[Quantization/NVFP4] Speed up TRTLLM NVFP4 MOE weight loading and fix K/V scale loading for MLA Attn (#25968)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>
Signed-off-by: yewentao256 <zhyanwentao@126.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

@@ -1,11 +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 -b 32 -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"
-    value: 0.90
-limit: 100
-num_fewshot: 0

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

@@ -1,11 +0,0 @@
-# For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -b 32 -l 250 -t 8 -f 5
-model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-backend: "vllm-vlm"
-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/run-performance-benchmarks.sh

@@ -454,6 +454,11 @@ 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-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

@@ -44,5 +44,6 @@ docker run \
     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/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_metrics
     pytest -v -s v1/test_serial_utils.py
 '

.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

@@ -296,7 +296,6 @@ steps:
     - tests/v1
   commands:
     # split the test to avoid interference
-    - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
     - pytest -v -s v1/kv_offload
     - pytest -v -s v1/sample
@@ -318,7 +317,7 @@ steps:
   no_gpu: true
   commands:
     # split the test to avoid interference
-    - pytest -v -s -m 'cpu_test' v1/core
+    - pytest -v -s 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
@@ -398,12 +397,12 @@ 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
@@ -432,9 +431,8 @@ 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
@@ -478,7 +476,6 @@ steps:
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
-  - vllm/model_executor/layers/mamba/ops
   commands:
     - pytest -v -s kernels/mamba
 
@@ -527,9 +524,8 @@ 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
@@ -734,16 +730,6 @@ 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
@@ -840,20 +826,18 @@ steps:
     - 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/moe/test_nvfp4_moe.py
-    - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
+    - 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_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
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.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
@@ -880,16 +864,6 @@ steps:
   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
-
 #####  1 GPU test  #####
 #####  multi gpus test  #####
 
@@ -1106,8 +1080,6 @@ steps:
   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/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

@@ -5,7 +5,9 @@
 /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
@@ -21,9 +23,9 @@ 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 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
 /vllm/v1/attention @LucasWilkinson
 /vllm/v1/attention/backends/flashinfer.py @mgoin
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
@@ -57,7 +59,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /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

@@ -11,8 +11,6 @@ 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

.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

.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

@@ -34,7 +34,7 @@ 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")
@@ -1007,7 +1007,6 @@ 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

@@ -149,7 +149,6 @@ 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
@@ -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
@@ -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/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

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_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

@@ -3,6 +3,7 @@
 
 import random
 import time
+from typing import Optional
 
 import torch
 
@@ -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_reshape_and_cache.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
 

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
 

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
 
@@ -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_triton_block_scaled_mm(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:

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 8f468e7da54a8e2f98abfa7c38636aac91c0cba1
+          GIT_TAG 4695e6bed5366c41e28c06cd86170166e4f43d00
           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))

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/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

@@ -572,70 +572,6 @@ __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.
@@ -1237,59 +1173,3 @@ 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

@@ -5,15 +5,12 @@
 
 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;
+// vllm_kernel_override_batch_invariant(); returns true
+// if env VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT=1
+inline bool vllm_kernel_override_batch_invariant() {
+  std::string env_key = "VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT";
+  const char* val = std::getenv(env_key.c_str());
+  return (val && std::atoi(val) != 0) ? 1 : 0;
 }
 
 }  // 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/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/layernorm_kernels.cu

@@ -2,7 +2,6 @@
 #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 +18,11 @@ __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;
@@ -426,7 +414,7 @@ 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();
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
   if (ptrs_are_aligned && offsets_are_multiple_of_vector_width &&
       !batch_invariant_launch) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
@@ -474,7 +462,7 @@ void poly_norm(torch::Tensor& out,     // [..., hidden_size]
   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;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
   if (ptrs_are_aligned && hidden_size % 8 == 0 && !batch_invariant_launch) {
     LAUNCH_FUSED_POLY_NORM(8);
   } else {

csrc/layernorm_quant_kernels.cu

@@ -10,7 +10,6 @@
 #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>
@@ -29,22 +28,10 @@ __global__ void rms_norm_static_fp8_quant_kernel(
   __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;
@@ -254,7 +241,7 @@ void fused_add_rms_norm_static_fp8_quant(
   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
   bool ptrs_are_aligned =
       inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
+  bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
   if (ptrs_are_aligned && hidden_size % 8 == 0 && input_stride % 8 == 0 &&
       !batch_invariant_launch) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -17,30 +17,25 @@ FILE_HEAD = """
 namespace MARLIN_NAMESPACE_NAME {
 """.strip()
 
-TEMPLATE = (
-    "template __global__ void Marlin<"
-    "{{scalar_t}}, "
-    "{{w_type_id}}, "
-    "{{s_type_id}}, "
-    "{{threads}}, "
-    "{{thread_m_blocks}}, "
-    "{{thread_n_blocks}}, "
-    "{{thread_k_blocks}}, "
-    "{{'true' if m_block_size_8 else 'false'}}, "
-    "{{stages}}, "
-    "{{group_blocks}}, "
-    "{{'true' if is_zp_float else 'false'}}>"
-    "( MARLIN_KERNEL_PARAMS );"
-)
+TEMPLATE = ("template __global__ void Marlin<"
+            "{{scalar_t}}, "
+            "{{w_type_id}}, "
+            "{{s_type_id}}, "
+            "{{threads}}, "
+            "{{thread_m_blocks}}, "
+            "{{thread_n_blocks}}, "
+            "{{thread_k_blocks}}, "
+            "{{'true' if m_block_size_8 else 'false'}}, "
+            "{{stages}}, "
+            "{{group_blocks}}, "
+            "{{'true' if is_zp_float else 'false'}}>"
+            "( MARLIN_KERNEL_PARAMS );")
 
 # int8 with zero point case (vllm::kU8) is also supported,
 # we don't add it to reduce wheel size.
 SCALAR_TYPES = [
-    "vllm::kU4",
-    "vllm::kU4B8",
-    "vllm::kU8B128",
-    "vllm::kFE4M3fn",
-    "vllm::kFE2M1f",
+    "vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn",
+    "vllm::kFE2M1f"
 ]
 THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128)]
 
@@ -63,12 +58,11 @@ def generate_new_kernels():
         all_template_str_list = []
 
         for group_blocks, m_blocks, thread_configs in itertools.product(
-            GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS
-        ):
+                GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS):
+
             # act order case only support gptq-int4 and gptq-int8
             if group_blocks == 0 and scalar_type not in [
-                "vllm::kU4B8",
-                "vllm::kU8B128",
+                    "vllm::kU4B8", "vllm::kU8B128"
             ]:
                 continue
             if thread_configs[2] == 256:

csrc/moe/moe_align_sum_kernels.cu

@@ -8,77 +8,12 @@
 
 #include "../cuda_compat.h"
 #include "../dispatch_utils.h"
-#include "core/math.hpp"
 
 #define CEILDIV(x, y) (((x) + (y) - 1) / (y))
 
 namespace vllm {
 namespace moe {
 
-namespace batched_moe_align_block_size {
-
-// Note num_threads needs to be 1024 for BlockScan Reduction in the kernel.
-static constexpr int32_t num_threads = 1024;
-static constexpr int32_t num_blocks = 1;
-__global__ void batched_moe_align_block_size_kernel(
-    int32_t const num_batches, int32_t const max_tokens_per_batch,
-    int32_t const block_size, int32_t const* __restrict__ batch_num_tokens,
-    int32_t* __restrict__ sorted_ids, int32_t* __restrict__ block_ids,
-    int32_t* __restrict__ num_tokens_post_pad) {
-  // TODO(varun): This is a naive implementation. Could be optimized.
-
-  size_t const batch_id = threadIdx.x;
-  size_t const stride = blockDim.x * gridDim.x;
-  int32_t const num_blocks_per_batch =
-      CEILDIV(max_tokens_per_batch, block_size);
-  int32_t const sorted_ids_size =
-      num_blocks_per_batch * num_batches * block_size;
-  int32_t const block_ids_size = sorted_ids_size / block_size;
-  int32_t const SENTINEL =
-      num_batches * max_tokens_per_batch;  // To denote invalid entries.
-  // Intialize sorted_ids
-  for (size_t i = threadIdx.x; i < sorted_ids_size; i += stride) {
-    sorted_ids[i] = SENTINEL;
-  }
-  // Intialize expert_ids with -1
-  for (size_t i = threadIdx.x; i < block_ids_size; i += stride) {
-    block_ids[i] = -1;
-  }
-
-  int32_t b_num_tokens = 0;
-  if (batch_id < num_batches) {
-    b_num_tokens = batch_num_tokens[batch_id];
-  }
-  int32_t const ceil_b_num_tokens =
-      CEILDIV(b_num_tokens, block_size) * block_size;
-
-  // Compute prefix sum over token counts per expert
-  using BlockScan = cub::BlockScan<int32_t, 1024>;
-  __shared__ typename BlockScan::TempStorage temp_storage;
-  int cumsum_val;
-  BlockScan(temp_storage).ExclusiveSum(ceil_b_num_tokens, cumsum_val);
-  __syncthreads();
-
-  bool const is_last_batch = batch_id == (num_batches - 1);
-  if (is_last_batch) {
-    *num_tokens_post_pad = cumsum_val + ceil_b_num_tokens;
-  }
-
-  if (batch_id < num_batches) {
-    int32_t const batch_offset = batch_id * max_tokens_per_batch;
-    for (size_t i = 0; i < b_num_tokens; ++i) {
-      sorted_ids[cumsum_val + i] = batch_offset + i;
-    }
-
-    int32_t const block_start = cumsum_val / block_size;
-    int32_t const num_blocks = ceil_b_num_tokens / block_size;
-    for (size_t i = 0; i < num_blocks; ++i) {
-      block_ids[block_start + i] = batch_id;
-    }
-  }
-}
-}  // namespace batched_moe_align_block_size
-
 template <typename scalar_t>
 __global__ void moe_align_block_size_kernel(
     const scalar_t* __restrict__ topk_ids,
@@ -345,33 +280,6 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
       });
 }
 
-void batched_moe_align_block_size(int64_t max_tokens_per_batch,
-                                  int64_t block_size,
-                                  torch::Tensor const& batch_num_tokens,
-                                  torch::Tensor sorted_ids,
-                                  torch::Tensor batch_ids,
-                                  torch::Tensor num_tokens_post_pad) {
-  namespace batched_kernel = vllm::moe::batched_moe_align_block_size;
-
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  int32_t const B = batch_num_tokens.size(0);
-  int32_t const num_blocks_per_batch =
-      round_to_next_multiple_of(max_tokens_per_batch, block_size) / block_size;
-  int32_t const num_blocks = num_blocks_per_batch * B;
-  int64_t const sorted_ids_size = num_blocks * block_size;
-
-  TORCH_CHECK(sorted_ids.size(0) == sorted_ids_size);
-  TORCH_CHECK(batch_ids.size(0) == sorted_ids_size / block_size);
-  TORCH_CHECK(num_tokens_post_pad.size(0) == 1);
-  TORCH_CHECK(B <= batched_kernel::num_threads);
-
-  batched_kernel::batched_moe_align_block_size_kernel<<<
-      batched_kernel::num_blocks, batched_kernel::num_threads, 0, stream>>>(
-      B, max_tokens_per_batch, block_size, batch_num_tokens.data_ptr<int32_t>(),
-      sorted_ids.data_ptr<int32_t>(), batch_ids.data_ptr<int32_t>(),
-      num_tokens_post_pad.data_ptr<int32_t>());
-}
-
 void moe_sum(torch::Tensor& input,   // [num_tokens, topk, hidden_size]
              torch::Tensor& output)  // [num_tokens, hidden_size]
 {

csrc/moe/moe_ops.h

@@ -12,14 +12,6 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                           int64_t block_size, torch::Tensor sorted_token_ids,
                           torch::Tensor experts_ids,
                           torch::Tensor num_tokens_post_pad);
-
-void batched_moe_align_block_size(int64_t max_tokens_per_batch,
-                                  int64_t block_size,
-                                  torch::Tensor const& expert_num_tokens,
-                                  torch::Tensor sorted_ids,
-                                  torch::Tensor expert_ids,
-                                  torch::Tensor num_tokens_post_pad);
-
 #ifndef USE_ROCM
 torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor b_qweight, torch::Tensor b_scales,

csrc/moe/topk_softmax_kernels.cu

@@ -21,6 +21,7 @@
 #include <c10/cuda/CUDAGuard.h>
 #include "../cuda_compat.h"
 #include "../cub_helpers.h"
+#include "../core/batch_invariant.hpp"
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -405,7 +406,8 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
     using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
     static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
-    const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
+    const bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant();
+    const int num_warps = batch_invariant_launch ? 32 : (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
     const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
 
     dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);

csrc/moe/torch_bindings.cpp

@@ -22,17 +22,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "                     Tensor! num_tokens_post_pad) -> ()");
   m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
 
-  // Aligning the number of tokens to be processed by each expert such
-  // that it is divisible by the block size, but for the batched case.
-  m.def(
-      "batched_moe_align_block_size(int max_tokens_per_batch,"
-      "                     int block_size, Tensor expert_num_tokens,"
-      "                     Tensor! sorted_token_ids,"
-      "                     Tensor! experts_ids,"
-      "                     Tensor! num_tokens_post_pad) -> ()");
-  m.impl("batched_moe_align_block_size", torch::kCUDA,
-         &batched_moe_align_block_size);
-
 #ifndef USE_ROCM
   m.def(
       "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "

csrc/ops.h

@@ -100,11 +100,6 @@ void apply_repetition_penalties_(torch::Tensor& logits,
                                  const torch::Tensor& output_mask,
                                  const torch::Tensor& repetition_penalties);
 
-void top_k_per_row(const torch::Tensor& logits, const torch::Tensor& rowStarts,
-                   const torch::Tensor& rowEnds, torch::Tensor& indices,
-                   torch::Tensor& values, int64_t numRows, int64_t stride0,
-                   int64_t stride1);
-
 void rms_norm_static_fp8_quant(torch::Tensor& out, torch::Tensor& input,
                                torch::Tensor& weight, torch::Tensor& scale,
                                double epsilon);
@@ -138,12 +133,12 @@ void silu_and_mul_nvfp4_quant(torch::Tensor& out,
                               torch::Tensor& input,
                               torch::Tensor& input_global_scale);
 #endif
-void persistent_masked_m_silu_mul_quant(
+void silu_mul_fp8_quant_deep_gemm_cuda(
     const at::Tensor& input,   // (E, T, 2*H)
     const at::Tensor& counts,  // (E)
     at::Tensor& y_q,           // (E, T, H) [OUT]
     at::Tensor& y_s,           // (E, T, H//group_size) [OUT]
-    bool use_ue8m0);
+    int64_t group_size, bool use_ue8m0, int64_t num_parallel_tokens);
 
 void mul_and_silu(torch::Tensor& out, torch::Tensor& input);
 

csrc/quantization/activation_kernels.cu

@@ -114,22 +114,13 @@ __global__ void act_and_mul_quant_kernel(
 }
 
 __device__ __forceinline__ float silu(float x) {
-  return __fdividef(x, (1.f + expf(-x)));
+  return (__fdividef(x, (1.f + expf(-x))));
 }
 
 __device__ __forceinline__ float2 silu2(float2 x) {
   return make_float2(silu(x.x), silu(x.y));
 }
 
-__device__ __forceinline__ __nv_bfloat162 silu2_v2(float2 x) {
-#ifndef USE_ROCM
-  return make_bfloat162(__float2bfloat16_rn(silu(x.x)),
-                        __float2bfloat16_rn(silu(x.y)));
-#else
-  return __float22bfloat162_rn(make_float2(silu(x.x), silu(x.y)));
-#endif
-}
-
 #ifndef USE_ROCM
 __device__ __forceinline__ float warp_max(float v) {
   static constexpr unsigned FULL_MASK = 0xffffffffu;
@@ -232,308 +223,224 @@ constexpr __nv_bfloat16 get_fp8_min() {
     return __nv_bfloat16(__nv_bfloat16_raw{.x = 50032});
   }
 }
-
-template <typename Idx_t>
-__device__ __forceinline__ int warp_expert_search(
-    int idx, int n, const Idx_t* __restrict__ input, Idx_t val) {
-  const Idx_t* input_ptr = input + idx;
-  int base_offset = 0;
-
-  for (;;) {
-    bool move_on = (idx < n && *input_ptr <= val);
-
-    unsigned mask = __ballot_sync(0xffffffff, move_on);
-
-    if (mask != 0xffffffffu) {
-      int last_lane = 31 - __clz(mask);
-      return base_offset + last_lane;
-    }
-
-    input_ptr += 32;
-    base_offset += 32;
-    idx += 32;
-  }
-}
-
-template <int num_parallel_tokens>
-__device__ __forceinline__ void token_bounds(int32_t n_tokens,
-                                             int32_t worker_id,
-                                             int32_t& n_tokens_lower,
-                                             int32_t& n_tokens_upper) {
-  if (n_tokens < num_parallel_tokens && worker_id < n_tokens) {
-    if (worker_id >= num_parallel_tokens) return;
-    n_tokens_lower = worker_id;
-    n_tokens_upper = worker_id + 1;
-  } else {
-    int32_t chunk_size = n_tokens / num_parallel_tokens;
-    int32_t residual = n_tokens - chunk_size * num_parallel_tokens;
-    auto calc_id = [&](int32_t id) {
-      if (id < residual)
-        return min(n_tokens, id * (chunk_size + 1));
-      else
-        return min(n_tokens, id * chunk_size + residual);
-    };
-    n_tokens_lower = calc_id(worker_id);
-    n_tokens_upper = calc_id(worker_id + 1);
-  }
-}
-
-template <int BLOCK_COUNT, int SMEM_SIZE_BYTES_Y, typename fp8_type,
-          int THREADS, typename Idx_t, bool USE_UE8M0, int GROUP_SIZE = 128,
+#ifndef USE_ROCM
+template <typename fp8_type, int32_t NUM_WARPS, typename Idx_t,
+          int NUM_PARALLEL_TOKENS, bool USE_UE8M0, int GROUP_SIZE = 128,
           int NUM_STAGES = 3>
 __global__ void silu_mul_fp8_quant_deep_gemm_kernel(
     const __nv_bfloat16* __restrict__ _input, fp8_type* __restrict__ _y_q,
-    float* __restrict__ _y_s, const int32_t* __restrict__ tokens_per_expert,
+    float* __restrict__ _y_s, const int32_t* __restrict__ counts,
+
     // sizes
-    Idx_t E, Idx_t T, Idx_t H,
+    int H, int G,
+
     // strides (in elements)
     Idx_t stride_i_e, Idx_t stride_i_t, Idx_t stride_i_h, Idx_t stride_yq_e,
     Idx_t stride_yq_t, Idx_t stride_yq_h, Idx_t stride_ys_e, Idx_t stride_ys_t,
     Idx_t stride_ys_g, Idx_t stride_counts_e) {
-#ifndef USE_ROCM
-  static constexpr int NUM_WARPS = THREADS / WARP_SIZE;
-
-  static constexpr int LOAD_STAGE_SIZE = 2 * GROUP_SIZE / 8;
-  static constexpr int LOAD_STAGE_MOD = NUM_STAGES * LOAD_STAGE_SIZE;
-
-  static constexpr int COMPUTE_STAGE_SIZE = 2 * GROUP_SIZE / 4;
-  static constexpr int COMPUTE_STAGE_MOD = COMPUTE_STAGE_SIZE * NUM_STAGES;
-
-  extern __shared__ __align__(16) __int128_t smem_128[];
-
-  int* s_expert_offsets =
-      reinterpret_cast<int*>(smem_128 + (SMEM_SIZE_BYTES_Y / 16));
-
   static constexpr __nv_bfloat16 fp8_min = get_fp8_min<fp8_type>();
   static constexpr __nv_bfloat16 fp8_max = get_fp8_max<fp8_type>();
-  // We assign EPS with it's 16-bit unsigned counterpart to allow constexpr.
+  // We assign EPS with its 16-bit unsigned counterpart to allow constexpr.
   static constexpr __nv_bfloat16 EPS = (__nv_bfloat16_raw{.x = 11996});
-  int tid = threadIdx.x;
-  int warp_id = tid >> 5;
-  int lane_id = tid & 0x1f;
 
-  int running_sum{};
-  if (!warp_id) {
-    for (int i = 0; i < E; i += WARP_SIZE) {
-      bool valid = (i + threadIdx.x) < E;
-      int value =
-          (valid ? tokens_per_expert[i + threadIdx.x * stride_counts_e] : 0) +
-          (!lane_id ? running_sum : 0);
+  // We pack 8 16-bit bfloat16 values into a 128-bit __int128_t.
+  static constexpr int32_t BFLOAT16_PER_GROUP = 8;
 
-      for (int offset = 1; offset < 32; offset *= 2) {
-        int n = __shfl_up_sync(0xFFFFFFFFu, value, offset);
-        if (lane_id >= offset) value += n;
-      }
+  // We split the shared memory in half, corresponding to gate and up matrices:
+  // [...gate_i, ...up_i]  where 0 <= i < stages.
+  static constexpr int32_t S_NUM_128 =
+      2u * (GROUP_SIZE / BFLOAT16_PER_GROUP) * NUM_WARPS * NUM_STAGES;
+  static constexpr auto THREAD_COUNT = NUM_WARPS * WARP_SIZE;
+  static constexpr int HALF_THREAD_COUNT = THREAD_COUNT / 2;
+  static constexpr int32_t S_NUM_64 = S_NUM_128 * 2;
+  __shared__ __int128_t __align__(16) s_buff_128[S_NUM_128];
 
-      if (valid) {
-        s_expert_offsets[i + threadIdx.x + 1] = value;
-      }
+  const int32_t tid = threadIdx.x;
+  const int32_t warp_id = tid / WARP_SIZE;
+  const int32_t lane_id = tid % WARP_SIZE;
 
-      running_sum = __shfl_sync(0xFFFFFFFFu, value, WARP_SIZE - 1);
-    }
+  auto s_buff_compute_32 = reinterpret_cast<__nv_bfloat162*>(s_buff_128);
 
-    if (!lane_id) {
-      s_expert_offsets[0] = 0;
-    }
+  // block handles one (expert e, group g)
+  int32_t pid = blockIdx.x;
+  int32_t e = pid / G;
+  int32_t g = pid % G;
+
+  const int32_t n_tokens = counts[e * stride_counts_e];
+
+  if (!n_tokens) {
+    return;  // Exit ASAP.
   }
 
-  __syncthreads();
+  const Idx_t stride_i_t_128 = stride_i_t / 8u;
 
-  int32_t total_tokens = s_expert_offsets[E];
+  int32_t n_tokens_lower, n_tokens_upper;
 
-  const int warp_position_yq = warp_id * (H / NUM_WARPS);
-  const int warp_position_scales = warp_id * (H / (GROUP_SIZE * NUM_WARPS));
-
-  // A single block will handle tokens_per_block tokens.
   // Each block i iterates over tokens of a slice of n_tokens =
   // expert_counts[i], with the size of chunk being
   // (n_tokens / NUM_PARALLEL_TOKENS) + residual, instead of
   // updiv(n_tokens, NUM_PARALLEL_TOKENS) for better scheduling.
-
-  // Each warp will get space to store its hidden dim for gate and up.
-  __int128_t* s_hidden_load = smem_128 + warp_id * ((2 * 128 / 8) * NUM_STAGES);
-  __int128_t* smem_load_ptr = s_hidden_load + lane_id;
-
-  const __nv_bfloat16 fp8_inv = __hdiv(__float2bfloat16(1.f), fp8_max);
-
-  int32_t compute_pipeline_offset_64 = 0;
-  int32_t load_stage_offset{};
-  const __nv_bfloat16 one_bf16 = __float2bfloat16_rn(1.f);
-
-  __int64_t* smem_compute_ptr = reinterpret_cast<__int64_t*>(smem_128) +
-                                warp_id * (2 * (GROUP_SIZE / 4) * NUM_STAGES) +
-                                lane_id;
-  __int64_t* s_gate64_ptr = smem_compute_ptr;
-  __int64_t* s_up64_ptr = smem_compute_ptr + GROUP_SIZE / 4;
-
-  int tokens_lower, tokens_upper;
-
-  token_bounds<BLOCK_COUNT>(total_tokens, blockIdx.x, tokens_lower,
-                            tokens_upper);
-
-  Idx_t expert_id{}, expert_offset{}, next_expert_offset{};
-  int token_id = tokens_lower;
-  int32_t t_load{};
-
-  if (token_id < tokens_upper) {
-    expert_id = warp_expert_search<int>(lane_id, E, s_expert_offsets, token_id);
-    expert_offset = s_expert_offsets[expert_id];
-    next_expert_offset = s_expert_offsets[expert_id + 1];
+  if (n_tokens < NUM_PARALLEL_TOKENS && blockIdx.y < n_tokens) {
+    // Specialize this, but can be likely fused.
+    if (blockIdx.y >= NUM_PARALLEL_TOKENS) {
+      return;
+    }
+    n_tokens_lower = blockIdx.y;
+    n_tokens_upper = blockIdx.y + 1;
   } else {
-    // This thread block has no work to do.
+    auto chunk_size = n_tokens / NUM_PARALLEL_TOKENS;
+    auto residual = n_tokens - chunk_size * NUM_PARALLEL_TOKENS;
+    auto calc_id = [&](int32_t id) {
+      if (id < residual) {
+        return min(n_tokens, id * (chunk_size + 1));
+      } else {
+        return min(n_tokens, id * chunk_size + residual);
+      }
+    };
+    n_tokens_lower = calc_id(blockIdx.y);
+    n_tokens_upper = calc_id(blockIdx.y + 1);
+  }
+
+  if (n_tokens_lower >= n_tokens_upper) {
     return;
   }
 
-  int t_load_bound = H / (GROUP_SIZE * NUM_WARPS);
+  // We do calculations here, using constexpr wherever possible.
+  const Idx_t base_i = e * stride_i_e + NUM_WARPS * g * GROUP_SIZE * stride_i_h;
+  const Idx_t base_ys = e * stride_ys_e + NUM_WARPS * g * stride_ys_g;
+  const Idx_t base_yq =
+      e * stride_yq_e + NUM_WARPS * g * GROUP_SIZE * stride_yq_h;
+  Idx_t gate_off_128 = (base_i / static_cast<Idx_t>(8u));
+  auto input_128_ptr = reinterpret_cast<const __int128_t*>(_input);
+  auto gate_128_ptr = input_128_ptr + gate_off_128 + (tid % HALF_THREAD_COUNT) +
+                      stride_i_t_128 * n_tokens_lower;
+  auto up_128_ptr = gate_128_ptr + (H * stride_i_h) / 8u;
+  auto y_s_ptr =
+      _y_s + base_ys + warp_id * stride_ys_g + n_tokens_lower * stride_ys_t;
+  auto y_q_ptr = _y_q + base_yq + warp_id * GROUP_SIZE +
+                 stride_yq_t * n_tokens_lower + 4 * lane_id;
+  int32_t t_load = n_tokens_lower, load_stage_id = 0;
+  auto s_buff_gate_load_128 = s_buff_128 + (tid % HALF_THREAD_COUNT);
+  auto s_buff_up_load_128 = s_buff_gate_load_128 + S_NUM_128 / 2u;
+  int32_t stage_offset{};
 
-  Idx_t base_i = ((expert_id * stride_i_e) / 8) +
-                 (token_id - expert_offset) * stride_i_t / 8;
-  const Idx_t gate_warp_offset =
-      warp_id * ((stride_i_h * H) / (8 * NUM_WARPS)) + (lane_id & 0b1111);
-
-  const __int128_t* input_128_ptr =
-      reinterpret_cast<const __int128_t*>(_input) + gate_warp_offset +
-      ((lane_id < 16) ? 0 : ((H * stride_i_h) / 8));
-  __int128_t* load_ptr = const_cast<__int128_t*>(input_128_ptr + base_i);
-
-  auto token_offset = token_id - expert_offset;
+  static constexpr int32_t LOAD_STAGE_SIZE = (NUM_WARPS * WARP_SIZE / 2);
+  static constexpr int32_t LOAD_STAGE_MOD =
+      NUM_STAGES * (NUM_WARPS * WARP_SIZE / 2);
 
+  // Two halves of all threads in a block conduct global loads for gate and up,
+  // repsectively.
   auto load_and_advance_y_pred = [&] {
-    if (t_load < t_load_bound) {
-      // Here we are simply continuing to load data
-      // from the current token.
-      auto smem_load_ptr_staged = smem_load_ptr + load_stage_offset;
+    if (t_load < n_tokens_upper) {
+      auto s_gate_stage_128_staged_ptr = s_buff_gate_load_128 + stage_offset;
+      auto s_up_stage_128_staged_ptr = s_buff_up_load_128 + stage_offset;
 
       // It is very important that LOAD_STAGE_SIZE is constexpr to avoid
       // unnecessary ALU ops.
-      load_stage_offset += LOAD_STAGE_SIZE;
-      load_stage_offset %= LOAD_STAGE_MOD;
+      stage_offset += LOAD_STAGE_SIZE;
+      stage_offset %= LOAD_STAGE_MOD;
 
-      cp_async4(smem_load_ptr_staged, load_ptr);
-      load_ptr += GROUP_SIZE / 8;
-      ++t_load;
-    } else if (token_id + 1 < tokens_upper) {
-      // We loaded everything from the current token, let's move on
-      // to the next one, and we checked that we have more tokens to load.
-      ++token_id;
-      t_load = 0;
-      if (token_id >= next_expert_offset) {
-        // We need to find the next expert.
-        do {
-          // This is a loop because it's possible
-          // that some experts are assigned 0 tokens.
-          // NOTE: We are guaranteed that there's at least
-          // one more token left so we don't have to check for
-          // expert_id bounds.
-          ++expert_id;
-          // This skips 1 memory read.
-          expert_offset = next_expert_offset;
-          next_expert_offset = s_expert_offsets[expert_id + 1];
-        } while (next_expert_offset == expert_offset);
-
-        base_i = expert_id * (stride_i_e / 8);
-        token_offset = 0;
-        load_ptr = const_cast<__int128_t*>(input_128_ptr + base_i);
+      if (tid < HALF_THREAD_COUNT) {
+        cp_async4(s_gate_stage_128_staged_ptr, gate_128_ptr);
+        gate_128_ptr += stride_i_t_128;
       } else {
-        // We remain within the same expert, so just
-        // move by H/4 __int128_t (2 * H/8).
-        base_i += stride_yq_t / 4;
-        token_offset++;
+        cp_async4(s_up_stage_128_staged_ptr, up_128_ptr);
+        up_128_ptr += stride_i_t_128;
       }
-
-      load_ptr = const_cast<__int128_t*>(input_128_ptr + base_i);
-
-      auto smem_load_ptr_staged = smem_load_ptr + load_stage_offset;
-
-      // It is very important that LOAD_STAGE_SIZE is constexpr to avoid
-      // unnecessary ALU ops.
-      load_stage_offset += LOAD_STAGE_SIZE;
-      load_stage_offset %= LOAD_STAGE_MOD;
-
-      cp_async4(smem_load_ptr_staged, load_ptr);
-      load_ptr += GROUP_SIZE / 8;
       ++t_load;
+      ++load_stage_id;
     }
     // We fence even if there is nothing to load to simplify pipelining.
     cp_async_fence();
   };
 
-  // We need to warm-up the pipeline.
   #pragma unroll
   for (int i = 0; i < NUM_STAGES - 1; i++) {
     load_and_advance_y_pred();
   }
 
-  __nv_fp8x4_e4m3* y_q_base_ptr =
-      reinterpret_cast<__nv_fp8x4_e4m3*>(_y_q) + lane_id;
-  auto y_scale_base_ptr = _y_s + warp_position_scales * stride_ys_g;
+  __int64_t* s_gate_ptr = reinterpret_cast<__int64_t*>(
+                              s_buff_compute_32 + warp_id * (GROUP_SIZE / 2)) +
+                          lane_id;
+  __int64_t* s_up_ptr = s_gate_ptr + S_NUM_64 / 2;
 
-  for (auto j = tokens_lower; j < tokens_upper; j++) {
-    const Idx_t base_ys = expert_id * stride_ys_e;
-    auto y_s_ptr = y_scale_base_ptr + base_ys + token_offset * stride_ys_t;
-    __nv_fp8x4_e4m3* y_q_ptr =
-        y_q_base_ptr + (expert_id * stride_yq_e + token_offset * stride_yq_t +
-                        warp_position_yq * stride_yq_h) /
-                           4;
-    const int COMPUTE_LIMIT = H / (GROUP_SIZE * NUM_WARPS);
+  static constexpr int32_t STAGE_SIZE = (GROUP_SIZE * NUM_WARPS) / 4u;
+  static constexpr int32_t STAGE_MOD = STAGE_SIZE * NUM_STAGES;
 
-    for (int i = 0; i < COMPUTE_LIMIT; i++) {
-      cp_async_wait<NUM_STAGES - 2>();
-      __syncthreads();
-      load_and_advance_y_pred();
+  int32_t compute_pipeline_offset_64 = 0;
 
-      __int64_t* gate64_ptr = s_gate64_ptr + compute_pipeline_offset_64;
-      __int64_t* up64_ptr = s_up64_ptr + compute_pipeline_offset_64;
+  for (int32_t t = n_tokens_lower; t < n_tokens_upper; ++t) {
+    __nv_bfloat162 results_bf162[2];
 
-      // COMPUTE_STAGE_SIZE/MOD must also be constexpr!
-      compute_pipeline_offset_64 += COMPUTE_STAGE_SIZE;
-      compute_pipeline_offset_64 %= COMPUTE_STAGE_MOD;
+    cp_async_wait<NUM_STAGES - 2>();
+    __syncthreads();
 
-      __int64_t gate64 = *gate64_ptr;
-      __int64_t up64 = *up64_ptr;
+    // We double-buffer pipelined loads so that the next load will
+    // concurrently run with compute without overwrites.
+    load_and_advance_y_pred();
 
-      // Compute
-      __nv_bfloat162 res[2];
-      __nv_bfloat162* s_up_comp = reinterpret_cast<__nv_bfloat162*>(&up64);
-      __nv_bfloat162* s_gate_comp = reinterpret_cast<__nv_bfloat162*>(&gate64);
+    auto s_gate_compute_64 = s_gate_ptr + compute_pipeline_offset_64;
+    auto s_up_compute_64 = s_up_ptr + compute_pipeline_offset_64;
+
+    // STAGE_SIZE must also be constexpr!
+    compute_pipeline_offset_64 += STAGE_SIZE;
+    compute_pipeline_offset_64 %= STAGE_MOD;
+
+    // Each thread loads (gate/up) 2X 4X bfloat16 values into registers.
+    __int64_t gate64 = *s_gate_compute_64;
+    __nv_bfloat162* s_gate_compute_32 =
+        reinterpret_cast<__nv_bfloat162*>(&gate64);
+
+    __int64_t up64 = *s_up_compute_64;
+    __nv_bfloat162* s_up_compute_32 = reinterpret_cast<__nv_bfloat162*>(&up64);
 
   #pragma unroll
-      for (int32_t k = 0; k < 2; ++k) {
-        __nv_bfloat162 gate = silu2_v2(__bfloat1622float2(s_gate_comp[k]));
-        res[k] = __hmul2(gate, s_up_comp[k]);
-      }
-
-      auto _y_max2 = __hmax2(__habs2(res[0]), __habs2(res[1]));
-
-      _y_max2.x = __hmax(__hmax(_y_max2.x, _y_max2.y), EPS);
-
-      __nv_bfloat16 y_s = __hmul(warp_max(_y_max2.x), fp8_inv);
-
-      if constexpr (USE_UE8M0) {
-        y_s = hexp2(hceil(hlog2(y_s)));
-      }
-
-      __nv_bfloat16 inv_y = __hdiv(one_bf16, y_s);
-
-      auto y_s2 = make_bfloat162(inv_y, inv_y);
+    for (int i = 0; i < 2; i++) {
+      // For silu, we make sure that div is emitted.
+      float2 gate = silu2(__bfloat1622float2(s_gate_compute_32[i]));
+      results_bf162[i] = __float22bfloat162_rn(gate);
+    }
 
   #pragma unroll
-      for (int32_t k = 0; k < 2; ++k) {
-        res[k] = clip(__hmul2(res[k], y_s2), __bfloat162bfloat162(fp8_min),
-                      __bfloat162bfloat162(fp8_max));
-      }
+    for (int i = 0; i < 2; i++) {
+      results_bf162[i] = __hmul2(results_bf162[i], s_up_compute_32[i]);
+    }
 
-      *y_q_ptr = __nv_fp8x4_e4m3(res[0], res[1]);
-      y_q_ptr += WARP_SIZE * stride_yq_h;
+    auto _y_max2 =
+        __hmax2(__habs2(results_bf162[0]), __habs2(results_bf162[1]));
 
-      if (!lane_id) {
-        *y_s_ptr = y_s;
-        y_s_ptr += stride_ys_g;
-      }
+    __nv_bfloat16 y_max_bf16 = __hmax(EPS, __hmax(_y_max2.x, _y_max2.y));
+
+    // An entire group is assigned to a single warp, so a simple warp reduce
+    // is used.
+    __nv_bfloat16 y_s = warp_max(y_max_bf16) / fp8_max;
+
+    if constexpr (USE_UE8M0) {
+      y_s = hexp2(hceil(hlog2(y_s)));
+    }
+
+    auto inv_y = __float2bfloat16_rn(1.f) / y_s;
+
+    auto y_s2 = make_bfloat162(inv_y, inv_y);
+
+  #pragma unroll
+    for (int32_t i = 0; i < 2; ++i) {
+      results_bf162[i] =
+          clip(__hmul2(results_bf162[i], y_s2), __bfloat162bfloat162(fp8_min),
+               __bfloat162bfloat162(fp8_max));
+    }
+
+    auto fp8x4 = __nv_fp8x4_e4m3(results_bf162[0], results_bf162[1]);
+    *reinterpret_cast<__nv_fp8x4_e4m3*>(y_q_ptr) = fp8x4;
+    y_q_ptr += stride_yq_t;
+
+    if (lane_id == 0) {
+      *y_s_ptr = y_s;
+      y_s_ptr += stride_ys_t;
     }
   }
-#endif
 }
+#endif
 
 }  // namespace vllm
 
@@ -568,14 +475,14 @@ void silu_and_mul_quant(torch::Tensor& out,    // [..., d]
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel);
 }
 
-void persistent_masked_m_silu_mul_quant(
-    const at::Tensor& input,              // (E, T, 2*H)
-    const at::Tensor& tokens_per_expert,  // (E)
-    at::Tensor& y_q,                      // (E, T, H) [OUT]
-    at::Tensor& y_s,                      // (E, T, H//group_size) [OUT]
-    bool use_ue8m0) {
+void silu_mul_fp8_quant_deep_gemm_cuda(
+    const at::Tensor& input,   // (E, T, 2*H)
+    const at::Tensor& counts,  // (E)
+    at::Tensor& y_q,           // (E, T, H) [OUT]
+    at::Tensor& y_s,           // (E, T, H//group_size) [OUT]
+    int64_t group_size, bool use_ue8m0, int64_t num_parallel_tokens) {
 #ifndef USE_ROCM
-
+  // This kernel relies heavily on cp.async and fp8 support.
   // This kernel currently only supports H % 128 == 0 and assumes a
   // fixed GROUP_SIZE of 128.
   TORCH_CHECK(input.dtype() == torch::kBFloat16);
@@ -584,6 +491,10 @@ void persistent_masked_m_silu_mul_quant(
   TORCH_CHECK(y_s.dtype() == torch::kFloat32);
   TORCH_CHECK(input.size(-1) % 256 == 0);
 
+  // Check that num_parallel_tokens is of power of 2 and between 1 and 64.
+  TORCH_CHECK(1 <= num_parallel_tokens && num_parallel_tokens <= 64);
+  TORCH_CHECK(!(num_parallel_tokens & (num_parallel_tokens - 1)));
+
   using Idx_t = int64_t;
 
   Idx_t E = input.size(0);
@@ -599,54 +510,81 @@ void persistent_masked_m_silu_mul_quant(
   Idx_t stride_ys_t = y_s.stride(1);
   Idx_t stride_ys_g = y_s.stride(2);
 
-  Idx_t stride_counts_e = tokens_per_expert.stride(0);
+  Idx_t stride_counts_e = counts.stride(0);
 
   static constexpr int GROUP_SIZE = 128;
 
+  #define KERNEL_FN                                                         \
+    if (use_ue8m0) {                                                        \
+      vllm::silu_mul_fp8_quant_deep_gemm_kernel<fp8_t, NUM_WARPS, Idx_t,    \
+                                                NUM_PARALLEL_TOKENS, true>  \
+          <<<grid, block, 0, stream>>>(                                     \
+              reinterpret_cast<__nv_bfloat16*>(input.data_ptr()),           \
+              (fp8_t*)y_q.data_ptr(), y_s.data_ptr<float>(),                \
+              reinterpret_cast<int32_t*>(counts.data_ptr<int>()), H, G,     \
+              stride_i_e, stride_i_t, stride_i_h, stride_yq_e, stride_yq_t, \
+              stride_yq_h, stride_ys_e, stride_ys_t, stride_ys_g,           \
+              stride_counts_e);                                             \
+    } else {                                                                \
+      vllm::silu_mul_fp8_quant_deep_gemm_kernel<fp8_t, NUM_WARPS, Idx_t,    \
+                                                NUM_PARALLEL_TOKENS, false> \
+          <<<grid, block, 0, stream>>>(                                     \
+              reinterpret_cast<__nv_bfloat16*>(input.data_ptr()),           \
+              (fp8_t*)y_q.data_ptr(), y_s.data_ptr<float>(),                \
+              reinterpret_cast<int32_t*>(counts.data_ptr<int>()), H, G,     \
+              stride_i_e, stride_i_t, stride_i_h, stride_yq_e, stride_yq_t, \
+              stride_yq_h, stride_ys_e, stride_ys_t, stride_ys_g,           \
+              stride_counts_e);                                             \
+    }
+
+  #define KERNEL_CALL_H                                       \
+    if (H % (4 * GROUP_SIZE) == 0) {                          \
+      static constexpr int NUM_WARPS = 4;                     \
+      populate_launch_params(NUM_WARPS, NUM_PARALLEL_TOKENS); \
+      KERNEL_FN                                               \
+    } else {                                                  \
+      static constexpr int NUM_WARPS = 1;                     \
+      populate_launch_params(NUM_WARPS, NUM_PARALLEL_TOKENS); \
+      KERNEL_FN                                               \
+    }
+
+  #define KERNEL_CALL_TOP_LEVEL                      \
+    if (num_parallel_tokens == 1) {                  \
+      static constexpr int NUM_PARALLEL_TOKENS = 1;  \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 2) {           \
+      static constexpr int NUM_PARALLEL_TOKENS = 2;  \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 4) {           \
+      static constexpr int NUM_PARALLEL_TOKENS = 4;  \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 8) {           \
+      static constexpr int NUM_PARALLEL_TOKENS = 8;  \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 16) {          \
+      static constexpr int NUM_PARALLEL_TOKENS = 16; \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 32) {          \
+      static constexpr int NUM_PARALLEL_TOKENS = 32; \
+      KERNEL_CALL_H                                  \
+    } else if (num_parallel_tokens == 64) {          \
+      static constexpr int NUM_PARALLEL_TOKENS = 64; \
+      KERNEL_CALL_H                                  \
+    }
+
+  Idx_t G;
+  dim3 block, grid;
+  auto populate_launch_params = [&](int num_warps, int _num_parallel_tokens) {
+    G = H / Idx_t(group_size * num_warps);
+    grid = dim3(E * G, _num_parallel_tokens);
+    block = dim3(num_warps * WARP_SIZE);
+  };
+
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  #define KERNEL(BLOCK_COUNT, USE_UE8M0, THREAD_COUNT, STAGES)                 \
-    static constexpr int NUM_WARPS = THREAD_COUNT / WARP_SIZE;                 \
-    int sms = SILU_V2_BLOCK_COUNT;                                             \
-    static constexpr int max_shared_mem_bytes =                                \
-        GROUP_SIZE * 2 * STAGES * NUM_WARPS * 2;                               \
-    dim3 grid(sms), block(THREAD_COUNT);                                       \
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(input));          \
-    VLLM_DISPATCH_FP8_TYPES(                                                   \
-        y_q.scalar_type(), "silu_mul_fp8_quant_deep_gemm_kernel", [&] {        \
-          vllm::silu_mul_fp8_quant_deep_gemm_kernel<                           \
-              BLOCK_COUNT, max_shared_mem_bytes, fp8_t, THREAD_COUNT, Idx_t,   \
-              USE_UE8M0, GROUP_SIZE, STAGES>                                   \
-              <<<grid, block, max_shared_mem_bytes + (E + 1) * 16, stream>>>(  \
-                  reinterpret_cast<__nv_bfloat16*>(input.data_ptr()),          \
-                  (fp8_t*)y_q.data_ptr(), y_s.data_ptr<float>(),               \
-                  reinterpret_cast<int32_t*>(tokens_per_expert.data_ptr()), E, \
-                  T, H, stride_i_e, stride_i_t, stride_i_h, stride_yq_e,       \
-                  stride_yq_t, stride_yq_h, stride_ys_e, stride_ys_t,          \
-                  stride_ys_g, stride_counts_e);                               \
-        });
-
-  static constexpr int SILU_V2_BLOCK_COUNT = 132 * 32;
-
-  if (!use_ue8m0) {
-    if (H >= 4096) {
-      static constexpr int NUM_STAGES = 4;
-      static constexpr int THREAD_COUNT = 256;
-      KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, NUM_STAGES);
-    } else {
-      static constexpr int THREAD_COUNT = 32;
-      KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, 2);
-    }
-  } else {
-    if (H >= 4096) {
-      static constexpr int NUM_STAGES = 4;
-      static constexpr int THREAD_COUNT = 256;
-      KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, NUM_STAGES);
-    } else {
-      static constexpr int THREAD_COUNT = 32;
-      KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, 2);
-    }
-  }
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  VLLM_DISPATCH_FP8_TYPES(y_q.scalar_type(),
+                          "silu_mul_fp8_quant_deep_gemm_kernel",
+                          [&] { KERNEL_CALL_TOP_LEVEL });
 
 #endif
 }

csrc/quantization/gptq_marlin/generate_kernels.py

@@ -17,32 +17,28 @@ FILE_HEAD = """
 namespace MARLIN_NAMESPACE_NAME {
 """.strip()
 
-TEMPLATE = (
-    "template __global__ void Marlin<"
-    "{{scalar_t}}, "
-    "{{w_type_id}}, "
-    "{{s_type_id}}, "
-    "{{threads}}, "
-    "{{thread_m_blocks}}, "
-    "{{thread_n_blocks}}, "
-    "{{thread_k_blocks}}, "
-    "{{'true' if m_block_size_8 else 'false'}}, "
-    "{{stages}}, "
-    "{{group_blocks}}, "
-    "{{'true' if is_zp_float else 'false'}}>"
-    "( MARLIN_KERNEL_PARAMS );"
-)
+TEMPLATE = ("template __global__ void Marlin<"
+            "{{scalar_t}}, "
+            "{{w_type_id}}, "
+            "{{s_type_id}}, "
+            "{{threads}}, "
+            "{{thread_m_blocks}}, "
+            "{{thread_n_blocks}}, "
+            "{{thread_k_blocks}}, "
+            "{{'true' if m_block_size_8 else 'false'}}, "
+            "{{stages}}, "
+            "{{group_blocks}}, "
+            "{{'true' if is_zp_float else 'false'}}>"
+            "( MARLIN_KERNEL_PARAMS );")
 
 # int8 with zero point case (vllm::kU8) is also supported,
 # we don't add it to reduce wheel size.
 SCALAR_TYPES = [
-    "vllm::kU4",
-    "vllm::kU4B8",
-    "vllm::kU8B128",
-    "vllm::kFE4M3fn",
-    "vllm::kFE2M1f",
+    "vllm::kU4", "vllm::kU4B8", "vllm::kU8B128", "vllm::kFE4M3fn",
+    "vllm::kFE2M1f"
 ]
-THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128), (128, 64, 128)]
+THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128),
+                  (128, 64, 128)]
 
 THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
 # group_blocks:
@@ -63,12 +59,11 @@ def generate_new_kernels():
         all_template_str_list = []
 
         for group_blocks, m_blocks, thread_configs in itertools.product(
-            GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS
-        ):
+                GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS):
+
             # act order case only support gptq-int4 and gptq-int8
             if group_blocks == 0 and scalar_type not in [
-                "vllm::kU4B8",
-                "vllm::kU8B128",
+                    "vllm::kU4B8", "vllm::kU8B128"
             ]:
                 continue
             if thread_configs[2] == 256:
@@ -98,7 +93,8 @@ def generate_new_kernels():
             c_dtype = "half" if dtype == "fp16" else "nv_bfloat16"
 
             is_zp_float_list = [False]
-            if dtype == "fp16" and scalar_type == "vllm::kU4" and group_blocks == 4:
+            if dtype == "fp16" and scalar_type == "vllm::kU4" and \
+                    group_blocks == 4:
                 # HQQ (is_zp_float = true) only supports
                 # 4bit quantization and fp16
                 is_zp_float_list.append(True)

csrc/quantization/machete/generate.py

@@ -9,23 +9,23 @@ from collections.abc import Iterable
 from copy import deepcopy
 from dataclasses import dataclass, fields
 from functools import reduce
+from typing import Optional, Union
 
 import jinja2
-from vllm_cutlass_library_extension import (
-    DataType,
-    EpilogueScheduleTag,
-    EpilogueScheduleType,
-    MixedInputKernelScheduleType,
-    TileSchedulerTag,
-    TileSchedulerType,
-    VLLMDataType,
-    VLLMDataTypeNames,
-    VLLMDataTypeSize,
-    VLLMDataTypeTag,
-    VLLMDataTypeTorchDataTypeTag,
-    VLLMDataTypeVLLMScalarTypeTag,
-    VLLMKernelScheduleTag,
-)
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm_cutlass_library_extension import (DataType, EpilogueScheduleTag,
+                                            EpilogueScheduleType,
+                                            MixedInputKernelScheduleType,
+                                            TileSchedulerTag,
+                                            TileSchedulerType, VLLMDataType,
+                                            VLLMDataTypeNames,
+                                            VLLMDataTypeSize, VLLMDataTypeTag,
+                                            VLLMDataTypeTorchDataTypeTag,
+                                            VLLMDataTypeVLLMScalarTypeTag,
+                                            VLLMKernelScheduleTag)
+
+# yapf: enable
 
 #
 #   Generator templating
@@ -258,7 +258,7 @@ class ScheduleConfig:
 @dataclass(frozen=True)
 class TypeConfig:
     a: DataType
-    b: DataType | VLLMDataType
+    b: Union[DataType, VLLMDataType]
     b_group_scale: DataType
     b_group_zeropoint: DataType
     b_channel_scale: DataType
@@ -279,30 +279,25 @@ class PrepackTypeConfig:
 class ImplConfig:
     types: TypeConfig
     schedules: list[ScheduleConfig]
-    heuristic: list[tuple[str | None, ScheduleConfig]]
+    heuristic: list[tuple[Optional[str], ScheduleConfig]]
 
 
 def generate_sch_sig(schedule_config: ScheduleConfig) -> str:
     tile_shape = (
         f"{schedule_config.tile_shape_mn[0]}x{schedule_config.tile_shape_mn[1]}"
     )
-    cluster_shape = (
-        f"{schedule_config.cluster_shape_mnk[0]}"
-        + f"x{schedule_config.cluster_shape_mnk[1]}"
-        + f"x{schedule_config.cluster_shape_mnk[2]}"
-    )
-    kernel_schedule = VLLMKernelScheduleTag[schedule_config.kernel_schedule].split(
-        "::"
-    )[-1]
-    epilogue_schedule = EpilogueScheduleTag[schedule_config.epilogue_schedule].split(
-        "::"
-    )[-1]
-    tile_scheduler = TileSchedulerTag[schedule_config.tile_scheduler].split("::")[-1]
+    cluster_shape = (f"{schedule_config.cluster_shape_mnk[0]}" +
+                     f"x{schedule_config.cluster_shape_mnk[1]}" +
+                     f"x{schedule_config.cluster_shape_mnk[2]}")
+    kernel_schedule = VLLMKernelScheduleTag[schedule_config.kernel_schedule]\
+        .split("::")[-1]
+    epilogue_schedule = EpilogueScheduleTag[
+        schedule_config.epilogue_schedule].split("::")[-1]
+    tile_scheduler = TileSchedulerTag[schedule_config.tile_scheduler]\
+        .split("::")[-1]
 
-    return (
-        f"{tile_shape}_{cluster_shape}_{kernel_schedule}"
-        + f"_{epilogue_schedule}_{tile_scheduler}"
-    )
+    return (f"{tile_shape}_{cluster_shape}_{kernel_schedule}" +
+            f"_{epilogue_schedule}_{tile_scheduler}")
 
 
 # mostly unique shorter sch_sig
@@ -321,24 +316,18 @@ def generate_terse_sch_sig(schedule_config: ScheduleConfig) -> str:
 
 # unique type_name
 def generate_type_signature(kernel_types: TypeConfig):
-    return str(
-        "".join(
-            [
-                VLLMDataTypeNames[getattr(kernel_types, field.name)]
-                for field in fields(TypeConfig)
-            ]
-        )
-    )
+    return str("".join([
+        VLLMDataTypeNames[getattr(kernel_types, field.name)]
+        for field in fields(TypeConfig)
+    ]))
 
 
 def generate_type_option_name(kernel_types: TypeConfig):
-    return ", ".join(
-        [
-            f"{field.name.replace('b_', 'with_') + '_type'}="
-            + VLLMDataTypeNames[getattr(kernel_types, field.name)]
-            for field in fields(TypeConfig)
-        ]
-    )
+    return ", ".join([
+        f"{field.name.replace('b_', 'with_')+'_type'}=" +
+        VLLMDataTypeNames[getattr(kernel_types, field.name)]
+        for field in fields(TypeConfig)
+    ])
 
 
 def is_power_of_two(n):
@@ -346,6 +335,7 @@ def is_power_of_two(n):
 
 
 def to_cute_constant(value: list[int]):
+
     def _to_cute_constant(value: int):
         if is_power_of_two(value):
             return f"_{value}"
@@ -360,11 +350,11 @@ def to_cute_constant(value: list[int]):
 
 def unique_schedules(impl_configs: list[ImplConfig]):
     # Use dict over set for deterministic ordering
-    return list(
-        {
-            sch: None for impl_config in impl_configs for sch in impl_config.schedules
-        }.keys()
-    )
+    return list({
+        sch: None
+        for impl_config in impl_configs
+        for sch in impl_config.schedules
+    }.keys())
 
 
 def unsigned_type_with_bitwidth(num_bits):
@@ -390,7 +380,7 @@ template_globals = {
     "gen_type_sig": generate_type_signature,
     "unique_schedules": unique_schedules,
     "unsigned_type_with_bitwidth": unsigned_type_with_bitwidth,
-    "gen_type_option_name": generate_type_option_name,
+    "gen_type_option_name": generate_type_option_name
 }
 
 
@@ -408,28 +398,23 @@ prepack_dispatch_template = create_template(PREPACK_TEMPLATE)
 def create_sources(impl_configs: list[ImplConfig], num_impl_files=8):
     sources = []
 
-    sources.append(
-        (
-            "machete_mm_dispatch",
-            mm_dispatch_template.render(impl_configs=impl_configs),
-        )
-    )
+    sources.append((
+        "machete_mm_dispatch",
+        mm_dispatch_template.render(impl_configs=impl_configs),
+    ))
 
     prepack_types = []
     for impl_config in impl_configs:
-        convert_type = (
-            impl_config.types.a
-            if impl_config.types.b_group_scale == DataType.void
-            else impl_config.types.b_group_scale
-        )
+        convert_type = impl_config.types.a \
+             if impl_config.types.b_group_scale == DataType.void \
+             else impl_config.types.b_group_scale
         prepack_types.append(
             PrepackTypeConfig(
                 a=impl_config.types.a,
                 b_num_bits=VLLMDataTypeSize[impl_config.types.b],
                 convert=convert_type,
                 accumulator=impl_config.types.accumulator,
-            )
-        )
+            ))
 
     def prepacked_type_key(prepack_type: PrepackTypeConfig):
         # For now, we can just use the first accumulator type seen since
@@ -445,14 +430,10 @@ def create_sources(impl_configs: list[ImplConfig], num_impl_files=8):
             unique_prepack_types.append(prepack_type)
             prepack_types_seen.add(key)
 
-    sources.append(
-        (
-            "machete_prepack",
-            prepack_dispatch_template.render(
-                types=unique_prepack_types,
-            ),
-        )
-    )
+    sources.append((
+        "machete_prepack",
+        prepack_dispatch_template.render(types=unique_prepack_types, ),
+    ))
 
     # Split up impls across files
     num_impls = reduce(lambda x, y: x + len(y.schedules), impl_configs, 0)
@@ -485,12 +466,10 @@ def create_sources(impl_configs: list[ImplConfig], num_impl_files=8):
         curr_impl_in_file += len(files_impls[-1][-1].schedules)
 
     for part, file_impls in enumerate(files_impls):
-        sources.append(
-            (
-                f"machete_mm_impl_part{part + 1}",
-                mm_impl_template.render(impl_configs=file_impls),
-            )
-        )
+        sources.append((
+            f"machete_mm_impl_part{part+1}",
+            mm_impl_template.render(impl_configs=file_impls),
+        ))
 
     return sources
 
@@ -535,7 +514,8 @@ def generate():
     # For now we use the same heuristic for all types
     # Heuristic is currently tuned for H100s
     default_heuristic = [
-        (cond, ScheduleConfig(*tile_config, **sch_common_params))  # type: ignore
+        (cond, ScheduleConfig(*tile_config,
+                              **sch_common_params))  # type: ignore
         for cond, tile_config in default_tile_heuristic_config.items()
     ]
 
@@ -561,18 +541,14 @@ def generate():
             a_token_scale=DataType.void,
             out=a,
             accumulator=DataType.f32,
-        )
-        for b in (VLLMDataType.u4b8, VLLMDataType.u8b128)
-        for a in (DataType.f16, DataType.bf16)
-    )
+        ) for b in (VLLMDataType.u4b8, VLLMDataType.u8b128)
+        for a in (DataType.f16, DataType.bf16))
 
     impl_configs += [
         ImplConfig(x[0], x[1], x[2])
-        for x in zip(
-            GPTQ_kernel_type_configs,
-            itertools.repeat(get_unique_schedules(default_heuristic)),
-            itertools.repeat(default_heuristic),
-        )
+        for x in zip(GPTQ_kernel_type_configs,
+                     itertools.repeat(get_unique_schedules(default_heuristic)),
+                     itertools.repeat(default_heuristic))
     ]
 
     AWQ_kernel_type_configs = list(
@@ -585,18 +561,14 @@ def generate():
             a_token_scale=DataType.void,
             out=a,
             accumulator=DataType.f32,
-        )
-        for b in (DataType.u4, DataType.u8)
-        for a in (DataType.f16, DataType.bf16)
-    )
+        ) for b in (DataType.u4, DataType.u8)
+        for a in (DataType.f16, DataType.bf16))
 
     impl_configs += [
         ImplConfig(x[0], x[1], x[2])
-        for x in zip(
-            AWQ_kernel_type_configs,
-            itertools.repeat(get_unique_schedules(default_heuristic)),
-            itertools.repeat(default_heuristic),
-        )
+        for x in zip(AWQ_kernel_type_configs,
+                     itertools.repeat(get_unique_schedules(default_heuristic)),
+                     itertools.repeat(default_heuristic))
     ]
 
     # TODO: Support W4A8 when ready

csrc/quickreduce/quick_reduce.h

@@ -22,14 +22,13 @@ template <typename AllReduceKernel, typename T>
 __global__ __quickreduce_launch_bounds_two_shot__ static void
 allreduce_prototype_twoshot(T const* A, T* B, uint32_t N, uint32_t num_blocks,
                             int rank, uint8_t** dbuffer_list,
-                            uint32_t data_offset, uint32_t flag_color,
-                            int64_t data_size_per_phase) {
+                            uint32_t data_offset, uint32_t flag_color) {
   int block = blockIdx.x;
   int grid = gridDim.x;
 
   while (block < num_blocks) {
     AllReduceKernel::run(A, B, N, block, rank, dbuffer_list, data_offset,
-                         flag_color, data_size_per_phase);
+                         flag_color);
     block += grid;
     flag_color++;
   }
@@ -42,21 +41,21 @@ allreduce_prototype_twoshot(T const* A, T* B, uint32_t N, uint32_t num_blocks,
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       flag_color);                                         \
   } else if (world_size == 4) {                                             \
     using LineCodec = __codec<T, 4>;                                        \
     using AllReduceKernel = AllReduceTwoshot<T, LineCodec, cast_bf2half>;   \
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       flag_color);                                         \
   } else if (world_size == 8) {                                             \
     using LineCodec = __codec<T, 8>;                                        \
     using AllReduceKernel = AllReduceTwoshot<T, LineCodec, cast_bf2half>;   \
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       flag_color);                                         \
   }
 
 enum QuickReduceQuantLevel {

csrc/quickreduce/quick_reduce_impl.cuh

@@ -553,12 +553,13 @@ struct AllReduceTwoshot {
       int const rank,                      // rank index
       uint8_t** __restrict__ buffer_list,  // communication buffers
       uint32_t const data_offset,          // offset to start of the data buffer
-      uint32_t flag_color, int64_t data_size_per_phase) {
+      uint32_t flag_color) {
     // Topology
     int thread = threadIdx.x + threadIdx.y * kWavefront;
     uint8_t* rank_buffer = buffer_list[rank];
     Codec codec(thread, rank);
     int block_id = blockIdx.x;
+    int grid_size = gridDim.x;
     // --------------------------------------------------------
     // Read input into registers
     int32x4_t tA[kAtoms];
@@ -587,10 +588,12 @@ struct AllReduceTwoshot {
     // rank responsible for this segment.
     uint32_t comm_data0_offset =
         data_offset + block_id * Codec::kTransmittedTileSize;
-    uint32_t comm_data1_offset = data_size_per_phase + comm_data0_offset;
+    uint32_t comm_data1_offset =
+        grid_size * Codec::kTransmittedTileSize + comm_data0_offset;
 
     uint32_t comm_flags0_offset = block_id * (kWorldSize * sizeof(uint32_t));
-    uint32_t comm_flags1_offset = (data_offset / 2) + comm_flags0_offset;
+    uint32_t comm_flags1_offset =
+        grid_size * (kWorldSize * sizeof(uint32_t)) + comm_flags0_offset;
 
     for (int r = 0; r < kWorldSize; r++) {
       int32x4_t* send_buffer =

csrc/sampler.cu

@@ -44,245 +44,6 @@ __global__ void apply_repetition_penalties_kernel(
   }
 }
 
-static inline __device__ uint16_t extractBinIdx(float x) {
-  union {
-    __half h;
-    uint16_t u16;
-  } tmp;
-  tmp.h = __float2half_rn(x);
-  tmp.u16 = (x < 0.f) ? (~tmp.u16 & 0xffff) : (tmp.u16 | 0x8000);
-  return 511 - (tmp.u16 >> 7);
-}
-
-template <int kNumThreadsPerBlock = 512>
-static __global__ void topKPerRow(const float* logits, const int* rowStarts,
-                                  const int* rowEnds, int* outIndices,
-                                  float* outLogits, int stride0, int stride1) {
-  // The number of bins in the histogram.
-  static constexpr int kNumBins = 512;
-
-  // The top-k width.
-  static constexpr int kTopK = 2048;
-  // The number of elements per thread for the final top-k sort.
-  static constexpr int kNumTopKItemsPerThread = kTopK / kNumThreadsPerBlock;
-  // The class to sort the elements during the final top-k sort.
-  using TopKSort = cub::BlockRadixSort<float, kNumThreadsPerBlock,
-                                       kNumTopKItemsPerThread, int>;
-
-  // The number of slots for the final pass.
-  static constexpr int kNumFinalItems = 3072;
-  // The number of elements per thread for the final sort.
-  static constexpr int kNumFinalItemsPerThread =
-      kNumFinalItems / kNumThreadsPerBlock;
-  // The class to sort the elements during the final pass.
-  using FinalSort = cub::BlockRadixSort<float, kNumThreadsPerBlock,
-                                        kNumFinalItemsPerThread, int>;
-
-  // The class to compute the inclusive prefix-sum over the histogram.
-  using Scan = cub::BlockScan<int, kNumThreadsPerBlock>;
-
-  // Shared memory to compute the block scan.
-  __shared__ typename Scan::TempStorage smemScan;
-
-  // The structure to store the final items (for the final pass).
-  struct FinalItems {
-    // Shared memory to store the indices for the final pass.
-    int indices[kNumFinalItems];
-    // Shared memory to store the logits for the final pass.
-    float logits[kNumFinalItems];
-  };
-
-  // Shared memory to compute the block sort.
-  __shared__ union {
-    FinalItems items;
-    typename FinalSort::TempStorage finalSort;
-    typename TopKSort::TempStorage topKSort;
-  } smemFinal;
-
-  // Shared memory to store the histogram.
-  __shared__ int smemHistogram[kNumBins];
-  // Shared memory to store the selected indices.
-  __shared__ int smemIndices[kTopK];
-  // Shared memory to store the selected logits.
-  __shared__ float smemLogits[kTopK];
-  // Shared memory to store the threshold bin.
-  __shared__ int smemThresholdBinIdx[1];
-  // Shared memory counter to register the candidates for the final phase.
-  __shared__ int smemFinalDstIdx[1];
-
-  // The row computed by this block.
-  int rowIdx = blockIdx.x;
-  // The range of logits within the row.
-  int rowStart = rowStarts[rowIdx], rowEnd = rowEnds[rowIdx];
-  // The length of the row.
-  int rowLen = rowEnd - rowStart;
-
-  // Shortcut if the length of the row is smaller than Top-K. Indices are not
-  // sorted by their corresponding logit.
-  if (rowLen <= kTopK) {
-    for (int rowIt = threadIdx.x; rowIt < rowLen;
-         rowIt += kNumThreadsPerBlock) {
-      int idx = rowStart + rowIt;
-      outIndices[rowIdx * kTopK + rowIt] = idx - rowStart;
-      outLogits[rowIdx * kTopK + rowIt] =
-          logits[rowIdx * stride0 + idx * stride1];
-    }
-    for (int rowIt = rowLen + threadIdx.x; rowIt < kTopK;
-         rowIt += kNumThreadsPerBlock) {
-      outIndices[rowIdx * kTopK + rowIt] = -1;
-      outLogits[rowIdx * kTopK + rowIt] = -FLT_MAX;
-    }
-    return;
-  }
-
-  // Clear the histogram.
-  if (threadIdx.x < kNumBins) {
-    smemHistogram[threadIdx.x] = 0;
-  }
-
-  // Make sure the histogram is ready.
-  __syncthreads();
-
-  // Fetch elements one-by-one.
-  for (int rowIt = rowStart + threadIdx.x; rowIt < rowEnd;
-       rowIt += kNumThreadsPerBlock) {
-    uint16_t idx = extractBinIdx(logits[rowIdx * stride0 + rowIt * stride1]);
-    atomicAdd(&smemHistogram[idx], 1);
-  }
-
-  // Make sure the histogram is ready.
-  __syncthreads();
-
-  // Read the values from SMEM.
-  int binCount{0};
-  if (threadIdx.x < kNumBins) {
-    binCount = smemHistogram[threadIdx.x];
-  }
-
-  // Make sure each thread has read its value.
-  __syncthreads();
-
-  // Compute the prefix sum.
-  int prefixSum{0}, totalSum{0};
-  Scan(smemScan).ExclusiveSum(binCount, prefixSum, totalSum);
-
-  // Update the histogram with the prefix sums.
-  if (threadIdx.x < kNumBins) {
-    smemHistogram[threadIdx.x] = prefixSum;
-  }
-
-  // Make sure the data is in shared memory.
-  __syncthreads();
-
-  // Find the last valid bin.
-  if (threadIdx.x < kNumBins) {
-    int nextPrefixSum =
-        threadIdx.x == kNumBins - 1 ? totalSum : smemHistogram[threadIdx.x + 1];
-    if (prefixSum < kTopK && nextPrefixSum >= kTopK) {
-      smemThresholdBinIdx[0] = threadIdx.x;
-    }
-  }
-
-  // Clear the counter to store the items for the final phase.
-  if (threadIdx.x == 0) {
-    smemFinalDstIdx[0] = 0;
-  }
-
-  // Make sure the data is in shared memory.
-  __syncthreads();
-
-  // The threshold bin.
-  int thresholdBinIdx = smemThresholdBinIdx[0];
-
-  // Fetch elements one-by-one and populate the shared memory buffers.
-  for (int rowIt = rowStart + threadIdx.x; rowIt < rowEnd;
-       rowIt += kNumThreadsPerBlock) {
-    float logit = logits[rowIdx * stride0 + rowIt * stride1];
-    uint16_t idx = extractBinIdx(logit);
-    if (idx < thresholdBinIdx) {
-      int dstIdx = atomicAdd(&smemHistogram[idx], 1);
-      smemLogits[dstIdx] = logit;
-      smemIndices[dstIdx] = rowIt;
-    } else if (idx == thresholdBinIdx) {
-      int dstIdx = atomicAdd(&smemFinalDstIdx[0], 1);
-      if (dstIdx < kNumFinalItems) {
-        smemFinal.items.logits[dstIdx] = logit;
-        smemFinal.items.indices[dstIdx] = rowIt;
-      }
-    }
-  }
-
-  // Make sure the elements are in shared memory.
-  __syncthreads();
-
-  // The logits of the elements to be sorted in the final pass.
-  float finalLogits[kNumFinalItemsPerThread];
-  // The indices of the elements to be sorted in the final pass.
-  int finalIndices[kNumFinalItemsPerThread];
-
-// Init.
-#pragma unroll
-  for (int ii = 0; ii < kNumFinalItemsPerThread; ++ii) {
-    finalLogits[ii] = -FLT_MAX;
-  }
-
-// Read the elements from SMEM.
-#pragma unroll
-  for (int ii = 0; ii < kNumFinalItemsPerThread; ++ii) {
-    int srcIdx = ii * kNumThreadsPerBlock + threadIdx.x;
-    if (srcIdx < smemFinalDstIdx[0]) {
-      finalLogits[ii] = smemFinal.items.logits[srcIdx];
-      finalIndices[ii] = smemFinal.items.indices[srcIdx];
-    }
-  }
-
-  // Make sure the shared memory has been read.
-  __syncthreads();
-
-  // Sort the elements.
-  FinalSort(smemFinal.finalSort)
-      .SortDescendingBlockedToStriped(finalLogits, finalIndices);
-
-  // Copy the data back to the shared memory storage.
-  int baseIdx = thresholdBinIdx > 0 ? smemHistogram[thresholdBinIdx - 1] : 0;
-#pragma unroll
-  for (int ii = 0; ii < kNumFinalItemsPerThread; ++ii) {
-    int srcIdx = ii * kNumThreadsPerBlock + threadIdx.x;
-    int dstIdx = baseIdx + srcIdx;
-    if (dstIdx < kTopK) {
-      smemLogits[dstIdx] = finalLogits[ii];
-      smemIndices[dstIdx] = finalIndices[ii];
-    }
-  }
-
-  // Make sure the data is in shared memory.
-  __syncthreads();
-
-  // The topK logits.
-  float topKLogits[kNumTopKItemsPerThread];
-  // The topK indices.
-  int topKIndices[kNumTopKItemsPerThread];
-
-// Load from shared memory.
-#pragma unroll
-  for (int ii = 0; ii < kNumTopKItemsPerThread; ++ii) {
-    topKLogits[ii] = smemLogits[ii * kNumThreadsPerBlock + threadIdx.x];
-    topKIndices[ii] = smemIndices[ii * kNumThreadsPerBlock + threadIdx.x];
-  }
-
-  // Sort the elements.
-  TopKSort(smemFinal.topKSort)
-      .SortDescendingBlockedToStriped(topKLogits, topKIndices);
-
-// Store to global memory.
-#pragma unroll
-  for (int ii = 0; ii < kNumTopKItemsPerThread; ++ii) {
-    int offset = rowIdx * kTopK + ii * kNumThreadsPerBlock + threadIdx.x;
-    outIndices[offset] = topKIndices[ii] - rowStart;
-    outLogits[offset] = topKLogits[ii];
-  }
-}
-
 }  // namespace vllm
 
 void apply_repetition_penalties_(
@@ -324,20 +85,4 @@ void apply_repetition_penalties_(
                 repetition_penalties.data_ptr<scalar_t>(), num_seqs, vocab_size,
                 tile_size);
       });
-}
-
-void top_k_per_row(const torch::Tensor& logits, const torch::Tensor& rowStarts,
-                   const torch::Tensor& rowEnds, torch::Tensor& indices,
-                   torch::Tensor& values, int64_t numRows, int64_t stride0,
-                   int64_t stride1) {
-  // Compute the results on the device.
-  constexpr int kNumThreadsPerBlock = 512;
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  vllm::topKPerRow<kNumThreadsPerBlock>
-      <<<numRows, kNumThreadsPerBlock, 0, stream>>>(
-          logits.data_ptr<float>(), rowStarts.data_ptr<int>(),
-          rowEnds.data_ptr<int>(), indices.data_ptr<int>(),
-          values.data_ptr<float>(), static_cast<int>(stride0),
-          static_cast<int>(stride1));
-}
+}

csrc/torch_bindings.cpp

@@ -33,11 +33,11 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 #endif
 
   ops.def(
-      "persistent_masked_m_silu_mul_quant(Tensor input, Tensor counts, Tensor! "
-      "y_q, Tensor! y_s,"
-      "bool use_ue8m0) -> ()");
-  ops.impl("persistent_masked_m_silu_mul_quant", torch::kCUDA,
-           &persistent_masked_m_silu_mul_quant);
+      "silu_mul_fp8_quant_deep_gemm_cuda(Tensor input, Tensor counts, Tensor! "
+      "y_q, Tensor! y_s, int group_size, "
+      "bool use_ue8m0, int num_parallel_tokens) -> ()");
+  ops.impl("silu_mul_fp8_quant_deep_gemm_cuda", torch::kCUDA,
+           &silu_mul_fp8_quant_deep_gemm_cuda);
 
   ops.def("weak_ref_tensor(Tensor input) -> Tensor");
   ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
@@ -188,13 +188,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("apply_repetition_penalties_", torch::kCUDA,
            &apply_repetition_penalties_);
 
-  // Optimized top-k per row operation
-  ops.def(
-      "top_k_per_row(Tensor logits, Tensor rowStarts, Tensor rowEnds, "
-      "Tensor! indices, Tensor! values, int numRows, int stride0, "
-      "int stride1) -> ()");
-  ops.impl("top_k_per_row", torch::kCUDA, &top_k_per_row);
-
   // Layernorm-quant
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(
@@ -727,12 +720,6 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
       "int quant_block_size, str kv_cache_dtype) -> ()");
   cache_ops.impl("indexer_k_quant_and_cache", torch::kCUDA,
                  &indexer_k_quant_and_cache);
-
-  cache_ops.def(
-      "cp_gather_indexer_k_quant_cache(Tensor kv_cache, Tensor! dst_k, Tensor! "
-      "dst_scale, Tensor block_table, Tensor cu_seq_lens) -> ()");
-  cache_ops.impl("cp_gather_indexer_k_quant_cache", torch::kCUDA,
-                 &cp_gather_indexer_k_quant_cache);
 }
 
 TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cuda_utils), cuda_utils) {

docker/Dockerfile

@@ -15,7 +15,7 @@ ARG PYTHON_VERSION=3.12
 # Example:
 # docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04
 
-# Important: We build with an old version of Ubuntu to maintain broad
+# Important: We build with an old version of Ubuntu to maintain broad 
 # compatibility with other Linux OSes. The main reason for this is that the
 # glibc version is baked into the distro, and binaries built with one glibc
 # version are not backwards compatible with OSes that use an earlier version.
@@ -229,7 +229,7 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
 # Check the size of the wheel if RUN_WHEEL_CHECK is true
 COPY .buildkite/check-wheel-size.py check-wheel-size.py
 # sync the default value with .buildkite/check-wheel-size.py
-ARG VLLM_MAX_SIZE_MB=500
+ARG VLLM_MAX_SIZE_MB=450
 ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
 ARG RUN_WHEEL_CHECK=true
 RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
@@ -356,14 +356,75 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
     uv pip install --system dist/*.whl --verbose \
         --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
-# Install FlashInfer pre-compiled kernel cache and binaries
-# https://docs.flashinfer.ai/installation.html
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system flashinfer-cubin==0.4.1 \
-    && uv pip install --system flashinfer-jit-cache==0.4.1 \
-        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-    && flashinfer show-config
+# If we need to build FlashInfer wheel before its release:
+# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
+# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0a 10.0a 12.0'
+# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
+# $ cd flashinfer
+# $ git checkout v0.2.6.post1
+# $ python -m flashinfer.aot
+# $ python -m build --no-isolation --wheel
+# $ ls -la dist
+# -rw-rw-r-- 1 mgoin mgoin 205M Jun  9 18:03 flashinfer_python-0.2.6.post1-cp39-abi3-linux_x86_64.whl
+# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/v0.2.6.post1/flashinfer_python-0.2.6.post1-cp39-abi3-linux_x86_64.whl
 
+# Install FlashInfer from source
+ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
+# Keep this in sync with "flashinfer" extra in setup.py
+ARG FLASHINFER_GIT_REF="v0.3.1"
+# Flag to control whether to compile FlashInfer AOT kernels
+# Set to "true" to enable AOT compilation:
+# docker build --build-arg FLASHINFER_AOT_COMPILE=true ...
+ARG FLASHINFER_AOT_COMPILE=false
+RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
+  . /etc/environment
+    git clone --depth 1 --recursive --shallow-submodules \
+        --branch ${FLASHINFER_GIT_REF} \
+        ${FLASHINFER_GIT_REPO} flashinfer
+    # Exclude CUDA arches for older versions (11.x and 12.0-12.7)
+    # TODO: Update this to allow setting TORCH_CUDA_ARCH_LIST as a build arg.
+    if [[ "${CUDA_VERSION}" == 11.* ]]; then
+        FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9"
+    elif [[ "${CUDA_VERSION}" == 12.[0-7]* ]]; then
+        FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a"
+    else
+        # CUDA 12.8+ supports 10.0a and 12.0
+        FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0"
+    fi
+    pushd flashinfer
+        if [[ "${CUDA_VERSION}" == 12.8.* ]] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then
+            # NOTE: To make new precompiled wheels, see tools/flashinfer-build.sh
+            echo "🏗️  Installing FlashInfer from pre-compiled wheel"
+            uv pip install --system https://wheels.vllm.ai/flashinfer-python/flashinfer_python-0.3.1-cp39-abi3-manylinux1_x86_64.whl \
+                --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+            if [ "${FLASHINFER_AOT_COMPILE}" = "true" ]; then
+                # Download pre-compiled cubins
+                TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
+                    python3 -m flashinfer --download-cubin || echo "WARNING: Failed to download flashinfer cubins."
+            fi
+        elif [ "${FLASHINFER_AOT_COMPILE}" = "true" ]; then
+            echo "🏗️  Installing FlashInfer with AOT compilation for arches: ${FI_TORCH_CUDA_ARCH_LIST}"
+            export FLASHINFER_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}"
+            # HACK: We need these to run flashinfer.aot before installing flashinfer, get from the package in the future
+            uv pip install --system cuda-python==$(echo $CUDA_VERSION | cut -d. -f1,2) pynvml==$(echo $CUDA_VERSION | cut -d. -f1) nvidia-nvshmem-cu$(echo $CUDA_VERSION | cut -d. -f1)
+            # Build AOT kernels
+            TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
+                python3 -m flashinfer.aot
+            # Install with no-build-isolation since we already built AOT kernels
+            TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
+                uv pip install --system --no-build-isolation . \
+                --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+            # Download pre-compiled cubins
+            TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
+                python3 -m flashinfer --download-cubin || echo "WARNING: Failed to download flashinfer cubins."
+        else
+            echo "🏗️  Installing FlashInfer without AOT compilation in JIT mode"
+            uv pip install --system . \
+                --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+        fi
+    popd
+    rm -rf flashinfer
+BASH
 COPY examples examples
 COPY benchmarks benchmarks
 COPY ./vllm/collect_env.py .
@@ -400,7 +461,7 @@ RUN set -eux; \
 # Install EP kernels(pplx-kernels and DeepEP)
 COPY tools/ep_kernels/install_python_libraries.sh install_python_libraries.sh
 ENV CUDA_HOME=/usr/local/cuda
-RUN export TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-9.0a 10.0a+PTX}" \
+RUN export TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-9.0a+PTX}" \
     && bash install_python_libraries.sh
 
 # CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will
@@ -481,7 +542,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     else \
         BITSANDBYTES_VERSION="0.46.1"; \
     fi; \
-    uv pip install --system accelerate hf_transfer modelscope "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm>=1.0.17' 'runai-model-streamer[s3,gcs]>=0.14.0'
+    uv pip install --system accelerate hf_transfer modelscope "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm>=1.0.17' 'runai-model-streamer[s3]>=0.14.0'
 
 ENV VLLM_USAGE_SOURCE production-docker-image