
   auto&& [scratchpad_storage, scratchpad_mem_desc] = get_runtime_memory_ptr(3);
   scratchpad_storage->set_data_handle(
@@ -516,13 +484,7 @@ dnnl::matmul::primitive_desc MatMulPrimitiveHandler::create_primitive_desc(
   } else {
     a_md = dnnl::memory::desc({key.a_m_size, b_k_size_}, b_type_,
                               {key.a_m_stride, 1});
-#ifdef VLLM_USE_ACL
-    // ACL's backend of oneDNN always expects the weight format to be "any"
-    b_md = dnnl::memory::desc({b_k_size_, b_n_size_}, b_type_,
-                              dnnl::memory::format_tag::any);
-#else
     b_md = b_target_mem_desc_;
-#endif
   }
   dnnl::memory::desc c_md({key.a_m_size, b_n_size_}, c_type_,
                           dnnl::memory::format_tag::ab);
@@ -532,18 +494,8 @@ dnnl::matmul::primitive_desc MatMulPrimitiveHandler::create_primitive_desc(
 
   if (key.use_bias) {
     dnnl::memory::desc bias_md({1, b_n_size_}, key.bias_type, {b_n_size_, 1});
-// Since ACL's matmuls don't support passing a bias_md, we apply the bias
-// through a fused-sum post-op
-#ifdef VLLM_USE_ACL
-    dnnl::post_ops post_ops;
-    post_ops.append_sum();
-    attr.set_post_ops(post_ops);
-    return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
-                                        attr);
-#else
     return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, bias_md,
                                         c_md, attr);
-#endif
   } else {
     return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
                                         attr);
@@ -559,23 +511,13 @@ void MatMulPrimitiveHandler::init_runtime_memory_cache(const Args& args) {
                    default_engine(), nullptr);
   set_runtime_memory_ptr(1, memory_cache_[DNNL_ARG_DST].get());
 
-// ACL matmuls don't support bias_md, so we don't need these
-#ifndef VLLM_USE_ACL
   memory_cache_[DNNL_ARG_BIAS] =
       dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
                    default_engine(), nullptr);
   set_runtime_memory_ptr(2, memory_cache_[DNNL_ARG_BIAS].get());
-#endif
+
   memory_cache_[DNNL_ARG_SCRATCHPAD] =
       dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
                    default_engine(), nullptr);
   set_runtime_memory_ptr(3, memory_cache_[DNNL_ARG_SCRATCHPAD].get());
 }
-
-bool is_onednn_acl_supported() {
-#ifdef VLLM_USE_ACL
-  return true;
-#else
-  return false;
-#endif
-}

csrc/cpu/dnnl_helper.h

@@ -101,7 +101,7 @@ class DNNLMatMulPrimitiveHandler {
  protected:
   DNNLMatMulPrimitiveHandler(const Args& args, dnnl::memory::data_type b_type);
 
-  void prepack_weight(void* original_b_ptr, dnnl::memory::desc original_b_md,
+  void prepack_weight(void* original_b_ptr,
                       dnnl::memory::desc b_target_mem_desc);
 
   void set_runtime_memory_ptr(size_t index, dnnl_memory* memory_ptr);

csrc/cpu/dnnl_kernels.cpp

@@ -527,42 +527,21 @@ void onednn_mm(torch::Tensor& c,        // [M, OC], row-major
   MatMulPrimitiveHandler* ptr =
       reinterpret_cast<MatMulPrimitiveHandler*>(handler);
 
-// ACL matmuls expect contiguous source tensors
-#ifdef VLLM_USE_ACL
-  torch::Tensor a_contig = a.contiguous();
-#endif
-
   MatMulPrimitiveHandler::ExecArgs exec_args;
-
-#ifdef VLLM_USE_ACL
-  exec_args.a_m_size = a_contig.size(0);
-  exec_args.a_m_stride = a_contig.stride(0);
-#else
   exec_args.a_m_size = a.size(0);
   exec_args.a_m_stride = a.stride(0);
-#endif
+
   VLLM_DISPATCH_FLOATING_TYPES(a.scalar_type(), "onednn_mm", [&] {
     if (bias.has_value()) {
       exec_args.use_bias = true;
       exec_args.bias_type = get_dnnl_type<scalar_t>();
-#ifdef VLLM_USE_ACL
-      // ACL matmuls in oneDNN do not support a bias.
-      // We handle a matmul with bias by doing: c = bias; c += matmul(a, b)
-      c.copy_(bias.value());
-#else
       exec_args.bias_ptr = bias->data_ptr<scalar_t>();
-#endif
     } else {
       exec_args.use_bias = false;
       exec_args.bias_type = get_dnnl_type<void>();
       exec_args.bias_ptr = nullptr;
     }
-#ifdef VLLM_USE_ACL
-    exec_args.a_ptr = a_contig.data_ptr<scalar_t>();
-#else
     exec_args.a_ptr = a.data_ptr<scalar_t>();
-
-#endif
     exec_args.c_ptr = c.data_ptr<scalar_t>();
 
     ptr->execute(exec_args);

csrc/cpu/torch_bindings.cpp

@@ -27,8 +27,6 @@ int64_t create_onednn_mm_handler(const torch::Tensor& b,
 void onednn_mm(torch::Tensor& c, const torch::Tensor& a,
                const std::optional<torch::Tensor>& bias, int64_t handler);
 
-bool is_onednn_acl_supported();
-
 void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
                         torch::Tensor& kv_cache, double scale,
                         torch::Tensor& block_tables, torch::Tensor& seq_lens);
@@ -183,9 +181,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int handler) -> ()");
   ops.impl("onednn_mm", torch::kCPU, &onednn_mm);
 
-  // Check if oneDNN was built with ACL backend
-  ops.def("is_onednn_acl_supported() -> bool", &is_onednn_acl_supported);
-
   // Create oneDNN W8A8 handler
   ops.def(
       "create_onednn_scaled_mm_handler(Tensor b, Tensor b_scales, ScalarType "

csrc/cub_helpers.h

@@ -12,7 +12,6 @@ using CubMaxOp = cub::Max;
   #endif  // CUB_VERSION
 #else
   #include <hipcub/hipcub.hpp>
-namespace cub = hipcub;
-using CubAddOp = hipcub::Sum;
-using CubMaxOp = hipcub::Max;
+using CubAddOp = cub::Sum;
+using CubMaxOp = cub::Max;
 #endif  // USE_ROCM

csrc/cutlass_extensions/vllm_cutlass_library_extension.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import enum
+from typing import Union
 
 from cutlass_library import *
 
@@ -21,31 +22,31 @@ class MixedInputKernelScheduleType(enum.Enum):
     TmaWarpSpecializedCooperative = enum_auto()
 
 
-VLLMDataTypeNames: dict[VLLMDataType | DataType, str] = {
+VLLMDataTypeNames: dict[Union[VLLMDataType, DataType], str] = {
     **DataTypeNames,  # type: ignore
     **{
         VLLMDataType.u4b8: "u4b8",
         VLLMDataType.u8b128: "u8b128",
-    },
+    }
 }
 
-VLLMDataTypeTag: dict[VLLMDataType | DataType, str] = {
+VLLMDataTypeTag: dict[Union[VLLMDataType, DataType], str] = {
     **DataTypeTag,  # type: ignore
     **{
         VLLMDataType.u4b8: "cutlass::vllm_uint4b8_t",
         VLLMDataType.u8b128: "cutlass::vllm_uint8b128_t",
-    },
+    }
 }
 
-VLLMDataTypeSize: dict[VLLMDataType | DataType, int] = {
+VLLMDataTypeSize: dict[Union[VLLMDataType, DataType], int] = {
     **DataTypeSize,  # type: ignore
     **{
         VLLMDataType.u4b8: 4,
         VLLMDataType.u8b128: 8,
-    },
+    }
 }
 
-VLLMDataTypeVLLMScalarTypeTag: dict[VLLMDataType | DataType, str] = {
+VLLMDataTypeVLLMScalarTypeTag: dict[Union[VLLMDataType, DataType], str] = {
     VLLMDataType.u4b8: "vllm::kU4B8",
     VLLMDataType.u8b128: "vllm::kU8B128",
     DataType.u4: "vllm::kU4",
@@ -56,7 +57,7 @@ VLLMDataTypeVLLMScalarTypeTag: dict[VLLMDataType | DataType, str] = {
     DataType.bf16: "vllm::kBfloat16",
 }
 
-VLLMDataTypeTorchDataTypeTag: dict[VLLMDataType | DataType, str] = {
+VLLMDataTypeTorchDataTypeTag: dict[Union[VLLMDataType, DataType], str] = {
     DataType.u8: "at::ScalarType::Byte",
     DataType.s8: "at::ScalarType::Char",
     DataType.e4m3: "at::ScalarType::Float8_e4m3fn",
@@ -66,11 +67,15 @@ VLLMDataTypeTorchDataTypeTag: dict[VLLMDataType | DataType, str] = {
     DataType.f32: "at::ScalarType::Float",
 }
 
-VLLMKernelScheduleTag: dict[MixedInputKernelScheduleType | KernelScheduleType, str] = {
-    **KernelScheduleTag,  # type: ignore
-    **{
-        MixedInputKernelScheduleType.TmaWarpSpecialized: "cutlass::gemm::KernelTmaWarpSpecialized",  # noqa: E501
-        MixedInputKernelScheduleType.TmaWarpSpecializedPingpong: "cutlass::gemm::KernelTmaWarpSpecializedPingpong",  # noqa: E501
-        MixedInputKernelScheduleType.TmaWarpSpecializedCooperative: "cutlass::gemm::KernelTmaWarpSpecializedCooperative",  # noqa: E501
-    },
-}
+VLLMKernelScheduleTag: dict[Union[
+    MixedInputKernelScheduleType, KernelScheduleType], str] = {
+        **KernelScheduleTag,  # type: ignore
+        **{
+            MixedInputKernelScheduleType.TmaWarpSpecialized:
+            "cutlass::gemm::KernelTmaWarpSpecialized",
+            MixedInputKernelScheduleType.TmaWarpSpecializedPingpong:
+            "cutlass::gemm::KernelTmaWarpSpecializedPingpong",
+            MixedInputKernelScheduleType.TmaWarpSpecializedCooperative:
+            "cutlass::gemm::KernelTmaWarpSpecializedCooperative",
+        }
+    }

csrc/launch_bounds_utils.h

@@ -8,37 +8,11 @@
   #define VLLM_LAUNCH_BLOCKS_CAP 4
 #endif
 
-// Compile-time estimate of max threads per SM for launch bounds.
-// Families: 1024, 1536, 2048 threads/SM.
+// compile-time estimate of max threads per SM for launch bounds.
 #ifndef VLLM_MAX_THREADS_PER_SM
-  #ifdef __CUDA_ARCH__
-
-    /* 1024 thr/SM: Turing (sm_75) */
-    #if (__CUDA_ARCH__ == 750)
-      #define VLLM_MAX_THREADS_PER_SM 1024
-
-    /* 1536 thr/SM: Ampere GA10x (sm_86/87), Ada (sm_89),
-        GB20x consumer (sm_120/121), Thor (sm_101 or sm_110) */
-    #elif (__CUDA_ARCH__ == 860) || (__CUDA_ARCH__ == 870) || \
-        (__CUDA_ARCH__ == 890) || (__CUDA_ARCH__ == 1010) ||  \
-        (__CUDA_ARCH__ == 1100) || (__CUDA_ARCH__ == 1200) || \
-        (__CUDA_ARCH__ == 1210)
-      #define VLLM_MAX_THREADS_PER_SM 1536
-
-    /* 2048 thr/SM: Volta (sm_70/72), Ampere GA100 (sm_80),
-        Hopper (sm_90), Blackwell (sm_100/103) */
-    #elif (__CUDA_ARCH__ == 700) || (__CUDA_ARCH__ == 720) || \
-        (__CUDA_ARCH__ == 800) || (__CUDA_ARCH__ == 900) ||   \
-        (__CUDA_ARCH__ == 1000) || (__CUDA_ARCH__ == 1030)
-      #define VLLM_MAX_THREADS_PER_SM 2048
-
-    /* Fallback: use 2048 for unknown future CCs */
-    #else
-      #define VLLM_MAX_THREADS_PER_SM 2048
-    #endif
-
+  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 300
+    #define VLLM_MAX_THREADS_PER_SM 1536
   #else
-  /* Host pass (no __CUDA_ARCH__): neutral default */
     #define VLLM_MAX_THREADS_PER_SM 2048
   #endif
 #endif

csrc/layernorm_kernels.cu

@@ -1,8 +1,6 @@
 #include "type_convert.cuh"
 #include "dispatch_utils.h"
 #include "cub_helpers.h"
-#include "core/batch_invariant.hpp"
-#include "quantization/vectorization_utils.cuh"
 
 #include <torch/cuda.h>
 #include <c10/cuda/CUDAGuard.h>
@@ -19,22 +17,11 @@ __global__ void rms_norm_kernel(
     const float epsilon, const int num_tokens, const int hidden_size) {
   __shared__ float s_variance;
   float variance = 0.0f;
-  const scalar_t* input_row = input + blockIdx.x * input_stride;
 
-  constexpr int VEC_SIZE = 8;
-  auto vec_op = [&variance](const vec_n_t<scalar_t, VEC_SIZE>& vec) {
-#pragma unroll
-    for (int i = 0; i < VEC_SIZE; ++i) {
-      float x = static_cast<float>(vec.val[i]);
-      variance += x * x;
-    }
-  };
-  auto scalar_op = [&variance](const scalar_t& val) {
-    float x = static_cast<float>(val);
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    const float x = (float)input[blockIdx.x * input_stride + idx];
     variance += x * x;
-  };
-  vllm::vectorize_read_with_alignment<VEC_SIZE>(
-      input_row, hidden_size, threadIdx.x, blockDim.x, vec_op, scalar_op);
+  }
 
   using BlockReduce = cub::BlockReduce<float, 1024>;
   __shared__ typename BlockReduce::TempStorage reduceStore;
@@ -148,6 +135,211 @@ fused_add_rms_norm_kernel(
   }
 }
 
+/* Function specialization in the case of FP16/BF16 tensors.
+   Additional optimizations we can make in this case are
+   packed and vectorized operations, which help with the
+   memory latency bottleneck.
+
+   _f16VecPN struct extends _f16Vec to add operations specifically required for
+   polynomial normalization (poly norm).
+   The original _f16Vec does not include the sum-of-powers computation or
+   in-place polynomial normalization logic. */
+template <typename scalar_t, int width>
+struct alignas(16) _f16VecPN : _f16Vec<scalar_t, width> {
+  using Base = _f16Vec<scalar_t, width>;
+  using Converter = typename Base::Converter;
+  using T1 = typename Base::T1;
+  using T2 = typename Base::T2;
+  using Base::data;
+
+  __device__ auto sum_pows() const {
+    float s2 = 0.0f, s4 = 0.0f, s6 = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+      float x2 = z.x * z.x;
+      float x4 = x2 * x2;
+      float x6 = x4 * x2;
+
+      float y2 = z.y * z.y;
+      float y4 = y2 * y2;
+      float y6 = y4 * y2;
+
+      s2 += x2 + y2;
+      s4 += x4 + y4;
+      s6 += x6 + y6;
+    }
+    return std::make_tuple(s2, s4, s6);
+  }
+
+  __device__ void poly_norm_inplace(const float w2_inv_std,
+                                    const float w1_inv_std2,
+                                    const float w0_inv_std3, const float bias) {
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+
+      float x2 = z.x * z.x;
+      float x3 = x2 * z.x;
+      z.x = w2_inv_std * z.x + w1_inv_std2 * x2 + w0_inv_std3 * x3 + bias;
+
+      float y2 = z.y * z.y;
+      float y3 = y2 * z.y;
+      z.y = w2_inv_std * z.y + w1_inv_std2 * y2 + w0_inv_std3 * y3 + bias;
+
+      auto out = Converter::convert(z);
+      data[i] = out.x;
+      data[i + 1] = out.y;
+    }
+  }
+};
+
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  // Sanity checks on our vector struct and type-punned pointer arithmetic
+  static_assert(std::is_pod_v<_f16VecPN<scalar_t, width>>);
+  static_assert(sizeof(_f16VecPN<scalar_t, width>) == sizeof(scalar_t) * width);
+
+  /* These and the argument pointers are all declared `restrict` as they are
+     not aliased in practice. Argument pointers should not be dereferenced
+     in this kernel as that would be undefined behavior */
+  auto* __restrict__ input_v =
+      reinterpret_cast<const _f16VecPN<scalar_t, width>*>(input);
+  const int vec_hidden_size = hidden_size / width;
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    auto [x2, x4, x6] = temp.sum_pows();
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  auto* __restrict__ out_v = reinterpret_cast<_f16VecPN<scalar_t, width>*>(out);
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    temp.poly_norm_inplace(s_w2_inv_std, s_w1_inv_std2, s_w0_inv_std3, s_bias);
+    out_v[id] = temp;
+  }
+}
+
+/* Generic poly_norm_kernel
+   The width field is not used here but necessary for other specializations.
+ */
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x4 = x2 * x2;
+    float x6 = x4 * x2;
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x3 = x2 * x;
+
+    out[blockIdx.x * hidden_size + idx] =
+        (scalar_t)(x * s_w2_inv_std + x2 * s_w1_inv_std2 + x3 * s_w0_inv_std3 +
+                   s_bias);
+  }
+}
+
 }  // namespace vllm
 
 void rms_norm(torch::Tensor& out,     // [..., hidden_size]
@@ -159,26 +351,18 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
   TORCH_CHECK(weight.is_contiguous());
 
   int hidden_size = input.size(-1);
-
-  // We cannot just use `input.stride(-2)` if the tensor is not row-major.
-  // Instead, we use a 2d view to get the second-innermost stride.
-  // That way the dimensions (except the last one) can be arbitrarily permuted.
-  torch::Tensor input_view = input.view({-1, hidden_size});
-
-  int num_tokens = input_view.numel() / hidden_size;
-  int64_t input_stride = input_view.stride(-2);
+  int num_tokens = input.numel() / hidden_size;
+  int64_t input_stride = input.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input_view));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      input_view.scalar_type(), "rms_norm_kernel", [&] {
-        vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            out.data_ptr<scalar_t>(), input_view.data_ptr<scalar_t>(),
-            input_stride, weight.data_ptr<scalar_t>(), epsilon, num_tokens,
-            hidden_size);
-      });
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
+    vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), input_stride,
+        weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size);
+  });
 }
 
 #define LAUNCH_FUSED_ADD_RMS_NORM(width)                                    \
@@ -195,8 +379,6 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                         torch::Tensor& residual,  // [..., hidden_size]
                         torch::Tensor& weight,    // [hidden_size]
                         double epsilon) {
-  TORCH_CHECK(weight.scalar_type() == input.scalar_type());
-  TORCH_CHECK(input.scalar_type() == residual.scalar_type());
   TORCH_CHECK(residual.is_contiguous());
   TORCH_CHECK(weight.is_contiguous());
   int hidden_size = input.size(-1);
@@ -231,11 +413,55 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                           wt_ptr % req_alignment_bytes == 0;
   bool offsets_are_multiple_of_vector_width =
       hidden_size % vector_width == 0 && input_stride % vector_width == 0;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
-  if (ptrs_are_aligned && offsets_are_multiple_of_vector_width &&
-      !batch_invariant_launch) {
+  if (ptrs_are_aligned && offsets_are_multiple_of_vector_width) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
   } else {
     LAUNCH_FUSED_ADD_RMS_NORM(0);
   }
 }
+
+#define LAUNCH_FUSED_POLY_NORM(width)                                         \
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "poly_norm_kernel", [&] { \
+    vllm::poly_norm_kernel<scalar_t, width><<<grid, block, 0, stream>>>(      \
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),                 \
+        weight.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(), epsilon,      \
+        hidden_size);                                                         \
+  });
+
+void poly_norm(torch::Tensor& out,     // [..., hidden_size]
+               torch::Tensor& input,   // [..., hidden_size]
+               torch::Tensor& weight,  // [3]
+               torch::Tensor& bias,    // [1]
+               double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(out.data_ptr() != input.data_ptr());
+
+  int hidden_size = input.size(-1);
+  int num_tokens = input.numel() / hidden_size;
+
+  dim3 grid(num_tokens);
+  /* This kernel is memory-latency bound in many scenarios.
+     When num_tokens is large, a smaller block size allows
+     for increased block occupancy on CUs and better latency
+     hiding on global mem ops. */
+  const int max_block_size = (num_tokens < 256) ? 1024 : 256;
+  dim3 block(std::min(hidden_size, max_block_size));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  /*If the tensor types are FP16/BF16, try to use the optimized kernel
+    with packed + vectorized ops.
+    Max optimization is achieved with a width-8 vector of FP16/BF16s
+    since we can load at most 128 bits at once in a global memory op.
+    However, this requires each tensor's data to be aligned to 16
+    bytes.
+   */
+  auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
+  auto out_ptr = reinterpret_cast<std::uintptr_t>(out.data_ptr());
+  bool ptrs_are_aligned = inp_ptr % 16 == 0 && out_ptr % 16 == 0;
+  if (ptrs_are_aligned && hidden_size % 8 == 0) {
+    LAUNCH_FUSED_POLY_NORM(8);
+  } else {
+    LAUNCH_FUSED_POLY_NORM(0);
+  }
+}