[CI] Implement uploading to PyPI and GitHub in the release pipeline, enable release image building for CUDA 13.0 (#31032)

(cherry picked from commit 8e61425ee6)

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

@@ -1,5 +0,0 @@
-Qwen2.5-1.5B-Instruct.yaml
-Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
-Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
-Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
-Qwen1.5-MoE-W4A16-compressed-tensors.yaml

.buildkite/release-pipeline.yaml

@@ -1,287 +1,277 @@
 steps:
+  # aarch64 + CUDA builds
+  - label: "Build wheel - aarch64 - CUDA 12.9"
+    depends_on: ~
+    id: build-wheel-arm64-cuda-12-9
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    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 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 ."
+      - "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-nightly-wheels.sh"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  - label: "Build wheel - aarch64 - CUDA 13.0"
+    depends_on: ~
+    id: build-wheel-arm64-cuda-13-0
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    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=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --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-nightly-wheels.sh manylinux_2_35"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # aarch64 build
+  - label: "Build wheel - aarch64 - CPU"
+    depends_on: ~
+    id: build-wheel-arm64-cpu
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
+      - "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-nightly-wheels.sh manylinux_2_35"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # x86 + CUDA builds
+  - label: "Build wheel - x86_64 - CUDA 12.9"
+    depends_on: ~
+    id: build-wheel-x86-cuda-12-9
+    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 ."
+      - "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-nightly-wheels.sh manylinux_2_31"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  - label: "Build wheel - x86_64 - CUDA 13.0"
+    depends_on: ~
+    id: build-wheel-x86-cuda-13-0
+    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=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --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-nightly-wheels.sh manylinux_2_35"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # x86 CPU wheel build
+  - label: "Build wheel - x86_64 - CPU"
+    depends_on: ~
+    id: build-wheel-x86-cpu
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
+      - "mkdir artifacts"
+      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
+      - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # Build release images (CUDA 12.9)
+  - label: "Build release image - x86_64 - CUDA 12.9"
+    depends_on: ~
+    id: build-release-image-x86
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+      # re-tag to default image tag and push, just in case arm64 build fails
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+
+  - label: "Build release image - aarch64 - CUDA 12.9"
+    depends_on: ~
+    id: build-release-image-arm64
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+
+  - label: "Create multi-arch manifest - CUDA 12.9"
+    depends_on:
+      - build-release-image-x86
+      - build-release-image-arm64
+    id: create-multi-arch-manifest
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
+      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+
+  - label: "Annotate release workflow - CUDA 12.9"
+    depends_on:
+      - create-multi-arch-manifest
+    id: annotate-release-workflow
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "bash .buildkite/scripts/annotate-release.sh"
+
+  - block: "Build CUDA 13.0 release images"
+    key: block-release-image-build-cuda-13-0
+    depends_on: ~
+
+  - label: "Build release image - x86_64 - CUDA 13.0"
+    depends_on: block-release-image-build-cuda-13-0
+    id: build-release-image-x86-cuda-13-0
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+      # re-tag to default image tag and push, just in case arm64 build fails
+      - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+
+  - label: "Build release image - aarch64 - CUDA 13.0"
+    depends_on: block-release-image-build-cuda-13-0
+    id: build-release-image-arm64-cuda-13-0
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
+
+  - label: "Create multi-arch manifest - CUDA 13.0"
+    depends_on:
+      - build-release-image-x86-cuda-13-0
+      - build-release-image-arm64-cuda-13-0
+    id: create-multi-arch-manifest-cuda-13-0
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
+      - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
+
   - input: "Provide Release version here"
     id: input-release-version
     fields:
       - text: "What is the release version?"
         key: release-version
 
-  - group: "Build Python wheels"
-    key: "build-wheels"
-    steps:
-      - label: "Build wheel - aarch64 - CUDA 12.9"
-        depends_on: ~
-        id: build-wheel-arm64-cuda-12-9
-        agents:
-          queue: arm64_cpu_queue_postmerge
-        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 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 ."
-          - "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-nightly-wheels.sh"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
+    key: block-upload-release-wheels
+    depends_on:
+      - input-release-version
+      - build-wheel-x86-cuda-12-9
+      - build-wheel-x86-cuda-13-0
+      - build-wheel-x86-cpu
+      - build-wheel-arm64-cuda-12-9
+      - build-wheel-arm64-cuda-13-0
+      - build-wheel-arm64-cpu
 
-      - label: "Build wheel - aarch64 - CUDA 13.0"
-        depends_on: ~
-        id: build-wheel-arm64-cuda-13-0
-        agents:
-          queue: arm64_cpu_queue_postmerge
-        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=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04  --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-nightly-wheels.sh manylinux_2_35"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - label: "Upload release wheels to PyPI and GitHub"
+    depends_on:
+      - block-upload-release-wheels
+    id: upload-release-wheels
+    agents:
+      queue: small_cpu_queue_postmerge
+    commands:
+      - "bash .buildkite/scripts/upload-release-wheels.sh"
 
-      - label: "Build wheel - aarch64 - CPU"
-        depends_on: ~
-        id: build-wheel-arm64-cpu
-        agents:
-          queue: arm64_cpu_queue_postmerge
-        commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
-          - "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-nightly-wheels.sh manylinux_2_35"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - block: "Build CPU release image"
+    key: block-cpu-release-image-build
+    depends_on: ~
 
-      - label: "Build wheel - x86_64 - CUDA 12.9"
-        depends_on: ~
-        id: build-wheel-x86-cuda-12-9
-        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 ."
-          - "mkdir artifacts"
-          - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - label: "Build and publish CPU release image"
+    depends_on: block-cpu-release-image-build
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
+    env:
+      DOCKER_BUILDKIT: "1"
 
-      - label: "Build wheel - x86_64 - CUDA 13.0"
-        depends_on: ~
-        id: build-wheel-x86-cuda-13-0
-        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=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --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-nightly-wheels.sh manylinux_2_35"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - block: "Build arm64 CPU release image"
+    key: block-arm64-cpu-release-image-build
+    depends_on: ~
 
-      - label: "Build wheel - x86_64 - CPU"
-        depends_on: ~
-        id: build-wheel-x86-cpu
-        agents:
-          queue: cpu_queue_postmerge
-        commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
-          - "mkdir artifacts"
-          - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
-        env:
-          DOCKER_BUILDKIT: "1"
+  - label: "Build and publish arm64 CPU release image"
+    depends_on: block-arm64-cpu-release-image-build
+    agents:
+      queue: arm64_cpu_queue_postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
+    env:
+      DOCKER_BUILDKIT: "1"
 
-  - group: "Build release Docker images"
-    key: "build-release-images"
-    steps:
-      - label: "Build release image - x86_64 - CUDA 12.9"
-        depends_on: ~
-        id: build-release-image-x86
-        agents:
-          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_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 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)"
-          # re-tag to default image tag and push, just in case arm64 build fails
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
+  - block: "Build ROCm release image"
+    key: block-rocm-release-image-build
+    depends_on: ~
 
-      - label: "Build release image - aarch64 - CUDA 12.9"
-        depends_on: ~
-        id: build-release-image-arm64
-        agents:
-          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 push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+  - label: "Build release image (ROCm)"
+    depends_on: block-rocm-release-image-build
+    id: build-release-image-rocm
+    agents:
+      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"
+      # Build base image first
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --tag rocm/vllm-dev:base-$BUILDKITE_COMMIT --target final --progress plain -f docker/Dockerfile.rocm_base ."
+      # Build vLLM ROCm image using the base
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg BASE_IMAGE=rocm/vllm-dev:base-$BUILDKITE_COMMIT --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm --target vllm-openai --progress plain -f docker/Dockerfile.rocm ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm"
 
-      - label: "Build release image - x86_64 - CUDA 13.0"
-        depends_on: ~
-        id: build-release-image-x86-cuda-13-0
-        agents:
-          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_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
-          # re-tag to default image tag and push, just in case arm64 build fails
-          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-
-      - label: "Build release image - aarch64 - CUDA 13.0"
-        depends_on: ~
-        id: build-release-image-arm64-cuda-13-0
-        agents:
-          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"
-          # compute capability 12.0 for RTX-50 series / RTX PRO 6000 Blackwell, 12.1 for DGX Spark
-          - "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=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
-
-      - block: "Build release image for x86_64 CPU"
-        key: block-cpu-release-image-build
-        depends_on: ~
-
-      - label: "Build release image - x86_64 - CPU"
-        depends_on:
-          - block-cpu-release-image-build
-          - input-release-version
-        agents:
-          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_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-        env:
-          DOCKER_BUILDKIT: "1"
-
-      - block: "Build release image for arm64 CPU"
-        key: block-arm64-cpu-release-image-build
-        depends_on: ~
-
-      - label: "Build release image - arm64 - CPU"
-        depends_on: 
-          - block-arm64-cpu-release-image-build
-          - input-release-version
-        agents:
-          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 GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-        env:
-          DOCKER_BUILDKIT: "1"
-
-      - block: "Build release image for x86_64 ROCm"
-        key: block-rocm-release-image-build
-        depends_on: ~
-
-      - label: "Build release image - x86_64 - ROCm"
-        depends_on: block-rocm-release-image-build
-        id: build-release-image-rocm
-        agents:
-          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"
-          # Build base image first
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --tag rocm/vllm-dev:base-$BUILDKITE_COMMIT --target final --progress plain -f docker/Dockerfile.rocm_base ."
-          # Build vLLM ROCm image using the base
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg BASE_IMAGE=rocm/vllm-dev:base-$BUILDKITE_COMMIT --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm --target vllm-openai --progress plain -f docker/Dockerfile.rocm ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm"
-
-  - group: "Publish release images"
-    key: "publish-release-images"
-    steps:
-      - label: "Create multi-arch manifest - CUDA 12.9"
-        depends_on:
-          - build-release-image-x86
-          - build-release-image-arm64
-        id: create-multi-arch-manifest
-        agents:
-          queue: small_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 manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
-
-      - label: "Annotate release workflow - CUDA 12.9"
-        depends_on:
-          - create-multi-arch-manifest
-        id: annotate-release-workflow
-        agents:
-          queue: small_cpu_queue_postmerge
-        commands:
-          - "bash .buildkite/scripts/annotate-release.sh"
-
-      - label: "Create multi-arch manifest - CUDA 13.0"
-        depends_on:
-          - build-release-image-x86-cuda-13-0
-          - build-release-image-arm64-cuda-13-0
-        id: create-multi-arch-manifest-cuda-13-0
-        agents:
-          queue: small_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 manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
-          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
-
-      - label: "Publish nightly multi-arch image to DockerHub"
-        depends_on:
-          - create-multi-arch-manifest
-        if: build.env("NIGHTLY") == "1"
-        agents:
-          queue: small_cpu_queue_postmerge
-        commands:
-          - "bash .buildkite/scripts/push-nightly-builds.sh"
-          # Clean up old nightly builds (keep only last 14)
-          - "bash .buildkite/scripts/cleanup-nightly-builds.sh"
-        plugins:
-          - docker-login#v3.0.0:
-              username: vllmbot
-              password-env: DOCKERHUB_TOKEN
-        env:
-          DOCKER_BUILDKIT: "1"
-          DOCKERHUB_USERNAME: "vllmbot"
-
-      - label: "Publish nightly multi-arch image to DockerHub - CUDA 13.0"
-        depends_on:
-          - create-multi-arch-manifest-cuda-13-0
-        if: build.env("NIGHTLY") == "1"
-        agents:
-          queue: small_cpu_queue_postmerge
-        commands:
-          - "bash .buildkite/scripts/push-nightly-builds.sh cu130"
-          # Clean up old nightly builds (keep only last 14)
-          - "bash .buildkite/scripts/cleanup-nightly-builds.sh cu130-nightly-"
-        plugins:
-          - docker-login#v3.0.0:
-              username: vllmbot
-              password-env: DOCKERHUB_TOKEN
-        env:
-          DOCKER_BUILDKIT: "1"
-          DOCKERHUB_USERNAME: "vllmbot"
-
-  - group: "Publish wheels"
-    key: "publish-wheels"
-    steps:
-      - block: "Confirm update release wheels to PyPI (experimental, use with caution)?"
-        key: block-upload-release-wheels
-        depends_on:
-          - input-release-version
-          - build-wheels
-
-      - label: "Upload release wheels to PyPI and GitHub"
-        depends_on:
-          - block-upload-release-wheels
-        id: upload-release-wheels
-        agents:
-          queue: small_cpu_queue_postmerge
-        commands:
-          - "bash .buildkite/scripts/upload-release-wheels.sh"
+  
+  - label: "Build and publish nightly multi-arch image to DockerHub"
+    depends_on:
+      - create-multi-arch-manifest
+    if: build.env("NIGHTLY") == "1"
+    agents:
+      queue: small_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"
+      # Clean up old nightly builds (keep only last 14)
+      - "bash .buildkite/scripts/cleanup-nightly-builds.sh"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"
 
   # =============================================================================
   # ROCm Release Pipeline (x86_64 only)

.buildkite/scripts/cherry-pick-from-milestone.sh

@@ -1,242 +0,0 @@
-#!/bin/bash
-#
-# cherry-pick-from-milestone.sh
-# Find commits from a GitHub milestone that are missing from the current branch
-# and output them in chronological order for cherry-picking.
-#
-# Usage: ./cherry-pick-from-milestone.sh <milestone> [--dry-run] [--execute]
-#
-
-set -euo pipefail
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-NC='\033[0m' # No Color
-
-usage() {
-    cat <<EOF
-Usage: $(basename "$0") <milestone> [options]
-
-Find commits from a GitHub milestone that need to be cherry-picked into the current branch.
-
-Arguments:
-    milestone       The GitHub milestone name (e.g., v0.14.0)
-
-Options:
-    --dry-run       Show the cherry-pick commands without executing (default)
-    --execute       Actually execute the cherry-picks
-    --main-branch   Specify the main branch name (default: main)
-    --help          Show this help message
-
-Examples:
-    $(basename "$0") v0.14.0
-    $(basename "$0") v0.14.0 --dry-run
-    $(basename "$0") v0.14.0 --execute
-    $(basename "$0") v0.14.0 --main-branch master
-EOF
-    exit 1
-}
-
-log_info() {
-    echo -e "${BLUE}[INFO]${NC} $1"
-}
-
-log_success() {
-    echo -e "${GREEN}[OK]${NC} $1"
-}
-
-log_warn() {
-    echo -e "${YELLOW}[WARN]${NC} $1"
-}
-
-log_error() {
-    echo -e "${RED}[ERROR]${NC} $1" >&2
-}
-
-# Default values
-MILESTONE=""
-DRY_RUN=true
-MAIN_BRANCH="main"
-
-# Parse arguments
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --dry-run)
-            DRY_RUN=true
-            shift
-            ;;
-        --execute)
-            DRY_RUN=false
-            shift
-            ;;
-        --main-branch)
-            MAIN_BRANCH="$2"
-            shift 2
-            ;;
-        --help|-h)
-            usage
-            ;;
-        -*)
-            log_error "Unknown option: $1"
-            usage
-            ;;
-        *)
-            if [[ -z "$MILESTONE" ]]; then
-                MILESTONE="$1"
-            else
-                log_error "Unexpected argument: $1"
-                usage
-            fi
-            shift
-            ;;
-    esac
-done
-
-# Validate milestone argument
-if [[ -z "$MILESTONE" ]]; then
-    log_error "Milestone is required"
-    usage
-fi
-
-# Check if we're in a git repository
-if ! git rev-parse --is-inside-work-tree &>/dev/null; then
-    log_error "Not in a git repository"
-    exit 1
-fi
-
-# Check if gh CLI is available
-if ! command -v gh &>/dev/null; then
-    log_error "GitHub CLI (gh) is not installed"
-    exit 1
-fi
-
-# Check if authenticated with gh
-if ! gh auth status &>/dev/null; then
-    log_error "Not authenticated with GitHub CLI. Run 'gh auth login' first."
-    exit 1
-fi
-
-CURRENT_BRANCH=$(git branch --show-current)
-log_info "Current branch: ${CURRENT_BRANCH}"
-log_info "Main branch: ${MAIN_BRANCH}"
-log_info "Milestone: ${MILESTONE}"
-echo ""
-
-# Fetch latest from remote
-log_info "Fetching latest from remote..."
-git fetch origin "$MAIN_BRANCH" --quiet
-
-# Get merged PRs from the milestone, sorted by merge date
-log_info "Fetching merged PRs from milestone '${MILESTONE}'..."
-
-# Store PR data in a temp file
-PR_DATA=$(mktemp)
-trap "rm -f $PR_DATA" EXIT
-
-if ! gh pr list --state merged --search "milestone:${MILESTONE}" \
-    --limit 1000 \
-    --json number,title,mergeCommit,mergedAt \
-    --jq 'sort_by(.mergedAt) | .[] | "\(.mergeCommit.oid)\t\(.number)\t\(.title)"' > "$PR_DATA" 2>/dev/null; then
-    log_error "Failed to fetch PRs from milestone '${MILESTONE}'"
-    log_error "This could be due to:"
-    log_error "  - Milestone does not exist"
-    log_error "  - Network/authentication issues"
-    log_error "  - Invalid milestone name format"
-    exit 1
-fi
-
-if [[ ! -s "$PR_DATA" ]]; then
-    log_warn "No merged PRs found for milestone '${MILESTONE}'"
-    exit 0
-fi
-
-TOTAL_PRS=$(wc -l < "$PR_DATA")
-log_info "Found ${TOTAL_PRS} merged PR(s) in milestone"
-echo ""
-
-# Find commits that are missing from current branch
-MISSING_COMMITS=()
-MISSING_INFO=()
-
-while IFS=$'\t' read -r sha pr_number title; do
-    # Skip if SHA is empty or null
-    if [[ -z "$sha" || "$sha" == "null" ]]; then
-        log_warn "PR #${pr_number} has no merge commit SHA, skipping"
-        continue
-    fi
-    
-    # Check if this commit is already in the current branch
-    if git merge-base --is-ancestor "$sha" HEAD 2>/dev/null; then
-        log_success "PR #${pr_number} already in branch: ${title:0:60}"
-    else
-        log_warn "PR #${pr_number} MISSING: ${title:0:60}"
-        MISSING_COMMITS+=("$sha")
-        MISSING_INFO+=("$sha PR #${pr_number}: ${title}")
-    fi
-done < "$PR_DATA"
-
-echo ""
-
-if [[ ${#MISSING_COMMITS[@]} -eq 0 ]]; then
-    log_success "All PRs from milestone '${MILESTONE}' are already in the current branch!"
-    exit 0
-fi
-
-log_info "Found ${#MISSING_COMMITS[@]} missing commit(s) to cherry-pick"
-echo ""
-
-# Output the cherry-pick commands
-echo "=========================================="
-echo "Cherry-pick commands (in chronological order):"
-echo "=========================================="
-echo ""
-
-for info in "${MISSING_INFO[@]}"; do
-    echo "# $info"
-done
-echo ""
-
-echo "# Run these commands to cherry-pick all missing commits:"
-echo "git cherry-pick ${MISSING_COMMITS[*]}"
-echo ""
-
-# Or one by one
-echo "# Or cherry-pick one at a time:"
-for sha in "${MISSING_COMMITS[@]}"; do
-    echo "git cherry-pick $sha"
-done
-echo ""
-
-# Execute if requested
-if [[ "$DRY_RUN" == false ]]; then
-    echo "=========================================="
-    log_info "Executing cherry-picks..."
-    echo "=========================================="
-    
-    for i in "${!MISSING_COMMITS[@]}"; do
-        sha="${MISSING_COMMITS[$i]}"
-        info="${MISSING_INFO[$i]}"
-        
-        echo ""
-        log_info "Cherry-picking: $info"
-        
-        if git cherry-pick "$sha"; then
-            log_success "Successfully cherry-picked $sha"
-        else
-            log_error "Failed to cherry-pick $sha"
-            log_error "Resolve conflicts and run 'git cherry-pick --continue', or 'git cherry-pick --abort' to cancel"
-            exit 1
-        fi
-    done
-    
-    echo ""
-    log_success "All cherry-picks completed successfully!"
-else
-    echo "=========================================="
-    echo -e "${YELLOW}Dry run mode - no changes made${NC}"
-    echo "Run with --execute to perform the cherry-picks"
-    echo "=========================================="
-fi

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

@@ -3,14 +3,7 @@
 set -ex
 
 # Clean up old nightly builds from DockerHub, keeping only the last 14 builds
-# This script uses DockerHub API to list and delete old tags with specified prefix
-# Usage: cleanup-nightly-builds.sh [TAG_PREFIX]
-# Example: cleanup-nightly-builds.sh "nightly-" or cleanup-nightly-builds.sh "cu130-nightly-"
-
-# Get tag prefix from argument, default to "nightly-" if not provided
-TAG_PREFIX="${1:-nightly-}"
-
-echo "Cleaning up tags with prefix: $TAG_PREFIX"
+# This script uses DockerHub API to list and delete old tags with "nightly-" prefix
 
 # DockerHub API endpoint for vllm/vllm-openai repository
 REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
@@ -52,7 +45,7 @@ get_all_tags() {
         set -x
         
         # Get both last_updated timestamp and tag name, separated by |
-        local tags=$(echo "$response" | jq -r --arg prefix "$TAG_PREFIX" '.results[] | select(.name | startswith($prefix)) | "\(.last_updated)|\(.name)"')
+        local tags=$(echo "$response" | jq -r '.results[] | select(.name | startswith("nightly-")) | "\(.last_updated)|\(.name)"')
         
         if [ -z "$tags" ]; then
             break

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

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Get tag variant from argument, default to empty if not provided, should be something like "cu130".
-# Due to limits in cleanup script, we must move variants to use separate tags like "cu130-nightly",
-# otherwise they will be cleaned up together with the main "nightly" tags.
-
-TAG_VARIANT="$1"
-if [ -n "$TAG_VARIANT" ]; then
-    ORIG_TAG_SUFFIX="-$TAG_VARIANT"
-    TAG_NAME="$TAG_VARIANT-nightly"
-else
-    ORIG_TAG_SUFFIX=""
-    TAG_NAME="nightly"
-fi
-
-ORIG_TAG_NAME="$BUILDKITE_COMMIT"
-
-echo "Pushing original tag $ORIG_TAG_NAME$ORIG_TAG_SUFFIX to new nightly tag name: $TAG_NAME"
-
-# pull original arch-dependent images from AWS ECR Public
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX
-# tag arch-dependent images
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-aarch64
-# push arch-dependent images to DockerHub
-docker push vllm/vllm-openai:$TAG_NAME-x86_64
-docker push vllm/vllm-openai:$TAG_NAME-aarch64
-# push arch-independent manifest to DockerHub
-docker manifest create vllm/vllm-openai:$TAG_NAME vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
-docker manifest create vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
-docker manifest push vllm/vllm-openai:$TAG_NAME
-docker manifest push vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -18,18 +18,15 @@ wait_for_server() {
 
 MODEL="Qwen/Qwen3-Next-80B-A3B-Instruct"
 
-# Set BACKENDS and platform-specific args based on platform
+# Set BACKENDS based on platform
 if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
   # ROCm platform
   BACKENDS=("allgather_reducescatter")
   # Disable MOE padding for ROCm since it is causing eplb to fail
   export VLLM_ROCM_MOE_PADDING=0
-  PLATFORM_ARGS=("--no-async-scheduling")
-  echo "Disabled async scheduling for ROCm platform due to issues with spec decode."
 else
   # Non-ROCm platform (CUDA/other)
   BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-  PLATFORM_ARGS=()
 fi
 
 cleanup() {
@@ -57,7 +54,6 @@ for BACK in "${BACKENDS[@]}"; do
     --trust-remote-code \
     --max-model-len 2048 \
     --gpu-memory-utilization 0.9 \
-    "${PLATFORM_ARGS[@]}" \
     --port $PORT &
   SERVER_PID=$!
   wait_for_server $PORT

.buildkite/scripts/trigger-ci-build.sh

@@ -1,227 +0,0 @@
-#!/bin/bash
-#
-# trigger-ci-build.sh
-# Trigger a Buildkite CI build using the bk CLI for the current commit and branch
-# with RUN_ALL=1 and NIGHTLY=1 environment variables.
-#
-# Usage: ./trigger-ci-build.sh [options]
-#
-# Requires: bk CLI (https://buildkite.com/docs/platform/cli)
-#
-# SAFETY: Dry-run by default. Use --execute to actually trigger a build.
-#
-
-set -euo pipefail
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-NC='\033[0m' # No Color
-
-# Default configuration
-PIPELINE="ci"
-DRY_RUN=true
-
-usage() {
-    cat <<EOF
-Usage: $(basename "$0") [options]
-
-Trigger a Buildkite CI build using the bk CLI for the current commit and branch.
-Sets RUN_ALL=1 and NIGHTLY=1 environment variables.
-
-SAFETY: Dry-run by default. Use --execute to actually trigger a build.
-
-Options:
-    --execute       Actually trigger the build (default: dry-run)
-    --pipeline      Buildkite pipeline slug (default: ${PIPELINE})
-    --commit        Override commit SHA (default: current HEAD)
-    --branch        Override branch name (default: current branch)
-    --message       Custom build message (default: auto-generated)
-    --help          Show this help message
-
-Prerequisites:
-    - bk CLI installed: brew tap buildkite/buildkite && brew install buildkite/buildkite/bk
-    - bk configured: bk configure
-
-Examples:
-    $(basename "$0")                        # Dry-run, show what would happen
-    $(basename "$0") --execute              # Actually trigger the build
-    $(basename "$0") --pipeline ci-shadow   # Dry-run with different pipeline
-EOF
-    exit 1
-}
-
-log_info() {
-    echo -e "${BLUE}[INFO]${NC} $1"
-}
-
-log_success() {
-    echo -e "${GREEN}[OK]${NC} $1"
-}
-
-log_warn() {
-    echo -e "${YELLOW}[WARN]${NC} $1"
-}
-
-log_error() {
-    echo -e "${RED}[ERROR]${NC} $1" >&2
-}
-
-# Parse arguments
-COMMIT=""
-BRANCH=""
-MESSAGE=""
-
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --execute)
-            DRY_RUN=false
-            shift
-            ;;
-        --pipeline)
-            PIPELINE="$2"
-            shift 2
-            ;;
-        --commit)
-            COMMIT="$2"
-            shift 2
-            ;;
-        --branch)
-            BRANCH="$2"
-            shift 2
-            ;;
-        --message)
-            MESSAGE="$2"
-            shift 2
-            ;;
-        --help|-h)
-            usage
-            ;;
-        -*)
-            log_error "Unknown option: $1"
-            usage
-            ;;
-        *)
-            log_error "Unexpected argument: $1"
-            usage
-            ;;
-    esac
-done
-
-# Check if bk CLI is installed
-if ! command -v bk &>/dev/null; then
-    log_error "Buildkite CLI (bk) is not installed"
-    echo ""
-    echo "Install with:"
-    echo "  brew tap buildkite/buildkite && brew install buildkite/buildkite/bk"
-    echo ""
-    echo "Then configure:"
-    echo "  bk configure"
-    exit 1
-fi
-
-# Check if we're in a git repository
-if ! git rev-parse --is-inside-work-tree &>/dev/null; then
-    log_error "Not in a git repository"
-    exit 1
-fi
-
-# Get current commit and branch if not overridden
-if [[ -z "$COMMIT" ]]; then
-    COMMIT=$(git rev-parse HEAD)
-fi
-
-if [[ -z "$BRANCH" ]]; then
-    BRANCH=$(git branch --show-current)
-    if [[ -z "$BRANCH" ]]; then
-        # Detached HEAD state - try to get branch from ref
-        BRANCH=$(git rev-parse --abbrev-ref HEAD)
-    fi
-fi
-
-# Generate default message if not provided
-if [[ -z "$MESSAGE" ]]; then
-    COMMIT_MSG=$(git log -1 --pretty=format:"%s" "$COMMIT" 2>/dev/null || echo "Manual build")
-    MESSAGE="[Manual] ${COMMIT_MSG}"
-fi
-
-# Safety check: Verify the commit exists on the remote
-log_info "Verifying commit exists on remote..."
-git fetch origin --quiet 2>/dev/null || true
-
-# Check if commit is reachable from any remote branch
-REMOTE_BRANCHES=$(git branch -r --contains "$COMMIT" 2>/dev/null || true)
-if [[ -z "$REMOTE_BRANCHES" ]]; then
-    log_error "Commit ${COMMIT} does not exist on any remote branch!"
-    echo ""
-    echo "The CI system will fail to checkout this commit."
-    echo "Please push your changes first:"
-    echo ""
-    echo "  git push origin ${BRANCH}"
-    echo ""
-    exit 1
-fi
-
-log_success "Commit found on remote branches:"
-echo "$REMOTE_BRANCHES" | head -5 | sed 's/^/  /'
-if [[ $(echo "$REMOTE_BRANCHES" | wc -l) -gt 5 ]]; then
-    echo "  ... and more"
-fi
-echo ""
-
-log_info "Pipeline: ${PIPELINE}"
-log_info "Branch: ${BRANCH}"
-log_info "Commit: ${COMMIT}"
-log_info "Message: ${MESSAGE}"
-log_info "Environment: RUN_ALL=1, NIGHTLY=1"
-echo ""
-
-# Build the command
-CMD=(bk build create
-    -y
-    -w
-    -i
-    --pipeline "${PIPELINE}"
-    --commit "${COMMIT}"
-    --branch "${BRANCH}"
-    --message "${MESSAGE}"
-    --env "RUN_ALL=1"
-    --env "NIGHTLY=1"
-)
-
-if [[ "$DRY_RUN" == true ]]; then
-    echo "=========================================="
-    log_warn "DRY-RUN MODE - No build will be triggered"
-    echo "=========================================="
-    echo ""
-    echo "Command that would be executed:"
-    echo ""
-    # Escape single quotes in values for safe shell display
-    escape_for_shell() {
-        printf '%s' "$1" | sed "s/'/'\\\\''/g"
-    }
-    echo "  bk build create \\"
-    echo "    -y \\"
-    echo "    -w \\"
-    echo "    -i \\"
-    echo "    --pipeline '$(escape_for_shell "${PIPELINE}")' \\"
-    echo "    --commit '$(escape_for_shell "${COMMIT}")' \\"
-    echo "    --branch '$(escape_for_shell "${BRANCH}")' \\"
-    echo "    --message '$(escape_for_shell "${MESSAGE}")' \\"
-    echo "    --env 'RUN_ALL=1' \\"
-    echo "    --env 'NIGHTLY=1'"
-    echo ""
-    echo "=========================================="
-    echo -e "${YELLOW}To actually trigger this build, run:${NC}"
-    echo ""
-    echo "  $0 --execute"
-    echo "=========================================="
-    exit 0
-fi
-
-log_info "Triggering build..."
-
-# Execute the command - bk will print the URL and open browser
-"${CMD[@]}"

.buildkite/scripts/upload-release-wheels.sh

@@ -16,7 +16,7 @@ else
     echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
 fi
 # sanity check for version mismatch
-if [ "$RELEASE_VERSION" != "$GIT_VERSION" ]; then
+if [ "v$RELEASE_VERSION" != "$GIT_VERSION" ]; then
   if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
     echo "[WARNING] Force release and ignore version mismatch"
   else
@@ -24,7 +24,6 @@ if [ "$RELEASE_VERSION" != "$GIT_VERSION" ]; then
     exit 1
   fi
 fi
-PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
 
 # check pypi token
 if [ -z "$PYPI_TOKEN" ]; then
@@ -82,16 +81,16 @@ echo "Existing wheels on S3:"
 aws s3 ls "$S3_COMMIT_PREFIX"
 echo "Copying wheels to local directory"
 mkdir -p $DIST_DIR
-# include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
-aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
+# include only wheels for the release version, ignore all files with "dev" or "rc" in the name
+aws s3 cp --recursive --exclude "*" --include "vllm-${RELEASE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc*" "$S3_COMMIT_PREFIX" $DIST_DIR
 echo "Wheels copied to local directory"
 # generate source tarball
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
+git archive --format=tar.gz --output="$DIST_DIR/vllm-${RELEASE_VERSION}.tar.gz" $BUILDKITE_COMMIT
 ls -la $DIST_DIR
 
 
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
-PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
+PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${RELEASE_VERSION}*.whl" -not -name "*+*")
 if [ -z "$PYPI_WHEEL_FILES" ]; then
   echo "No default variant wheels found, quitting..."
   exit 1

.buildkite/test-amd.yaml

@@ -71,7 +71,6 @@ steps:
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/multimodal
-  - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
   - tests/tool_parsers
@@ -83,7 +82,6 @@ steps:
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s renderers
   - pytest -v -s tokenizers_
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
@@ -430,8 +428,6 @@ steps:
   timeout_in_minutes: 30
   gpu: h100
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
@@ -456,12 +452,10 @@ steps:
   timeout_in_minutes: 30
   gpu: b200
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - pytest -v -s v1/attention
+    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
 
 - label: V1 Test others (CPU) # 5 mins
   mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
@@ -709,17 +703,6 @@ steps:
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
 
-- label: Kernels Helion Test
-  timeout_in_minutes: 30
-  mirror_hardwares: [amdexperimental, amdproduction]
-  agent_pool: mi325_1
-  source_file_dependencies:
-  - vllm/utils/import_utils.py
-  - tests/kernels/helion/
-  commands:
-    - pip install helion
-    - pytest -v -s kernels/helion/
-
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
   torch_nightly: true
@@ -872,7 +855,7 @@ steps:
 
 - label: Language Models Tests (Standard)
   timeout_in_minutes: 25
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   torch_nightly: true
@@ -1468,7 +1451,7 @@ steps:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
 
 - label: NixlConnector PD accuracy tests (Distributed) # 30min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_4
   # grade: Blocking
   timeout_in_minutes: 30
@@ -1479,10 +1462,10 @@ steps:
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-    - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+    - VLLM_ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: DP EP NixlConnector PD accuracy tests (Distributed) # 15min
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_4
   # grade: Blocking
   timeout_in_minutes: 15
@@ -1493,7 +1476,7 @@ steps:
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-    - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+    - VLLM_ATTENTION_BACKEND=ROCM_ATTN DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 ##### multi gpus test #####
 ##### A100 test #####
@@ -1679,6 +1662,17 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
+- label: DeepSeek V2-Lite Async EPLB Accuracy
+  timeout_in_minutes: 60
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_4
+  # grade: Blocking
+  gpu: h100
+  optional: true
+  num_gpus: 4
+  working_dir: "/vllm-workspace"
+  commands:
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
 
 - label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
   timeout_in_minutes: 60

.buildkite/test-pipeline.yaml

@@ -64,7 +64,6 @@ steps:
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/multimodal
-  - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
   - tests/tool_parsers
@@ -76,7 +75,6 @@ steps:
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s renderers
   - pytest -v -s tokenizers_
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
@@ -376,8 +374,6 @@ steps:
   timeout_in_minutes: 30
   gpu: h100
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
@@ -400,12 +396,10 @@ steps:
   timeout_in_minutes: 30
   gpu: b200
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - pytest -v -s v1/attention
+    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
 
 - label: V1 Test others (CPU) # 5 mins
   source_file_dependencies:
@@ -630,56 +624,6 @@ steps:
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
 
-- label: Kernels Helion Test
-  timeout_in_minutes: 30
-  gpu: h100
-  source_file_dependencies:
-  - vllm/utils/import_utils.py
-  - tests/kernels/helion/
-  commands:
-    - pip install helion
-    - pytest -v -s kernels/helion/
-
-  
-- label: Kernels FP8 MoE Test (1 H100)
-  timeout_in_minutes: 90
-  gpu: h100
-  num_gpus: 1
-  optional: true
-  commands:
-    - pytest -v -s kernels/moe/test_cutlass_moe.py
-    - pytest -v -s kernels/moe/test_flashinfer.py
-    - pytest -v -s kernels/moe/test_gpt_oss_triton_kernels.py
-    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py
-    - pytest -v -s kernels/moe/test_moe.py
-    # - pytest -v -s kernels/moe/test_block_fp8.py - failing on main
-    - pytest -v -s kernels/moe/test_block_int8.py
-    - pytest -v -s kernels/moe/test_triton_moe_no_act_mul.py
-    - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
-
-- label: Kernels FP8 MoE Test (2 H100s)
-  timeout_in_minutes: 90
-  gpu: h100
-  num_gpus: 2
-  optional: true
-  commands:
-    - pytest -v -s kernels/moe/test_deepep_deepgemm_moe.py
-    - pytest -v -s kernels/moe/test_deepep_moe.py
-    - pytest -v -s kernels/moe/test_pplx_cutlass_moe.py
-    # - pytest -v -s kernels/moe/test_pplx_moe.py - failing on main
-  
-- label: Kernels Fp4 MoE Test (B200)
-  timeout_in_minutes: 60
-  gpu: b200
-  num_gpus: 1
-  optional: true
-  commands:
-    - pytest -v -s kernels/moe/test_cutedsl_moe.py
-    - pytest -v -s kernels/moe/test_flashinfer_moe.py
-    - pytest -v -s kernels/moe/test_nvfp4_moe.py
-    - pytest -v -s kernels/moe/test_ocp_mx_moe.py
-
-
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
   torch_nightly: true
@@ -1007,7 +951,7 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 23 min
+- label: Blackwell Test # 21 min
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   gpu: b200
@@ -1047,8 +991,6 @@ steps:
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
-    # e2e
-    - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Blackwell Fusion and Compile Tests # 30 min
   timeout_in_minutes: 40
@@ -1103,48 +1045,6 @@ steps:
     # Run all e2e fusion tests
     - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
 
-- label: Hopper Fusion E2E Tests (H100) # 10min
-  timeout_in_minutes: 70
-  working_dir: "/vllm-workspace/"
-  gpu: h100
-  optional: true
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/test_fusion_attn.py
-  commands:
-    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-    # skip Llama-4 since it does not fit on this device
-    - pytest -v -s tests/compile/test_fusion_attn.py -k 'not Llama-4'
-
-- label: Hopper Fusion Distributed E2E Tests (2xH100)  # 70min
-  timeout_in_minutes: 70
-  working_dir: "/vllm-workspace/"
-  gpu: h100
-  optional: true
-  num_gpus: 2
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  commands:
-    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-    # Run all e2e fusion tests
-    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
@@ -1444,31 +1344,22 @@ steps:
     - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
-- label: Sequence Parallel Tests (H100) # 60 min
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  gpu: h100
-  optional: true
-  num_gpus: 2
-  commands:
-    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-    # Run sequence parallel tests
-    - pytest -v -s tests/distributed/test_sequence_parallel.py
-    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
-
-- label: Distributed Tests (H100) # optional
-  gpu: h100
+##### H200 test #####
+- label: Distributed Tests (H200) # optional
+  gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
   num_gpus: 2
   commands:
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
+    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
+    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
+    - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
+    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
 
-##### H200 test #####
-
 - label: LM Eval Large Models (H200) # optional
   timeout_in_minutes: 60
   gpu: h200

.buildkite/test_areas/attention.yaml

@@ -6,8 +6,6 @@ steps:
   timeout_in_minutes: 30
   gpu: h100
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
@@ -17,9 +15,7 @@ steps:
   timeout_in_minutes: 30
   gpu: b200
   source_file_dependencies:
-    - vllm/config/attention.py
-    - vllm/model_executor/layers/attention
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - pytest -v -s v1/attention
+    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this

.buildkite/test_areas/misc.yaml

@@ -121,7 +121,6 @@ steps:
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/multimodal
-  - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
   - tests/tool_parsers
@@ -133,7 +132,6 @@ steps:
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s -m 'cpu_test' multimodal
-  - pytest -v -s renderers
   - pytest -v -s tokenizers_
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils

.github/mergify.yml

@@ -414,18 +414,6 @@ pull_request_rules:
       remove:
         - needs-rebase
 
-- name: label-bug
-  description: Automatically apply bug label
-  conditions:
-    - label != stale
-    - or:
-      - title~=(?i)\bbug\b
-      - title~=(?i)\bbugfix\b
-  actions:
-    label:
-      add:
-        - bug
-
 - name: label-kv-connector
   description: Automatically apply kv-connector label
   conditions:

.github/workflows/macos-smoke-test.yml

@@ -29,9 +29,8 @@ jobs:
 
       - name: Install dependencies and build vLLM
         run: |
-          uv pip install -r requirements/cpu-build.txt --index-strategy unsafe-best-match
           uv pip install -r requirements/cpu.txt --index-strategy unsafe-best-match
-          uv pip install -e . --no-build-isolation
+          uv pip install -e .
         env:
           CMAKE_BUILD_PARALLEL_LEVEL: 4
 

.gitignore

@@ -7,9 +7,6 @@ vllm/vllm_flash_attn/*
 # OpenAI triton kernels copied from source
 vllm/third_party/triton_kernels/*
 
-# FlashMLA interface copied from source
-vllm/third_party/flashmla/flash_mla_interface.py
-
 # triton jit
 .triton
 
@@ -194,9 +191,6 @@ CLAUDE.md
 AGENTS.md
 .codex/
 
-# Cursor
-.cursor/
-
 # DS Store
 .DS_Store
 

.pre-commit-config.yaml

@@ -147,13 +147,6 @@ repos:
     entry: python tools/pre_commit/validate_config.py
     language: python
     additional_dependencies: [regex]
-  - id: validate-docker-versions
-    name: Validate docker/versions.json matches Dockerfile
-    entry: python tools/generate_versions_json.py --check
-    language: python
-    files: ^docker/(Dockerfile|versions\.json)$
-    pass_filenames: false
-    additional_dependencies: [dockerfile-parse]
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

CMakeLists.txt

@@ -377,7 +377,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     # preselected input type pairs and schedules.
     # Generate sources:
     set(MARLIN_GEN_SCRIPT
-      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/marlin/generate_kernels.py)
+      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/gptq_marlin/generate_kernels.py)
     file(MD5 ${MARLIN_GEN_SCRIPT} MARLIN_GEN_SCRIPT_HASH)
     list(JOIN CUDA_ARCHS "," CUDA_ARCHS_STR)
     set(MARLIN_GEN_SCRIPT_HASH_AND_ARCH "${MARLIN_GEN_SCRIPT_HASH}(ARCH:${CUDA_ARCHS_STR})")
@@ -412,7 +412,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
 
     if (MARLIN_ARCHS)
-      file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/marlin/sm80_kernel_*_float16.cu")
+      file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/sm80_kernel_*_float16.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
         CUDA_ARCHS "${MARLIN_ARCHS}")
@@ -422,7 +422,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       endif()
       list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
 
-      file(GLOB MARLIN_TEMPLATE_BF16_KERNEL_SRC "csrc/quantization/marlin/sm80_kernel_*_bfloat16.cu")
+      file(GLOB MARLIN_TEMPLATE_BF16_KERNEL_SRC "csrc/quantization/gptq_marlin/sm80_kernel_*_bfloat16.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_BF16_KERNEL_SRC}"
         CUDA_ARCHS "${MARLIN_BF16_ARCHS}")
@@ -434,7 +434,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
 
     if (MARLIN_SM75_ARCHS) 
-      file(GLOB MARLIN_TEMPLATE_SM75_KERNEL_SRC "csrc/quantization/marlin/sm75_kernel_*.cu")
+      file(GLOB MARLIN_TEMPLATE_SM75_KERNEL_SRC "csrc/quantization/gptq_marlin/sm75_kernel_*.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_SM75_KERNEL_SRC}"
         CUDA_ARCHS "${MARLIN_SM75_ARCHS}")
@@ -446,7 +446,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
 
     if (MARLIN_FP8_ARCHS) 
-      file(GLOB MARLIN_TEMPLATE_FP8_KERNEL_SRC "csrc/quantization/marlin/sm89_kernel_*.cu")
+      file(GLOB MARLIN_TEMPLATE_FP8_KERNEL_SRC "csrc/quantization/gptq_marlin/sm89_kernel_*.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_FP8_KERNEL_SRC}"
         CUDA_ARCHS "${MARLIN_FP8_ARCHS}")
@@ -459,10 +459,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
     set(MARLIN_SRCS
        "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
-       "csrc/quantization/marlin/marlin.cu"
-       "csrc/quantization/marlin/marlin_int4_fp8_preprocess.cu"
-       "csrc/quantization/marlin/gptq_marlin_repack.cu"
-       "csrc/quantization/marlin/awq_marlin_repack.cu")
+       "csrc/quantization/gptq_marlin/gptq_marlin.cu"
+       "csrc/quantization/gptq_marlin/marlin_int4_fp8_preprocess.cu"
+       "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
+       "csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
     set_gencode_flags_for_srcs(
       SRCS "${MARLIN_SRCS}"
       CUDA_ARCHS "${MARLIN_OTHER_ARCHS}")

benchmarks/kernels/bench_nvfp4_quant.py

@@ -20,12 +20,8 @@ FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
 FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
 
 PROVIDER_CFGS = {
-    "vllm": dict(backend="vllm", is_sf_swizzled_layout=False, enabled=True),
-    "vllm-swizzle": dict(backend="vllm", is_sf_swizzled_layout=True, enabled=True),
-    "flashinfer": dict(backend="flashinfer", is_sf_swizzled_layout=False, enabled=True),
-    "flashinfer-swizzle": dict(
-        backend="flashinfer", is_sf_swizzled_layout=True, enabled=True
-    ),
+    "vllm": dict(backend="vllm", enabled=True),
+    "flashinfer": dict(backend="flashinfer", enabled=True),
 }
 
 _enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
@@ -40,7 +36,7 @@ def compute_global_scale(tensor: torch.Tensor) -> torch.Tensor:
 @triton.testing.perf_report(
     triton.testing.Benchmark(
         x_names=["batch_size"],
-        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192],
+        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
         x_log=False,
         line_arg="provider",
         line_vals=_enabled,
@@ -67,36 +63,19 @@ def benchmark(batch_size, provider, N, K):
 
     if cfg["backend"] == "vllm":
         # vLLM's FP4 quantization
-        if cfg["is_sf_swizzled_layout"]:
-            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-                lambda: ops.scaled_fp4_quant(
-                    a, a_global_scale, is_sf_swizzled_layout=True
-                ),
-                quantiles=quantiles,
-            )
-        else:
-            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-                lambda: ops.scaled_fp4_quant(
-                    a, a_global_scale, is_sf_swizzled_layout=False
-                ),
-                quantiles=quantiles,
-            )
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: ops.scaled_fp4_quant(a, a_global_scale),
+            quantiles=quantiles,
+        )
     elif cfg["backend"] == "flashinfer":
         # FlashInfer's FP4 quantization
-        if cfg["is_sf_swizzled_layout"]:
-            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-                lambda: flashinfer_fp4_quantize(
-                    a, a_global_scale, is_sf_swizzled_layout=True
-                ),
-                quantiles=quantiles,
-            )
-        else:
-            ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-                lambda: flashinfer_fp4_quantize(
-                    a, a_global_scale, is_sf_swizzled_layout=False
-                ),
-                quantiles=quantiles,
-            )
+        # Use is_sf_swizzled_layout=True to match vLLM's output format
+        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+            lambda: flashinfer_fp4_quantize(
+                a, a_global_scale, is_sf_swizzled_layout=True
+            ),
+            quantiles=quantiles,
+        )
 
     # Convert ms to us for better readability at small batch sizes
     to_us = lambda t_ms: t_ms * 1000
@@ -113,9 +92,7 @@ def prepare_shapes(args):
     return out
 
 
-def _test_accuracy_once(
-    M: int, K: int, dtype: torch.dtype, device: str, is_sf_swizzled_layout: bool
-):
+def _test_accuracy_once(M: int, K: int, dtype: torch.dtype, device: str):
     """Test accuracy between vLLM and FlashInfer FP4 quantization."""
     # Create input tensor
     a = torch.randn((M, K), device=device, dtype=dtype)
@@ -124,13 +101,11 @@ def _test_accuracy_once(
     a_global_scale = compute_global_scale(a)
 
     # vLLM quantization
-    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(
-        a, a_global_scale, is_sf_swizzled_layout=is_sf_swizzled_layout
-    )
+    vllm_fp4, vllm_scale = ops.scaled_fp4_quant(a, a_global_scale)
 
     # FlashInfer quantization (with swizzled layout to match vLLM's output)
     flashinfer_fp4, flashinfer_scale = flashinfer_fp4_quantize(
-        a, a_global_scale, is_sf_swizzled_layout=is_sf_swizzled_layout
+        a, a_global_scale, is_sf_swizzled_layout=True
     )
     flashinfer_scale = flashinfer_scale.view(torch.float8_e4m3fn)
 
@@ -139,14 +114,7 @@ def _test_accuracy_once(
         vllm_fp4,
         flashinfer_fp4,
     )
-    # Compare scales
-    torch.testing.assert_close(
-        vllm_scale,
-        flashinfer_scale,
-    )
-    print(
-        f"M={M}, K={K}, dtype={dtype}, is_sf_swizzled_layout={is_sf_swizzled_layout}: PASSED"  # noqa: E501
-    )
+    print(f"M={M}, K={K}, dtype={dtype}: PASSED")
 
 
 def test_accuracy():
@@ -162,10 +130,9 @@ def test_accuracy():
     Ms = [1, 1024]
     Ks = [4096]
 
-    for is_sf_swizzled_layout in [True, False]:
-        for M in Ms:
-            for K in Ks:
-                _test_accuracy_once(M, K, dtype, device, is_sf_swizzled_layout)
+    for M in Ms:
+        for K in Ks:
+            _test_accuracy_once(M, K, dtype, device)
 
     print("\nAll accuracy tests passed!")
 
@@ -178,7 +145,7 @@ if __name__ == "__main__":
         "--models",
         nargs="+",
         type=str,
-        default=["meta-llama/Llama-3.3-70B-Instruct"],
+        default=["meta-llama/Llama-3.1-8B-Instruct"],
         choices=list(WEIGHT_SHAPES.keys()),
     )
     parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])

benchmarks/kernels/benchmark_activation.py

@@ -7,7 +7,7 @@ import itertools
 import torch
 
 import vllm.model_executor.layers.activation  # noqa F401
-from vllm.model_executor.custom_op import op_registry
+from vllm.model_executor.custom_op import CustomOp
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
@@ -33,14 +33,14 @@ def benchmark_activation(
     torch.set_default_device(device)
 
     if func_name == "gelu_and_mul":
-        layer = op_registry[func_name](approximate="none")
+        layer = CustomOp.op_registry[func_name](approximate="none")
     elif func_name == "gelu_and_mul_tanh":
-        layer = op_registry["gelu_and_mul"](approximate="tanh")
+        layer = CustomOp.op_registry["gelu_and_mul"](approximate="tanh")
     elif func_name == "fatrelu_and_mul":
         threshold = 0.5
-        layer = op_registry[func_name](threshold)
+        layer = CustomOp.op_registry[func_name](threshold)
     else:
-        layer = op_registry[func_name]()
+        layer = CustomOp.op_registry[func_name]()
 
     x = torch.randn(num_tokens, dim, dtype=dtype, device=device)
     compiled_layer = torch.compile(layer.forward_native)

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -9,7 +9,6 @@ but use different quantization strategies and backends.
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
@@ -139,13 +138,12 @@ def bench_run(
     fn = mk.FusedMoEModularKernel(
         MoEPrepareAndFinalizeNoEP(),
         CutlassExpertsFp8(
-            moe_config=make_dummy_moe_config(
-                num_experts=num_experts,
-                hidden_dim=k,
-                intermediate_size_per_partition=n,
-                in_dtype=a.dtype,
-            ),
+            out_dtype=a.dtype,
+            e=num_experts,
+            n=n,
+            k=k,
             quant_config=quant_config,
+            device=w1.device,
         ),
     )
 

benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py

@@ -12,7 +12,6 @@ import torch
 import torch.utils.benchmark as benchmark
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import (
@@ -199,7 +198,8 @@ def bench_run(
         kernel = mk.FusedMoEModularKernel(
             MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
             CutlassExpertsFp4(
-                make_dummy_moe_config(),
+                out_dtype=dtype,
+                max_experts_per_worker=e,
                 quant_config=quant_config,
             ),
         )
@@ -244,7 +244,8 @@ def bench_run(
         kernel = mk.FusedMoEModularKernel(
             MoEPrepareAndFinalizeNoEP(defer_input_quant=True),
             CutlassExpertsFp4(
-                make_dummy_moe_config(),
+                out_dtype=dtype,
+                max_experts_per_worker=e,
                 quant_config=quant_config,
             ),
         )

benchmarks/kernels/benchmark_fused_topk.py

@@ -1,99 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import itertools
-
-import torch
-
-from vllm.model_executor.layers.fused_moe.router.fused_topk_router import fused_topk
-from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-
-num_tokens_range = [2**i for i in range(0, 8, 2)]
-num_experts_range = [16, 32, 64, 128, 256, 512]
-topk_range = [3, 4]
-configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range))
-
-
-def torch_topk(
-    gating_output: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-    scoring_func: str = "softmax",
-):
-    if scoring_func == "softmax":
-        scores = torch.softmax(gating_output.float(), dim=-1)
-    else:
-        scores = torch.sigmoid(gating_output.float())
-    topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
-
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-
-    return topk_weights, topk_ids
-
-
-def get_benchmark(scoring_func):
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=["num_tokens", "num_experts", "topk"],
-            x_vals=[list(_) for _ in configs],
-            line_arg="provider",
-            line_vals=["torch", "vllm"],
-            line_names=["Torch", "vLLM"],
-            styles=[("blue", "-"), ("red", "-")],
-            ylabel="us",
-            plot_name=f"fused-topk-perf-{scoring_func}",
-            args={},
-        )
-    )
-    def benchmark(num_tokens, num_experts, topk, provider):
-        dtype = torch.bfloat16
-        hidden_size = 1024
-        renormalize = True
-        hidden_states = torch.randn(
-            (num_tokens, hidden_size), dtype=dtype, device="cuda"
-        )
-        gating_output = torch.randn(
-            (num_tokens, num_experts), dtype=dtype, device="cuda"
-        )
-
-        quantiles = [0.5, 0.2, 0.8]
-
-        if provider == "torch":
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: torch_topk(
-                    gating_output=gating_output,
-                    topk=topk,
-                    renormalize=renormalize,
-                    scoring_func=scoring_func,
-                ),
-                quantiles=quantiles,
-            )
-        else:
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: fused_topk(
-                    hidden_states=hidden_states,
-                    gating_output=gating_output,
-                    topk=topk,
-                    renormalize=renormalize,
-                    scoring_func=scoring_func,
-                ),
-                quantiles=quantiles,
-            )
-
-        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
-
-    return benchmark
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(description="Benchmark the MoE topk kernel.")
-    parser.add_argument("--scoring-func", type=str, default="softmax")
-    parser.add_argument("--save-path", type=str, default="./configs/fused_topk/")
-    args = parser.parse_args()
-
-    # Get the benchmark function
-    benchmark = get_benchmark(args.scoring_func)
-    # Run performance benchmark
-    benchmark.run(print_data=True, save_path=args.save_path)

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -6,7 +6,6 @@ import torch.utils.benchmark as benchmark
 from benchmark_shapes import WEIGHT_SHAPES_MOE
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
@@ -135,13 +134,13 @@ def bench_run(
         fn = mk.FusedMoEModularKernel(
             MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp8(
-                moe_config=make_dummy_moe_config(
-                    num_experts=w2.shape[0],
-                    hidden_dim=w2.shape[1],
-                    intermediate_size_per_partition=w2.shape[2],
-                    in_dtype=a.dtype,
-                ),
+                out_dtype=a.dtype,
+                # NOTE(rob): w2 is shaped as [E, hidden, intermediate]
+                e=w2.shape[0],
+                n=w2.shape[2],
+                k=w2.shape[1],
                 quant_config=quant_config,
+                device=w1.device,
             ),
         )
 
@@ -167,13 +166,13 @@ def bench_run(
         fn = mk.FusedMoEModularKernel(
             MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp8(
-                moe_config=make_dummy_moe_config(
-                    num_experts=w2.shape[0],
-                    hidden_dim=w2.shape[1],
-                    intermediate_size_per_partition=w2.shape[2],
-                    in_dtype=a.dtype,
-                ),
+                out_dtype=a.dtype,
+                # NOTE(rob): w2 is shaped as [E, hidden, intermediate]
+                e=w2.shape[0],
+                n=w2.shape[2],
+                k=w2.shape[1],
                 quant_config=quant_config,
+                device=w1.device,
             ),
         )
 

benchmarks/kernels/benchmark_machete.py

@@ -231,7 +231,7 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
         assert bt.w_tok_s is None
         assert bt.group_size is not None
 
-        fn = lambda: ops.marlin_gemm(
+        fn = lambda: ops.gptq_marlin_gemm(
             a=bt.a,
             c=None,
             b_q_weight=w_q,

benchmarks/kernels/benchmark_marlin.py

@@ -239,7 +239,7 @@ def bench_run(
         "sm_version": sm_version,
         "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD,
         # Kernels
-        "marlin_gemm": ops.marlin_gemm,
+        "gptq_marlin_gemm": ops.gptq_marlin_gemm,
         "gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
         "gptq_marlin_repack": ops.gptq_marlin_repack,
         "allspark_w8a16_gemm": ops.allspark_w8a16_gemm,
@@ -263,21 +263,21 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="output = marlin_gemm(a, None, marlin_q_w, marlin_s, None, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, None, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
-            description="marlin_gemm",
+            description="gptq_marlin_gemm",
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
     results.append(
         benchmark.Timer(
-            stmt="output = marlin_gemm(a, None, marlin_q_w, marlin_s, None, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, None, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
-            description="marlin_gemm_fp32",
+            description="gptq_marlin_gemm_fp32",
         ).blocked_autorange(min_run_time=min_run_time)
     )
 

benchmarks/kernels/benchmark_moe.py

@@ -15,18 +15,11 @@ import ray
 import torch
 from ray.experimental.tqdm_ray import tqdm
 
-from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.config import (
-    FusedMoEConfig,
-    FusedMoEParallelConfig,
     FusedMoEQuantConfig,
-    RoutingMethodType,
     _get_config_dtype_str,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import *
-from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
-    TritonOrDeepGemmExperts,
-)
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
@@ -201,36 +194,10 @@ def benchmark_config(
             block_shape=block_quant_shape,
         )
 
-        deep_gemm_experts = None
-        if use_deep_gemm:
-            deep_gemm_experts = mk.FusedMoEModularKernel(
-                prepare_finalize=MoEPrepareAndFinalizeNoEP(),
-                fused_experts=TritonOrDeepGemmExperts(
-                    moe_config=FusedMoEConfig(
-                        num_experts=num_experts,
-                        experts_per_token=topk,
-                        hidden_dim=hidden_size,
-                        intermediate_size_per_partition=shard_intermediate_size,
-                        num_local_experts=num_experts,
-                        activation="silu",
-                        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
-                        in_dtype=init_dtype,
-                        routing_method=RoutingMethodType.TopK,
-                        device="cuda",
-                    ),
-                    quant_config=quant_config,
-                ),
-            )
-
         with override_config(config):
             topk_weights, topk_ids, token_expert_indices = fused_topk(
                 x, input_gating, topk, renormalize=not use_deep_gemm
             )
-
-            if use_deep_gemm:
-                return deep_gemm_experts(
-                    x, w1, w2, topk_weights, topk_ids, inplace=True
-                )
             return fused_experts(
                 x,
                 w1,
@@ -239,6 +206,7 @@ def benchmark_config(
                 topk_ids,
                 inplace=True,
                 quant_config=quant_config,
+                allow_deep_gemm=use_deep_gemm,
             )
 
     # JIT compilation & warmup
@@ -675,7 +643,6 @@ def main(args: argparse.Namespace):
         "DeepseekV3ForCausalLM",
         "DeepseekV32ForCausalLM",
         "Glm4MoeForCausalLM",
-        "Glm4MoeLiteForCausalLM",
         "NemotronHForCausalLM",
     ):
         E = config.n_routed_experts

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -8,7 +8,7 @@ import ray
 import torch
 from transformers import AutoConfig
 
-from vllm.model_executor.layers.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
     _moe_permute,
     _moe_unpermute_and_reduce,
@@ -86,7 +86,9 @@ def benchmark_permute(
                 sorted_token_ids,
                 expert_ids,
                 inv_perm,
-            ) = _moe_permute(qhidden_states, None, topk_ids, num_experts, None, 16)
+            ) = _moe_permute(
+                qhidden_states, None, topk_ids, num_experts, None, align_block_size
+            )
 
     # JIT compilation & warmup
     run()
@@ -180,7 +182,7 @@ def benchmark_unpermute(
                 expert_ids,
                 inv_perm,
             ) = _moe_permute(
-                qhidden_states, None, topk_ids, num_experts, None, block_m=16
+                qhidden_states, None, topk_ids, num_experts, None, align_block_size
             )
             # convert to fp16/bf16 as gemm output
             return (
@@ -328,7 +330,6 @@ def main(args: argparse.Namespace):
         config.architectures[0] == "DeepseekV3ForCausalLM"
         or config.architectures[0] == "DeepseekV2ForCausalLM"
         or config.architectures[0] == "Glm4MoeForCausalLM"
-        or config.architectures[0] == "Glm4MoeLiteForCausalLM"
     ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -14,6 +14,7 @@ from vllm.triton_utils import triton
 from vllm.utils.deep_gemm import (
     calc_diff,
     fp8_gemm_nt,
+    get_col_major_tma_aligned_tensor,
     per_block_cast_to_fp8,
 )
 
@@ -47,9 +48,8 @@ def benchmark_shape(
     block_size = [128, 128]
 
     # Pre-quantize A for all implementations
-    A_deepgemm, A_scale_deepgemm = per_token_group_quant_fp8(
-        A, block_size[1], column_major_scales=True, tma_aligned_scales=True
-    )
+    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)
     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(

cmake/cpu_extension.cmake

@@ -13,8 +13,6 @@ endif()
 #
 # Define environment variables for special configurations
 #
-set(ENABLE_AVX2 $ENV{VLLM_CPU_AVX2})
-set(ENABLE_AVX512 $ENV{VLLM_CPU_AVX512})
 set(ENABLE_AVX512BF16 $ENV{VLLM_CPU_AVX512BF16})
 set(ENABLE_AVX512VNNI $ENV{VLLM_CPU_AVX512VNNI})
 set(ENABLE_AMXBF16 $ENV{VLLM_CPU_AMXBF16})
@@ -105,16 +103,6 @@ else()
     find_isa(${CPUINFO} "bf16" ARM_BF16_FOUND) # Check for ARM BF16 support
     find_isa(${CPUINFO} "S390" S390_FOUND)
     find_isa(${CPUINFO} "v" RVV_FOUND) # Check for RISC-V RVV support
-
-    # Support cross-compilation by allowing override via environment variables
-    if (ENABLE_AVX2)
-        set(AVX2_FOUND ON)
-        message(STATUS "AVX2 support enabled via VLLM_CPU_AVX2 environment variable")
-    endif()
-    if (ENABLE_AVX512)
-        set(AVX512_FOUND ON)
-        message(STATUS "AVX512 support enabled via VLLM_CPU_AVX512 environment variable")
-    endif()
 endif()
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
@@ -391,12 +379,6 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
     endif()
 endif()
 
-if (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND)
-    set(VLLM_EXT_SRC
-        "csrc/cpu/shm.cpp"
-        ${VLLM_EXT_SRC})
-endif()
-
 if(USE_ONEDNN)
     set(VLLM_EXT_SRC
         "csrc/cpu/dnnl_kernels.cpp"

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG c2afa9cb93e674d5a9120a170a6da57b89267208
+        GIT_TAG 46d64a8ebef03fa50b4ae74937276a5c940e3f95
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -30,24 +30,6 @@ endif()
 FetchContent_MakeAvailable(flashmla)
 message(STATUS "FlashMLA is available at ${flashmla_SOURCE_DIR}")
 
-# Vendor FlashMLA interface into vLLM with torch-ops shim.
-set(FLASHMLA_VENDOR_DIR "${CMAKE_SOURCE_DIR}/vllm/third_party/flashmla")
-file(MAKE_DIRECTORY "${FLASHMLA_VENDOR_DIR}")
-file(READ "${flashmla_SOURCE_DIR}/flash_mla/flash_mla_interface.py"
-     FLASHMLA_INTERFACE_CONTENT)
-string(REPLACE "import flash_mla.cuda as flash_mla_cuda"
-               "import vllm._flashmla_C\nflash_mla_cuda = torch.ops._flashmla_C"
-               FLASHMLA_INTERFACE_CONTENT
-               "${FLASHMLA_INTERFACE_CONTENT}")
-file(WRITE "${FLASHMLA_VENDOR_DIR}/flash_mla_interface.py"
-     "${FLASHMLA_INTERFACE_CONTENT}")
-
-# Install the generated flash_mla_interface.py to the wheel
-# Use COMPONENT _flashmla_C to ensure it's installed with the C extension
-install(FILES "${FLASHMLA_VENDOR_DIR}/flash_mla_interface.py"
-        DESTINATION vllm/third_party/flashmla/
-        COMPONENT _flashmla_C)
-
 # The FlashMLA kernels only work on hopper and require CUDA 12.3 or later.
 # Only build FlashMLA kernels if we are building for something compatible with 
 # sm90a
@@ -73,42 +55,16 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_SOURCES
         ${flashmla_SOURCE_DIR}/csrc/torch_api.cpp
-
-        # Misc kernels for decoding
-        ${flashmla_SOURCE_DIR}/csrc/smxx/decode/get_decoding_sched_meta/get_decoding_sched_meta.cu
-        ${flashmla_SOURCE_DIR}/csrc/smxx/decode/combine/combine.cu
-
-        # sm90 dense decode
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/instantiations/fp16.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/instantiations/bf16.cu
-
-        # sm90 sparse decode
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/model1_persistent_h64.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/model1_persistent_h128.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/v32_persistent_h64.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/instantiations/v32_persistent_h128.cu
-
-        # sm90 sparse prefill
+        ${flashmla_SOURCE_DIR}/csrc/pybind.cpp
+        ${flashmla_SOURCE_DIR}/csrc/smxx/get_mla_metadata.cu
+        ${flashmla_SOURCE_DIR}/csrc/smxx/mla_combine.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/splitkv_mla.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/splitkv_mla.cu
         ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/fwd.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k512.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k512_topklen.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k576.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/instantiations/phase1_k576_topklen.cu
-
-        # sm100 dense prefill & backward
+        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/sparse_fp8/splitkv_mla.cu
         ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_fwd_sm100.cu
-
-        # sm100 sparse prefill
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head64/instantiations/phase1_k512.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head64/instantiations/phase1_k576.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head128/instantiations/phase1_k512.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd/head128/instantiations/phase1_k576.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd_for_small_topk/head128/instantiations/phase1_prefill_k512.cu
-
-        # sm100 sparse decode
-        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/head64/instantiations/v32.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/decode/head64/instantiations/model1.cu
-        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd_for_small_topk/head128/instantiations/phase1_decode_k512.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_bwd_sm100.cu
+        ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd.cu
     )
 
     set(FlashMLA_Extension_SOURCES
@@ -120,7 +76,6 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc
-        ${flashmla_SOURCE_DIR}/csrc/kerutils/include
         ${flashmla_SOURCE_DIR}/csrc/sm90
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include
         ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include
@@ -128,6 +83,7 @@ if(FLASH_MLA_ARCHS)
 
     set(FlashMLA_Extension_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc
+        ${flashmla_SOURCE_DIR}/csrc/sm90
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include
         ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include
@@ -154,12 +110,9 @@ if(FLASH_MLA_ARCHS)
 
     # Keep Stable ABI for the module, but *not* for CUDA/C++ files.
     # This prevents Py_LIMITED_API from affecting nvcc and C++ compiles.
-    # Also enable C++20 for the FlashMLA sources (required for std::span, requires, etc.)
     target_compile_options(_flashmla_C PRIVATE
         $<$<COMPILE_LANGUAGE:CUDA>:-UPy_LIMITED_API>
-        $<$<COMPILE_LANGUAGE:CXX>:-UPy_LIMITED_API>
-        $<$<COMPILE_LANGUAGE:CXX>:-std=c++20>
-        $<$<COMPILE_LANGUAGE:CUDA>:-std=c++20>)
+        $<$<COMPILE_LANGUAGE:CXX>:-UPy_LIMITED_API>)
 
     define_extension_target(
         _flashmla_extension_C

csrc/cache.h

@@ -7,7 +7,6 @@
 #include <vector>
 
 void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
-                 int64_t block_size_in_bytes,
                  const torch::Tensor& block_mapping);
 
 void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,

csrc/cache_kernels.cu

@@ -25,7 +25,6 @@ typedef __hip_bfloat16 __nv_bfloat16;
 #endif
 
 void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
-                 int64_t block_size_in_bytes,
                  const torch::Tensor& block_mapping) {
   torch::Device src_device = src.device();
   torch::Device dst_device = dst.device();
@@ -50,6 +49,10 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
   char* src_ptr = static_cast<char*>(src.data_ptr());
   char* dst_ptr = static_cast<char*>(dst.data_ptr());
 
+  // We use the stride instead of numel in case the cache is padded for memory
+  // alignment reasons, we assume the blocks data (inclusive of any padding)
+  // is contiguous in memory
+  const int64_t block_size_in_bytes = src.element_size() * src.stride(0);
   const at::cuda::OptionalCUDAGuard device_guard(
       src_device.is_cuda() ? src_device : dst_device);
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -202,8 +205,7 @@ __global__ void reshape_and_cache_flash_kernel(
     const int64_t block_stride, const int64_t page_stride,
     const int64_t head_stride, const int64_t key_stride,
     const int64_t value_stride, const int num_heads, const int head_size,
-    const int block_size, const float* k_scale, const float* v_scale,
-    const int kv_scale_stride) {
+    const int block_size, const float* k_scale, const float* v_scale) {
   const int64_t token_idx = blockIdx.x;
   const int64_t slot_idx = slot_mapping[token_idx];
   // NOTE: slot_idx can be -1 if the token is padded
@@ -227,23 +229,21 @@ __global__ void reshape_and_cache_flash_kernel(
   // this is true for the NHD layout where `head_stride == head_size`
   const bool is_contiguous_heads = (head_stride == head_size);
 
+  float k_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *k_scale;
+  float v_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *v_scale;
   constexpr int VEC_SIZE = (sizeof(scalar_t) == 2) ? 8 : 4;
-
-  if (is_contiguous_heads && kv_scale_stride == 0) {
-    // NHD layout and k/v_scales are [1] (i.e. single scale for all heads)
+  CopyWithScaleOp<cache_t, scalar_t, kv_dt> k_op{k_scale_val};
+  CopyWithScaleOp<cache_t, scalar_t, kv_dt> v_op{v_scale_val};
+  if (is_contiguous_heads) {
+    // NHD layout
     // kv cache: [num_blocks, block_size, num_heads, head_size]
-    float k_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *k_scale;
-    float v_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *v_scale;
-
-    CopyWithScaleOp<cache_t, scalar_t, kv_dt> k_op{k_scale_val};
-    CopyWithScaleOp<cache_t, scalar_t, kv_dt> v_op{v_scale_val};
-
     vectorize_with_alignment<VEC_SIZE>(key_src, key_dst, n_elems, threadIdx.x,
                                        blockDim.x, k_op);
+
     vectorize_with_alignment<VEC_SIZE>(value_src, value_dst, n_elems,
                                        threadIdx.x, blockDim.x, v_op);
+
   } else {
-    // HND layout OR k/v_scales are [num_heads] (i.e. per-attn-head)
     // HND layout: heads are strided, but each head_size segment is contiguous
     // kv cache: [num_blocks, num_heads, block_size, head_size]
     const int lane = threadIdx.x & 31;     // 0..31 within warp
@@ -259,16 +259,6 @@ __global__ void reshape_and_cache_flash_kernel(
       cache_t* __restrict__ v_dst_h =
           value_dst + static_cast<int64_t>(head) * head_stride;
 
-      float k_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto)
-                              ? 0.f
-                              : k_scale[head * kv_scale_stride];
-      float v_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto)
-                              ? 0.f
-                              : v_scale[head * kv_scale_stride];
-
-      CopyWithScaleOp<cache_t, scalar_t, kv_dt> k_op{k_scale_val};
-      CopyWithScaleOp<cache_t, scalar_t, kv_dt> v_op{v_scale_val};
-
       // within each head, let the 32 threads of the warp perform the vector
       // copy
       vectorize_with_alignment<VEC_SIZE>(k_src_h, k_dst_h, head_size, lane, 32,
@@ -618,8 +608,7 @@ void reshape_and_cache(
           slot_mapping.data_ptr<int64_t>(), block_stride, page_stride,    \
           head_stride, key_stride, value_stride, num_heads, head_size,    \
           block_size, reinterpret_cast<const float*>(k_scale.data_ptr()), \
-          reinterpret_cast<const float*>(v_scale.data_ptr()),             \
-          kv_scale_stride);
+          reinterpret_cast<const float*>(v_scale.data_ptr()));
 
 void reshape_and_cache_flash(
     torch::Tensor& key,        // [num_tokens, num_heads, head_size]
@@ -628,9 +617,8 @@ void reshape_and_cache_flash(
     torch::Tensor&
         value_cache,  // [num_blocks, block_size, num_heads, head_size]
     torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
-    const std::string& kv_cache_dtype,
-    torch::Tensor& k_scale,    // [1] or [num_heads]
-    torch::Tensor& v_scale) {  // [1] or [num_heads]
+    const std::string& kv_cache_dtype, torch::Tensor& k_scale,
+    torch::Tensor& v_scale) {
   // NOTE(woosuk): In vLLM V1, key.size(0) can be different from
   // slot_mapping.size(0) because of padding for CUDA graphs.
   // In vLLM V0, key.size(0) is always equal to slot_mapping.size(0) because
@@ -653,12 +641,6 @@ void reshape_and_cache_flash(
   int64_t head_stride = key_cache.stride(2);
   TORCH_CHECK(key_cache.stride(0) == value_cache.stride(0));
 
-  TORCH_CHECK(k_scale.sizes() == v_scale.sizes(),
-              "k_scale and v_scale must have the same shape");
-  TORCH_CHECK(k_scale.numel() == 1 || k_scale.numel() == num_heads,
-              "k_scale and v_scale must be of shape [1] or [num_heads]");
-  int kv_scale_stride = (k_scale.numel() > 1) ? 1 : 0;
-
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(key));

csrc/cpu/cpu_types_arm.hpp

@@ -80,10 +80,8 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
     reg.val[1] = vld1q_f16(reinterpret_cast<const __fp16*>(ptr) + 8);
   }
 
-  // ASIMD does not support non-temporal loads
-  explicit FP16Vec16(bool, const void* ptr) : FP16Vec16(ptr) {}
-
   explicit FP16Vec16(const FP32Vec16& vec);
+
   void save(void* ptr) const {
     vst1q_f16(reinterpret_cast<__fp16*>(ptr), reg.val[0]);
     vst1q_f16(reinterpret_cast<__fp16*>(ptr) + 8, reg.val[1]);
@@ -192,9 +190,6 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
   explicit BF16Vec16(const void* ptr)
       : reg(*reinterpret_cast<const bfloat16x8x2_t*>(ptr)) {};
 
-  // ASIMD does not support non-temporal loads
-  explicit BF16Vec16(bool, const void* ptr) : BF16Vec16(ptr) {}
-
   explicit BF16Vec16(bfloat16x8x2_t data) : reg(data) {};
 
   explicit BF16Vec16(const FP32Vec16&);
@@ -479,9 +474,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
       : reg({vld1q_f32(ptr), vld1q_f32(ptr + 4), vld1q_f32(ptr + 8),
              vld1q_f32(ptr + 12)}) {}
 
-  // ASIMD does not support non-temporal loads
-  explicit FP32Vec16(bool, const float* ptr) : FP32Vec16(ptr) {}
-
   explicit FP32Vec16(float32x4x4_t data) : reg(data) {}
 
   explicit FP32Vec16(const FP32Vec8& data) {
@@ -764,96 +756,6 @@ struct INT8Vec16 : public Vec<INT8Vec16> {
   };
 };
 
-struct INT8Vec64 : public Vec<INT8Vec64> {
-  constexpr static int VEC_ELEM_NUM = 64;
-  union AliasReg {
-    int8x16x4_t reg;
-    int8_t values[VEC_ELEM_NUM];
-  };
-  int8x16x4_t reg;
-
-  explicit INT8Vec64(const int8_t* ptr) { reg = vld1q_s8_x4(ptr); }
-
-  // ASIMD does not support non-temporal loads
-  explicit INT8Vec64(bool, const int8_t* ptr) : INT8Vec64(ptr) {}
-
-  void save(int8_t* ptr) const { vst1q_s8_x4(ptr, reg); }
-
-  // masked store
-  void save(int8_t* p, int elem_num) const {
-    TORCH_CHECK(elem_num <= VEC_ELEM_NUM && elem_num > 0);
-
-    if (elem_num == VEC_ELEM_NUM) {
-      vst1q_s8_x4(p, reg);
-      return;
-    }
-
-    const int full_quadwords = elem_num / 16;
-    const int remaining_bytes = elem_num % 16;
-
-    for (int i = 0; i < full_quadwords; ++i) {
-      vst1q_s8(p + 16 * i, reg.val[i]);
-    }
-
-    if (remaining_bytes) {
-      const int8x16_t v = reg.val[full_quadwords];
-      int8_t* tail = p + 16 * full_quadwords;
-      switch (remaining_bytes) {
-        case 15:
-          tail[14] = vgetq_lane_s8(v, 14);
-          [[fallthrough]];
-        case 14:
-          tail[13] = vgetq_lane_s8(v, 13);
-          [[fallthrough]];
-        case 13:
-          tail[12] = vgetq_lane_s8(v, 12);
-          [[fallthrough]];
-        case 12:
-          tail[11] = vgetq_lane_s8(v, 11);
-          [[fallthrough]];
-        case 11:
-          tail[10] = vgetq_lane_s8(v, 10);
-          [[fallthrough]];
-        case 10:
-          tail[9] = vgetq_lane_s8(v, 9);
-          [[fallthrough]];
-        case 9:
-          tail[8] = vgetq_lane_s8(v, 8);
-          [[fallthrough]];
-        case 8:
-          tail[7] = vgetq_lane_s8(v, 7);
-          [[fallthrough]];
-        case 7:
-          tail[6] = vgetq_lane_s8(v, 6);
-          [[fallthrough]];
-        case 6:
-          tail[5] = vgetq_lane_s8(v, 5);
-          [[fallthrough]];
-        case 5:
-          tail[4] = vgetq_lane_s8(v, 4);
-          [[fallthrough]];
-        case 4:
-          tail[3] = vgetq_lane_s8(v, 3);
-          [[fallthrough]];
-        case 3:
-          tail[2] = vgetq_lane_s8(v, 2);
-          [[fallthrough]];
-        case 2:
-          tail[1] = vgetq_lane_s8(v, 1);
-          [[fallthrough]];
-        case 1:
-          tail[0] = vgetq_lane_s8(v, 0);
-          break;
-        default:
-          break;
-      }
-    }
-  }
-
-  // ASIMD does not support non-temporal stores
-  void nt_save(int8_t* ptr) const { save(ptr); }
-};  // INT8Vec64
-
 template <typename T>
 struct VecType {
   using vec_type = void;

csrc/cpu/shm.cpp

@@ -5,10 +5,6 @@
 #include <sys/stat.h>
 #include <unistd.h>
 
-#ifdef __aarch64__
-  #include <atomic>
-#endif
-
 namespace {
 #define MAX_SHM_RANK_NUM 8
 #define PER_THREAD_SHM_BUFFER_BYTES (4 * 1024 * 1024)
@@ -38,17 +34,8 @@ struct KernelVecType<c10::Half> {
 };
 
 struct ThreadSHMContext {
-#ifdef __aarch64__
-  // memory model is weaker on AArch64, so we use atomic variables for
-  // consumer (load-acquire) and producer (store-release) to make sure
-  // that a stamp cannot be ready before the corresponding data is ready.
-  std::atomic<char> _curr_thread_stamp[2];
-  std::atomic<char> _ready_thread_stamp[2];
-  static_assert(std::atomic<char>::is_always_lock_free);
-#else
   volatile char _curr_thread_stamp[2];
   volatile char _ready_thread_stamp[2];
-#endif  // __aarch64__
   int local_stamp_buffer_idx;
   int remote_stamp_buffer_idx;
   int thread_id;
@@ -75,17 +62,10 @@ struct ThreadSHMContext {
     TORCH_CHECK(group_size <= MAX_SHM_RANK_NUM);
     TORCH_CHECK((size_t)this % 64 == 0);
     TORCH_CHECK((size_t)thread_shm_ptr % 64 == 0);
-#ifdef __aarch64__
-    _curr_thread_stamp[0].store(1, std::memory_order_relaxed);
-    _curr_thread_stamp[1].store(1, std::memory_order_relaxed);
-    _ready_thread_stamp[0].store(0, std::memory_order_relaxed);
-    _ready_thread_stamp[1].store(0, std::memory_order_relaxed);
-#else
     _curr_thread_stamp[0] = 1;
     _curr_thread_stamp[1] = 1;
     _ready_thread_stamp[0] = 0;
     _ready_thread_stamp[1] = 0;
-#endif  // __aarch64__
     _thread_buffer_mask[0] = 0;
     _thread_buffer_mask[1] = 0;
     for (int i = 0; i < MAX_SHM_RANK_NUM; ++i) {
@@ -123,43 +103,19 @@ struct ThreadSHMContext {
     _thread_buffer_mask[local_stamp_buffer_idx] ^= 0xFFFFFFFFFFFFFFFF;
   }
 
-  char get_curr_stamp(int idx) const {
-#ifdef __aarch64__
-    return _curr_thread_stamp[idx].load(std::memory_order_acquire);
-#else
-    return _curr_thread_stamp[idx];
-#endif  // __aarch64__
-  }
+  char get_curr_stamp(int idx) const { return _curr_thread_stamp[idx]; }
 
-  char get_ready_stamp(int idx) const {
-#ifdef __aarch64__
-    return _ready_thread_stamp[idx].load(std::memory_order_acquire);
-#else
-    return _ready_thread_stamp[idx];
-#endif  // __aarch64__
-  }
+  char get_ready_stamp(int idx) const { return _ready_thread_stamp[idx]; }
 
   void next_stamp() {
-#ifdef __aarch64__
-    _curr_thread_stamp[local_stamp_buffer_idx].fetch_add(
-        1, std::memory_order_release);
-#else
     _mm_mfence();
     _curr_thread_stamp[local_stamp_buffer_idx] += 1;
-#endif  // __aarch64__
   }
 
   void commit_ready_stamp() {
-#ifdef __aarch64__
-    _ready_thread_stamp[local_stamp_buffer_idx].store(
-        _curr_thread_stamp[local_stamp_buffer_idx].load(
-            std::memory_order_relaxed),
-        std::memory_order_release);
-#else
     _mm_mfence();
     _ready_thread_stamp[local_stamp_buffer_idx] =
         _curr_thread_stamp[local_stamp_buffer_idx];
-#endif  // __aarch64__
   }
 
   int get_swizzled_rank(int idx) { return swizzled_ranks[idx]; }
@@ -186,11 +142,7 @@ struct ThreadSHMContext {
         break;
       }
       ++_spinning_count;
-#ifdef __aarch64__
-      __asm__ __volatile__("yield");
-#else
       _mm_pause();
-#endif  // __aarch64__
     }
   }
 

csrc/cpu/torch_bindings.cpp

@@ -230,7 +230,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 #endif
 
 // SHM CCL
-#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__))
+#ifdef __AVX512F__
   ops.def("init_shm_manager(str name, int group_size, int rank) -> int",
           &init_shm_manager);
   ops.def("join_shm_manager(int handle, str name) -> str", &join_shm_manager);
@@ -250,7 +250,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("shm_send_tensor_list", torch::kCPU, &shm_send_tensor_list);
   ops.def("shm_recv_tensor_list(int handle, int src) -> Tensor[](a)",
           &shm_recv_tensor_list);
-#endif  // #if defined(__AVX512F__) || defined(__aarch64__)
+#endif
 
   // sgl-kernels
 #if defined(__AVX512BF16__) && defined(__AVX512F__) && defined(__AVX512VNNI__)

csrc/moe/grouped_topk_kernels.cu

@@ -31,6 +31,8 @@ namespace moe {
 
 constexpr unsigned FULL_WARP_MASK = 0xffffffff;
 constexpr int32_t WARP_SIZE = 32;
+constexpr int32_t BLOCK_SIZE = 512;
+constexpr int32_t NUM_WARPS_PER_BLOCK = BLOCK_SIZE / WARP_SIZE;
 
 namespace warp_topk {
 
@@ -63,6 +65,14 @@ __forceinline__ __device__ bool is_better_than(T val, T baseline, idxT index,
   return res;
 }
 
+template <typename T, typename idxT>
+int calc_smem_size_for_block_wide(int num_of_warp, int64_t k) {
+  int64_t cache_topk = (sizeof(T) + sizeof(idxT)) * num_of_warp * k;
+  int64_t n = std::max<int>(num_of_warp / 2 * k, num_of_warp * WARP_SIZE);
+  return max(cache_topk,
+             round_up_to_multiple_of<256>(n * sizeof(T)) + n * sizeof(idxT));
+}
+
 template <int size, bool ascending, bool reverse, typename T, typename idxT,
           bool is_stable>
 struct BitonicMerge {
@@ -257,15 +267,6 @@ class WarpSort {
     }
   }
 
-  // Accessors for per-lane selected value/index.
-  // NOTE: For the common case `capacity == WARP_SIZE`, `max_arr_len_ == 1`
-  // and callers should use `i == 0`.
-  __device__ __forceinline__ idxT get_idx(int i = 0) const {
-    return idx_arr_[i];
-  }
-
-  __device__ __forceinline__ T get_val(int i = 0) const { return val_arr_[i]; }
-
  protected:
   static constexpr int max_arr_len_ = capacity / WARP_SIZE;
 
@@ -284,7 +285,6 @@ class WarpSelect : public WarpSort<capacity, greater, T, idxT, is_stable> {
   __device__ WarpSelect(idxT k, T dummy)
       : WarpSort<capacity, greater, T, idxT, is_stable>(k, dummy),
         k_th_(dummy),
-        k_th_idx_(0),
         k_th_lane_((k - 1) % WARP_SIZE) {
     extern __shared__ char smem_buf[];  // extern __shared__ T smem_buf[];
 
@@ -346,6 +346,9 @@ class WarpSelect : public WarpSort<capacity, greater, T, idxT, is_stable> {
       idxT idx = (lane_ < smem_buf_len_) ? idx_smem_[lane_] : 0;
       merge_buf_(val, idx);
     }
+
+    // after done(), smem is used for merging results among warps
+    __syncthreads();
   }
 
  private:
@@ -500,186 +503,255 @@ __device__ void topk_with_k2(T* output, T const* input, BiasT const* bias,
   }
 }
 
-template <typename T, typename BiasT, typename IdxT, ScoringFunc SF>
-__global__ void grouped_topk_fused_kernel(
-    T* scores, float* topk_values, IdxT* topk_indices, BiasT const* bias,
-    int64_t const num_tokens, int64_t const num_experts, int64_t const n_group,
-    int64_t const topk_group, int64_t const topk, bool renormalize,
+template <typename T, typename BiasT, ScoringFunc SF>
+__global__ void topk_with_k2_kernel(T* output, T* input, BiasT const* bias,
+                                    int64_t const num_tokens,
+                                    int64_t const num_cases,
+                                    int64_t const n_group,
+                                    int64_t const num_experts_per_group) {
+  int32_t warp_id = threadIdx.x / WARP_SIZE;
+  int32_t lane_id = threadIdx.x % WARP_SIZE;
+
+  int32_t case_id = blockIdx.x * NUM_WARPS_PER_BLOCK + warp_id;
+  if (case_id < num_cases) {
+    input += case_id * num_experts_per_group;
+    // bias is per expert group, offset to current group
+    int32_t group_id = case_id % n_group;
+    BiasT const* group_bias = bias + group_id * num_experts_per_group;
+    output += case_id;
+
+    cg::thread_block block = cg::this_thread_block();
+    cg::thread_block_tile<32> tile = cg::tiled_partition<32>(block);
+
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+    asm volatile("griddepcontrol.wait;");
+#endif
+    topk_with_k2<T, BiasT, SF>(output, input, group_bias, tile, lane_id,
+                               num_experts_per_group);
+  }
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+  asm volatile("griddepcontrol.launch_dependents;");
+#endif
+}
+
+template <typename T, typename BiasT, typename IdxT, ScoringFunc SF,
+          int NGroup = -1>
+__global__ void group_idx_and_topk_idx_kernel(
+    T* scores, T const* group_scores, float* topk_values, IdxT* topk_indices,
+    BiasT const* bias, int64_t const num_tokens, int64_t const n_group,
+    int64_t const topk_group, int64_t const topk, int64_t const num_experts,
+    int64_t const num_experts_per_group, bool renormalize,
     double routed_scaling_factor) {
-  int32_t const token_id = static_cast<int32_t>(blockIdx.x);
-  if (token_id >= num_tokens) {
-    return;
-  }
+  int32_t warp_id = threadIdx.x / WARP_SIZE;
+  int32_t lane_id = threadIdx.x % WARP_SIZE;
+  int32_t case_id =
+      blockIdx.x * NUM_WARPS_PER_BLOCK + warp_id;  // one per token
+  scores += case_id * num_experts;
+  group_scores += case_id * n_group;
+  topk_values += case_id * topk;
+  topk_indices += case_id * topk;
 
-  int32_t const warp_id = threadIdx.x / WARP_SIZE;
-  int32_t const lane_id = threadIdx.x % WARP_SIZE;
+  constexpr bool kUseStaticNGroup = (NGroup > 0);
+  // use int32 to avoid implicit conversion
+  int32_t const n_group_i32 =
+      kUseStaticNGroup ? NGroup : static_cast<int32_t>(n_group);
 
-  int32_t const n_group_i32 = static_cast<int32_t>(n_group);
-  int32_t const topk_group_i32 = static_cast<int32_t>(topk_group);
-  int32_t const topk_i32 = static_cast<int32_t>(topk);
-  int32_t const num_experts_i32 = static_cast<int32_t>(num_experts);
-
-  int32_t const num_warps = blockDim.x / WARP_SIZE;
-  if (warp_id >= n_group_i32 || num_warps < n_group_i32) {
-    return;
-  }
-
-  int32_t const num_experts_per_group = num_experts_i32 / n_group_i32;
-
-  T* scores_token = scores + static_cast<int64_t>(token_id) * num_experts;
+  int32_t align_num_experts_per_group =
+      warp_topk::round_up_to_multiple_of<WARP_SIZE>(num_experts_per_group);
 
   cg::thread_block block = cg::this_thread_block();
   cg::thread_block_tile<32> tile = cg::tiled_partition<32>(block);
 
-  extern __shared__ char smem_buf[];
-  // warpSelect internal staging buffer layout
-  size_t const val_bytes =
-      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
-  size_t const val_bytes_aligned =
-      warp_topk::round_up_to_multiple_of<256>(val_bytes);
-  size_t const idx_bytes =
-      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
-  size_t const internal_bytes = val_bytes_aligned + idx_bytes;
+  extern __shared__ char smem_buf[];  // NOTE: reuse the shared memory here to
+                                      // store the target topk idx
+  int32_t* s_topk_idx = reinterpret_cast<int32_t*>(smem_buf);
+  T* s_topk_value =
+      reinterpret_cast<T*>(s_topk_idx + NUM_WARPS_PER_BLOCK * topk) +
+      warp_id * topk;
+  s_topk_idx += warp_id * topk;
 
-  // user-managed shared memory starts after warpSelect internal staging.
-  uintptr_t ptr_u = reinterpret_cast<uintptr_t>(smem_buf + internal_bytes);
-  ptr_u = (ptr_u + 15) & ~static_cast<uintptr_t>(15);  // align to 16B
-  T* s_group_scores = reinterpret_cast<T*>(ptr_u);
+  T value = neg_inf<T>();
+  T topk_group_value = neg_inf<T>();
+  int32_t num_equalto_topkth_group;
 
 #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
   asm volatile("griddepcontrol.wait;");  // I think all prolog can be put before
                                          // acqbulk because it's ptr arithmetic
 #endif
 
-  // phase 1: per-group scan
-  int32_t const group_offset = warp_id * num_experts_per_group;
-  topk_with_k2<T, BiasT, SF>(s_group_scores + warp_id,
-                             scores_token + group_offset, bias + group_offset,
-                             tile, lane_id, num_experts_per_group);
+  if (case_id < num_tokens) {
+    // calculate group_idx
+    int32_t target_num_min =
+        WARP_SIZE - n_group_i32 + static_cast<int32_t>(topk_group);
+    // The check is necessary to avoid abnormal input
+    if (lane_id < n_group_i32 && is_finite(group_scores[lane_id])) {
+      value = group_scores[lane_id];
+    }
+
+    int count_equal_to_top_value = WARP_SIZE - n_group_i32;
+    int pre_count_equal_to_top_value = 0;
+    // Use loop to find the largset top_group
+    while (count_equal_to_top_value < target_num_min) {
+      topk_group_value = cg::reduce(tile, value, cg::greater<T>());
+      if (value == topk_group_value) {
+        value = neg_inf<T>();
+      }
+      pre_count_equal_to_top_value = count_equal_to_top_value;
+      count_equal_to_top_value =
+          __popc(__ballot_sync(FULL_WARP_MASK, (value == neg_inf<T>())));
+    }
+    num_equalto_topkth_group = target_num_min - pre_count_equal_to_top_value;
+  }
+  __syncthreads();
+
+  warp_topk::WarpSelect</*capability*/ WARP_SIZE, /*greater*/ true, T, int32_t,
+                        /* is_stable */ true>
+      queue((int32_t)topk, neg_inf<T>());
+
+  int count_equalto_topkth_group = 0;
+  bool if_proceed_next_topk = topk_group_value != neg_inf<T>();
+  if (case_id < num_tokens && if_proceed_next_topk) {
+    auto process_group = [&](int i_group) {
+      if ((group_scores[i_group] > topk_group_value) ||
+          ((group_scores[i_group] == topk_group_value) &&
+           (count_equalto_topkth_group < num_equalto_topkth_group))) {
+        int32_t offset = i_group * num_experts_per_group;
+        for (int32_t i = lane_id; i < align_num_experts_per_group;
+             i += WARP_SIZE) {
+          T candidates = neg_inf<T>();
+          if (i < num_experts_per_group) {
+            // apply scoring function (if any) and add bias
+            T input = scores[offset + i];
+            if (is_finite(input)) {
+              T score = apply_scoring<SF>(input);
+              candidates = score + static_cast<T>(bias[offset + i]);
+            }
+          }
+          queue.add(candidates, offset + i);
+        }
+        if (group_scores[i_group] == topk_group_value) {
+          count_equalto_topkth_group++;
+        }
+      }
+    };
+
+    if constexpr (kUseStaticNGroup) {
+#pragma unroll
+      for (int i_group = 0; i_group < NGroup; ++i_group) {
+        process_group(i_group);
+      }
+    } else {
+      for (int i_group = 0; i_group < n_group_i32; ++i_group) {
+        process_group(i_group);
+      }
+    }
+    queue.done();
+    // Get the topk_idx
+    queue.dumpIdx(s_topk_idx);
+  }
+
+  // Load the valid score value
+  // Calculate the summation
+  float topk_sum = 1e-20;
+  if (case_id < num_tokens && if_proceed_next_topk) {
+    for (int i = lane_id;
+         i < warp_topk::round_up_to_multiple_of<WARP_SIZE>(topk);
+         i += WARP_SIZE) {
+      T value = cuda_cast<T, float>(0.0f);
+      if (i < topk) {
+        // Load the score value (without bias) for normalization
+        T input = scores[s_topk_idx[i]];
+        value = apply_scoring<SF>(input);
+        s_topk_value[i] = value;
+      }
+      if (renormalize) {
+        topk_sum +=
+            cg::reduce(tile, cuda_cast<float, T>(value), cg::plus<float>());
+      }
+    }
+  }
 
   __syncthreads();
 
-  // phase 2: warp0 selects groups + merges candidates to final topk
-  if (warp_id != 0) {
-    return;
-  }
-
-  topk_values += static_cast<int64_t>(token_id) * topk;
-  topk_indices += static_cast<int64_t>(token_id) * topk;
-
-  // select topk_group groups by group score
-  warp_topk::WarpSelect</*capability*/ WARP_SIZE, /*greater*/ true, T, int32_t,
-                        /* is_stable */ true>
-      group_sel(static_cast<int32_t>(topk_group_i32), neg_inf<T>());
-
-  // all lanes must participate in WarpSelect::add().
-  T gscore = (lane_id < n_group_i32) ? s_group_scores[lane_id] : neg_inf<T>();
-  group_sel.add(gscore, lane_id);
-  group_sel.done();
-
-  // proceed only if the k-th selected group score is not -inf
-  bool proceed = false;
-  if (topk_group_i32 > 0) {
-    int const kth_lane = topk_group_i32 - 1;
-    // broadcast the k-th selected group score to all lanes
-    T kth_val = __shfl_sync(FULL_WARP_MASK, group_sel.get_val(0), kth_lane);
-    proceed = (kth_val != neg_inf<T>());
-  }
-
-  if (!proceed) {
-    for (int i = lane_id; i < topk_i32; i += WARP_SIZE) {
-      topk_indices[i] = static_cast<IdxT>(i);
-      topk_values[i] = 1.0f / static_cast<float>(topk_i32);
-    }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-    asm volatile("griddepcontrol.launch_dependents;");
-#endif
-    return;
-  }
-
-  // merge per-group topk candidates for selected groups, then select topk
-  warp_topk::WarpSelect</*capability*/ WARP_SIZE, /*greater*/ true, T, int32_t,
-                        /* is_stable */ true>
-      expert_sel(static_cast<int32_t>(topk_i32), neg_inf<T>());
-
-  // selected group ids reside in lanes [0, topk_group)
-  int32_t sel_gid_lane = (lane_id < topk_group_i32) ? group_sel.get_idx(0) : 0;
-
-  // add candidates from selected groups to expert_sel
-  for (int32_t g = 0; g < topk_group_i32; ++g) {
-    int32_t gid = __shfl_sync(FULL_WARP_MASK, sel_gid_lane, g);
-    int32_t const offset = gid * num_experts_per_group;
-    int32_t const align_num_experts_per_group =
-        warp_topk::round_up_to_multiple_of<WARP_SIZE>(num_experts_per_group);
-    for (int32_t i = lane_id; i < align_num_experts_per_group; i += WARP_SIZE) {
-      // all lanes must call `add()` the same number of times.
-      T cand = neg_inf<T>();
-      int32_t idx = 0;
-      if (i < num_experts_per_group) {
-        idx = offset + i;
-        T input = scores_token[idx];
-        if (is_finite(input)) {
-          T score = apply_scoring<SF>(input);
-          cand = score + static_cast<T>(bias[idx]);
-        }
+  if (case_id < num_tokens) {
+    if (if_proceed_next_topk) {
+      float scale = routed_scaling_factor;
+      if (renormalize) {
+        scale /= topk_sum;
+      }
+      for (int i = lane_id; i < topk; i += WARP_SIZE) {
+        float base = cuda_cast<float, T>(s_topk_value[i]);
+        float value = base * scale;
+        topk_indices[i] = s_topk_idx[i];
+        topk_values[i] = value;
+      }
+    } else {
+      for (int i = lane_id; i < topk; i += WARP_SIZE) {
+        topk_indices[i] = i;
+        topk_values[i] = 1.0f / topk;
       }
-      expert_sel.add(cand, idx);
     }
+    // Note: when if_proceed_next_topk==false, choose the first 8 experts as the
+    // default result.
   }
-  expert_sel.done();
-
-  // compute unbiased routing weights + optional renorm.
-  float lane_unbiased = 0.0f;
-  IdxT lane_idx = 0;
-  if (lane_id < topk_i32) {
-    lane_idx = static_cast<IdxT>(expert_sel.get_idx(0));
-    T in = scores_token[static_cast<int32_t>(lane_idx)];
-    lane_unbiased = cuda_cast<float, T>(apply_scoring<SF>(in));
-  }
-
-  float topk_sum = 1e-20f;
-  if (renormalize) {
-    topk_sum += cg::reduce(tile, lane_unbiased, cg::plus<float>());
-  }
-
-  float scale = static_cast<float>(routed_scaling_factor);
-  if (renormalize) {
-    scale /= topk_sum;
-  }
-
-  if (lane_id < topk_i32) {
-    topk_indices[lane_id] = lane_idx;
-    topk_values[lane_id] = lane_unbiased * scale;
-  }
-
 #if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
   asm volatile("griddepcontrol.launch_dependents;");
 #endif
 }
 
+template <typename T, typename BiasT, typename IdxT, ScoringFunc SF>
+inline void launch_group_idx_and_topk_kernel(
+    cudaLaunchConfig_t const& config, T* scores, T* group_scores,
+    float* topk_values, IdxT* topk_indices, BiasT const* bias,
+    int64_t const num_tokens, int64_t const n_group, int64_t const topk_group,
+    int64_t const topk, int64_t const num_experts,
+    int64_t const num_experts_per_group, bool const renormalize,
+    double const routed_scaling_factor) {
+  auto launch = [&](auto* kernel_instance2) {
+    cudaLaunchKernelEx(&config, kernel_instance2, scores, group_scores,
+                       topk_values, topk_indices, bias, num_tokens, n_group,
+                       topk_group, topk, num_experts, num_experts_per_group,
+                       renormalize, routed_scaling_factor);
+  };
+
+  switch (n_group) {
+    case 4: {
+      launch(&group_idx_and_topk_idx_kernel<T, BiasT, IdxT, SF, 4>);
+      break;
+    }
+    case 8: {
+      launch(&group_idx_and_topk_idx_kernel<T, BiasT, IdxT, SF, 8>);
+      break;
+    }
+    case 16: {
+      launch(&group_idx_and_topk_idx_kernel<T, BiasT, IdxT, SF, 16>);
+      break;
+    }
+    case 32: {
+      launch(&group_idx_and_topk_idx_kernel<T, BiasT, IdxT, SF, 32>);
+      break;
+    }
+    default: {
+      launch(&group_idx_and_topk_idx_kernel<T, BiasT, IdxT, SF>);
+      break;
+    }
+  }
+}
+
 template <typename T, typename BiasT, typename IdxT>
-void invokeNoAuxTc(T* scores, float* topk_values, IdxT* topk_indices,
-                   BiasT const* bias, int64_t const num_tokens,
-                   int64_t const num_experts, int64_t const n_group,
-                   int64_t const topk_group, int64_t const topk,
-                   bool const renormalize, double const routed_scaling_factor,
-                   int const scoring_func, bool enable_pdl = false,
-                   cudaStream_t const stream = 0) {
+void invokeNoAuxTc(T* scores, T* group_scores, float* topk_values,
+                   IdxT* topk_indices, BiasT const* bias,
+                   int64_t const num_tokens, int64_t const num_experts,
+                   int64_t const n_group, int64_t const topk_group,
+                   int64_t const topk, bool const renormalize,
+                   double const routed_scaling_factor, int const scoring_func,
+                   bool enable_pdl = false, cudaStream_t const stream = 0) {
+  int64_t num_cases = num_tokens * n_group;
+  int64_t topk_with_k2_num_blocks = (num_cases - 1) / NUM_WARPS_PER_BLOCK + 1;
   cudaLaunchConfig_t config;
-  // One block per token; one warp per group.
-  config.gridDim = static_cast<uint32_t>(num_tokens);
-  config.blockDim = static_cast<uint32_t>(n_group) * WARP_SIZE;
-  // Dynamic shared memory: WarpSelect staging + per-group topk buffers.
-  int32_t const num_warps = static_cast<int32_t>(n_group);
-  size_t const val_bytes =
-      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
-  size_t const val_bytes_aligned =
-      warp_topk::round_up_to_multiple_of<256>(val_bytes);
-  size_t const idx_bytes =
-      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
-  size_t const internal_bytes = val_bytes_aligned + idx_bytes;
-  size_t const extra_bytes = 16 + static_cast<size_t>(n_group) * sizeof(T);
-  config.dynamicSmemBytes = internal_bytes + extra_bytes;
+  config.gridDim = topk_with_k2_num_blocks;
+  config.blockDim = BLOCK_SIZE;
+  config.dynamicSmemBytes = 0;
   config.stream = stream;
   cudaLaunchAttribute attrs[1];
   attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
@@ -687,35 +759,66 @@ void invokeNoAuxTc(T* scores, float* topk_values, IdxT* topk_indices,
   config.numAttrs = 1;
   config.attrs = attrs;
   auto const sf = static_cast<ScoringFunc>(scoring_func);
+  int64_t const num_experts_per_group = num_experts / n_group;
+  auto launch_topk_with_k2 = [&](auto* kernel_instance1) {
+    cudaLaunchKernelEx(&config, kernel_instance1, group_scores, scores, bias,
+                       num_tokens, num_cases, n_group, num_experts_per_group);
+  };
   switch (sf) {
     case SCORING_NONE: {
-      auto* kernel_instance =
-          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_NONE>;
-      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                         topk_indices, bias, num_tokens, num_experts, n_group,
-                         topk_group, topk, renormalize, routed_scaling_factor);
-      return;
+      auto* kernel_instance1 = &topk_with_k2_kernel<T, BiasT, SCORING_NONE>;
+      launch_topk_with_k2(kernel_instance1);
+      break;
     }
     case SCORING_SIGMOID: {
-      auto* kernel_instance =
-          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_SIGMOID>;
-      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                         topk_indices, bias, num_tokens, num_experts, n_group,
-                         topk_group, topk, renormalize, routed_scaling_factor);
-      return;
+      auto* kernel_instance1 = &topk_with_k2_kernel<T, BiasT, SCORING_SIGMOID>;
+      launch_topk_with_k2(kernel_instance1);
+      break;
     }
     default:
       // should be guarded by higher level checks.
       TORCH_CHECK(false, "Unsupported scoring_func in invokeNoAuxTc");
   }
+
+  int64_t topk_with_k_group_num_blocks =
+      (num_tokens - 1) / NUM_WARPS_PER_BLOCK + 1;
+  size_t dynamic_smem_in_bytes =
+      warp_topk::calc_smem_size_for_block_wide<T, int32_t>(NUM_WARPS_PER_BLOCK,
+                                                           topk);
+  config.gridDim = topk_with_k_group_num_blocks;
+  config.blockDim = BLOCK_SIZE;
+  config.dynamicSmemBytes = dynamic_smem_in_bytes;
+  config.stream = stream;
+  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
+  attrs[0].val.programmaticStreamSerializationAllowed = enable_pdl;
+  config.numAttrs = 1;
+  config.attrs = attrs;
+  switch (sf) {
+    case SCORING_NONE: {
+      launch_group_idx_and_topk_kernel<T, BiasT, IdxT, SCORING_NONE>(
+          config, scores, group_scores, topk_values, topk_indices, bias,
+          num_tokens, n_group, topk_group, topk, num_experts,
+          num_experts_per_group, renormalize, routed_scaling_factor);
+      break;
+    }
+    case SCORING_SIGMOID: {
+      launch_group_idx_and_topk_kernel<T, BiasT, IdxT, SCORING_SIGMOID>(
+          config, scores, group_scores, topk_values, topk_indices, bias,
+          num_tokens, n_group, topk_group, topk, num_experts,
+          num_experts_per_group, renormalize, routed_scaling_factor);
+      break;
+    }
+    default:
+      TORCH_CHECK(false, "Unsupported scoring_func in invokeNoAuxTc");
+  }
 }
 
-#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT)                                 \
-  template void invokeNoAuxTc<T, BiasT, IdxT>(                               \
-      T * scores, float* topk_values, IdxT* topk_indices, BiasT const* bias, \
-      int64_t const num_tokens, int64_t const num_experts,                   \
-      int64_t const n_group, int64_t const topk_group, int64_t const topk,   \
-      bool const renormalize, double const routed_scaling_factor,            \
+#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT)                                  \
+  template void invokeNoAuxTc<T, BiasT, IdxT>(                                \
+      T * scores, T * group_scores, float* topk_values, IdxT* topk_indices,   \
+      BiasT const* bias, int64_t const num_tokens, int64_t const num_experts, \
+      int64_t const n_group, int64_t const topk_group, int64_t const topk,    \
+      bool const renormalize, double const routed_scaling_factor,             \
       int const scoring_func, bool enable_pdl, cudaStream_t const stream);
 
 INSTANTIATE_NOAUX_TC(float, float, int32_t);
@@ -740,21 +843,17 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
   int64_t num_tokens = input_size[0];
   int64_t num_experts = input_size[1];
   TORCH_CHECK(input_size.size() == 2, "scores must be a 2D Tensor");
-  TORCH_CHECK(n_group > 0, "n_group must be positive");
-  TORCH_CHECK(topk > 0, "topk must be positive");
-  TORCH_CHECK(topk_group > 0, "topk_group must be positive");
-  TORCH_CHECK(topk_group <= n_group, "topk_group must be <= n_group");
   TORCH_CHECK(num_experts % n_group == 0,
               "num_experts should be divisible by n_group");
   TORCH_CHECK(n_group <= 32,
               "n_group should be smaller than or equal to 32 for now");
   TORCH_CHECK(topk <= 32, "topk should be smaller than or equal to 32 for now");
-  TORCH_CHECK(topk <= topk_group * (num_experts / n_group),
-              "topk must be <= topk_group * (num_experts / n_group)");
   TORCH_CHECK(scoring_func == vllm::moe::SCORING_NONE ||
                   scoring_func == vllm::moe::SCORING_SIGMOID,
               "scoring_func must be SCORING_NONE (0) or SCORING_SIGMOID (1)");
 
+  torch::Tensor group_scores = torch::empty(
+      {num_tokens, n_group}, torch::dtype(data_type).device(torch::kCUDA));
   // Always output float32 for topk_values (eliminates Python-side conversion)
   torch::Tensor topk_values = torch::empty(
       {num_tokens, topk}, torch::dtype(torch::kFloat32).device(torch::kCUDA));
@@ -769,6 +868,7 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       case torch::kFloat16:                                                  \
         vllm::moe::invokeNoAuxTc<T, half, IdxT>(                             \
             reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<T*>(group_scores.mutable_data_ptr()),           \
             reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
             reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
             reinterpret_cast<half const*>(bias.data_ptr()), num_tokens,      \
@@ -779,6 +879,7 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       case torch::kFloat32:                                                  \
         vllm::moe::invokeNoAuxTc<T, float, IdxT>(                            \
             reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<T*>(group_scores.mutable_data_ptr()),           \
             reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
             reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
             reinterpret_cast<float const*>(bias.data_ptr()), num_tokens,     \
@@ -789,6 +890,7 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       case torch::kBFloat16:                                                 \
         vllm::moe::invokeNoAuxTc<T, __nv_bfloat16, IdxT>(                    \
             reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<T*>(group_scores.mutable_data_ptr()),           \
             reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
             reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
             reinterpret_cast<__nv_bfloat16 const*>(bias.data_ptr()),         \

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -58,7 +58,7 @@ TEMPLATE = (
     "( MARLIN_KERNEL_PARAMS );"
 )
 
-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)]
 
 THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
 

csrc/moe/marlin_moe_wna16/kernel.h

@@ -3,8 +3,8 @@
   #define MARLIN_NAMESPACE_NAME marlin_moe_wna16
 #endif
 
-#include "quantization/marlin/marlin.cuh"
-#include "quantization/marlin/marlin_dtypes.cuh"
+#include "quantization/gptq_marlin/marlin.cuh"
+#include "quantization/gptq_marlin/marlin_dtypes.cuh"
 #include "core/scalar_type.hpp"
 
 #define MARLIN_KERNEL_PARAMS                                          \

csrc/moe/marlin_moe_wna16/marlin_template.h

@@ -23,10 +23,10 @@
   #define MARLIN_NAMESPACE_NAME marlin_moe_wna16
 #endif
 
-#include "quantization/marlin/marlin.cuh"
-#include "quantization/marlin/marlin_dtypes.cuh"
-#include "quantization/marlin/dequant.h"
-#include "quantization/marlin/marlin_mma.h"
+#include "quantization/gptq_marlin/marlin.cuh"
+#include "quantization/gptq_marlin/marlin_dtypes.cuh"
+#include "quantization/gptq_marlin/dequant.h"
+#include "quantization/gptq_marlin/marlin_mma.h"
 #include "core/scalar_type.hpp"
 
 #define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t)               \

csrc/moe/marlin_moe_wna16/ops.cu

@@ -126,16 +126,14 @@ thread_config_t small_batch_thread_configs[] = {
 
     // thread_k, thread_n, num_threads
     {128, 128, 256},
-    {64, 128, 128},
-    {128, 64, 128}};
+    {64, 128, 128}};
 
 thread_config_t large_batch_thread_configs[] = {
     // Ordered by priority
 
     // thread_k, thread_n, num_threads
     {64, 256, 256},
-    {64, 128, 128},
-    {128, 64, 128}};
+    {64, 128, 128}};
 
 typedef struct {
   int blocks_per_sm;

csrc/moe/moe_ops.h

@@ -4,13 +4,7 @@
 
 void topk_softmax(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
                   torch::Tensor& token_expert_indices,
-                  torch::Tensor& gating_output, bool renormalize,
-                  std::optional<torch::Tensor> bias);
-
-void topk_sigmoid(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
-                  torch::Tensor& token_expert_indices,
-                  torch::Tensor& gating_output, bool renormalize,
-                  std::optional<torch::Tensor> bias);
+                  torch::Tensor& gating_output, bool renormalize);
 
 void moe_sum(torch::Tensor& input, torch::Tensor& output);
 

csrc/moe/moe_permute_unpermute_op.cu

@@ -42,7 +42,7 @@ void moe_permute(
   auto sort_workspace = torch::empty(
       {sorter_size},
       torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
-  torch::Tensor topk_ids_for_sort = topk_ids;
+  auto copy_topk_ids = topk_ids.clone();  // copy topk_ids for preprocess
   auto permuted_experts_id = torch::empty_like(topk_ids);
   auto sorted_row_idx = torch::empty_like(inv_permuted_idx);
 
@@ -62,13 +62,12 @@ void moe_permute(
     const int* expert_map_ptr = get_ptr<int>(expert_map.value());
     valid_num_ptr =
         get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
-    topk_ids_for_sort = topk_ids.clone();
-    preprocessTopkIdLauncher(get_ptr<int>(topk_ids_for_sort), n_token * topk,
+    preprocessTopkIdLauncher(get_ptr<int>(copy_topk_ids), n_token * topk,
                              expert_map_ptr, n_expert, stream);
   }
   // expert sort topk expert id and scan expert id get expert_first_token_offset
   sortAndScanExpert(
-      get_ptr<const int>(topk_ids_for_sort), get_ptr<int>(token_expert_indices),
+      get_ptr<int>(copy_topk_ids), get_ptr<int>(token_expert_indices),
       get_ptr<int>(permuted_experts_id), get_ptr<int>(sorted_row_idx),
       get_ptr<int64_t>(expert_first_token_offset), n_token, n_expert,
       n_local_expert, topk, sorter, get_ptr<int>(sort_workspace), stream);

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -109,7 +109,7 @@ void computeExpertFirstTokenOffset(int const* sorted_indices,
       sorted_indices, total_indices, num_experts, expert_first_token_offset);
 }
 
-void sortAndScanExpert(const int* expert_for_source_row, const int* source_rows,
+void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
                        int* permuted_experts, int* permuted_rows,
                        int64_t* expert_first_token_offset, int num_rows,
                        int num_experts, int num_experts_per_node, int k,

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.h

@@ -48,7 +48,7 @@ void computeExpertFirstTokenOffset(int const* sorted_indices,
                                    int64_t* expert_first_token_offset,
                                    cudaStream_t stream);
 
-void sortAndScanExpert(const int* expert_for_source_row, const int* source_rows,
+void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
                        int* permuted_experts, int* permuted_rows,
                        int64_t* expert_first_token_offset, int num_rows,
                        int num_experts, int num_experts_per_node, int k,

csrc/moe/topk_softmax_kernels.cu

@@ -62,12 +62,6 @@ __device__ __forceinline__ float toFloat(T value) {
     }
 }
 
-// Scoring function enums
-enum ScoringFunc {
-  SCORING_SOFTMAX = 0, // apply softmax
-  SCORING_SIGMOID = 1  // apply sigmoid
-};
-
 // ====================== Softmax things ===============================
 // We have our own implementation of softmax here so we can support transposing the output
 // in the softmax kernel when we extend this module to support expert-choice routing.
@@ -131,27 +125,6 @@ __launch_bounds__(TPB) __global__
     }
 }
 
-template <int TPB, typename InputType>
-__launch_bounds__(TPB) __global__
-    void moeSigmoid(const InputType* input, const bool* finished, float* output, const int num_cols)
-{
-    const int thread_row_offset = blockIdx.x * num_cols;
-
-    // Don't touch finished rows.
-    if ((finished != nullptr) && finished[blockIdx.x])
-    {
-        return;
-    }
-
-    for (int ii = threadIdx.x; ii < num_cols; ii += TPB)
-    {
-        const int idx = thread_row_offset + ii;
-        const float val = toFloat(input[idx]);
-        const float sigmoid_val = 1.0f / (1.0f + __expf(-val));
-        output[idx] = sigmoid_val;
-    }
-}
-
 template <int TPB, typename IndType>
 __launch_bounds__(TPB) __global__ void moeTopK(
     const float* inputs_after_softmax,
@@ -163,8 +136,7 @@ __launch_bounds__(TPB) __global__ void moeTopK(
     const int k,
     const int start_expert,
     const int end_expert,
-    const bool renormalize,
-    const float* bias)
+    const bool renormalize)
 {
 
     using cub_kvp = cub::KeyValuePair<int, float>;
@@ -190,13 +162,7 @@ __launch_bounds__(TPB) __global__ void moeTopK(
         {
             const int idx = thread_read_offset + expert;
             inp_kvp.key = expert;
-
-            // Apply correction bias if provided
-            if (bias != nullptr) {
-              inp_kvp.value = inputs_after_softmax[idx] + bias[expert];
-            } else {
-              inp_kvp.value = inputs_after_softmax[idx];
-            }
+            inp_kvp.value = inputs_after_softmax[idx];
 
             for (int prior_k = 0; prior_k < k_idx; ++prior_k)
             {
@@ -220,13 +186,12 @@ __launch_bounds__(TPB) __global__ void moeTopK(
             const bool should_process_row = row_is_active && node_uses_expert;
 
             const int idx = k * block_row + k_idx;
-            // Return the unbiased scores for output weights
-            output[idx] = inputs_after_softmax[thread_read_offset + expert];
+            output[idx] = result_kvp.value;
             indices[idx] = should_process_row ? (expert - start_expert) : num_experts;
             assert(indices[idx] >= 0);
             source_rows[idx] = k_idx * num_rows + block_row;
             if (renormalize) {
-                selected_sum += inputs_after_softmax[thread_read_offset + expert];
+                selected_sum += result_kvp.value;
             }
         }
         __syncthreads();
@@ -260,12 +225,10 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   2) This implementation assumes k is small, but will work for any k.
 */
 
-template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename IndType,
-          typename InputType = float, ScoringFunc SF>
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename IndType, typename InputType = float>
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
-    void topkGating(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
-        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-        const float* bias)
+    void topkGatingSoftmax(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
+        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize)
 {
     static_assert(std::is_same_v<InputType, float> || std::is_same_v<InputType, __nv_bfloat16> ||
                       std::is_same_v<InputType, __half>,
@@ -390,89 +353,61 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
         }
     }
 
-    if constexpr (SF == SCORING_SOFTMAX) {
-      // First, we perform a max reduce within the thread.
-      float thread_max = row_chunk[0];
+    // First, we perform a max reduce within the thread. We can do the max in fp16 safely (I think) and just
+    // convert to float afterwards for the exp + sum reduction.
+    float thread_max = row_chunk[0];
 #pragma unroll
-      for (int ii = 1; ii < VPT; ++ii) {
+    for (int ii = 1; ii < VPT; ++ii)
+    {
         thread_max = max(thread_max, row_chunk[ii]);
-      }
+    }
 
 // Now, we find the max within the thread group and distribute among the threads. We use a butterfly reduce.
 #pragma unroll
-      for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2)
-      {
+    for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2)
+    {
         thread_max = max(thread_max, VLLM_SHFL_XOR_SYNC_WIDTH(thread_max, mask, THREADS_PER_ROW));
-      }
+    }
 
-      // From this point, thread max in all the threads have the max within the row.
-      // Now, we subtract the max from each element in the thread and take the exp. We also compute the thread local sum.
-      float row_sum = 0;
+    // From this point, thread max in all the threads have the max within the row.
+    // Now, we subtract the max from each element in the thread and take the exp. We also compute the thread local sum.
+    float row_sum = 0;
 #pragma unroll
-      for (int ii = 0; ii < VPT; ++ii)
-      {
+    for (int ii = 0; ii < VPT; ++ii)
+    {
         row_chunk[ii] = expf(row_chunk[ii] - thread_max);
         row_sum += row_chunk[ii];
-      }
+    }
 
 // Now, we perform the sum reduce within each thread group. Similar to the max reduce, we use a bufferfly pattern.
 #pragma unroll
-      for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2)
-      {
+    for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2)
+    {
         row_sum += VLLM_SHFL_XOR_SYNC_WIDTH(row_sum, mask, THREADS_PER_ROW);
-      }
+    }
 
-      // From this point, all threads have the max and the sum for their rows in the thread_max and thread_sum variables
-      // respectively. Finally, we can scale the rows for the softmax. Technically, for top-k gating we don't need to
-      // compute the entire softmax row. We can likely look at the maxes and only compute for the top-k values in the row.
-      // However, this kernel will likely not be a bottle neck and it seems better to closer match torch and find the
-      // argmax after computing the softmax.
-      const float reciprocal_row_sum = 1.f / row_sum;
+    // From this point, all threads have the max and the sum for their rows in the thread_max and thread_sum variables
+    // respectively. Finally, we can scale the rows for the softmax. Technically, for top-k gating we don't need to
+    // compute the entire softmax row. We can likely look at the maxes and only compute for the top-k values in the row.
+    // However, this kernel will likely not be a bottle neck and it seems better to closer match torch and find the
+    // argmax after computing the softmax.
+    const float reciprocal_row_sum = 1.f / row_sum;
 
 #pragma unroll
-      for (int ii = 0; ii < VPT; ++ii)
-      {
+    for (int ii = 0; ii < VPT; ++ii)
+    {
         row_chunk[ii] = row_chunk[ii] * reciprocal_row_sum;
-      }
-    } else if constexpr (SF == SCORING_SIGMOID) {
-#pragma unroll
-      for (int ii = 0; ii < VPT; ++ii)
-      {
-        row_chunk[ii] = 1.0f / (1.0f + __expf(-row_chunk[ii]));
-      }
     }
 
-    static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
-
-    // If bias is not null, use biased value for selection
-    float row_chunk_for_choice[VPT];
-    // Apply correction bias
-    if (bias != nullptr) {
-#pragma unroll
-      for (int ldg = 0; ldg < LDG_PER_THREAD; ++ldg) {
-#pragma unroll
-        for (int ii = 0; ii < ELTS_PER_LDG; ++ii) {
-          const int expert = first_elt_read_by_thread + ldg * COLS_PER_GROUP_LDG + ii;
-          float bias_val = expert < NUM_EXPERTS ? bias[expert] : 0.0f;
-          row_chunk_for_choice[ldg * ELTS_PER_LDG + ii] = row_chunk[ldg * ELTS_PER_LDG + ii] + bias_val;
-        }
-      }
-    } else {
-#pragma unroll
-      for (int ii = 0; ii < VPT; ++ii) {
-        row_chunk_for_choice[ii] = row_chunk[ii];
-      }
-    }
-
-    // Now, row_chunk contains the softmax / sigmoid of the row chunk. Now, I want to find the topk elements in each row, along
+    // Now, softmax_res contains the softmax of the row chunk. Now, I want to find the topk elements in each row, along
     // with the max index.
     int start_col = first_elt_read_by_thread;
+    static constexpr int COLS_PER_GROUP_LDG = ELTS_PER_LDG * THREADS_PER_ROW;
 
     float selected_sum = 0.f;
     for (int k_idx = 0; k_idx < k; ++k_idx)
     {
         // First, each thread does the local argmax
-        float max_val_for_choice = row_chunk_for_choice[0];
         float max_val = row_chunk[0];
         int expert = start_col;
 #pragma unroll
@@ -481,14 +416,12 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
 #pragma unroll
             for (int ii = 0; ii < ELTS_PER_LDG; ++ii)
             {
-                float val_for_choice = row_chunk_for_choice[ldg * ELTS_PER_LDG + ii];
                 float val = row_chunk[ldg * ELTS_PER_LDG + ii];
 
                 // No check on the experts here since columns with the smallest index are processed first and only
                 // updated if > (not >=)
-                if (val_for_choice > max_val_for_choice)
+                if (val > max_val)
                 {
-                    max_val_for_choice = val_for_choice;
                     max_val = val;
                     expert = col + ii;
                 }
@@ -501,14 +434,12 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
 #pragma unroll
         for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2)
         {
-            float other_max_for_choice = VLLM_SHFL_XOR_SYNC_WIDTH(max_val_for_choice, mask, THREADS_PER_ROW);
             float other_max = VLLM_SHFL_XOR_SYNC_WIDTH(max_val, mask, THREADS_PER_ROW);
             int other_expert = VLLM_SHFL_XOR_SYNC_WIDTH(expert, mask, THREADS_PER_ROW);
 
             // We want lower indices to "win" in every thread so we break ties this way
-            if (other_max_for_choice > max_val_for_choice || (other_max_for_choice == max_val_for_choice && other_expert < expert))
+            if (other_max > max_val || (other_max == max_val && other_expert < expert))
             {
-                max_val_for_choice = other_max_for_choice;
                 max_val = other_max;
                 expert = other_expert;
             }
@@ -543,7 +474,7 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
             {
                 const int offset_for_expert = expert % ELTS_PER_LDG;
                 // Safe to set to any negative value since row_chunk values must be between 0 and 1.
-                row_chunk_for_choice[ldg_group_for_expert * ELTS_PER_LDG + offset_for_expert] = -10000.f;
+                row_chunk[ldg_group_for_expert * ELTS_PER_LDG + offset_for_expert] = -10000.f;
             }
         }
     }
@@ -577,10 +508,10 @@ struct TopkConstants
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType, ScoringFunc SF>
-void topkGatingLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType>
+void topkGatingSoftmaxLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
     int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-    const float* bias, cudaStream_t stream)
+    cudaStream_t stream)
 {
     static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
     using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
@@ -590,51 +521,43 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
     const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
 
     dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
-    topkGating<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM, IndType, InputType, SF><<<num_blocks, block_dim, 0, stream>>>(
-        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize, bias);
+    topkGatingSoftmax<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM, IndType, InputType><<<num_blocks, block_dim, 0, stream>>>(
+        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize);
 }
 
 #ifndef USE_ROCM
-  #define LAUNCH_TOPK(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                   \
-    static_assert(WARP_SIZE == 32,                                            \
-                  "Unsupported warp size. Only 32 is supported for CUDA");    \
-    topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES, \
-                             IndType, InputType, SF>(                         \
-        gating_output, nullptr, topk_weights, topk_indices,                   \
-        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,  \
-        bias, stream);
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                          \
+    static_assert(WARP_SIZE == 32,                                                    \
+                  "Unsupported warp size. Only 32 is supported for CUDA");            \
+    topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES>( \
+        gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,     \
+        num_tokens, topk, 0, num_experts, renormalize, stream);
 #else
-  #define LAUNCH_TOPK(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                    \
-    if (WARP_SIZE == 64) {                                                     \
-      topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64, MAX_BYTES,       \
-                               IndType, InputType, SF>(                        \
-          gating_output, nullptr, topk_weights, topk_indices,                  \
-          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, stream);                                                       \
-    } else if (WARP_SIZE == 32) {                                              \
-      topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES,       \
-                               IndType, InputType, SF>(                        \
-          gating_output, nullptr, topk_weights, topk_indices,                  \
-          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, stream);                                                       \
-    } else {                                                                   \
-      assert(false &&                                                          \
-             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                             \
+    if (WARP_SIZE == 64) {                                                               \
+        topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64, MAX_BYTES>(       \
+            gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,    \
+            num_tokens, topk, 0, num_experts, renormalize, stream);                      \
+    } else if (WARP_SIZE == 32) {                                                        \
+        topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES>(       \
+            gating_output, nullptr, topk_weights, topk_indices, token_expert_indices,    \
+            num_tokens, topk, 0, num_experts, renormalize, stream);                      \
+    } else {                                                                             \
+        assert(false && "Unsupported warp size. Only 32 and 64 are supported for ROCm"); \
     }
 #endif
 
-template <typename IndType, typename InputType, ScoringFunc SF>
-void topkGatingKernelLauncher(
+template <typename IndType, typename InputType>
+void topkGatingSoftmaxKernelLauncher(
     const InputType* gating_output,
     float* topk_weights,
     IndType* topk_indices,
     int* token_expert_indices,
-    float* workspace,
+    float* softmax_workspace,
     const int num_tokens,
     const int num_experts,
     const int topk,
     const bool renormalize,
-    const float* bias,
     cudaStream_t stream) {
     static constexpr int WARPS_PER_TB = 4;
     static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
@@ -646,71 +569,64 @@ void topkGatingKernelLauncher(
 #endif
     switch (num_experts) {
         case 1:
-            LAUNCH_TOPK(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 2:
-            LAUNCH_TOPK(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 4:
-            LAUNCH_TOPK(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 8:
-            LAUNCH_TOPK(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 16:
-            LAUNCH_TOPK(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 32:
-            LAUNCH_TOPK(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 64:
-            LAUNCH_TOPK(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 128:
-            LAUNCH_TOPK(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 256:
-            LAUNCH_TOPK(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 512:
-            LAUNCH_TOPK(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            LAUNCH_SOFTMAX(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         // (CUDA only) support multiples of 64 when num_experts is not power of 2.
         // ROCm uses WARP_SIZE 64 so 8 bytes loading won't fit for some of num_experts,
         // alternatively we can test 4 bytes loading and enable it in future.
 #ifndef USE_ROCM
         case 192:
-            LAUNCH_TOPK(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            LAUNCH_SOFTMAX(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
             break;
         case 320:
-            LAUNCH_TOPK(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            LAUNCH_SOFTMAX(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
             break;
         case 384:
-            LAUNCH_TOPK(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            LAUNCH_SOFTMAX(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
             break;
         case 448:
-            LAUNCH_TOPK(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            LAUNCH_SOFTMAX(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
             break;
         case 576:
-            LAUNCH_TOPK(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            LAUNCH_SOFTMAX(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
             break;
 #endif
         default: {
-            TORCH_CHECK(workspace != nullptr,
-                "workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
+            TORCH_CHECK(softmax_workspace != nullptr,
+                "softmax_workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
             static constexpr int TPB = 256;
-            if constexpr (SF == SCORING_SOFTMAX) {
-              moeSoftmax<TPB, InputType><<<num_tokens, TPB, 0, stream>>>(
-                gating_output, nullptr, workspace, num_experts);
-            } else if constexpr (SF == SCORING_SIGMOID) {
-              moeSigmoid<TPB, InputType><<<num_tokens, TPB, 0, stream>>>(
-                gating_output, nullptr, workspace, num_experts);
-            } else {
-                TORCH_CHECK(false, "Unsupported scoring func");
-            }
+            moeSoftmax<TPB, InputType><<<num_tokens, TPB, 0, stream>>>(
+                gating_output, nullptr, softmax_workspace, num_experts);
             moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
-                workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
-                num_experts, topk, 0, num_experts, renormalize, bias);
+                softmax_workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
+                num_experts, topk, 0, num_experts, renormalize);
         }
     }
 }
@@ -719,55 +635,40 @@ void topkGatingKernelLauncher(
 } // namespace vllm
 
 
-template<typename ComputeType, vllm::moe::ScoringFunc SF>
-void dispatch_topk_launch(
+template<typename ComputeType>
+void dispatch_topk_softmax_launch(
     torch::Tensor& gating_output,
     torch::Tensor& topk_weights,
     torch::Tensor& topk_indices,
     torch::Tensor& token_expert_indices,
     torch::Tensor& softmax_workspace,
-    int num_tokens, int num_experts, int topk, bool renormalize,
-    std::optional<torch::Tensor> bias,
-    cudaStream_t stream)
- {
-    const float* bias_ptr = nullptr;
-    if (bias.has_value()) {
-      const torch::Tensor& bias_tensor = bias.value();
-      TORCH_CHECK(bias_tensor.scalar_type() == at::ScalarType::Float, "bias tensor must be float32");
-      TORCH_CHECK(bias_tensor.dim() == 1, "bias tensor must be 1D");
-      TORCH_CHECK(bias_tensor.size(0) == num_experts, "bias size mismatch, expected: ", num_experts);
-      TORCH_CHECK(bias_tensor.is_contiguous(), "bias tensor must be contiguous");
-      bias_ptr = bias_tensor.data_ptr<float>();
-    }
-
+    int num_tokens, int num_experts, int topk, bool renormalize, cudaStream_t stream)
+{
     if (topk_indices.scalar_type() == at::ScalarType::Int) {
-        vllm::moe::topkGatingKernelLauncher<int, ComputeType, SF>(
+        vllm::moe::topkGatingSoftmaxKernelLauncher<int, ComputeType>(
             reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
             topk_weights.data_ptr<float>(),
             topk_indices.data_ptr<int>(),
             token_expert_indices.data_ptr<int>(),
             softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, stream);
+            num_tokens, num_experts, topk, renormalize, stream);
     } else if (topk_indices.scalar_type() == at::ScalarType::UInt32) {
-        vllm::moe::topkGatingKernelLauncher<uint32_t, ComputeType, SF>(
+        vllm::moe::topkGatingSoftmaxKernelLauncher<uint32_t, ComputeType>(
             reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
             topk_weights.data_ptr<float>(),
             topk_indices.data_ptr<uint32_t>(),
             token_expert_indices.data_ptr<int>(),
             softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, stream);
+            num_tokens, num_experts, topk, renormalize, stream);
     } else {
         TORCH_CHECK(topk_indices.scalar_type() == at::ScalarType::Long);
-        vllm::moe::topkGatingKernelLauncher<int64_t, ComputeType, SF>(
+        vllm::moe::topkGatingSoftmaxKernelLauncher<int64_t, ComputeType>(
             reinterpret_cast<const ComputeType*>(gating_output.data_ptr()),
             topk_weights.data_ptr<float>(),
             topk_indices.data_ptr<int64_t>(),
             token_expert_indices.data_ptr<int>(),
             softmax_workspace.data_ptr<float>(),
-            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, stream);
+            num_tokens, num_experts, topk, renormalize, stream);
     }
 }
 
@@ -776,8 +677,7 @@ void topk_softmax(
     torch::Tensor& topk_indices,                // [num_tokens, topk]
     torch::Tensor& token_expert_indices,        // [num_tokens, topk]
     torch::Tensor& gating_output,               // [num_tokens, num_experts]
-    bool renormalize,
-    std::optional<torch::Tensor> bias)
+    bool renormalize)
 {
     const int num_experts = gating_output.size(-1);
     const auto num_tokens = gating_output.numel() / num_experts;
@@ -793,55 +693,14 @@ void topk_softmax(
     torch::Tensor softmax_workspace = torch::empty({workspace_size}, workspace_options);
 
     if (gating_output.scalar_type() == at::ScalarType::Float) {
-        dispatch_topk_launch<float, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
+        dispatch_topk_softmax_launch<float>(gating_output, topk_weights, topk_indices, 
+            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
     } else if (gating_output.scalar_type() == at::ScalarType::Half) {
-        dispatch_topk_launch<__half, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
+        dispatch_topk_softmax_launch<__half>(gating_output, topk_weights, topk_indices, 
+            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
     } else if (gating_output.scalar_type() == at::ScalarType::BFloat16) {
-        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
-    } else {
-        TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
-    }
-}
-
-void topk_sigmoid(
-    torch::Tensor& topk_weights,                // [num_tokens, topk]
-    torch::Tensor& topk_indices,                // [num_tokens, topk]
-    torch::Tensor& token_expert_indices,        // [num_tokens, topk]
-    torch::Tensor& gating_output,               // [num_tokens, num_experts]
-    bool renormalize,
-    std::optional<torch::Tensor> bias)
-{
-    const int num_experts = gating_output.size(-1);
-    const auto num_tokens = gating_output.numel() / num_experts;
-    const int topk = topk_weights.size(-1);
-
-    const bool is_pow_2 = (num_experts != 0) && ((num_experts & (num_experts - 1)) == 0);
-    const bool needs_workspace = !is_pow_2 || num_experts > 256;
-    const int64_t workspace_size = needs_workspace ? num_tokens * num_experts : 0;
-
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
-    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-    const auto workspace_options = gating_output.options().dtype(at::ScalarType::Float);
-    torch::Tensor workspace = torch::empty({workspace_size}, workspace_options);
-
-    if (gating_output.scalar_type() == at::ScalarType::Float) {
-        dispatch_topk_launch<float, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
-    } else if (gating_output.scalar_type() == at::ScalarType::Half) {
-        dispatch_topk_launch<__half, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
-    } else if (gating_output.scalar_type() == at::ScalarType::BFloat16) {
-        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
-            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, stream);
+        dispatch_topk_softmax_launch<__nv_bfloat16>(gating_output, topk_weights, topk_indices, 
+            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize, stream);
     } else {
         TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
     }

csrc/moe/torch_bindings.cpp

@@ -5,17 +5,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
   // Apply topk softmax to the gating outputs.
   m.def(
       "topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
-      "token_expert_indices, Tensor gating_output, bool renormalize, Tensor? "
-      "bias) -> ()");
+      "token_expert_indices, Tensor gating_output, bool renormalize) -> ()");
   m.impl("topk_softmax", torch::kCUDA, &topk_softmax);
 
-  // Apply topk sigmoid to the gating outputs.
-  m.def(
-      "topk_sigmoid(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
-      "token_expert_indices, Tensor gating_output, bool renormalize, Tensor? "
-      "bias) -> ()");
-  m.impl("topk_sigmoid", torch::kCUDA, &topk_sigmoid);
-
   // Calculate the result of moe by summing up the partial results
   // from all selected experts.
   m.def("moe_sum(Tensor input, Tensor! output) -> ()");

csrc/ops.h

@@ -260,6 +260,12 @@ void get_cutlass_moe_mm_data(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
+void get_cutlass_moe_mm_problem_sizes(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
+    std::optional<bool> force_swap_ab = std::nullopt);
+
 void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
     const torch::Tensor& expert_first_token_offset,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
@@ -293,8 +299,7 @@ std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a);
 
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
                       torch::Tensor& output_scale,
-                      torch::Tensor const& input_scale,
-                      bool is_sf_swizzled_layout);
+                      torch::Tensor const& input_scale);
 
 void scaled_fp4_experts_quant(
     torch::Tensor& output, torch::Tensor& output_scale,

csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu

@@ -27,24 +27,17 @@
 
 #include "cuda_utils.h"
 #include "launch_bounds_utils.h"
-
-// Define before including nvfp4_utils.cuh so the header
-// can use this macro during compilation.
-#define NVFP4_ENABLE_ELTS16 1
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
 
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
-__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
-    silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols,
-                             int32_t num_padded_cols,
-                             Type const* __restrict__ in,
-                             float const* __restrict__ SFScale,
-                             uint32_t* __restrict__ out,
-                             uint32_t* __restrict__ SFout) {
-  using PackedVec = vllm::PackedVec<Type>;
+__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
+    silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
+                             float const* SFScale, uint32_t* out,
+                             uint32_t* SFout) {
+  using PackedVec = PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -56,60 +49,34 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
-  float const SFScaleVal = (SFScale == nullptr) ? 1.0f : SFScale[0];
-
-  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
-  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
+  float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[0];
 
   // Input tensor row/col loops.
   for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    if (colIdx < num_padded_cols) {
-      PackedVec in_vec;
-      PackedVec in_vec2;
+    for (int colIdx = threadIdx.x; colIdx < numCols / CVT_FP4_ELTS_PER_THREAD;
+         colIdx += blockDim.x) {
       int64_t inOffset =
           rowIdx * (numCols * 2 / CVT_FP4_ELTS_PER_THREAD) + colIdx;
       int64_t inOffset2 = rowIdx * (numCols * 2 / CVT_FP4_ELTS_PER_THREAD) +
                           numCols / CVT_FP4_ELTS_PER_THREAD + colIdx;
+      PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+      PackedVec in_vec2 = reinterpret_cast<PackedVec const*>(in)[inOffset2];
 
-      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
-      if constexpr (CVT_FP4_PACK16) {
-        ld256_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-            valid);
-        ld256_or_zero_cg_u32<Type>(
-            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 8],
-            valid);
-      } else {
-        ld128_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-            valid);
-        ld128_or_zero_cg_u32<Type>(
-            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 4],
-            valid);
-      }
+      // Get the output tensor offset.
+      // Same as inOffset because 8 elements are packed into one uint32_t.
+      int64_t outOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
+      auto& out_pos = out[outOffset];
 
       // Compute silu and mul
-      PackedVec out_silu_mul = compute_silu_mul<Type>(in_vec, in_vec2);
+      PackedVec out_silu_mul = compute_silu_mul(in_vec, in_vec2);
 
       auto sf_out =
           cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
                                              CVT_FP4_NUM_THREADS_PER_SF>(
               rowIdx, colIdx, numKTiles, SFout);
 
-      auto out_val =
-          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
-              out_silu_mul, SFScaleVal, sf_out);
-
-      if (valid) {
-        if constexpr (CVT_FP4_PACK16) {
-          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
-          uint64_t packed64 =
-              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
-          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
-        } else {
-          out[inOffset] = out_val;
-        }
-      }
+      out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(out_silu_mul, SFScaleVal,
+                                                     sf_out);
     }
   }
 }
@@ -136,23 +103,17 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,  // [..., d]
   auto output_ptr = static_cast<int64_t*>(output.data_ptr());
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
-  dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
+  dim3 block(std::min(int(n / ELTS_PER_THREAD), 1024));
   int const numBlocksPerSM =
       vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
-
-  int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
-
-  int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
-  int grid_x = std::min(
-      int(m), std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
-  dim3 grid(grid_x, grid_y);
+  dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
 
   VLLM_DISPATCH_HALF_TYPES(
       input.scalar_type(), "silu_and_mul_nvfp4_quant_kernel", [&] {
         using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
         auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
         vllm::silu_mul_cvt_fp16_to_fp4<cuda_type><<<grid, block, 0, stream>>>(
-            m, n, sf_n_unpadded, input_ptr, input_sf_ptr,
+            m, n, input_ptr, input_sf_ptr,
             reinterpret_cast<uint32_t*>(output_ptr),
             reinterpret_cast<uint32_t*>(sf_out));
       });

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -140,8 +140,8 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
                                            CVT_FP4_NUM_THREADS_PER_SF>(
             rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos = cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
-        quant_input, SFScaleVal, sf_out);
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
   }
 }
 
@@ -246,8 +246,8 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
                                            CVT_FP4_NUM_THREADS_PER_SF>(
             rowIdx_in_expert, colIdx, numKTiles, SFout_in_expert);
 
-    out_pos = cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
-        quant_input, SFScaleVal, sf_out);
+    out_pos =
+        cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(quant_input, SFScaleVal, sf_out);
   }
 }
 

csrc/quantization/fp4/nvfp4_quant_entry.cu

@@ -21,8 +21,7 @@
 void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
                              torch::Tensor const& input,
                              torch::Tensor const& output_sf,
-                             torch::Tensor const& input_sf,
-                             bool is_sf_swizzled_layout);
+                             torch::Tensor const& input_sf);
 #endif
 
 #if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
@@ -52,12 +51,10 @@ void silu_and_mul_scaled_fp4_experts_quant_sm1xxa(
 #endif
 
 void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
-                      torch::Tensor& output_sf, torch::Tensor const& input_sf,
-                      bool is_sf_swizzled_layout) {
+                      torch::Tensor& output_sf, torch::Tensor const& input_sf) {
 #if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
     (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
-  return scaled_fp4_quant_sm1xxa(output, input, output_sf, input_sf,
-                                 is_sf_swizzled_layout);
+  return scaled_fp4_quant_sm1xxa(output, input, output_sf, input_sf);
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(false, "No compiled nvfp4 quantization kernel");
 }

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -27,23 +27,29 @@
 
 #include "cuda_utils.h"
 #include "launch_bounds_utils.h"
-
-// Define before including nvfp4_utils.cuh so the header
-// can use this macro during compilation.
-#define NVFP4_ENABLE_ELTS16 1
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
 
+template <typename Int>
+__host__ __device__ inline Int round_up(Int x, Int y) {
+  static_assert(std::is_integral_v<Int>,
+                "round_up argument must be integral type");
+  return ((x + y - 1) / y) * y;
+}
+
+// Compute effective rows for grid configuration with swizzled SF layouts.
+inline int computeEffectiveRows(int m) {
+  constexpr int ROW_TILE = 128;
+  return round_up(m, ROW_TILE);
+}
+
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
 __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
-    cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, int32_t num_padded_cols,
-                    Type const* __restrict__ in,
-                    float const* __restrict__ SFScale,
-                    uint32_t* __restrict__ out, uint32_t* __restrict__ SFout) {
-  using PackedVec = vllm::PackedVec<Type>;
-
+    cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
+                    float const* SFScale, uint32_t* out, uint32_t* SFout) {
+  using PackedVec = PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -53,31 +59,33 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   int32_t const numKTiles = (numCols + 63) / 64;
 
   int sf_m = round_up<int>(numRows, 128);
-  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
-  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
+  int sf_n_unpadded = numCols / CVT_FP4_SF_VEC_SIZE;
+  int sf_n_int = round_up<int>(sf_n_unpadded, 4) / 4;
+  int num_padded_cols = sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE;
 
   // Get the global scaling factor, which will be applied to the SF.
   // Note SFScale is the same as next GEMM's alpha, which is
   // (448.f / (Alpha_A / 6.f)).
-  float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
+  float const global_scale = SFScale == nullptr ? 1.0f : SFScale[0];
 
   // Iterate over all rows and cols including padded ones -
   //  ensures we visit every single scale factor address to initialize it.
   for (int rowIdx = blockIdx.x; rowIdx < sf_m; rowIdx += gridDim.x) {
-    if (colIdx < num_padded_cols) {
+    for (int colIdx = threadIdx.x;
+         colIdx < num_padded_cols / CVT_FP4_ELTS_PER_THREAD;
+         colIdx += blockDim.x) {
+      int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
+
       PackedVec in_vec;
       int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
 
       // If we are outside valid rows OR outside valid columns -> Use Zeros
-      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
-      if constexpr (CVT_FP4_PACK16) {
-        ld256_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-            valid);
+      if (rowIdx >= numRows || elem_idx >= numCols) {
+        memset(&in_vec, 0, sizeof(PackedVec));
+
       } else {
-        ld128_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-            valid);
+        // Valid Region: Load actual data
+        in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
       }
 
       auto sf_out =
@@ -86,85 +94,13 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
               rowIdx, colIdx, numKTiles, SFout);
 
       auto out_val =
-          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
-              in_vec, global_scale, sf_out);
+          cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, global_scale, sf_out);
 
       // We do NOT write output for padding because the 'out' tensor is not
       // padded.
-      if (valid) {
-        if constexpr (CVT_FP4_PACK16) {
-          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
-          uint64_t packed64 =
-              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
-          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
-        } else {
-          out[inOffset] = out_val;
-        }
-      }
-    }
-  }
-}
-
-// Use UE4M3 by default.
-template <class Type, bool UE8M0_SF = false>
-__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
-    cvt_fp16_to_fp4_sf_major(int32_t numRows, int32_t numCols,
-                             int32_t sf_n_unpadded, Type const* __restrict__ in,
-                             float const* __restrict__ SFScale,
-                             uint32_t* __restrict__ out,
-                             uint32_t* __restrict__ SFout) {
-  using PackedVec = PackedVec<Type>;
-
-  static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
-      (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
-  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
-                "Vec size is not matched.");
-
-  int32_t const colIdx = blockDim.x * blockIdx.y + threadIdx.x;
-  int elem_idx = colIdx * CVT_FP4_ELTS_PER_THREAD;
-
-  // Get the global scaling factor, which will be applied to the SF.
-  // Note SFScale is the same as next GEMM's alpha, which is
-  // (448.f / (Alpha_A / 6.f)).
-  float const global_scale = (SFScale == nullptr) ? 1.0f : SFScale[0];
-
-  // Iterate over all rows and cols including padded ones -
-  //  ensures we visit every single scale factor address to initialize it.
-  for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    if (colIdx < sf_n_unpadded) {
-      PackedVec in_vec;
-      int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
-
-      // If we are outside valid rows OR outside valid columns -> Use Zeros
-      bool valid = (rowIdx < numRows) && (elem_idx < numCols);
-      if constexpr (CVT_FP4_PACK16) {
-        ld256_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-            valid);
-      } else {
-        ld128_or_zero_cg_u32<Type>(
-            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-            valid);
-      }
-
-      auto sf_out =
-          sf_out_rowmajor_u8<uint32_t>(rowIdx, colIdx, sf_n_unpadded, SFout);
-
-      auto out_val =
-          cvt_warp_fp16_to_fp4<Type, CVT_FP4_NUM_THREADS_PER_SF, UE8M0_SF>(
-              in_vec, global_scale, sf_out);
-
-      // We do NOT write output for padding because the 'out' tensor is not
-      // padded.
-      if (valid) {
-        if constexpr (CVT_FP4_PACK16) {
-          int64_t outOffset = rowIdx * (numCols / 8) + colIdx * 2;
-          uint64_t packed64 =
-              (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
-          reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
-        } else {
-          out[inOffset] = out_val;
-        }
+      if (rowIdx < numRows && elem_idx < numCols) {
+        // Same as inOffset because 8 elements are packed into one uint32_t.
+        out[inOffset] = out_val;
       }
     }
   }
@@ -175,8 +111,7 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
 void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
                              torch::Tensor const& input,
                              torch::Tensor const& output_sf,
-                             torch::Tensor const& input_sf,
-                             bool is_sf_swizzled_layout) {
+                             torch::Tensor const& input_sf) {
   int32_t m = input.size(0);
   int32_t n = input.size(1);
 
@@ -194,48 +129,19 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
 
-  int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
-
   // Grid, Block size. Each thread converts 8 values.
   dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
   int const numBlocksPerSM =
       vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
+  int effectiveRows = vllm::computeEffectiveRows(m);
+  dim3 grid(std::min(effectiveRows, multiProcessorCount * numBlocksPerSM));
 
-  if (is_sf_swizzled_layout) {
-    int sf_n_int = int(vllm::round_up(sf_n_unpadded, 4) / 4);
-    int32_t num_padded_cols =
-        sf_n_int * 4 * CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD;
-
-    int grid_y = vllm::div_round_up(num_padded_cols, static_cast<int>(block.x));
-    int grid_x =
-        std::min(vllm::computeEffectiveRows(m),
-                 std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
-    dim3 grid(grid_x, grid_y);
-
-    VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
-      using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
-      auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
-      // NOTE: We don't support e8m0 scales at this moment.
-      vllm::cvt_fp16_to_fp4<cuda_type, false><<<grid, block, 0, stream>>>(
-          m, n, num_padded_cols, input_ptr, input_sf_ptr,
-          reinterpret_cast<uint32_t*>(output_ptr),
-          reinterpret_cast<uint32_t*>(sf_out));
-    });
-  } else {
-    int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
-    int grid_x = std::min(
-        m, std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
-    dim3 grid(grid_x, grid_y);
-
-    VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
-      using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
-      auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
-      // NOTE: We don't support e8m0 scales at this moment.
-      vllm::cvt_fp16_to_fp4_sf_major<cuda_type, false>
-          <<<grid, block, 0, stream>>>(m, n, sf_n_unpadded, input_ptr,
-                                       input_sf_ptr,
-                                       reinterpret_cast<uint32_t*>(output_ptr),
-                                       reinterpret_cast<uint32_t*>(sf_out));
-    });
-  }
-}
+  VLLM_DISPATCH_HALF_TYPES(input.scalar_type(), "nvfp4_quant_kernel", [&] {
+    using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
+    auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
+    // NOTE: We don't support e8m0 scales at this moment.
+    vllm::cvt_fp16_to_fp4<cuda_type, false><<<grid, block, 0, stream>>>(
+        m, n, input_ptr, input_sf_ptr, reinterpret_cast<uint32_t*>(output_ptr),
+        reinterpret_cast<uint32_t*>(sf_out));
+  });
+}

csrc/quantization/fp4/nvfp4_utils.cuh

@@ -19,17 +19,9 @@
 #include <cuda_runtime.h>
 #include <cuda_fp8.h>
 
-#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
-     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
-  #define ELTS_PER_THREAD 16
-constexpr int CVT_FP4_ELTS_PER_THREAD = 16;
-constexpr bool CVT_FP4_PACK16 = true;
-#else
-  #define ELTS_PER_THREAD 8
-constexpr int CVT_FP4_ELTS_PER_THREAD = 8;
-constexpr bool CVT_FP4_PACK16 = false;
-#endif
+#define ELTS_PER_THREAD 8
 
+constexpr int CVT_FP4_ELTS_PER_THREAD = 8;
 constexpr int CVT_FP4_SF_VEC_SIZE = 16;
 
 namespace vllm {
@@ -76,46 +68,19 @@ struct TypeConverter<__nv_bfloat16> {
   using Type = __nv_bfloat162;
 };
 
-#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
-     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
-// Define a 32 bytes packed data type.
-template <class Type>
-struct alignas(32) PackedVec {
-  typename TypeConverter<Type>::Type elts[8];
-};
-#else
 // Define a 16 bytes packed data type.
 template <class Type>
-struct alignas(16) PackedVec {
+struct PackedVec {
   typename TypeConverter<Type>::Type elts[4];
 };
-#endif
 
 template <>
 struct PackedVec<__nv_fp8_e4m3> {
   __nv_fp8x2_e4m3 elts[8];
 };
 
-template <typename Int>
-__host__ __device__ inline Int round_up(Int x, Int y) {
-  static_assert(std::is_integral_v<Int>,
-                "round_up argument must be integral type");
-  return ((x + y - 1) / y) * y;
-}
-
-template <typename Int>
-__host__ __device__ __forceinline__ Int div_round_up(Int x, Int y) {
-  return (x + y - 1) / y;
-}
-
-// Compute effective rows for grid configuration with swizzled SF layouts.
-inline int computeEffectiveRows(int m) {
-  constexpr int ROW_TILE = 128;
-  return round_up(m, ROW_TILE);
-}
-
 // Convert 8 float32 values into 8 e2m1 values (represented as one uint32_t).
-inline __device__ uint32_t fp32_vec8_to_e2m1(float (&array)[8]) {
+inline __device__ uint32_t fp32_vec_to_e2m1(float (&array)[8]) {
   uint32_t val;
   asm volatile(
       "{\n"
@@ -136,7 +101,7 @@ inline __device__ uint32_t fp32_vec8_to_e2m1(float (&array)[8]) {
 }
 
 // Convert 4 float2 values into 8 e2m1 values (represented as one uint32_t).
-__device__ __forceinline__ uint32_t fp32_vec8_to_e2m1(float2 (&array)[4]) {
+inline __device__ uint32_t fp32_vec_to_e2m1(float2 (&array)[4]) {
   uint32_t val;
   asm volatile(
       "{\n"
@@ -149,115 +114,20 @@ __device__ __forceinline__ uint32_t fp32_vec8_to_e2m1(float2 (&array)[4]) {
       "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
       "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
       "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
-      "}\n"
+      "}"
       : "=r"(val)
       : "f"(array[0].x), "f"(array[0].y), "f"(array[1].x), "f"(array[1].y),
         "f"(array[2].x), "f"(array[2].y), "f"(array[3].x), "f"(array[3].y));
   return val;
 }
 
-struct u32x2 {
-  uint32_t lo, hi;
-};
-
-using fp4_packed_t = std::conditional_t<CVT_FP4_PACK16, u32x2, uint32_t>;
-
-__device__ __forceinline__ u32x2 fp32_vec16_to_e2m1(float2 (&array)[8]) {
-  u32x2 out;
-  asm volatile(
-      "{\n"
-      ".reg .b8 b0;\n"
-      ".reg .b8 b1;\n"
-      ".reg .b8 b2;\n"
-      ".reg .b8 b3;\n"
-      ".reg .b8 b4;\n"
-      ".reg .b8 b5;\n"
-      ".reg .b8 b6;\n"
-      ".reg .b8 b7;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b0,  %3,  %2;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b1,  %5,  %4;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b2,  %7,  %6;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b3,  %9,  %8;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b4, %11, %10;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b5, %13, %12;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b6, %15, %14;\n"
-      "cvt.rn.satfinite.e2m1x2.f32   b7, %17, %16;\n"
-      "mov.b32 %0, {b0, b1, b2, b3};\n"
-      "mov.b32 %1, {b4, b5, b6, b7};\n"
-      "}\n"
-      : "=r"(out.lo), "=r"(out.hi)
-      : "f"(array[0].x), "f"(array[0].y), "f"(array[1].x), "f"(array[1].y),
-        "f"(array[2].x), "f"(array[2].y), "f"(array[3].x), "f"(array[3].y),
-        "f"(array[4].x), "f"(array[4].y), "f"(array[5].x), "f"(array[5].y),
-        "f"(array[6].x), "f"(array[6].y), "f"(array[7].x), "f"(array[7].y));
-  return out;
-}
-
-__device__ __forceinline__ uint32_t pack_fp4(float2 (&v)[4]) {
-  return fp32_vec8_to_e2m1(v);
-}
-
-__device__ __forceinline__ u32x2 pack_fp4(float2 (&v)[8]) {
-  return fp32_vec16_to_e2m1(v);
-}
-
 // Fast reciprocal.
-__device__ __forceinline__ float reciprocal_approximate_ftz(float a) {
+inline __device__ float reciprocal_approximate_ftz(float a) {
   float b;
-  asm volatile("rcp.approx.ftz.f32 %0, %1;" : "=f"(b) : "f"(a));
+  asm volatile("rcp.approx.ftz.f32 %0, %1;\n" : "=f"(b) : "f"(a));
   return b;
 }
 
-template <class Type>
-__device__ __forceinline__ void ld128_or_zero_cg_u32(PackedVec<Type>& out,
-                                                     const void* ptr,
-                                                     bool pred) {
-  uint32_t r0, r1, r2, r3;
-
-  asm volatile(
-      "{\n"
-      "  .reg .pred pr;\n"
-      "  setp.ne.u32 pr, %4, 0;\n"
-      "  mov.u32 %0, 0;\n"
-      "  mov.u32 %1, 0;\n"
-      "  mov.u32 %2, 0;\n"
-      "  mov.u32 %3, 0;\n"
-      "  @pr ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%5];\n"
-      "}\n"
-      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3)
-      : "r"((int)pred), "l"(ptr));
-
-  *reinterpret_cast<uint4*>(&out) = uint4{r0, r1, r2, r3};
-}
-
-template <class Type>
-__device__ __forceinline__ void ld256_or_zero_cg_u32(PackedVec<Type>& out,
-                                                     const void* ptr,
-                                                     bool pred) {
-  uint32_t r0, r1, r2, r3, r4, r5, r6, r7;
-
-  asm volatile(
-      "{\n"
-      "  .reg .pred pr;\n"
-      "  setp.ne.u32 pr, %8, 0;\n"
-      "  mov.u32 %0, 0;\n"
-      "  mov.u32 %1, 0;\n"
-      "  mov.u32 %2, 0;\n"
-      "  mov.u32 %3, 0;\n"
-      "  mov.u32 %4, 0;\n"
-      "  mov.u32 %5, 0;\n"
-      "  mov.u32 %6, 0;\n"
-      "  mov.u32 %7, 0;\n"
-      "  @pr ld.global.cg.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%9];\n"
-      "}\n"
-      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3), "=r"(r4), "=r"(r5), "=r"(r6),
-        "=r"(r7)
-      : "r"((int)pred), "l"(ptr));
-
-  reinterpret_cast<uint4*>(&out)[0] = uint4{r0, r1, r2, r3};
-  reinterpret_cast<uint4*>(&out)[1] = uint4{r4, r5, r6, r7};
-}
-
 // Compute SF output offset for swizzled tensor core layout.
 // SF layout: [numMTiles, numKTiles, 32, 4, 4]
 // Caller must precompute: numKTiles = (numCols + 63) / 64
@@ -296,41 +166,21 @@ __device__ __forceinline__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(
   return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
 }
 
-template <class SFType>
-__device__ __forceinline__ uint8_t* sf_out_rowmajor_u8(int row, int pack,
-                                                       int packs_per_row_sf,
-                                                       SFType* SFout) {
-  constexpr int PACK = CVT_FP4_ELTS_PER_THREAD;
-  constexpr int THREADS_PER_SF =
-      CVT_FP4_SF_VEC_SIZE / PACK;  // 1 if PACK=16, 2 else PACK=8
-
-  if (threadIdx.x % THREADS_PER_SF != 0) return nullptr;
-
-  int sf_col =
-      pack / THREADS_PER_SF;  // PACK=16 => sf_col=pack; PACK=8 => sf_col=pack/2
-  int64_t off = (int64_t)row * packs_per_row_sf + sf_col;
-
-  return (uint8_t*)SFout + off;
-}
-
 // Quantizes the provided PackedVec into the uint32_t output
-template <class Type, int CVT_FP4_NUM_THREADS_PER_SF, bool UE8M0_SF = false>
-__device__ __forceinline__ fp4_packed_t
-cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
+template <class Type, bool UE8M0_SF = false>
+__device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
+                                         uint8_t* SFout) {
   // Get absolute maximum values among the local 8 values.
   auto localMax = __habs2(vec.elts[0]);
 
-  // Local maximum value.
+// Local maximum value.
 #pragma unroll
   for (int i = 1; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
     localMax = __hmax2(localMax, __habs2(vec.elts[i]));
   }
 
   // Get the absolute maximum among all 16 values (two threads).
-
-  if constexpr (CVT_FP4_NUM_THREADS_PER_SF == 2) {
-    localMax = __hmax2(__shfl_xor_sync(0xffffffffu, localMax, 1), localMax);
-  }
+  localMax = __hmax2(__shfl_xor_sync(uint32_t(-1), localMax, 1), localMax);
   // Get the final absolute maximum values.
   float vecMax = float(__hmax(localMax.x, localMax.y));
 
@@ -355,17 +205,18 @@ cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
     // Convert back to fp32.
     SFValue = float(tmp);
   }
-
-  // Write the SF to global memory (STG.8).
-  if (SFout) *SFout = fp8SFVal;
-
   // Get the output scale.
   // Recipe: final_scale = reciprocal(fp32(fp8(SFValue * SFScaleVal))) *
   //                       reciprocal(SFScaleVal))
   float outputScale =
-      SFValue != 0.0f ? reciprocal_approximate_ftz(
-                            SFValue * reciprocal_approximate_ftz(SFScaleVal))
-                      : 0.0f;
+      SFValue != 0 ? reciprocal_approximate_ftz(
+                         SFValue * reciprocal_approximate_ftz(SFScaleVal))
+                   : 0.0f;
+
+  if (SFout) {
+    // Write the SF to global memory (STG.8).
+    *SFout = fp8SFVal;
+  }
 
   // Convert the input to float.
   float2 fp2Vals[CVT_FP4_ELTS_PER_THREAD / 2];
@@ -382,7 +233,10 @@ cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
   }
 
   // Convert to e2m1 values.
-  return pack_fp4(fp2Vals);
+  uint32_t e2m1Vec = fp32_vec_to_e2m1(fp2Vals);
+
+  // Write the e2m1 values to global memory.
+  return e2m1Vec;
 }
 
 // silu in float32

csrc/quantization/gptq_allspark/allspark_utils.cuh

@@ -7,7 +7,7 @@
 #include <cuda_fp16.h>
 #include <cuda_bf16.h>
 #include <iostream>
-#include "../marlin/marlin_dtypes.cuh"
+#include "../gptq_marlin/marlin_dtypes.cuh"
 using marlin::MarlinScalarType2;
 
 namespace allspark {

csrc/quantization/gptq_marlin/generate_kernels.py

@@ -70,6 +70,15 @@ QUANT_CONFIGS = [
         "thread_m_blocks": THREAD_M_BLOCKS,
         "group_blocks": [-1, 2, 4, 8],
     },
+    # HQQ
+    {
+        "a_type": ["kFloat16"],
+        "b_type": "kU4",
+        "thread_configs": THREAD_CONFIGS,
+        "thread_m_blocks": THREAD_M_BLOCKS,
+        "group_blocks": [4],
+        "is_zp_float": True,
+    },
     # GPTQ-INT4
     {
         "b_type": "kU4B8",

csrc/quantization/gptq_marlin/gptq_marlin.cu

@@ -46,7 +46,7 @@ __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
 
 }  // namespace marlin
 
-torch::Tensor marlin_gemm(
+torch::Tensor gptq_marlin_gemm(
     torch::Tensor& a, std::optional<torch::Tensor> c_or_none,
     torch::Tensor& b_q_weight,
     std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
@@ -528,7 +528,7 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* b_bias,
 
 }  // namespace marlin
 
-torch::Tensor marlin_gemm(
+torch::Tensor gptq_marlin_gemm(
     torch::Tensor& a, std::optional<torch::Tensor> c_or_none,
     torch::Tensor& b_q_weight,
     std::optional<torch::Tensor> const& b_bias_or_none, torch::Tensor& b_scales,
@@ -856,5 +856,5 @@ torch::Tensor marlin_gemm(
 #endif
 
 TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("marlin_gemm", &marlin_gemm);
+  m.impl("gptq_marlin_gemm", &gptq_marlin_gemm);
 }

csrc/quantization/w8a8/cutlass/moe/moe_data.cu

@@ -130,6 +130,26 @@ inline void launch_compute_problem_sizes(const torch::Tensor& topk_ids,
 }
 }  // namespace
 
+void get_cutlass_moe_mm_problem_sizes_caller(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
+    std::optional<bool> force_swap_ab = std::nullopt) {
+  auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
+  auto options_int32 =
+      torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
+  torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
+
+  // Swap-AB should be disabled for FP4 path
+  bool may_swap_ab =
+      force_swap_ab.value_or((!blockscale_offsets.has_value()) &&
+                             (topk_ids.numel() <= SWAP_AB_THRESHOLD));
+
+  launch_compute_problem_sizes(topk_ids, problem_sizes1, problem_sizes2,
+                               atomic_buffer, num_experts, n, k, stream,
+                               may_swap_ab);
+}
+
 template <bool SWAP_AB>
 __global__ void compute_problem_sizes_from_expert_offsets(
     const int64_t* __restrict__ expert_first_token_offset,

csrc/quantization/w8a8/cutlass/scaled_mm_entry.cu

@@ -77,6 +77,12 @@ void get_cutlass_moe_mm_data_caller(
     const int64_t num_experts, const int64_t n, const int64_t k,
     const std::optional<torch::Tensor>& blockscale_offsets);
 
+void get_cutlass_moe_mm_problem_sizes_caller(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
+    std::optional<bool> force_swap_ab = std::nullopt);
+
 void get_cutlass_moe_mm_problem_sizes_from_expert_offsets_caller(
     const torch::Tensor& expert_first_token_offset,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
@@ -300,6 +306,27 @@ void get_cutlass_moe_mm_data(
       version_num, ". Required capability: 90, 100, or 120");
 }
 
+void get_cutlass_moe_mm_problem_sizes(
+    const torch::Tensor& topk_ids, torch::Tensor& problem_sizes1,
+    torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
+    const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets,
+    std::optional<bool> force_swap_ab = std::nullopt) {
+  int32_t version_num = get_sm_version_num();
+#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
+    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
+    (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
+  get_cutlass_moe_mm_problem_sizes_caller(topk_ids, problem_sizes1,
+                                          problem_sizes2, num_experts, n, k,
+                                          blockscale_offsets, force_swap_ab);
+  return;
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled get_cutlass_moe_mm_problem_sizes: no cutlass_scaled_mm "
+      "kernel for CUDA device capability: ",
+      version_num, ". Required capability: 90, 100, or 120");
+}
+
 void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
     const torch::Tensor& expert_first_token_offset,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,

csrc/rocm/ops.h

@@ -9,10 +9,6 @@ torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
                        const std::optional<at::Tensor>& in_bias,
                        const int64_t CuCount);
 
-torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
-                         const std::optional<at::Tensor>& in_bias,
-                         const int64_t CuCount);
-
 void wvSplitKQ(const at::Tensor& in_a, const at::Tensor& in_b,
                const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
                const at::Tensor& scale_a, const at::Tensor& scale_b,

csrc/rocm/skinny_gemms.cu

@@ -13,13 +13,6 @@
 #include "dispatch_utils.h"
 #include "quantization/w8a8/fp8/common.cuh"
 
-// TODO(rasmith): The kernels in this file are susceptible to integer overflow
-// issues, do not take strides, and are unable to handle PyTorch tensors that
-// return is_contiguous() as False (the tensors may actually be contiguous
-// in memory).
-//
-// However, it may be possible to fix these kernels to handle both issues.
-
 #if defined(__HIPCC__) && \
     (defined(__gfx90a__) || defined(__gfx942__) || defined(__gfx950__))
   #define __HIP__GFX9__
@@ -294,11 +287,6 @@ torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
     V0 += (s.x + s.y);                                                        \
   }
 
-// To avoid LLVM silently upcasting to double
-__device__ inline unsigned int min__(uint32_t a, uint32_t b) {
-  return min(a, b);
-}
-
 #if defined(__HIP__GFX9__)  // TODO: Add NAVI support
 // This version targets cases where A[] fits LDS capacity
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
@@ -346,11 +334,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min__(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -645,11 +633,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min__(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -966,11 +954,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   //----------------------------------------------------
   #define PCML
   #ifndef PCML
-  for (uint32_t k = 0; k < min__(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, max_lds_len);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min__(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, max_lds_len)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -987,7 +975,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
              ? kFit
              : (kFit - kFit % TUC);  // round up to multiple of TUC
   // if (kFit == 0) kFit = TUC;
-  kFit = min__(kFit, K);
+  kFit = min(kFit, K);
 
   float sum[N][YTILE];
   scalar8 sum4[N][YTILE];
@@ -1263,7 +1251,6 @@ int mindiv(int N, int div1, int div2) {
   }
   for (int i = 12; i >= 0; i--)
     if (rnds[0] == rnds[i]) return (div2 - i);
-  return 0;
 }
 
 torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
@@ -1365,536 +1352,6 @@ torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
   return out_c;
 }
 
-#if defined(__gfx950__)  // TODO: Add NAVI support
-  // This version targets big A[] cases, where it is much larger than LDS
-  // capacity
-  #define WVSPLITKRC_1KPASS
-template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
-          int UNRL, int N, int GrpsShrB>
-
-__global__ void __launch_bounds__(WvPrGrp* THRDS)
-    __attribute__((amdgpu_waves_per_eu(1, 1)))
-    wvSplitKrc_(const int actlN, const int K, const int M, const int Bx,
-                const int By, const scalar_t* __restrict__ B,
-                const scalar_t* __restrict__ A,
-                const scalar_t* __restrict__ BIAS, float* glbl, scalar_t* C,
-                const int CuCount) {
-  // Use upper half of glbl buffer for atomic reduce counting
-  int* cntr = (int*)(&glbl[M * N]);
-
-  constexpr int NTILE = 16;
-  constexpr int WVLDS_ = (NTILE * THRDS * A_CHUNK);
-  constexpr int APAD = 1;
-  constexpr int ASTRD = 64;
-  constexpr int BPAD = 1;
-  constexpr int BSTRD = 64;
-  constexpr int WVLDS = ((WVLDS_ + (WVLDS_ / BSTRD) * 4 * BPAD));
-
-  constexpr int max_lds_len = LDS_SIZE / 2;
-
-  using scalar16 =
-      __attribute__((__vector_size__((A_CHUNK * 2) * sizeof(float)))) float;
-  using scalar8 =
-      __attribute__((__vector_size__((A_CHUNK / 2) * sizeof(float)))) float;
-  using half4 =
-      __attribute__((__vector_size__((A_CHUNK / 2) * sizeof(__bf16)))) __bf16;
-  union bigType {
-    scalar_t h[A_CHUNK];
-    float f[A_CHUNK / 2];
-    unsigned int i[A_CHUNK / 2];
-    float2 f2[A_CHUNK / 4];
-    unsigned long l[A_CHUNK / 4];
-    double d[A_CHUNK / 4];
-    half4 h4[A_CHUNK / 4];
-    scalar8 h8;
-  };
-  using big4 = __attribute__((__vector_size__(4 * sizeof(bigType)))) __bf16;
-
-  __shared__ scalar_t stg[WvPrGrp * WVLDS / GrpsShrB];
-  unsigned int* myStg = (unsigned int*)(&stg[WVLDS * (threadIdx.y / GrpsShrB)]);
-  __shared__ scalar_t s[max_lds_len - WvPrGrp * WVLDS / GrpsShrB];
-
-  #ifndef WVSPLITKRC_1KPASS
-  constexpr int TUC_ = (THRDS * UNRL * A_CHUNK);
-  // find biggest k size that fits padded into LDS
-  constexpr uint32_t kFit__ = (max_lds_len - WvPrGrp * WVLDS / GrpsShrB) / N;
-  constexpr uint32_t kFit_ = (kFit__ * ASTRD) / (APAD + ASTRD);
-  uint32_t kFit = kFit_ - (kFit_ % TUC_);
-  uint32_t kfitsPerRdc = (K + kFit - 1) / kFit;
-
-  // find best k split to fill the CUs
-  if (((K + kfitsPerRdc * kFit - 1) / (kfitsPerRdc * kFit)) * numCuWithFullK <=
-      CuCount)
-    while (true) {
-      while (kFit > TUC_) {
-        uint32_t kFit_ = kFit - TUC_;
-        if (((K + (kfitsPerRdc * kFit_ - 1)) / (kfitsPerRdc * kFit_)) *
-                numCuWithFullK >
-            CuCount)
-          break;
-        kFit = kFit_;
-      }
-      if (((K + ((kfitsPerRdc - 1) * kFit - 1)) / ((kfitsPerRdc - 1) * kFit)) *
-              numCuWithFullK <=
-          CuCount)
-        kfitsPerRdc--;
-      else
-        break;
-    }
-  #else
-  int constexpr kFit = 512;
-  int constexpr kfitsPerRdc = 1;
-  #endif
-
-  bool doRdc = (kfitsPerRdc * kFit < K);
-  uint32_t numCuWithFullK =
-      ((M + (WvPrGrp * YTILE / GrpsShrB) - 1) / (WvPrGrp * YTILE / GrpsShrB));
-  uint32_t Mmod = numCuWithFullK * (WvPrGrp * YTILE / GrpsShrB);
-
-  // given above k-split, find this wave's position
-  uint32_t kFitPdd = kFit + (kFit / ASTRD) * APAD;
-  uint32_t m0 = (blockIdx.x * WvPrGrp / GrpsShrB) * YTILE;
-  uint32_t m1 = ((threadIdx.y % WvPrGrp) / GrpsShrB) * YTILE;
-  uint32_t m = (m0 + m1) % Mmod;
-  const uint32_t k_str = (m0 / Mmod) * kFit * kfitsPerRdc;
-  uint32_t k_end = (m0 / Mmod + 1) * kFit * kfitsPerRdc;
-  const uint32_t k_rnd = (K + kFit * kfitsPerRdc - 1) / (kFit * kfitsPerRdc);
-
-  scalar8 sum4[N / NTILE / GrpsShrB][1];
-  bigType bigB_[YTILE / GrpsShrB][UNRL];
-  const uint32_t bLoader = (threadIdx.y % GrpsShrB);
-  uint32_t kBase = 0;
-  if (k_str >= K) return;
-  if (m >= Mmod) return;
-
-  bool noreloada = false;
-  constexpr bool FAST_UNSAFE_RDC_INIT = false;
-
-  #ifdef WVSPLITKRC_1KPASS
-  // Early glbl init, B[] loading, if 1KPASS
-  if constexpr (FAST_UNSAFE_RDC_INIT) {
-    if (m + (threadIdx.x % 16) < M)
-      if (doRdc)
-        if (k_str == 0) {
-          int mindx = m + (threadIdx.x % 16);
-          int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
-                       (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-          int adr_ = mindx + M * nindx_ / 4;
-          __hip_atomic_store(&cntr[adr_], 0, __ATOMIC_RELAXED,
-                             __HIP_MEMORY_SCOPE_AGENT);
-          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-            for (uint32_t j = 0; j < 4; j++) {
-              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-              int adr = mindx + M * nindx;
-              __hip_atomic_store(&glbl[adr], 0, __ATOMIC_RELAXED,
-                                 __HIP_MEMORY_SCOPE_AGENT);
-            }
-          }
-        }
-  }
-
-    // Load first B[] chunk
-    #pragma unroll
-  for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-    uint32_t k = k_str + k2 * THRDS * A_CHUNK;
-    uint32_t k_ = k + threadIdx.x * A_CHUNK;
-    const scalar_t* B_ = &B[min__(k_, K - A_CHUNK)];
-    #pragma unroll
-    for (uint32_t y = 0; y < YTILE / GrpsShrB; y++)
-      bigB_[y][k2].h8 = (loadnt(
-          (scalar8*)(&B_[min__(y * GrpsShrB + bLoader + m, M - 1) * K])));
-  }
-  {
-  #else
-  while (m < Mmod) {
-  #endif
-
-  #ifndef WVSPLITKRC_1KPASS
-    if constexpr (FAST_UNSAFE_RDC_INIT) {
-      if (m + (threadIdx.x % 16) < M)
-        if (doRdc)
-          if (k_str == 0) {
-            int mindx = m + (threadIdx.x % 16);
-            int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
-                         (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-            int adr_ = mindx + M * nindx_ / 4;
-            __hip_atomic_store(&cntr[adr_], 0, __ATOMIC_RELAXED,
-                               __HIP_MEMORY_SCOPE_AGENT);
-            for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-              for (uint32_t j = 0; j < 4; j++) {
-                int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                            (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-                int adr = mindx + M * nindx;
-                __hip_atomic_store(&glbl[adr], 0, __ATOMIC_RELAXED,
-                                   __HIP_MEMORY_SCOPE_AGENT);
-              }
-            }
-          }
-    }
-
-  #endif
-
-  #ifndef WVSPLITKRC_1KPASS
-    for (uint32_t k1 = k_str; k1 < k_end; k1 += THRDS * A_CHUNK * UNRL) {
-  #else
-    const uint32_t k1 = k_str;
-    {
-  #endif
-  #ifndef WVSPLITKRC_1KPASS
-      const bool reloada = (!noreloada) &&
-                           ((k1 == k_str) || (k1 == k_str + kBase + kFit)) &&
-                           (k1 < k_end);
-      // load next chunk of A[] to LDS
-      if (reloada) {
-        if (k1 != k_str) kBase += kFit;
-        __syncthreads();
-  #else
-      const bool reloada = (!noreloada) &&
-                           ((k1 == k_str) || (k1 == k_str + kBase + kFit)) &&
-                           (k1 < k_end);
-      if (reloada) {
-  #endif
-        constexpr int sprdN = 4;
-        const uint32_t thrd = ((threadIdx.y / sprdN) * THRDS + threadIdx.x);
-
-  #ifndef WVSPLITKRC_1KPASS
-    #pragma unroll
-        for (int k = 0; k < kFit; k += THRDS * (WvPrGrp / sprdN) * A_CHUNK) {
-  #else
-        const unsigned int k = 0;
-        {
-  #endif
-          unsigned int kOff = k + (thrd * A_CHUNK);
-          unsigned int kOffcp = min__(K - A_CHUNK, k_str + kOff);
-          const unsigned int k_in = kOffcp + ((threadIdx.y % sprdN)) * K;
-          const unsigned int k_ot = kOff + ((threadIdx.y % sprdN)) * kFitPdd;
-          for (unsigned int n = 0; n < N / 2; n += sprdN) {
-            __builtin_amdgcn_global_load_lds((int*)(&A[k_in + n * K]),
-                                             (int*)(&s[(k_ot + n * kFitPdd)]),
-                                             16, 0, 0);
-            if (((threadIdx.y % sprdN)) + n + N / 2 >= actlN) continue;
-            __builtin_amdgcn_global_load_lds(
-                (int*)(&A[k_in + (n + N / 2) * K]),
-                (int*)(&s[(k_ot + (n + N / 2) * kFitPdd)]), 16, 0, 0);
-          }
-
-          // Stage loaded B[] to LDS for MFMA swizzling...
-          for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-            uint32_t k = k1 + k2 * THRDS * A_CHUNK;
-            uint32_t k_ = k + threadIdx.x * A_CHUNK;
-            const bool oob_k = (k_ >= K);
-            for (uint32_t y = 0; y < YTILE / GrpsShrB; y++) {
-              uint32_t idx = threadIdx.x * 4 +
-                             (y * GrpsShrB + bLoader) * ((THRDS + BPAD) * 4);
-              // zero out if oob
-              *((scalar8*)&myStg[idx]) =
-                  (oob_k || (y * GrpsShrB + bLoader + m >= M))
-                      ? 0
-                      : bigB_[y][k2].h8;
-            }
-          }
-        }
-      }
-    }
-
-  #ifndef WVSPLITKRC_1KPASS
-    // Fire load of next B[] chunk...
-    if ((k1 + THRDS * A_CHUNK * UNRL < k_end) &&
-        (k1 + THRDS * A_CHUNK * UNRL < K))
-    #pragma unroll
-      for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-        uint32_t k = k1 + THRDS * A_CHUNK * UNRL + k2 * THRDS * A_CHUNK;
-        uint32_t k_ = k + threadIdx.x * A_CHUNK;
-        const scalar_t* B_ = &B[min__(k_, K - A_CHUNK)];
-    #pragma unroll
-        for (uint32_t y = 0; y < YTILE / GrpsShrB; y++)
-          bigB_[y][k2].h8 = (loadnt(
-              (scalar8*)(&B_[min__(y * GrpsShrB + bLoader + m, M - 1) * K])));
-      }
-  #endif
-
-    // B[] staging is cooperative across GrpsShrB, so sync here before reading
-    // back
-    __syncthreads();
-
-    // read back B[] swizzled for MFMA...
-    bigType bigB[YTILE][UNRL];
-    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-      for (uint32_t y = 0; y < YTILE; y++) {
-        unsigned int idx = (threadIdx.x % YTILE) * ((THRDS + BPAD) * 4) +
-                           (threadIdx.x / YTILE) * 4 + y * 16;
-        bigB[y][k2].h8 = *((scalar8*)&myStg[idx]);
-      }
-    }
-
-    // rReadback A[] swizzled for MFMA...
-    bigType bigA[N / GrpsShrB][UNRL];
-  #pragma unroll
-    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-      uint32_t k = k1 + k2 * THRDS * A_CHUNK - kBase - k_str;
-  #pragma unroll
-      for (uint32_t nt = 0; nt < N / GrpsShrB; nt += NTILE)
-  #pragma unroll
-        for (uint32_t n = 0; n < NTILE; n++) {
-          uint32_t idxa = (nt + (threadIdx.x % NTILE) +
-                           (N / GrpsShrB) * (threadIdx.y % GrpsShrB)) *
-                              kFitPdd +
-                          A_CHUNK * ((threadIdx.x / NTILE) + n * 4) + k;
-          bigA[nt + n][k2] = *((const bigType*)(&(s[idxa])));
-        }
-    }
-
-    // Do the MFMAs
-  #pragma unroll
-    for (uint32_t k2 = 0; k2 < UNRL; k2++) {
-  #pragma unroll
-      for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-        if constexpr (std::is_same_v<scalar_t, half>) {
-          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
-              bigA[nt * NTILE + 0][k2].h4[0], bigB[0][k2].h4[0],
-              (k1 == k_str) ? ((scalar8){0}) : sum4[nt][0], 0, 0, 0);
-          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
-              bigA[nt * NTILE + 0][k2].h4[1], bigB[0][k2].h4[1], sum4[nt][0], 0,
-              0, 0);
-        } else {  // bf16
-          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
-              bigA[nt * NTILE + 0][k2].h4[0], bigB[0][k2].h4[0],
-              (k1 == k_str) ? ((scalar8){0}) : sum4[nt][0], 0, 0, 0);
-          sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
-              bigA[nt * NTILE + 0][k2].h4[1], bigB[0][k2].h4[1], sum4[nt][0], 0,
-              0, 0);
-        }
-  #pragma unroll
-        for (uint32_t j = 1; j < YTILE; j++) {
-          if constexpr (std::is_same_v<scalar_t, half>) {
-            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
-                bigA[nt * NTILE + j][k2].h4[0], bigB[j][k2].h4[0], sum4[nt][0],
-                0, 0, 0);
-            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16f16(
-                bigA[nt * NTILE + j][k2].h4[1], bigB[j][k2].h4[1], sum4[nt][0],
-                0, 0, 0);
-          } else {  // bf16
-            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
-                bigA[nt * NTILE + j][k2].h4[0], bigB[j][k2].h4[0], sum4[nt][0],
-                0, 0, 0);
-            sum4[nt][0] = __builtin_amdgcn_mfma_f32_16x16x16bf16_1k(
-                bigA[nt * NTILE + j][k2].h4[1], bigB[j][k2].h4[1], sum4[nt][0],
-                0, 0, 0);
-          }
-        }
-      }
-    }
-  }
-
-  if (!doRdc) {
-    if (m + (threadIdx.x % 16) < M) {
-      scalar_t biases[N / NTILE / GrpsShrB][4] = {0};
-      if (BIAS)
-        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-          for (uint32_t j = 0; j < 4; j++) {
-            int mindx = m + (threadIdx.x % 16);
-            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-            biases[nt][j] = BIAS[(mindx % Bx) + (nindx % By) * M];
-          }
-        }
-      for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-        for (uint32_t j = 0; j < 4; j++) {
-          int mindx = m + (threadIdx.x % 16);
-          int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                      (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-          int adr = mindx + M * nindx;
-          if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
-            if (BIAS) sum4[nt][0][j] += __bfloat162float(biases[nt][j]);
-            C[adr] = __float2bfloat16(sum4[nt][0][j]);
-          } else {
-            if (BIAS) sum4[nt][0][j] += __half2float(biases[nt][j]);
-            C[adr] = __float2half(sum4[nt][0][j]);
-          }
-        }
-      }
-    }
-  } else {
-    if (m + (threadIdx.x % 16) < M) {
-      int my_cntr;
-      if (!BIAS) {
-        int mindx = m + (threadIdx.x % 16);
-        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++)
-          for (uint32_t j = 0; j < 4; j++) {
-            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-            int adr = mindx + M * nindx;
-            atomicAdd(&glbl[adr], sum4[nt][0][j]);
-          }
-        int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
-                     (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-        int adr_ = mindx + M * nindx_ / 4;
-        my_cntr = atomicAdd(&cntr[adr_], 1);
-        float vals[N / NTILE / GrpsShrB][4] = {};
-        if (my_cntr + 1 == k_rnd) {
-          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-            for (uint32_t j = 0; j < 4; j++) {
-              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-              int adr = mindx + M * nindx;
-              vals[nt][j] = glbl[adr];
-            }
-          }
-          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-            for (uint32_t j = 0; j < 4; j++) {
-              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-              if (nindx >= actlN) break;
-              int adr = mindx + M * nindx;
-              if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
-                C[adr] = __float2bfloat16(vals[nt][j]);
-              } else {
-                C[adr] = __float2half(vals[nt][j]);
-              }
-            }
-          }
-        }
-      } else {
-        int mindx = m + (threadIdx.x % 16);
-        scalar_t biases[N / NTILE / GrpsShrB][4] = {};
-        // Atomic add the output, read biases
-        for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++)
-          for (uint32_t j = 0; j < 4; j++) {
-            int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                        (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-            int adr = mindx + M * nindx;
-            atomicAdd(&glbl[adr], sum4[nt][0][j]);
-            biases[nt][j] = BIAS[(mindx % Bx) + (nindx % By) * M];
-          }
-        int nindx_ = (0 + (threadIdx.x / 16) * 4) + 0 * NTILE +
-                     (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-        int adr_ = mindx + M * nindx_ / 4;
-        // Update the complete counter
-        my_cntr = atomicAdd(&cntr[adr_], 1);
-        float vals[N / NTILE / GrpsShrB][4] = {};
-        // If we're the last k-shard, read back the value and convert...
-        if (my_cntr + 1 == k_rnd) {
-          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-            for (uint32_t j = 0; j < 4; j++) {
-              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-              int adr = mindx + M * nindx;
-              vals[nt][j] = glbl[adr];
-            }
-          }
-          for (uint32_t nt = 0; nt < N / NTILE / GrpsShrB; nt++) {
-            for (uint32_t j = 0; j < 4; j++) {
-              int nindx = (j + (threadIdx.x / 16) * 4) + nt * NTILE +
-                          (N / GrpsShrB) * (threadIdx.y % GrpsShrB);
-              if (nindx >= actlN) break;
-              int adr = mindx + M * nindx;
-              if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
-                vals[nt][j] += __bfloat162float(biases[nt][j]);
-                C[adr] = __float2bfloat16(vals[nt][j]);
-              } else {
-                vals[nt][j] += __half2float(biases[nt][j]);
-                C[adr] = __float2half(vals[nt][j]);
-              }
-            }
-          }
-        }
-      }
-    }
-
-  #ifndef WVSPLITKRC_1KPASS
-    m0 += CuCount * WvPrGrp * YTILE / GrpsShrB;
-    m = (m0 + m1) % Mmod;
-    k_str = (m0 / Mmod) * kFit * kfitsPerRdc;
-    k_end = (m0 / Mmod + 1) * kFit * kfitsPerRdc;
-    if (k_str >= K) break;
-    kBase = 0;
-  #endif
-  }
-}
-#else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
-template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
-          int UNRL, int N, int GrpsShrB>
-__global__ void wvSplitKrc_(const int actlN, const int K, const int M,
-                            const int Bx, const int By, const scalar_t* B,
-                            const scalar_t* __restrict__ A,
-                            const scalar_t* __restrict__ BIAS, float* glbl,
-                            // int* cntr,
-                            scalar_t* C, const int CuCount){UNREACHABLE_CODE}
-#endif  // defined(__HIP__GFX9__) TODO: Add NAVI support
-
-torch::Tensor wvSplitKrc(const at::Tensor& in_a, const at::Tensor& in_b,
-                         const std::optional<at::Tensor>& in_bias,
-                         const int64_t CuCount) {
-  auto M_in = in_a.size(0);
-  auto N_in = in_b.size(0);
-  auto K_in = in_a.size(1);
-  auto Bx_in =
-      (in_bias.has_value() && in_bias->numel() > 0)
-          ? (in_bias->sizes().size() == 2) ? in_bias->size(1) : in_bias->size(0)
-          : 1;
-  auto By_in = (in_bias.has_value() && in_bias->numel() > 0 &&
-                in_bias->sizes().size() == 2)
-                   ? in_bias->size(0)
-                   : 1;
-
-  TORCH_CHECK(in_a.dtype() == in_b.dtype());
-  TORCH_CHECK(K_in % 8 == 0, "k % 8 == 0");
-  TORCH_CHECK(in_a.dtype() == torch::kFloat16 ||
-              in_a.dtype() == torch::kBFloat16);
-
-  auto out_c = torch::empty(
-      {N_in, M_in},
-      torch::TensorOptions().dtype(in_b.dtype()).device(in_b.device()));
-
-  auto N_p2 = 1U << (32 - __builtin_clz(N_in - 1));
-  auto axl_glbl = torch::empty(
-      {N_p2 + N_p2 / 4, M_in + M_in / 4},
-      torch::TensorOptions().dtype(torch::kFloat32).device(in_b.device()));
-  axl_glbl.zero_();  // disable for FAST_UNSAFE_RDC_INIT
-
-  dim3 grid(CuCount);
-
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(in_a));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  // const int max_lds_len = get_lds_size() / 2;
-
-#define WVSPLITKrc(_WvPrGrp, _YTILE, _UNRL, _N, _GrpsShrB)                     \
-  {                                                                            \
-    dim3 block(64, _WvPrGrp);                                                  \
-    wvSplitKrc_<fptype, 64, _YTILE, _WvPrGrp, 8, _UNRL, _N, _GrpsShrB>         \
-        <<<grid, block, 0, stream>>>(N_in, K_in, M_in, Bx_in, By_in, af4, bf4, \
-                                     biasf4, glbl, c, CuCount);                \
-  }
-
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(in_b.scalar_type(), "wvSplitKrc", [&] {
-    using fptype = typename scalar<scalar_t>::type;
-    fptype* af4 = reinterpret_cast<fptype*>(in_a.data_ptr());
-    const fptype* bf4 = reinterpret_cast<const fptype*>(in_b.data_ptr());
-    const fptype* biasf4 =
-        (in_bias.has_value() && in_bias->numel() > 0)
-            ? reinterpret_cast<const fptype*>(in_bias->data_ptr())
-            : nullptr;
-    fptype* c = reinterpret_cast<fptype*>(out_c.data_ptr());
-    auto glbl = axl_glbl.data_ptr<float>();
-    switch (N_p2) {
-      case 16:
-        WVSPLITKrc(4, 16, 1, 16, 1) break;
-      case 32:
-        WVSPLITKrc(4, 16, 1, 32, 2) break;
-      case 64:
-        WVSPLITKrc(4, 16, 1, 64, 2) break;
-      case 128:
-        WVSPLITKrc(4, 16, 1, 128, 4) break;
-      default:
-        throw std::runtime_error(
-            "Unsupported N value: " + std::to_string(M_in) + "," +
-            std::to_string(K_in) + "," + std::to_string(N_in));
-    }
-  });
-  return out_c;
-}
-
 #if defined(__HIP__MI3XX__)  // TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
@@ -1924,7 +1381,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   __shared__ fp8_t s[max_lds_len];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min__(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();
@@ -2113,7 +1570,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   __shared__ fp8_t s[max_lds_len];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min__(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();

csrc/rocm/torch_bindings.cpp

@@ -26,12 +26,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, rocm_ops) {
       "Tensor");
   rocm_ops.impl("wvSplitK", torch::kCUDA, &wvSplitK);
 
-  // Custom gemm op for skinny matrix-matrix multiplication
-  rocm_ops.def(
-      "wvSplitKrc(Tensor in_a, Tensor in_b, Tensor? in_bias, int CuCount) -> "
-      "Tensor");
-  rocm_ops.impl("wvSplitKrc", torch::kCUDA, &wvSplitKrc);
-
   // wvSplitK for fp8
   rocm_ops.def(
       "wvSplitKQ(Tensor in_a, Tensor in_b, Tensor? in_bias, Tensor! out_c, "

csrc/torch_bindings.cpp

@@ -303,9 +303,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("permute_cols(Tensor A, Tensor perm) -> Tensor");
   ops.impl("permute_cols", torch::kCUDA, &permute_cols);
 
-  // Marlin Optimized Quantized GEMM (supports GPTQ, AWQ, FP8, NVFP4, MXFP4).
+  // gptq_marlin Optimized Quantized GEMM for GPTQ.
   ops.def(
-      "marlin_gemm(Tensor a, Tensor? c_or_none, Tensor b_q_weight, "
+      "gptq_marlin_gemm(Tensor a, Tensor? c_or_none, Tensor b_q_weight, "
       "Tensor? b_bias_or_none,Tensor b_scales, "
       "Tensor? a_scales, Tensor? global_scale, Tensor? b_zeros_or_none, "
       "Tensor? "
@@ -474,6 +474,19 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "()");
   ops.impl("get_cutlass_moe_mm_data", torch::kCUDA, &get_cutlass_moe_mm_data);
 
+  // A function that computes problem sizes for each expert's multiplication
+  // used by the two mms called from fused MoE operation. It takes topk_ids as
+  // an input, and computes problem_sizes1 and problem_sizes2 only.
+  ops.def(
+      "get_cutlass_moe_mm_problem_sizes(Tensor topk_ids, "
+      "                                 Tensor! problem_sizes1, "
+      "                                 Tensor! problem_sizes2, "
+      "                                 int num_experts, int n, int k, "
+      "                                 Tensor? blockscale_offsets, "
+      "                                 bool? force_swap_ab) -> ()");
+  ops.impl("get_cutlass_moe_mm_problem_sizes", torch::kCUDA,
+           &get_cutlass_moe_mm_problem_sizes);
+
   // compute per-expert problem sizes from expert_first_token_offset
   // produced by vLLM's moe_permute kernel
   ops.def(
@@ -546,8 +559,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // Compute NVFP4 block quantized tensor.
   ops.def(
       "scaled_fp4_quant(Tensor! output, Tensor input,"
-      "                 Tensor! output_scale, Tensor input_scale, bool "
-      "is_sf_swizzled_layout) -> ()");
+      "                 Tensor! output_scale, Tensor input_scale) -> ()");
   ops.impl("scaled_fp4_quant", torch::kCUDA, &scaled_fp4_quant);
 
   // Compute NVFP4 experts quantization.
@@ -693,8 +705,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
   // Cache ops
   // Swap in (out) the cache blocks from src to dst.
   cache_ops.def(
-      "swap_blocks(Tensor src, Tensor! dst,"
-      "            int block_size_in_bytes, Tensor block_mapping) -> ()");
+      "swap_blocks(Tensor src, Tensor! dst, Tensor block_mapping) -> ()");
   cache_ops.impl("swap_blocks", torch::kCUDA, &swap_blocks);
 
   // Reshape the key and value tensors and cache them.

docker/Dockerfile

@@ -5,23 +5,6 @@
 # docs/contributing/dockerfile/dockerfile.md and
 # docs/assets/contributing/dockerfile-stages-dependency.png
 
-# =============================================================================
-# VERSION MANAGEMENT
-# =============================================================================
-# ARG defaults in this Dockerfile are the source of truth for pinned versions.
-# docker/versions.json is auto-generated for use with docker buildx bake.
-#
-# When updating versions:
-# 1. Edit the ARG defaults below
-# 2. Run: python tools/generate_versions_json.py
-#
-# To query versions programmatically:
-#   jq -r '.variable.CUDA_VERSION.default' docker/versions.json
-#
-# To build with bake:
-#   docker buildx bake -f docker/docker-bake.hcl -f docker/versions.json
-# =============================================================================
-
 ARG CUDA_VERSION=12.9.1
 ARG PYTHON_VERSION=3.12
 
@@ -134,8 +117,8 @@ ENV UV_LINK_MODE=copy
 # Verify GCC version
 RUN gcc --version
 
-# Ensure CUDA compatibility library is loaded
-RUN echo "/usr/local/cuda-$(echo "$CUDA_VERSION" | cut -d. -f1,2)/compat/" > /etc/ld.so.conf.d/00-cuda-compat.conf && ldconfig
+# Workaround for triton/pytorch issues
+RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 
 # ============================================================
 # SLOW-CHANGING DEPENDENCIES BELOW
@@ -148,41 +131,16 @@ ARG PYTORCH_CUDA_INDEX_BASE_URL
 
 WORKDIR /workspace
 
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build and runtime dependencies, including PyTorch
-# Check whether to install torch nightly instead of release for this build
+# install build and runtime dependencies
 COPY requirements/common.txt requirements/common.txt
 COPY requirements/cuda.txt requirements/cuda.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY pyproject.toml pyproject.toml
 RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 torch torchaudio torchvision --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && echo "Installing other requirements..." \
-        && /opt/venv/bin/python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-# Track PyTorch lib versions used during build and match in downstream instances.
-# We do this for both nightly and release so we can strip dependencies/*.txt as needed.
-# Otherwise library dependencies can upgrade/downgrade torch incorrectly.
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip freeze | grep -i "^torch=\|^torchvision=\|^torchaudio=" > torch_lib_versions.txt \
-    && TORCH_LIB_VERSIONS=$(cat torch_lib_versions.txt | xargs) \
-    && echo "Installed torch libs: ${TORCH_LIB_VERSIONS}"
+    uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
+    --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
 # CUDA arch list used by torch
 # Explicitly set the list to avoid issues with torch 2.2
 # See https://github.com/pytorch/pytorch/pull/123243
-# From versions.json: .torch.cuda_arch_list
 ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0 12.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 #################### BUILD BASE IMAGE ####################
@@ -195,13 +153,8 @@ ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ARG PYTORCH_CUDA_INDEX_BASE_URL
 
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build dependencies
+# install build dependencies
 COPY requirements/build.txt requirements/build.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
 
 # This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
 # Reference: https://github.com/astral-sh/uv/pull/1694
@@ -211,18 +164,8 @@ ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE=copy
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing build requirements without torch..." \
-        && python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
-        && echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing build requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
+    uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
+    --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
 WORKDIR /workspace
 
@@ -254,13 +197,6 @@ ARG VLLM_MAIN_CUDA_VERSION=""
 # Use dummy version for csrc-build wheel (only .so files are extracted, version doesn't matter)
 ENV SETUPTOOLS_SCM_PRETEND_VERSION="0.0.0+csrc.build"
 
-# Use existing torch for nightly builds
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        python3 use_existing_torch.py --prefix; \
-    fi
-
-# Build the vLLM wheel
 # if USE_SCCACHE is set, use sccache to speed up compilation
 RUN --mount=type=cache,target=/root/.cache/uv \
     if [ "$USE_SCCACHE" = "1" ]; then \
@@ -304,7 +240,6 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
         export VLLM_DOCKER_BUILD_CONTEXT=1 && \
         python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
     fi
-
 #################### CSRC BUILD IMAGE ####################
 
 #################### EXTENSIONS BUILD IMAGE ####################
@@ -321,8 +256,7 @@ ENV UV_LINK_MODE=copy
 WORKDIR /workspace
 
 # Build DeepGEMM wheel
-# Default moved here from tools/install_deepgemm.sh for centralized version management
-ARG DEEPGEMM_GIT_REF=594953acce41793ae00a1233eb516044d604bcb6
+ARG DEEPGEMM_GIT_REF
 COPY tools/install_deepgemm.sh /tmp/install_deepgemm.sh
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p /tmp/deepgemm/dist && \
@@ -337,9 +271,8 @@ RUN mkdir -p /tmp/deepgemm/dist && touch /tmp/deepgemm/dist/.deepgemm_skipped
 
 # Build pplx-kernels and DeepEP wheels
 COPY tools/ep_kernels/install_python_libraries.sh /tmp/install_python_libraries.sh
-# Defaults moved here from tools/ep_kernels/install_python_libraries.sh for centralized version management
-ARG PPLX_COMMIT_HASH=12cecfd
-ARG DEEPEP_COMMIT_HASH=73b6ea4
+ARG PPLX_COMMIT_HASH
+ARG DEEPEP_COMMIT_HASH
 ARG NVSHMEM_VER
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p /tmp/ep_kernels_workspace/dist && \
@@ -361,13 +294,8 @@ ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ARG PYTORCH_CUDA_INDEX_BASE_URL
 
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build dependencies
+# install build dependencies
 COPY requirements/build.txt requirements/build.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
 
 # This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
 # Reference: https://github.com/astral-sh/uv/pull/1694
@@ -377,23 +305,14 @@ ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE=copy
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing build requirements without torch..." \
-        && python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
-        && echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing build requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
+    uv pip install --python /opt/venv/bin/python3 -r requirements/build.txt \
+    --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
 WORKDIR /workspace
 
 # Copy pre-built csrc wheel directly
 COPY --from=csrc-build /workspace/dist /precompiled-wheels
+
 COPY . .
 
 ARG GIT_REPO_CHECK=0
@@ -406,13 +325,6 @@ ENV VLLM_TARGET_DEVICE=${vllm_target_device}
 # Skip adding +precompiled suffix to version (preserves git-derived version)
 ENV VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX=1
 
-# Use existing torch for nightly builds
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        python3 use_existing_torch.py --prefix; \
-    fi
-
-# Build the vLLM wheel
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=.git,target=.git \
     if [ "${vllm_target_device}" = "cuda" ]; then \
@@ -435,8 +347,7 @@ RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
     else \
         echo "Skipping wheel size check."; \
     fi
-
-#################### WHEEL BUILD IMAGE ####################
+#################### EXTENSION Build IMAGE ####################
 
 #################### DEV IMAGE ####################
 FROM base AS dev
@@ -454,34 +365,12 @@ ENV UV_LINK_MODE=copy
 
 # Install libnuma-dev, required by fastsafetensors (fixes #20384)
 RUN apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*
-
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install development dependencies
 COPY requirements/lint.txt requirements/lint.txt
-COPY requirements/test.in requirements/test.in
 COPY requirements/test.txt requirements/test.txt
 COPY requirements/dev.txt requirements/dev.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing dev requirements plus torch nightly..." \
-        && python3 use_existing_torch.py --prefix \
-        && cat torch_lib_versions.txt >> requirements/test.in \
-        && uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | xargs) --pre \
-        -r requirements/dev.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing dev requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/dev.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
+    uv pip install --python /opt/venv/bin/python3 -r requirements/dev.txt \
+    --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 #################### DEV IMAGE ####################
 #################### vLLM installation IMAGE ####################
 # image with vLLM installed
@@ -564,8 +453,8 @@ ENV UV_HTTP_TIMEOUT=500
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE=copy
 
-# Ensure CUDA compatibility library is loaded
-RUN echo "/usr/local/cuda-$(echo "$CUDA_VERSION" | cut -d. -f1,2)/compat/" > /etc/ld.so.conf.d/00-cuda-compat.conf && ldconfig
+# Workaround for triton/pytorch issues
+RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 
 # ============================================================
 # SLOW-CHANGING DEPENDENCIES BELOW
@@ -585,8 +474,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Install FlashInfer pre-compiled kernel cache and binaries
 # This is ~1.1GB and only changes when FlashInfer version bumps
 # https://docs.flashinfer.ai/installation.html
-# From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.1
+ARG FLASHINFER_VERSION=0.5.3
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system flashinfer-cubin==${FLASHINFER_VERSION} \
     && uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
@@ -615,20 +503,14 @@ RUN set -eux; \
 
 # Install vllm-openai dependencies (saves ~2.6s per build)
 # These are stable packages that don't depend on vLLM itself
-# From versions.json: .bitsandbytes.x86_64, .bitsandbytes.arm64
-# From versions.json: .openai_server_extras.timm, .openai_server_extras.runai_model_streamer
-ARG BITSANDBYTES_VERSION_X86=0.46.1
-ARG BITSANDBYTES_VERSION_ARM64=0.42.0
-ARG TIMM_VERSION=">=1.0.17"
-ARG RUNAI_MODEL_STREAMER_VERSION=">=0.15.3"
 RUN --mount=type=cache,target=/root/.cache/uv \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_ARM64}"; \
+        BITSANDBYTES_VERSION="0.42.0"; \
     else \
-        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_X86}"; \
+        BITSANDBYTES_VERSION="0.46.1"; \
     fi; \
     uv pip install --system accelerate hf_transfer modelscope \
-        "bitsandbytes>=${BITSANDBYTES_VERSION}" "timm${TIMM_VERSION}" "runai-model-streamer[s3,gcs]${RUNAI_MODEL_STREAMER_VERSION}"
+        "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm>=1.0.17' 'runai-model-streamer[s3,gcs]>=0.15.3'
 
 # ============================================================
 # VLLM INSTALLATION (depends on build stage)
@@ -639,26 +521,11 @@ ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ARG PYTORCH_CUDA_INDEX_BASE_URL
 ARG PIP_KEYRING_PROVIDER UV_KEYRING_PROVIDER
 
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install vLLM wheel first, so that torch etc will be installed.
-# Check whether to install torch nightly instead of release for this build.
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+# Install vllm wheel first, so that torch etc will be installed.
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
     --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing torch nightly..." \
-        && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && echo "Installing vLLM..." \
-        && uv pip install --system dist/*.whl --verbose \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing vLLM..." \
-        && 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 '.'); \
-    fi
+    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 '.')
 
 RUN --mount=type=cache,target=/root/.cache/uv \
 . /etc/environment && \
@@ -718,33 +585,12 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
     && apt-get update -y \
     && apt-get install -y git
 
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install development dependencies (for testing)
-COPY requirements/lint.txt requirements/lint.txt
-COPY requirements/test.in requirements/test.in
-COPY requirements/test.txt requirements/test.txt
-COPY requirements/dev.txt requirements/dev.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+# install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \
     CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
     if [ "$CUDA_MAJOR" -ge 12 ]; then \
-        if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-            echo "Installing dev requirements plus torch nightly..." \
-            && python3 use_existing_torch.py --prefix \
-            && cat torch_lib_versions.txt >> requirements/test.in \
-            && uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-            && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
-            -r requirements/dev.txt \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-        else \
-            echo "Installing dev requirements..." \
-            && uv pip install --system -r requirements/dev.txt \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-        fi \
+        uv pip install --system -r requirements/dev.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
     fi
 
 # install development dependencies (for testing)

docker/Dockerfile.cpu

@@ -15,11 +15,9 @@
 # Build arguments:
 #   PYTHON_VERSION=3.13|3.12 (default)|3.11|3.10
 #   VLLM_CPU_DISABLE_AVX512=false (default)|true
-#   VLLM_CPU_AVX2=false (default)|true (for cross-compilation)
-#   VLLM_CPU_AVX512=false (default)|true (for cross-compilation)
-#   VLLM_CPU_AVX512BF16=false (default)|true (for cross-compilation)
-#   VLLM_CPU_AVX512VNNI=false (default)|true (for cross-compilation)
-#   VLLM_CPU_AMXBF16=false (default)|true (for cross-compilation)
+#   VLLM_CPU_AVX512BF16=false (default)|true
+#   VLLM_CPU_AVX512VNNI=false (default)|true
+#   VLLM_CPU_AMXBF16=false |true (default)
 #
 
 ######################### COMMON BASE IMAGE #########################
@@ -56,12 +54,9 @@ ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"
-
-# Copy requirements files for installation
-COPY requirements/common.txt requirements/common.txt
-COPY requirements/cpu.txt requirements/cpu.txt
-
 RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
+    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
     uv pip install --upgrade pip && \
     uv pip install -r requirements/cpu.txt
 
@@ -93,12 +88,6 @@ ARG GIT_REPO_CHECK=0
 # Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
 ARG VLLM_CPU_DISABLE_AVX512=0
 ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
-# Support for cross-compilation with AVX2 ISA: docker build --build-arg VLLM_CPU_AVX2="1" ...
-ARG VLLM_CPU_AVX2=0
-ENV VLLM_CPU_AVX2=${VLLM_CPU_AVX2}
-# Support for cross-compilation with AVX512 ISA: docker build --build-arg VLLM_CPU_AVX512="1" ...
-ARG VLLM_CPU_AVX512=0
-ENV VLLM_CPU_AVX512=${VLLM_CPU_AVX512}
 # Support for building with AVX512BF16 ISA: docker build --build-arg VLLM_CPU_AVX512BF16="true" ...
 ARG VLLM_CPU_AVX512BF16=0
 ENV VLLM_CPU_AVX512BF16=${VLLM_CPU_AVX512BF16}
@@ -111,19 +100,18 @@ ENV VLLM_CPU_AMXBF16=${VLLM_CPU_AMXBF16}
 
 WORKDIR /workspace/vllm
 
-# Copy build requirements
-COPY requirements/cpu-build.txt requirements/build.txt
-
 RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,src=requirements/cpu-build.txt,target=requirements/build.txt \
     uv pip install -r requirements/build.txt
 
 COPY . .
-
-RUN if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
+RUN --mount=type=bind,source=.git,target=.git \
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/workspace/vllm/.deps,sharing=locked \
+    --mount=type=bind,source=.git,target=.git \
     VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38
 
 ######################### TEST DEPS #########################
@@ -131,11 +119,9 @@ FROM base AS vllm-test-deps
 
 WORKDIR /workspace/vllm
 
-# Copy test requirements
-COPY requirements/test.in requirements/cpu-test.in
-
 # TODO: Update to 2.9.0 when there is a new build for intel_extension_for_pytorch for that version
-RUN \
+RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
+    cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
     remove_packages_not_supported_on_aarch64() { \
       case "$(uname -m)" in \
@@ -146,7 +132,7 @@ RUN \
       esac; \
     }; \
     remove_packages_not_supported_on_aarch64 && \
-    sed -i 's/^torch==.*/torch==2.10.0/g' requirements/cpu-test.in && \
+    sed -i 's/^torch==.*/torch==2.9.1/g' requirements/cpu-test.in && \
     sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
     sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
@@ -214,29 +200,4 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \
     uv pip install dist/*.whl
 
-# Add labels to document build configuration
-LABEL org.opencontainers.image.title="vLLM CPU"
-LABEL org.opencontainers.image.description="vLLM inference engine for CPU platforms"
-LABEL org.opencontainers.image.vendor="vLLM Project"
-LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm"
-
-# Build configuration labels
-ARG TARGETARCH
-ARG VLLM_CPU_DISABLE_AVX512
-ARG VLLM_CPU_AVX2
-ARG VLLM_CPU_AVX512
-ARG VLLM_CPU_AVX512BF16
-ARG VLLM_CPU_AVX512VNNI
-ARG VLLM_CPU_AMXBF16
-ARG PYTHON_VERSION
-
-LABEL ai.vllm.build.target-arch="${TARGETARCH}"
-LABEL ai.vllm.build.cpu-disable-avx512="${VLLM_CPU_DISABLE_AVX512:-false}"
-LABEL ai.vllm.build.cpu-avx2="${VLLM_CPU_AVX2:-false}"
-LABEL ai.vllm.build.cpu-avx512="${VLLM_CPU_AVX512:-false}"
-LABEL ai.vllm.build.cpu-avx512bf16="${VLLM_CPU_AVX512BF16:-false}"
-LABEL ai.vllm.build.cpu-avx512vnni="${VLLM_CPU_AVX512VNNI:-false}"
-LABEL ai.vllm.build.cpu-amxbf16="${VLLM_CPU_AMXBF16:-false}"
-LABEL ai.vllm.build.python-version="${PYTHON_VERSION:-3.12}"
-
 ENTRYPOINT ["vllm", "serve"]

docker/Dockerfile.nightly_torch

@@ -1,11 +1,3 @@
-#######
-#
-# THIS FILE IS DEPRECATED AND WILL BE REMOVED SHORTLY
-#
-# Please use the standard Dockerfile with PYTORCH_NIGHTLY=1 instead
-#
-#######
-
 # The vLLM Dockerfile is used to construct vLLM image against torch nightly that can be directly used for testing
 
 # for torch nightly, cuda >=12.6 is required,
@@ -221,14 +213,15 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.6.1
+# release version: v0.5.2
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     echo "git clone flashinfer..." \
-    && git clone --depth 1 --branch v0.6.1 --recursive https://github.com/flashinfer-ai/flashinfer.git \
+    && git clone --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
+    && git checkout v0.5.2 \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \
     && rm -rf build \

docker/Dockerfile.rocm

@@ -385,5 +385,5 @@ RUN echo "VLLM_BASE_IMAGE=${BASE_IMAGE}" >> ${COMMON_WORKDIR}/versions.txt
 CMD ["/bin/bash"]
 
 #Set entrypoint for vllm-openai official images
-FROM final AS vllm-openai
+FROM final As vllm-openai
 ENTRYPOINT ["vllm", "serve"]

docker/versions.json

@@ -1,92 +0,0 @@
-{
-  "_comment": "Auto-generated from Dockerfile ARGs. Do not edit manually. Run: python tools/generate_versions_json.py",
-  "variable": {
-    "CUDA_VERSION": {
-      "default": "12.9.1"
-    },
-    "PYTHON_VERSION": {
-      "default": "3.12"
-    },
-    "BUILD_BASE_IMAGE": {
-      "default": "nvidia/cuda:12.9.1-devel-ubuntu20.04"
-    },
-    "FINAL_BASE_IMAGE": {
-      "default": "nvidia/cuda:12.9.1-base-ubuntu22.04"
-    },
-    "GET_PIP_URL": {
-      "default": "https://bootstrap.pypa.io/get-pip.py"
-    },
-    "PYTORCH_CUDA_INDEX_BASE_URL": {
-      "default": "https://download.pytorch.org/whl"
-    },
-    "PIP_KEYRING_PROVIDER": {
-      "default": "disabled"
-    },
-    "UV_KEYRING_PROVIDER": {
-      "default": "disabled"
-    },
-    "INSTALL_KV_CONNECTORS": {
-      "default": "false"
-    },
-    "TORCH_CUDA_ARCH_LIST": {
-      "default": "7.0 7.5 8.0 8.9 9.0 10.0 12.0"
-    },
-    "MAX_JOBS": {
-      "default": "2"
-    },
-    "NVCC_THREADS": {
-      "default": "8"
-    },
-    "SCCACHE_BUCKET_NAME": {
-      "default": "vllm-build-sccache"
-    },
-    "SCCACHE_REGION_NAME": {
-      "default": "us-west-2"
-    },
-    "SCCACHE_S3_NO_CREDENTIALS": {
-      "default": "0"
-    },
-    "vllm_target_device": {
-      "default": "cuda"
-    },
-    "DEEPGEMM_GIT_REF": {
-      "default": "594953acce41793ae00a1233eb516044d604bcb6"
-    },
-    "PPLX_COMMIT_HASH": {
-      "default": "12cecfd"
-    },
-    "DEEPEP_COMMIT_HASH": {
-      "default": "73b6ea4"
-    },
-    "GIT_REPO_CHECK": {
-      "default": "0"
-    },
-    "VLLM_MAX_SIZE_MB": {
-      "default": "500"
-    },
-    "RUN_WHEEL_CHECK": {
-      "default": "true"
-    },
-    "FLASHINFER_VERSION": {
-      "default": "0.6.1"
-    },
-    "GDRCOPY_CUDA_VERSION": {
-      "default": "12.8"
-    },
-    "GDRCOPY_OS_VERSION": {
-      "default": "Ubuntu22_04"
-    },
-    "BITSANDBYTES_VERSION_X86": {
-      "default": "0.46.1"
-    },
-    "BITSANDBYTES_VERSION_ARM64": {
-      "default": "0.42.0"
-    },
-    "TIMM_VERSION": {
-      "default": ">=1.0.17"
-    },
-    "RUNAI_MODEL_STREAMER_VERSION": {
-      "default": ">=0.15.3"
-    }
-  }
-}

docs/api/README.md

@@ -82,6 +82,10 @@ Internal data structures.
 
 - [vllm.multimodal.processing][]
 
+### Memory Profiling
+
+- [vllm.multimodal.profiling][]
+
 ### Registry
 
 - [vllm.multimodal.registry][]

docs/benchmarking/dashboard.md

@@ -13,14 +13,14 @@ For x86 CPU environment, please use the image with "-cpu" postfix. For AArch64 C
 Here is an example for docker run command for CPU. For GPUs skip setting the `ON_CPU` env var.
 
 ```bash
-export VLLM_COMMIT=7f42dc20bb2800d09faa72b26f25d54e26f1b694 # use full commit hash from the main branch
+export VLLM_COMMIT=1da94e673c257373280026f75ceb4effac80e892 # use full commit hash from the main branch
 export HF_TOKEN=<valid Hugging Face token>
 if [[ "$(uname -m)" == aarch64 || "$(uname -m)" == arm64 ]]; then
   IMG_SUFFIX="arm64-cpu"
 else
   IMG_SUFFIX="cpu"
 fi
-docker run -it --entrypoint /bin/bash -v /data/huggingface:/root/.cache/huggingface -e HF_TOKEN=$HF_TOKEN -e ON_CPU=1 --shm-size=16g --name vllm-cpu-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${VLLM_COMMIT}-${IMG_SUFFIX}
+docker run -it --entrypoint /bin/bash -v /data/huggingface:/root/.cache/huggingface -e HF_TOKEN=$HF_TOKEN -e ON_ARM64_CPU=1 --shm-size=16g --name vllm-cpu-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${VLLM_COMMIT}-${IMG_SUFFIX}
 ```
 
 Then, run below command inside the docker instance.

docs/benchmarking/sweeps.md

@@ -139,63 +139,6 @@ The algorithm for adjusting the SLA variable is as follows:
 
     For a given combination of `--serve-params` and `--bench-params`, we share the benchmark results across `--sla-params` to avoid rerunning benchmarks with the same SLA variable value.
 
-### Startup
-
-`vllm bench sweep startup` runs `vllm bench startup` across parameter combinations to compare cold/warm startup time for different engine settings.
-
-Follow these steps to run the script:
-
-1. (Optional) Construct the base command to `vllm bench startup`, and pass it to `--startup-cmd` (default: `vllm bench startup`).
-2. (Optional) Reuse a `--serve-params` JSON from `vllm bench sweep serve` to vary engine settings. Only parameters supported by `vllm bench startup` are applied.
-3. (Optional) Create a `--startup-params` JSON to vary startup-specific options like iteration counts.
-4. Determine where you want to save the results, and pass that to `--output-dir`.
-
-Example `--serve-params`:
-
-```json
-[
-    {
-        "_benchmark_name": "tp1",
-        "model": "Qwen/Qwen3-0.6B",
-        "tensor_parallel_size": 1,
-        "gpu_memory_utilization": 0.9
-    },
-    {
-        "_benchmark_name": "tp2",
-        "model": "Qwen/Qwen3-0.6B",
-        "tensor_parallel_size": 2,
-        "gpu_memory_utilization": 0.9
-    }
-]
-```
-
-Example `--startup-params`:
-
-```json
-[
-    {
-        "_benchmark_name": "qwen3-0.6",
-        "num_iters_cold": 2,
-        "num_iters_warmup": 1,
-        "num_iters_warm": 2
-    }
-]
-```
-
-Example command:
-
-```bash
-vllm bench sweep startup \
-    --startup-cmd 'vllm bench startup --model Qwen/Qwen3-0.6B' \
-    --serve-params benchmarks/serve_hparams.json \
-    --startup-params benchmarks/startup_hparams.json \
-    -o benchmarks/results
-```
-
-!!! important
-    By default, unsupported parameters in `--serve-params` or `--startup-params` are ignored with a warning.
-    Use `--strict-params` to fail fast on unknown keys.
-
 ## Visualization
 
 ### Basic

docs/contributing/README.md

@@ -43,16 +43,10 @@ If you are only developing vLLM's Python code, install vLLM using:
 VLLM_USE_PRECOMPILED=1 uv pip install -e .
 ```
 
-If you are developing vLLM's Python and CUDA/C++ code, install Pytorch first:
+If you are developing vLLM's Python and CUDA/C++ code, install vLLM using:
 
 ```bash
-uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129
-```
-
-then install vLLM using:
-
-```bash
-uv pip install -e . --no-build-isolation
+uv pip install -e .
 ```
 
 For more details about installing from source and installing for other hardware, check out the [installation instructions](../getting_started/installation/README.md) for your hardware and head to the "Build wheel from source" section.

docs/contributing/model/multimodal.md

@@ -23,32 +23,29 @@ Further update the model as follows:
                 raise ValueError("Only image modality is supported")
         ```
 
-- Inside `__init__` method, initialize the language components of the model inside [_mark_language_model][vllm.model_executor.models.interfaces.SupportsMultiModal._mark_language_model], and the multimodal components of the model inside [_mark_tower_model][vllm.model_executor.models.interfaces.SupportsMultiModal._mark_tower_model], e.g.:
+- Reserve a keyword parameter in [forward][torch.nn.Module.forward] for each input tensor that corresponds to a multi-modal input, as shown in the following example:
 
-    ```python
-        def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
-            super().__init__()
-
-            config = vllm_config.model_config.hf_config
-
-            with self._mark_tower_model(vllm_config, "image"):
-                self.vision_encoder = ...
-                self.multi_modal_projector = ...
-
-            with self._mark_language_model(vllm_config):
-                self.language_model = init_vllm_registered_model(
-                    vllm_config=vllm_config,
-                    hf_config=config.text_config,
-                    prefix=maybe_prefix(prefix, "language_model"),
-                )
-    ```
+  ```diff
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+  +     pixel_values: torch.Tensor,
+    ) -> SamplerOutput:
+  ```
+  
+  More conveniently, you can simply pass `**kwargs` to the [forward][torch.nn.Module.forward] method and retrieve the keyword parameters for multimodal inputs from it.
 
 - Implement [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal] that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
 
     ??? code
 
         ```python
+        class YourModelForImage2Seq(nn.Module):
+            ...
+
             def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:
+                assert self.vision_encoder is not None
                 image_features = self.vision_encoder(image_input)
                 return self.multi_modal_projector(image_features)
 
@@ -74,7 +71,18 @@ Further update the model as follows:
     [PlaceholderRange][vllm.multimodal.inputs.PlaceholderRange] from input processing.
     This logic can be found at [embed_input_ids][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_input_ids].
 
-    You may override this method if additional logic is required for your model when merging embeddings.
+    You may override this method if additional logic is required for your model when merging embeddings. 
+
+- Implement [get_language_model][vllm.model_executor.models.interfaces.SupportsMultiModal.get_language_model] getter to provide stable access to the underlying language model.
+
+    ```python
+    class YourModelForImage2Seq(nn.Module):
+        ...
+
+        def get_language_model(self) -> torch.nn.Module:
+            # Change `language_model` according to your implementation.
+            return self.language_model
+    ```
 
 - Once the above steps are done, update the model class with the [SupportsMultiModal][vllm.model_executor.models.interfaces.SupportsMultiModal] interface.
 
@@ -108,10 +116,12 @@ def get_supported_mm_limits(self) -> Mapping[str, int | None]:
 
 ## 3. Specify dummy inputs
 
-Then, inherit [BaseDummyInputsBuilder][vllm.multimodal.processing.BaseDummyInputsBuilder] to construct dummy inputs for
-HF processing. The processed outputs are also used for memory profiling.
+Then, inherit [BaseDummyInputsBuilder][vllm.multimodal.profiling.BaseDummyInputsBuilder] to construct dummy inputs for
+HF processing as well as memory profiling.
 
-Override the abstract methods [get_dummy_text][vllm.multimodal.processing.BaseDummyInputsBuilder.get_dummy_text] and [get_dummy_mm_data][vllm.multimodal.processing.BaseDummyInputsBuilder.get_dummy_mm_data] to construct dummy inputs. These dummy inputs should result in the worst-case memory usage of the model so that vLLM can reserve the correct amount of memory for it.
+### For memory profiling
+
+Override the abstract methods [get_dummy_text][vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_text] and [get_dummy_mm_data][vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_mm_data] to construct dummy inputs for memory profiling. These dummy inputs should result in the worst-case memory usage of the model so that vLLM can reserve the correct amount of memory for it.
 
 Assuming that the memory usage increases with the number of tokens, the dummy inputs can be constructed to maximize the number of output embeddings, which is the same number as placeholder feature tokens.
 
@@ -793,7 +803,7 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
 ## 5. Register processor-related classes
 
 After you have defined [BaseProcessingInfo][vllm.multimodal.processing.BaseProcessingInfo] (Step 2),
-[BaseDummyInputsBuilder][vllm.multimodal.processing.BaseDummyInputsBuilder] (Step 3),
+[BaseDummyInputsBuilder][vllm.multimodal.profiling.BaseDummyInputsBuilder] (Step 3),
 and [BaseMultiModalProcessor][vllm.multimodal.processing.BaseMultiModalProcessor] (Step 4),
 decorate the model class with [MULTIMODAL_REGISTRY.register_processor][vllm.multimodal.registry.MultiModalRegistry.register_processor]
 to register them to the multi-modal registry:

docs/design/custom_op.md

@@ -8,6 +8,15 @@ This document will introduce how CustomOp works in vLLM and how to implement a n
 
 `CustomOp` manages two dictionaries of all custom ops (i.e., op classes, indexed by registered name) in its class, for vLLM and OOT plugins respectively.
 
+??? code
+
+    ```python
+    class CustomOp(nn.Module):
+
+        op_registry: dict[str, type["CustomOp"]] = {}
+        op_registry_oot: dict[str, type["CustomOp"]] = {}
+    ```
+
 We can use `@CustomOp.register("op_name")` to register an op class to the `CustomOp` system. After this, the `op_name` and its class will be added into the `op_registry` dictionary. In addition, We can also register an OOT op by `@CustomOp.register_oot("op_name")`. We will introduce this mechanism in detail later.
 
 When a `CustomOp` is called (i.e., call its `forward()` method), if it is enabled (i.e., with `--compilation_config.custom_ops '["+op_name"]'`), it will automatically dispatch the forward method to the appropriate backend according to `current_platform`. Otherwise (i.e., it is disabled), it will only call the `forward_native()` method to use PyTorch-native implementation of this forward method.

docs/design/io_processor_plugins.md

@@ -79,7 +79,7 @@ The `post_process*` methods take `PoolingRequestOutput` objects as input and gen
 The `validate_or_generate_params` method is used for validating with the plugin any `SamplingParameters`/`PoolingParameters` received with the user request, or to generate new ones if none are specified. The function always returns the validated/generated parameters.
 The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/pooling` serving endpoint is available here [vllm/entrypoints/openai/serving_pooling.py](../../vllm/entrypoints/pooling/pooling/serving.py).
 
-An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_online.py](../../examples/pooling/plugin/prithvi_geospatial_mae_online.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.
+An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_client.py](../../examples/pooling/plugin/prithvi_geospatial_mae_client.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.
 
 ## Using an IO Processor plugin
 

docs/design/metrics.md

@@ -49,7 +49,7 @@ The subset of metrics exposed in the Grafana dashboard gives us an indication of
 - `vllm:e2e_request_latency_seconds_bucket` - End to end request latency measured in seconds.
 - `vllm:prompt_tokens` - Prompt tokens.
 - `vllm:generation_tokens` - Generation tokens.
-- `vllm:inter_token_latency_seconds` - Inter-token latency (Time Per Output Token, TPOT) in seconds.
+- `vllm:time_per_output_token_seconds` - Inter-token latency (Time Per Output Token, TPOT) in seconds.
 - `vllm:time_to_first_token_seconds` - Time to First Token (TTFT) latency in seconds.
 - `vllm:num_requests_running` (also, `_swapped` and `_waiting`) - Number of requests in the RUNNING, WAITING, and SWAPPED states.
 - `vllm:kv_cache_usage_perc` - Percentage of used cache blocks by vLLM.

docs/design/mm_processing.md

@@ -43,7 +43,7 @@ Moreover, since the tokenized text has not passed through the HF processor, we h
 
 ### Dummy text
 
-We work around the first issue by requiring each model to define how to generate dummy text based on the number of multi-modal inputs, via [get_dummy_text][vllm.multimodal.processing.BaseDummyInputsBuilder.get_dummy_text]. This lets us generate dummy text corresponding to the multi-modal inputs and input them together to obtain the processed multi-modal data.
+We work around the first issue by requiring each model to define how to generate dummy text based on the number of multi-modal inputs, via [get_dummy_text][vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_text]. This lets us generate dummy text corresponding to the multi-modal inputs and input them together to obtain the processed multi-modal data.
 
 ### Automatic prompt updating
 

docs/design/moe_kernel_features.md

@@ -85,13 +85,14 @@ To be used with a particular `FusedMoEPrepareAndFinalize` subclass, MoE kernels
 |--------|-------------------|--------------|---------------|---------------------|-----------------------|---------|--------|
 | triton | standard | all<sup>1</sup> | G,A,T | silu, gelu,</br>swigluoai,</br>silu_no_mul,</br>gelu_no_mul | Y | Y | [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts],</br>[`TritonExperts`][vllm.model_executor.layers.fused_moe.fused_moe.TritonExperts] |
 | triton (batched) | batched | all<sup>1</sup> | G,A,T | silu, gelu | <sup>6</sup> | Y | [`BatchedTritonExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedTritonExperts] |
-| deep gemm | standard,</br>batched | fp8 | G(128),A,T | silu, gelu | <sup>6</sup> | Y | </br>[`DeepGemmExperts`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.DeepGemmExperts],</br>[`BatchedDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe.BatchedDeepGemmExperts] |
+| deep gemm | standard,</br>batched | fp8 | G(128),A,T | silu, gelu | <sup>6</sup> | Y | [`deep_gemm_moe_fp8`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.deep_gemm_moe_fp8],</br>[`DeepGemmExperts`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.DeepGemmExperts],</br>[`BatchedDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe.BatchedDeepGemmExperts] |
 | cutlass_fp4 | standard,</br>batched | nvfp4 | A,T | silu | Y | Y | [`CutlassExpertsFp4`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp4] |
 | cutlass_fp8 | standard,</br>batched | fp8 | A,T | silu, gelu | Y | Y | [`CutlassExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp8],</br>[`CutlasBatchedExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassBatchedExpertsFp8] |
-| flashinfer | standard | nvfp4,</br>fp8 | T | <sup>5</sup> | N | Y | [`FlashInferExperts`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts] |
+| flashinfer | standard | nvfp4,</br>fp8 | T | <sup>5</sup> | N | Y | [`flashinfer_cutlass_moe_fp4`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.flashinfer_cutlass_moe_fp4],</br>[`FlashInferExperts`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts] |
 | gpt oss triton | standard | N/A | N/A | <sup>5</sup> | Y | Y | [`triton_kernel_fused_experts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.triton_kernel_fused_experts],</br>[`OAITritonExperts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.OAITritonExperts] |
 | marlin | standard,</br>batched | <sup>3</sup> / N/A | <sup>3</sup> / N/A | silu,</br>swigluoai | Y | Y | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.fused_marlin_moe],</br>[`MarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.MarlinExperts],</br>[`BatchedMarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.BatchedMarlinExperts] |
 | trtllm | standard | mxfp4,</br>nvfp4 | G(16),G(32) | <sup>5</sup> | N | Y | [`TrtLlmGenExperts`][vllm.model_executor.layers.fused_moe.trtllm_moe.TrtLlmGenExperts] |
+| iterative | standard | N/A | N/A | silu | N | N | [`fused_moe`][vllm.model_executor.layers.fused_moe.moe_torch_iterative.fused_moe] |
 | rocm aiter moe | standard | fp8 | G(128),A,T | silu, gelu | Y | N | [`rocm_aiter_fused_experts`][vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe.rocm_aiter_fused_experts] |
 | cpu_fused_moe | standard | N/A | N/A | silu | N | N | [`CPUFusedMOE`][vllm.model_executor.layers.fused_moe.cpu_fused_moe.CPUFusedMOE] |
 | naive batched<sup>4</sup> | batched | int8,</br>fp8 | G,A,T | silu, gelu | <sup>6</sup> | Y | [`NaiveBatchedExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.NaiveBatchedExperts] |

docs/design/prefix_caching.md

@@ -22,13 +22,8 @@ In the example above, the KV cache in the first block can be uniquely identified
     We only cache full blocks.
 
 !!! note "Note 2"
-    In previous versions, the hash key was not guaranteed to be collision-free. As of v0.11, the default hashing algorithm is `sha256`, which addresses collision risks.
-
-    For `vllm serve`, you can control the hashing algorithm via `--prefix-caching-hash-algo`:
-    - `sha256` (default): Uses Python's `pickle` for serialization. Hashes may not be reproducible across different Python or vLLM versions.
-    - `sha256_cbor`: Uses `cbor2` for serialization, providing a reproducible, cross-language compatible hash. This is recommended for deterministic caching across environments.
-    - `xxhash`: `Uses Pickle serialization with xxHash (128-bit) for faster, non-cryptographic hashing. Requires the optional `xxhash` package. IMPORTANT: Use of a hashing algorithm that is not considered cryptographically secure theoretically increases the risk of hash collisions, which can cause undefined behavior or even leak private information in multi-tenant environments. Even if collisions are still very unlikely, it is important to consider your security risk tolerance against the performance benefits before turning this on.
-    - `xxhash_cbor` combines canonical CBOR serialization with xxHash for reproducible hashing. Requires the optional `xxhash` package.    
+    The above hash key structure is not 100% collision free. Theoretically it’s still possible for the different prefix tokens to have the same hash value. To avoid any hash collisions **in a multi-tenant setup, we use SHA256** as hash function instead of the builtin hash.
+    SHA256 is supported since vLLM v0.8.3 and the default since v0.10.2. It comes with a negligible performance impact of about 75ns per token (<4ms for 50k tokens of context).
 
 **A hashing example with multi-modality inputs**  
 In this example, we illustrate how prefix caching works with multi-modality inputs (e.g., images). Assuming we have a request with the following messages:

docs/design/torch_compile_multimodal.md

@@ -11,14 +11,14 @@ to new models to improve performance.
 
 ## Overview
 
-We have recently enabled the `@support_torch_compile` decorator to work for multiple nn module components within a model type; this enables
+We have recently enabled the `@supports_torch_compile` decorator to work for multiple nn module components within a model type; this enables
 turning compile on for multimodal encoders, bringing performance improvements to additional components of the stack.
 
 When applied to the vision block of [`Qwen2_5_vl`](https://github.com/vllm-project/vllm/pull/23207) we observe ~4.5% e2e perf improvements with
 some increase in compilation time
 
 This feature is off by default, but can be enabled by setting `compile_mm_encoder: true` in the compilation config when models have the
-`@support_torch_compile` decorator.
+`@supports_torch_compile` decorator.
 
 ## How Compilation Works for Multimodal Components
 
@@ -26,7 +26,7 @@ This feature is off by default, but can be enabled by setting `compile_mm_encode
 
 To compile a multimodal component such as an encoder, we follow the same mechanism as the LLM text backbone, with a few additional scaffoldings:
 
-1. The `@support_torch_compile` decorator should include `enable_if=should_torch_compile_mm_vit`. This will gate the compilation behind our
+1. The `@supports_torch_compile` decorator should include `enable_if=should_torch_compile_mm_vit`. This will gate the compilation behind our
 `compile_mm_encoder` configuration
 
 2. `with set_model_tag("<component_name>", is_encoder=True)` context manager should be used around the nn.Module's instantiation. Since torch.compile
@@ -44,9 +44,9 @@ this for more configuration in the future.
 
 ## Applying torch.compile to a New Multimodal Model/Component
 
-To apply `support_torch_compile` to a new general nn.Module, we advise following the same steps in [`debug_vllm_compile`](./debug_vllm_compile.md); this includes:
+To apply `supports_torch_compile` to a new general nn.Module, we advise following the same steps in [`debug_vllm_compile`](./debug_vllm_compile.md); this includes:
 
-1. Applying `support_torch_compile` on initially small modules (such as basic MLP layers), then raising to more general modules until one reaches a good performance
+1. Applying `supports_torch_compile` on initially small modules (such as basic MLP layers), then raising to more general modules until one reaches a good performance
 tradeoff
 
 2. Leveraging [`tlparse`](https://github.com/meta-pytorch/tlparse) to identify and eliminate the source of recompiles and graph breaks

docs/features/batch_invariance.md

@@ -106,7 +106,6 @@ Batch invariance has been tested and verified on the following models:
 - **DeepSeek series**: `deepseek-ai/DeepSeek-V3`, `deepseek-ai/DeepSeek-V3-0324`, `deepseek-ai/DeepSeek-R1`, `deepseek-ai/DeepSeek-V3.1`
 - **Qwen3 (Dense)**: `Qwen/Qwen3-1.7B`, `Qwen/Qwen3-8B`
 - **Qwen3 (MoE)**: `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct`
-- **Qwen2.5**: `Qwen/Qwen2.5-0.5B-Instruct`, `Qwen/Qwen2.5-1.5B-Instruct`, `Qwen/Qwen2.5-3B-Instruct`, `Qwen/Qwen2.5-7B-Instruct`, `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-32B-Instruct`
 - **Llama 3**: `meta-llama/Llama-3.1-8B-Instruct`, `meta-llama/Llama-3.2-1B-Instruct`
 
 Other models may also work, but these have been explicitly validated. If you encounter issues with a specific model, please report them on the [GitHub issue tracker](https://github.com/vllm-project/vllm/issues/new/choose).

docs/features/lora.md

@@ -210,24 +210,6 @@ Alternatively, follow these example steps to implement your own plugin:
 
     For more details, refer to the [vLLM's Plugins System](../design/plugin_system.md).
 
-### In-Place LoRA Reloading
-
-When dynamically loading LoRA adapters, you may need to replace an existing adapter with updated weights while keeping the same name. The `load_inplace` parameter enables this functionality. This commonly occurs in asynchronous reinforcement learning setups, where adapters are continuously updated and swapped in without interrupting ongoing inference.
-
-When `load_inplace=True`, vLLM will replace the existing adapter with the new one.
-
-Example request to load or replace a LoRA adapter with the same name:
-
-```bash
-curl -X POST http://localhost:8000/v1/load_lora_adapter \
--H "Content-Type: application/json" \
--d '{
-    "lora_name": "my-adapter",
-    "lora_path": "/path/to/adapter/v2",
-    "load_inplace": true
-}'
-```
-
 ## New format for `--lora-modules`
 
 In the previous version, users would provide LoRA modules via the following format, either as a key-value pair or in JSON format. For example:

docs/features/multimodal_inputs.md

@@ -20,6 +20,67 @@ To input multi-modal data, follow this schema in [vllm.inputs.PromptType][]:
 - `prompt`: The prompt should follow the format that is documented on HuggingFace.
 - `multi_modal_data`: This is a dictionary that follows the schema defined in [vllm.multimodal.inputs.MultiModalDataDict][].
 
+### Stable UUIDs for Caching (multi_modal_uuids)
+
+When using multi-modal inputs, vLLM normally hashes each media item by content to enable caching across requests. You can optionally pass `multi_modal_uuids` to provide your own stable IDs for each item so caching can reuse work across requests without rehashing the raw content.
+
+??? code
+
+    ```python
+    from vllm import LLM
+    from PIL import Image
+
+    # Qwen2.5-VL example with two images
+    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct")
+
+    prompt = "USER: <image><image>\nDescribe the differences.\nASSISTANT:"
+    img_a = Image.open("/path/to/a.jpg")
+    img_b = Image.open("/path/to/b.jpg")
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": {"image": [img_a, img_b]},
+        # Provide stable IDs for caching.
+        # Requirements (matched by this example):
+        #  - Include every modality present in multi_modal_data.
+        #  - For lists, provide the same number of entries.
+        #  - Use None to fall back to content hashing for that item.
+        "multi_modal_uuids": {"image": ["sku-1234-a", None]},
+    })
+
+    for o in outputs:
+        print(o.outputs[0].text)
+    ```
+
+Using UUIDs, you can also skip sending media data entirely if you expect cache hits for respective items. Note that the request will fail if the skipped media doesn't have a corresponding UUID, or if the UUID fails to hit the cache.
+
+??? code
+
+    ```python
+    from vllm import LLM
+    from PIL import Image
+
+    # Qwen2.5-VL example with two images
+    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct")
+
+    prompt = "USER: <image><image>\nDescribe the differences.\nASSISTANT:"
+    img_b = Image.open("/path/to/b.jpg")
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": {"image": [None, img_b]},
+        # Since img_a is expected to be cached, we can skip sending the actual
+        # image entirely.
+        "multi_modal_uuids": {"image": ["sku-1234-a", None]},
+    })
+
+    for o in outputs:
+        print(o.outputs[0].text)
+    ```
+
+!!! warning
+    If both multimodal processor caching and prefix caching are disabled, user-provided `multi_modal_uuids` are ignored.
+
 ### Image Inputs
 
 You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:
@@ -336,8 +397,7 @@ No manual conversion is needed - vLLM handles the channel normalization automati
 ### Embedding Inputs
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
-pass a tensor of shape `(..., hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
-The exact shape depends on the model being used.
+pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
 
 You must enable this feature via `enable_mm_embeds=True`.
 
@@ -358,7 +418,8 @@ You must enable this feature via `enable_mm_embeds=True`.
     # Refer to the HuggingFace repo for the correct format to use
     prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
 
-    # For most models, `image_embeds` has shape: (num_images, image_feature_size, hidden_size)
+    # Embeddings for single image
+    # torch.Tensor of shape (1, image_feature_size, hidden_size of LM)
     image_embeds = torch.load(...)
 
     outputs = llm.generate({
@@ -369,8 +430,21 @@ You must enable this feature via `enable_mm_embeds=True`.
     for o in outputs:
         generated_text = o.outputs[0].text
         print(generated_text)
+    ```
 
-    # Additional examples for models that require extra fields
+For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings:
+
+??? code
+
+    ```python
+    # Construct the prompt based on your model
+    prompt = ...
+
+    # Embeddings for multiple images
+    # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM)
+    image_embeds = torch.load(...)
+
+    # Qwen2-VL
     llm = LLM(
         "Qwen/Qwen2-VL-2B-Instruct",
         limit_mm_per_prompt={"image": 4},
@@ -378,15 +452,13 @@ You must enable this feature via `enable_mm_embeds=True`.
     )
     mm_data = {
         "image": {
-            # Shape: (total_feature_size, hidden_size)
-            # total_feature_size = sum(image_feature_size for image in images)
-            "image_embeds": torch.load(...),
-            # Shape: (num_images, 3)
+            "image_embeds": image_embeds,
             # image_grid_thw is needed to calculate positional encoding.
-            "image_grid_thw": torch.load(...),
+            "image_grid_thw": torch.load(...),  # torch.Tensor of shape (1, 3),
         }
     }
 
+    # MiniCPM-V
     llm = LLM(
         "openbmb/MiniCPM-V-2_6",
         trust_remote_code=True,
@@ -395,14 +467,20 @@ You must enable this feature via `enable_mm_embeds=True`.
     )
     mm_data = {
         "image": {
-            # Shape: (num_images, num_slices, hidden_size)
-            # num_slices can differ for each image
-            "image_embeds": [torch.load(...) for image in images],  
-            # Shape: (num_images, 2)
+            "image_embeds": image_embeds,
             # image_sizes is needed to calculate details of the sliced image.
-            "image_sizes": [image.size for image in images],
+            "image_sizes": [image.size for image in images],  # list of image sizes
         }
     }
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": mm_data,
+    })
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
     ```
 
 For Qwen3-VL, the `image_embeds` should contain both the base image embedding and deepstack features.
@@ -423,8 +501,8 @@ You can pass pre-computed audio embeddings similar to image embeddings:
     # Refer to the HuggingFace repo for the correct format to use
     prompt = "USER: <audio>\nWhat is in this audio?\nASSISTANT:"
 
-    # Load pre-computed audio embeddings, usually with shape:
-    # (num_audios, audio_feature_size, hidden_size of LM)
+    # Load pre-computed audio embeddings
+    # torch.Tensor of shape (1, audio_feature_size, hidden_size of LM)
     audio_embeds = torch.load(...)
 
     outputs = llm.generate({
@@ -437,67 +515,6 @@ You can pass pre-computed audio embeddings similar to image embeddings:
         print(generated_text)
     ```
 
-### Cached Inputs
-
-When using multi-modal inputs, vLLM normally hashes each media item by content to enable caching across requests. You can optionally pass `multi_modal_uuids` to provide your own stable IDs for each item so caching can reuse work across requests without rehashing the raw content.
-
-??? code
-
-    ```python
-    from vllm import LLM
-    from PIL import Image
-
-    # Qwen2.5-VL example with two images
-    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct")
-
-    prompt = "USER: <image><image>\nDescribe the differences.\nASSISTANT:"
-    img_a = Image.open("/path/to/a.jpg")
-    img_b = Image.open("/path/to/b.jpg")
-
-    outputs = llm.generate({
-        "prompt": prompt,
-        "multi_modal_data": {"image": [img_a, img_b]},
-        # Provide stable IDs for caching.
-        # Requirements (matched by this example):
-        #  - Include every modality present in multi_modal_data.
-        #  - For lists, provide the same number of entries.
-        #  - Use None to fall back to content hashing for that item.
-        "multi_modal_uuids": {"image": ["sku-1234-a", None]},
-    })
-
-    for o in outputs:
-        print(o.outputs[0].text)
-    ```
-
-Using UUIDs, you can also skip sending media data entirely if you expect cache hits for respective items. Note that the request will fail if the skipped media doesn't have a corresponding UUID, or if the UUID fails to hit the cache.
-
-??? code
-
-    ```python
-    from vllm import LLM
-    from PIL import Image
-
-    # Qwen2.5-VL example with two images
-    llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct")
-
-    prompt = "USER: <image><image>\nDescribe the differences.\nASSISTANT:"
-    img_b = Image.open("/path/to/b.jpg")
-
-    outputs = llm.generate({
-        "prompt": prompt,
-        "multi_modal_data": {"image": [None, img_b]},
-        # Since img_a is expected to be cached, we can skip sending the actual
-        # image entirely.
-        "multi_modal_uuids": {"image": ["sku-1234-a", None]},
-    })
-
-    for o in outputs:
-        print(o.outputs[0].text)
-    ```
-
-!!! warning
-    If both multimodal processor caching and prefix caching are disabled, user-provided `multi_modal_uuids` are ignored.
-
 ## Online Serving
 
 Our OpenAI-compatible server accepts multi-modal data via the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat). Media inputs also support optional UUIDs users can provide to uniquely identify each media, which is used to cache the media results across requests.
@@ -862,11 +879,7 @@ Full example: [examples/online_serving/openai_chat_completion_client_for_multimo
 ### Embedding Inputs
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
-pass a tensor of shape `(..., hidden_size of LM)` for each item to the corresponding field of the multi-modal dictionary.
-
-!!! important
-    Unlike offline inference, the embeddings for each item must be passed separately
-    in order for placeholder tokens to be applied correctly by the chat template.
+pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
 
 You must enable this feature via the `--enable-mm-embeds` flag in `vllm serve`.
 
@@ -884,6 +897,11 @@ The following example demonstrates how to pass image embeddings to the OpenAI se
     ```python
     from vllm.utils.serial_utils import tensor2base64
 
+    image_embedding = torch.load(...)
+    grid_thw = torch.load(...) # Required by Qwen/Qwen2-VL-2B-Instruct
+
+    base64_image_embedding = tensor2base64(image_embedding)
+
     client = OpenAI(
         # defaults to os.environ.get("OPENAI_API_KEY")
         api_key=openai_api_key,
@@ -894,33 +912,29 @@ The following example demonstrates how to pass image embeddings to the OpenAI se
     model = "llava-hf/llava-1.5-7b-hf"
     embeds = {
         "type": "image_embeds",
-        "image_embeds": tensor2base64(torch.load(...)),  # Shape: (image_feature_size, hidden_size)
+        "image_embeds": f"{base64_image_embedding}",
         "uuid": image_url,  # Optional
     }
 
-
-    # Additional examples for models that require extra fields
+    # Pass additional parameters (available to Qwen2-VL and MiniCPM-V)
     model = "Qwen/Qwen2-VL-2B-Instruct"
     embeds = {
         "type": "image_embeds",
         "image_embeds": {
-            "image_embeds": tensor2base64(torch.load(...)),  # Shape: (image_feature_size, hidden_size)
-            "image_grid_thw": tensor2base64(torch.load(...)),  # Shape: (3,)
+            "image_embeds": f"{base64_image_embedding}",  # Required
+            "image_grid_thw": f"{base64_image_grid_thw}",  # Required by Qwen/Qwen2-VL-2B-Instruct
         },
         "uuid": image_url,  # Optional
     }
-
     model = "openbmb/MiniCPM-V-2_6"
     embeds = {
         "type": "image_embeds",
         "image_embeds": {
-            "image_embeds": tensor2base64(torch.load(...)),  # Shape: (num_slices, hidden_size)
-            "image_sizes": tensor2base64(torch.load(...)),  # Shape: (2,)
+            "image_embeds": f"{base64_image_embedding}",  # Required
+            "image_sizes": f"{base64_image_sizes}",  # Required by openbmb/MiniCPM-V-2_6
         },
         "uuid": image_url,  # Optional
     }
-
-    # Single image input
     chat_completion = client.chat.completions.create(
         messages=[
             {
@@ -940,55 +954,9 @@ The following example demonstrates how to pass image embeddings to the OpenAI se
         ],
         model=model,
     )
-
-    # Multi image input
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "system",
-                "content": "You are a helpful assistant.",
-            },
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": "What's in this image?",
-                    },
-                    embeds,
-                    embeds,
-                ],
-            },
-        ],
-        model=model,
-    )
-
-    # Multi image input (interleaved)
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "system",
-                "content": "You are a helpful assistant.",
-            },
-            {
-                "role": "user",
-                "content": [
-                    embeds,
-                    {
-                        "type": "text",
-                        "text": "What's in this image?",
-                    },
-                    embeds,
-                ],
-            },
-        ],
-        model=model,
-    )
     ```
 
-### Cached Inputs
-
-Just like with offline inference, you can skip sending media if you expect cache hits with provided UUIDs. You can do so by sending media like this:
+For Online Serving, you can also skip sending media if you expect cache hits with provided UUIDs. You can do so by sending media like this:
 
 ??? code
 
@@ -1022,3 +990,13 @@ Just like with offline inference, you can skip sending media if you expect cache
         },
 
     ```
+
+!!! note
+    Multiple messages can now contain `{"type": "image_embeds"}`, enabling you to pass multiple image embeddings in a single request (similar to regular images). The number of embeddings is limited by `--limit-mm-per-prompt`.
+
+    **Important**: The embedding shape format differs based on the number of embeddings:
+
+    - **Single embedding**: 3D tensor of shape `(1, feature_size, hidden_size)`
+    - **Multiple embeddings**: List of 2D tensors, each of shape `(feature_size, hidden_size)`
+
+    If used with a model that requires additional parameters, you must also provide a tensor for each of them, e.g. `image_grid_thw`, `image_sizes`, etc.

docs/features/nixl_connector_usage.md

@@ -50,7 +50,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
 vllm serve Qwen/Qwen3-0.6B \
   --port 8100 \
   --enforce-eager \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
 ```
 
 ### Consumer (Decoder) Configuration
@@ -65,7 +65,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5601 \
 vllm serve Qwen/Qwen3-0.6B \
   --port 8200 \
   --enforce-eager \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
 ```
 
 ### Proxy Server
@@ -110,7 +110,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
 UCX_NET_DEVICES=all \
 vllm serve Qwen/Qwen3-0.6B --port 8000 \
   --tensor-parallel-size 8 \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'
 
 # Prefiller 2 on Machine B (example IP: ${IP2})
 VLLM_NIXL_SIDE_CHANNEL_HOST=${IP2} \
@@ -118,7 +118,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
 UCX_NET_DEVICES=all \
 vllm serve Qwen/Qwen3-0.6B --port 8000 \
   --tensor-parallel-size 8 \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'
 ```
 
 ### Multiple Decoder Instances on Different Machines
@@ -130,7 +130,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
 UCX_NET_DEVICES=all \
 vllm serve Qwen/Qwen3-0.6B --port 8000 \
   --tensor-parallel-size 8 \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'
 
 # Decoder 2 on Machine D (example IP: ${IP4})
 VLLM_NIXL_SIDE_CHANNEL_HOST=${IP4} \
@@ -138,7 +138,7 @@ VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
 UCX_NET_DEVICES=all \
 vllm serve Qwen/Qwen3-0.6B --port 8000 \
   --tensor-parallel-size 8 \
-  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer","kv_load_failure_policy":"fail"}'
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'
 ```
 
 ### Proxy for Multiple Instances
@@ -164,16 +164,6 @@ For multi-host DP deployment, only need to provide the host/port of the head ins
     NixlConnector currently does not distinguish `kv_role`; the actual prefiller/decoder roles are determined by the upper-level proxy (e.g., `toy_proxy_server.py` using `--prefiller-hosts` and `--decoder-hosts`).
     Therefore, `kv_role` in `--kv-transfer-config` is effectively a placeholder and does not affect NixlConnector's behavior.
 
-### KV Load Failure Policy
-
-The `kv_load_failure_policy` setting controls how the system handles failures when the decoder instance loads KV cache blocks from the prefiller instance:
-
-- **fail** (recommended): Immediately fail the request with an error when KV load fails. This prevents performance degradation by avoiding recomputation of prefill work on the decode instance.
-- **recompute** (default): Recompute failed blocks locally on the decode instance. This may cause performance _jitter_ on decode instances as the scheduled prefill will delay and interfere with other decodes. Furthermore, decode instances are typically configured with low-latency optimizations.
-
-!!! warning
-    Using `kv_load_failure_policy="recompute"` can lead to performance degradation in production deployments. When KV loads fail, the decode instance will execute prefill work with decode-optimized configurations, which is inefficient and defeats the purpose of disaggregated prefilling. This also increases tail latency for other ongoing decode requests.
-
 ## Experimental Feature
 
 ### Heterogeneous KV Layout support
@@ -184,15 +174,6 @@ Support use case: Prefill with 'HND' and decode with 'NHD' with experimental con
 --kv-transfer-config '{..., "enable_permute_local_kv":"True"}'
 ```
 
-### Cross layers blocks
-
-By default, this feature is disabled. On attention backends that support this feature, each logical block is contiguous in physical memory. This reduces the number of buffers that need to be transferred.
-To enable this feature:
-
-```bash
---kv-transfer-config '{..., "kv_connector_extra_config": {"enable_cross_layers_blocks": "True"}}'
-```
-
 ## Example Scripts/Code
 
 Refer to these example scripts in the vLLM repository:

docs/features/quantization/README.md

@@ -5,11 +5,12 @@ Quantization trades off model precision for smaller memory footprint, allowing l
 Contents:
 
 - [AutoAWQ](auto_awq.md)
+- [AutoRound](auto_round.md)
 - [BitsAndBytes](bnb.md)
 - [BitBLAS](bitblas.md)
 - [GGUF](gguf.md)
 - [GPTQModel](gptqmodel.md)
-- [Intel Neural Compressor](inc.md)
+- [INC](inc.md)
 - [INT4 W4A16](int4.md)
 - [INT8 W8A8](int8.md)
 - [FP8 W8A8](fp8.md)
@@ -42,23 +43,23 @@ th:not(:first-child) {
 }
 </style>
 
-| Implementation        | Volta   | Turing   | Ampere   | Ada   | Hopper   | AMD GPU   | Intel GPU   | x86 CPU   |
-|-----------------------|---------|----------|----------|-------|----------|-----------|-------------|-----------|
-| AWQ                   | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ✅︎        |
-| GPTQ                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ✅︎        |
-| Marlin (GPTQ/AWQ/FP8) | ❌      | ❌       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌        |
-| INT8 (W8A8)           | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ✅︎        |
-| FP8 (W8A8)            | ❌      | ❌       | ❌       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌        |
-| BitBLAS               | ✅︎      | ✅       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌        |
-| BitBLAS (GPTQ)        | ❌      | ❌       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌        |
-| bitsandbytes          | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌        |
-| DeepSpeedFP           | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌        |
-| GGUF                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌        |
+| Implementation        | Volta   | Turing   | Ampere   | Ada   | Hopper   | AMD GPU   | Intel GPU   | Intel Gaudi | x86 CPU   |
+|-----------------------|---------|----------|----------|-------|----------|-----------|-------------|-------------|-----------|
+| AWQ                   | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ❌         | ✅︎        |
+| GPTQ                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ✅︎          | ❌         | ✅︎        |
+| Marlin (GPTQ/AWQ/FP8) | ❌      | ❌       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        |
+| INT8 (W8A8)           | ❌      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ✅︎        |
+| FP8 (W8A8)            | ❌      | ❌       | ❌       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌         | ❌        |
+| BitBLAS               | ✅︎      | ✅       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        |
+| BitBLAS (GPTQ)        | ❌      | ❌       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        |
+| bitsandbytes          | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        |
+| DeepSpeedFP           | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ❌         | ❌          | ❌         | ❌        |
+| GGUF                  | ✅︎      | ✅︎       | ✅︎       | ✅︎    | ✅︎       | ✅︎         | ❌          | ❌         | ❌        |
+| INC (W8A8)            | ❌      | ❌       | ❌       | ❌    | ❌       | ❌         | ❌          | ✅︎         | ❌        |
 
 - Volta refers to SM 7.0, Turing to SM 7.5, Ampere to SM 8.0/8.6, Ada to SM 8.9, and Hopper to SM 9.0.
 - ✅︎ indicates that the quantization method is supported on the specified hardware.
 - ❌ indicates that the quantization method is not supported on the specified hardware.
-- All Intel Gaudi quantization support has been migrated to [vLLM-Gaudi](https://github.com/vllm-project/vllm-gaudi).
 
 !!! note
     For information on quantization support on Google TPU, please refer to the [TPU-Inference Recommended Models and Features](https://docs.vllm.ai/projects/tpu/en/latest/recommended_models_features/) documentation.
@@ -67,160 +68,3 @@ th:not(:first-child) {
     This compatibility chart is subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods.
 
     For the most up-to-date information on hardware support and quantization methods, please refer to [vllm/model_executor/layers/quantization](../../../vllm/model_executor/layers/quantization) or consult with the vLLM development team.
-
-## Out-of-Tree Quantization Plugins
-
-vLLM supports registering custom, out-of-tree quantization methods using the `@register_quantization_config` decorator. This allows you to implement and use your own quantization schemes without modifying the vLLM codebase.
-
-### Registering a Custom Quantization Method
-
-To register a custom quantization method, create a class that inherits from `QuantizationConfig` and decorate it with `@register_quantization_config`. The `get_quant_method` dispatches to the appropriate quantize method based on the layer type:
-
-```python
-import torch
-from vllm.model_executor.layers.quantization import (
-    register_quantization_config,
-)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
-from vllm.model_executor.layers.linear import LinearBase
-from vllm.model_executor.layers.fused_moe import FusedMoE
-
-@register_quantization_config("my_quant")
-class MyQuantConfig(QuantizationConfig):
-    """Custom quantization config."""
-
-    def get_name(self) -> str:
-        return "my_quant"
-
-    def get_supported_act_dtypes(self) -> list:
-        return [torch.float16, torch.bfloat16]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        # Minimum GPU compute capability, -1 for no restriction
-        return -1
-
-    @staticmethod
-    def get_config_filenames() -> list[str]:
-        # Config files to search for in model directory
-        return []
-
-    @classmethod
-    def from_config(cls, config: dict) -> "MyQuantConfig":
-        # Create config from model's quantization config
-        return cls()
-
-    def get_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> QuantizeMethodBase | None:
-        # Dispatch based on layer type
-        # NOTE: you only need to implement methods you care about
-        if isinstance(layer, LinearBase):
-            return MyQuantLinearMethod()
-        elif isinstance(layer, FusedMoE):
-            return MyQuantMoEMethod(layer.moe_config)
-        return None
-```
-
-### Required QuantizationConfig Methods
-
-Your custom `QuantizationConfig` subclass must implement these abstract methods:
-
-| Method | Description |
-|--------|-------------|
-| `get_name()` | Returns the name of the quantization method |
-| `get_supported_act_dtypes()` | Returns list of supported activation dtypes (e.g., `torch.float16`) |
-| `get_min_capability()` | Returns minimum GPU compute capability (e.g., 80 for Ampere, -1 for no restriction) |
-| `get_config_filenames()` | Returns list of config filenames to search for in model directory |
-| `from_config(config)` | Class method to create config from model's quantization config dict |
-| `get_quant_method(layer, prefix)` | Returns the quantization method for a given layer, or `None` to skip |
-
-### Implementing a Quantized Linear Method
-
-For linear layers, return a `QuantizeMethodBase` subclass from `get_quant_method`. You can extend `UnquantizedLinearMethod` as a starting point:
-
-```python
-from vllm.model_executor.layers.linear import UnquantizedLinearMethod
-
-class MyQuantLinearMethod(UnquantizedLinearMethod):
-    """Custom quantization method for linear layers."""
-
-    def create_weights(
-        self, layer: torch.nn.Module, *weight_args, **extra_weight_attrs
-    ):
-        # Create quantized weights for the layer
-        ...
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        # Apply custom quantization logic here
-        ...
-```
-
-### Implementing a Quantized MoE Method
-
-For Mixture of Experts (MoE) models, return a `FusedMoEMethodBase` subclass from `get_quant_method`. You can use `UnquantizedFusedMoEMethod` to skip MoE quantization:
-
-```python
-from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
-from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
-
-class MyQuantMoEMethod(FusedMoEMethodBase):
-    """Custom quantization method for MoE layers."""
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        num_experts: int,
-        hidden_size: int,
-        intermediate_size_per_partition: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-        # Create quantized weights for the MoE layer
-        ...
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        router: "FusedMoERouter",
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> torch.Tensor:
-        # Apply MoE computation with quantized weights
-        ...
-
-    def get_fused_moe_quant_config(
-        self, layer: torch.nn.Module
-    ) -> FusedMoEQuantConfig | None:
-        # Return the MoE quantization configuration
-        ...
-```
-
-See existing implementations like `Fp8MoEMethod` in `vllm/model_executor/layers/quantization/fp8.py` for reference.
-
-### Using the Plugin
-
-Once registered, you can use your custom quantization method with vLLM:
-
-```python
-# Register your quantization method (import the module containing your config)
-import my_quant_plugin
-
-from vllm import LLM
-
-# Use the custom quantization method
-llm = LLM(model="your-model", quantization="my_quant")
-```
-
-For more information on the plugin system, see the [Plugin System documentation](../../design/plugin_system.md).

docs/features/quantization/auto_round.md

@@ -0,0 +1,103 @@
+# AutoRound
+
+[AutoRound](https://github.com/intel/auto-round) is Intel’s advanced quantization algorithm designed to produce highly efficient **INT2, INT3, INT4, and INT8**
+quantized large language models—striking an optimal balance between accuracy and deployment performance.
+
+AutoRound applies weight-only quantization to transformer-based models, enabling significant memory savings and faster
+inference while maintaining near-original accuracy. It supports a wide range of hardware platforms, including **CPUs,
+Intel GPUs, HPUs, and CUDA-enabled devices**.
+
+Please refer to the [AutoRound guide](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md) for more details.
+
+Key Features:
+
+✅ **AutoRound, AutoAWQ, AutoGPTQ, and GGUF** are supported
+
+✅ **10+ vision-language models (VLMs)** are supported
+
+✅ **Per-layer mixed-bit quantization** for fine-grained control
+
+✅ **RTN (Round-To-Nearest) mode** for quick quantization with slight accuracy loss
+
+✅ **Multiple quantization recipes**: best, base, and light
+
+✅ Advanced utilities such as immediate packing and support for **10+ backends**
+
+## Installation
+
+```bash
+uv pip install auto-round
+```
+
+## Quantizing a model
+
+For VLMs, please change to `auto-round-mllm` in CLI usage and `AutoRoundMLLM` in API usage.
+
+### CLI usage
+
+```bash
+auto-round \
+    --model Qwen/Qwen3-0.6B \
+    --bits 4 \
+    --group_size 128 \
+    --format "auto_round" \
+    --output_dir ./tmp_autoround
+```
+
+```bash
+auto-round \
+    --model Qwen/Qwen3-0.6B \
+    --format "gguf:q4_k_m" \
+    --output_dir ./tmp_autoround
+```
+
+### API usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from auto_round import AutoRound
+
+model_name = "Qwen/Qwen3-0.6B"
+model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+bits, group_size, sym = 4, 128, True
+autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym)
+
+# the best accuracy, 4-5X slower, low_gpu_mem_usage could save ~20G but ~30% slower
+# autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=True, bits=bits, group_size=group_size, sym=sym)
+
+# 2-3X speedup, slight accuracy drop at W4G128
+# autoround = AutoRound(model, tokenizer, nsamples=128, iters=50, lr=5e-3, bits=bits, group_size=group_size, sym=sym )
+
+output_dir = "./tmp_autoround"
+# format= 'auto_round'(default), 'auto_gptq', 'auto_awq'
+autoround.quantize_and_save(output_dir, format="auto_round")
+```
+
+## Running a quantized model with vLLM
+
+Here is some example code to run auto-round format in vLLM:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+]
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95)
+model_name = "Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound"
+llm = LLM(model=model_name)
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+## Acknowledgement
+
+Special thanks to open-source low precision libraries such as AutoGPTQ, AutoAWQ, GPTQModel, Triton, Marlin, and
+ExLLaMAV2 for providing low-precision CUDA kernels, which are leveraged in AutoRound.

docs/features/quantization/inc.md

@@ -1,89 +1,50 @@
-# Intel Quantization Support
+# FP8 INC
 
-[AutoRound](https://github.com/intel/auto-round) is Intel’s advanced quantization algorithm designed for large language models(LLMs). It produces highly efficient **INT2, INT3, INT4, INT8, MXFP8, MXFP4, NVFP4**, and **GGUF** quantized models, balancing accuracy and inference performance. AutoRound is also part of the [Intel® Neural Compressor](https://github.com/intel/neural-compressor). For a deeper introduction, see the [AutoRound step-by-step guide](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md).
+vLLM supports FP8 (8-bit floating point) weight and activation quantization using Intel® Neural Compressor (INC) on Intel® Gaudi® 2 and Intel® Gaudi® 3 AI accelerators.
+Currently, quantization is validated only in Llama models.
 
-## Key Features
-
-✅ Superior Accuracy Delivers strong performance even at 2–3 bits [example models](https://huggingface.co/collections/OPEA/2-3-bits)
-
-✅ Fast Mixed `Bits`/`Dtypes` Scheme Generation Automatically configure in minutes
-
-✅ Support for exporting **AutoRound, AutoAWQ, AutoGPTQ, and GGUF** formats
-
-✅ **10+ vision-language models (VLMs)** are supported
-
-✅ **Per-layer mixed-bit quantization** for fine-grained control
-
-✅ **RTN (Round-To-Nearest) mode** for quick quantization with slight accuracy loss
-
-✅ **Multiple quantization recipes**: best, base, and light
-
-✅ Advanced utilities such as immediate packing and support for **10+ backends**
-
-## Supported Recipes on Intel Platforms
-
-On Intel platforms, AutoRound recipes are being enabled progressively by format and hardware. Currently, vLLM supports:
-
-- **`W4A16`**: weight-only, 4-bit weights with 16-bit activations
-- **`W8A16`**: weight-only, 8-bit weights with 16-bit activations
-
-Additional recipes and formats will be supported in future releases.
-
-## Quantizing a Model
-
-### Installation
-
-```bash
-uv pip install auto-round
-```
-
-### Quantize with CLI
-
-```bash
-auto-round \
-    --model Qwen/Qwen3-0.6B \
-    --scheme W4A16 \
-    --format auto_round \
-    --output_dir ./tmp_autoround
-```
-
-### Quantize with Python API
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from auto_round import AutoRound
-
-model_name = "Qwen/Qwen3-0.6B"
-autoround = AutoRound(model_name, scheme="W4A16")
-
-# the best accuracy, 4-5X slower, low_gpu_mem_usage could save ~20G but ~30% slower
-# autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=True, bits=bits, group_size=group_size, sym=sym)
-
-# 2-3X speedup, slight accuracy drop at W4G128
-# autoround = AutoRound(model, tokenizer, nsamples=128, iters=50, lr=5e-3, bits=bits, group_size=group_size, sym=sym )
-
-output_dir = "./tmp_autoround"
-# format= 'auto_round'(default), 'auto_gptq', 'auto_awq'
-autoround.quantize_and_save(output_dir, format="auto_round")
-```
-
-## Deploying AutoRound Quantized Models in vLLM
-
-```bash
-vllm serve Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound \
-    --gpu-memory-utilization 0.8 \
-    --max-model-len 4096
-```
+Intel Gaudi supports quantization of various modules and functions, including, but not limited to `Linear`, `KVCache`, `Matmul` and `Softmax`. For more information, please refer to:
+[Supported Modules\\Supported Functions\\Custom Patched Modules](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html#supported-modules).
 
 !!! note
-     To deploy `wNa16` models on Intel GPU/CPU, please add `--enforce-eager` for now.
+    Measurement files are required to run quantized models with vLLM on Gaudi accelerators. The FP8 model calibration procedure is described in the [vLLM HPU extension](https://github.com/HabanaAI/vllm-hpu-extension/tree/main/calibration/README.md) package.
 
-## Evaluating the Quantized Model with vLLM
+!!! note
+    `QUANT_CONFIG` is an environment variable that points to the measurement or quantization [JSON config file](https://docs.habana.ai/en/latest/PyTorch/Inference_on_PyTorch/Quantization/Inference_Using_FP8.html#supported-json-config-file-options).
+    The measurement configuration file is used during the calibration procedure to collect measurements for a given model. The quantization configuration is used during inference.
+
+## Run Online Inference Using FP8
+
+Once you've completed the model calibration process and collected the measurements, you can run FP8 inference with vLLM using the following command:
 
 ```bash
-lm_eval --model vllm \
-  --model_args pretrained="Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound,max_model_len=8192,max_num_batched_tokens=32768,max_num_seqs=128,gpu_memory_utilization=0.8,dtype=bfloat16,max_gen_toks=2048,enforce_eager=True" \
-  --tasks gsm8k \
-  --num_fewshot 5 \
-  --batch_size 128
+export QUANT_CONFIG=/path/to/quant/config/inc/meta-llama-3.1-405b-instruct/maxabs_measure_g3.json
+vllm serve meta-llama/Llama-3.1-405B-Instruct --quantization inc --kv-cache-dtype fp8_inc --tensor-parallel-size 8
 ```
+
+!!! tip
+    When using FP8 models, you may experience timeouts caused by the long compilation time of FP8 operations. To mitigate this problem, you can use the below environment variables:
+    `VLLM_ENGINE_ITERATION_TIMEOUT_S` - to adjust the vLLM server timeout. You can set the value in seconds, e.g., 600 equals 10 minutes.
+    `VLLM_RPC_TIMEOUT` - to adjust the RPC protocol timeout used by the OpenAI-compatible API. This value is in microseconds, e.g., 600000 equals 10 minutes.
+
+## Run Offline Inference Using FP8
+
+To run offline inference (after completing the model calibration process):
+
+* Set the "QUANT_CONFIG" environment variable to point to a JSON configuration file with QUANTIZE mode.
+* Pass `quantization=inc` and `kv_cache_dtype=fp8_inc` as parameters to the `LLM` object.
+* Call shutdown method of the model_executor at the end of the run.
+
+```python
+from vllm import LLM
+llm = LLM("llama3.1/Meta-Llama-3.1-8B-Instruct", quantization="inc", kv_cache_dtype="fp8_inc")
+...
+# Call llm.generate on the required prompts and sampling params.
+...
+llm.llm_engine.model_executor.shutdown()
+```
+
+## Device for the Model's Weights Uploading
+
+The unquantized weights are first loaded onto the CPU, then quantized and transferred to the target device (HPU) for model execution.
+This reduces the device memory footprint of model weights, as only quantized weights are stored in the device memory.

docs/features/quantization/quantized_kvcache.md

@@ -1,187 +1,162 @@
 # Quantized KV Cache
 
-## FP8 KV Cache Overview
+## FP8 KV Cache
 
-Efficient memory usage is crucial for working with large language models. Quantizing the KV (Key-Value) cache to FP8 format can significantly reduce its memory footprint. This optimization enables you to store more tokens in memory, leading to improved throughput and support for longer context windows.
+Quantizing the KV cache to FP8 reduces its memory footprint. This increases the number of tokens that can be stored in the cache, improving throughput.
 
-> **Note:** When using the Flash Attention 3 backend with FP8 KV cache, attention operations are also performed in the quantized (FP8) domain. In this configuration, queries are quantized to FP8 in addition to keys and values.
+### FP8 Formats
 
-### Supported FP8 KV-Cache Quantization Schemes
+[OCP (Open Compute Project)](https://www.opencompute.org) specifies two common 8-bit floating point data formats:
 
-vLLM supports two main quantization strategies for the FP8 KV-cache:
+- E5M2 (5 exponent bits and 2 mantissa bits)
+- E4M3FN (4 exponent bits and 3 mantissa bits, often shortened as E4M3)
 
-- **Per-tensor quantization:**  
-  A single scale is applied for each Q, K, and V tensor individually. (`q/k/v_scale = [1]`)
-- **Per-attention-head quantization:**  
-  Each scale corresponds to an attention head: `q_scale = [num_heads]`, `k/v_scale = [num_kv_heads]`.
+The E4M3 format offers higher precision compared to E5M2. However, due to its small dynamic range (±240.0), E4M3 typically requires a higher-precision (FP32) scaling factor alongside each quantized tensor.
 
-> **Note:**  
-> Per-attention-head quantization is currently available **only with the Flash Attention backend** and requires the calibration pathway provided by **llm-compressor**.
+### Current Limitations
 
-### Scale Calibration Approaches
+For now, only per-tensor (scalar) scaling factors are supported. Development is ongoing to support scaling factors of a finer granularity (e.g. per-channel).
 
-You can configure how the quantization scales are computed in vLLM using three different approaches:
+### How FP8 KV Cache Works
 
-1. **No calibration (default scales):**  
-   All quantization scales are set to `1.0`.  
-   _Configure with:_  
-   ```python
-   kv_cache_dtype="fp8"
-   calculate_kv_scales=False
-   ```
+The FP8 KV cache implementation follows this workflow:
 
-2. **Random token calibration (on-the-fly):**  
-   Scales are automatically estimated from a single batch of random tokens during warmup and then fixed.  
-   _Configure with:_  
-   ```python
-   kv_cache_dtype="fp8"
-   calculate_kv_scales=True
-   ```
+1. **Storage**: Key and Value tensors are quantized to FP8 format using scaling factors before being stored in the KV cache
+2. **Retrieval**: When needed for attention computation, cached KV tensors are dequantized back to higher precision (FP16/BF16)
+3. **Attention**: The attention-value multiplication (softmax output × V) is performed using the dequantized higher-precision V tensor
 
-3. **[Recommended] Calibration with a dataset (via llm-compressor):**  
-   Scales are estimated using a curated calibration dataset for maximum accuracy.  
-   This requires the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.  
-   _See example below!_
+This means the final attention computation operates on dequantized values, not FP8 tensors. The quantization reduces memory usage during storage but maintains computation accuracy by using higher precision during the actual attention operations.
 
-#### Additional `kv_cache_dtype` Options
+### Performance Impact
 
-- `kv_cache_dtype="auto"`: Use the model's default data type
-- `kv_cache_dtype="fp8_e4m3"`: Supported on CUDA 11.8+ and ROCm (AMD GPUs)
-- `kv_cache_dtype="fp8_e5m2"`: Supported on CUDA 11.8+
+The current FP8 KV cache implementation primarily benefits throughput by allowing approximately double the amount of space for KV cache allocation. This enables either:
 
----
+- Processing longer context lengths for individual requests, or
+- Handling more concurrent request batches
 
-## Examples
+However, there are currently no latency improvements as the implementation does not yet include fused dequantization and attention operations. Future releases will support quantized attention with hardware acceleration, which should provide additional performance benefits. While the most recent silicon offerings (e.g. AMD MI300, NVIDIA Hopper or later) support native hardware conversion between FP8 and other formats (fp32, fp16, bf16), this benefit is not yet fully realized.
 
-### 1. No Calibration (`kv_cache_dtype="fp8"`, `calculate_kv_scales=False`)
+Studies have shown that FP8 E4M3 quantization typically only minimally degrades inference accuracy, making it a practical choice for throughput optimization.
 
-All quantization scales are set to 1.0.
+## Usage Example
 
-```python
-from vllm import LLM, SamplingParams
+Here is an example of how to enable FP8 quantization:
 
-sampling_params = SamplingParams(temperature=0.7, top_p=0.8)
-llm = LLM(
-    model="meta-llama/Llama-2-7b-chat-hf",
-    kv_cache_dtype="fp8",
-    calculate_kv_scales=False,
-)
-prompt = "London is the capital of"
-out = llm.generate(prompt, sampling_params)[0].outputs[0].text
-print(out)
-```
+??? code
 
----
+    ```python
+    # To calculate kv cache scales on the fly enable the calculate_kv_scales
+    # parameter
 
-### 2. Random Token Calibration (`kv_cache_dtype="fp8"`, `calculate_kv_scales=True`)
+    from vllm import LLM, SamplingParams
 
-Scales are automatically estimated from a single batch of tokens during warmup.
+    sampling_params = SamplingParams(temperature=0.7, top_p=0.8)
+    llm = LLM(
+        model="meta-llama/Llama-2-7b-chat-hf",
+        kv_cache_dtype="fp8",
+        calculate_kv_scales=True,
+    )
+    prompt = "London is the capital of"
+    out = llm.generate(prompt, sampling_params)[0].outputs[0].text
+    print(out)
+    ```
 
-```python
-from vllm import LLM, SamplingParams
+The `kv_cache_dtype` argument specifies the data type for KV cache storage:
 
-sampling_params = SamplingParams(temperature=0.7, top_p=0.8)
-llm = LLM(
-    model="meta-llama/Llama-2-7b-chat-hf",
-    kv_cache_dtype="fp8",
-    calculate_kv_scales=True,
-)
-prompt = "London is the capital of"
-out = llm.generate(prompt, sampling_params)[0].outputs[0].text
-print(out)
-```
+- `"auto"`: Uses the model's default "unquantized" data type
+- `"fp8"` or `"fp8_e4m3"`: Supported on CUDA 11.8+ and ROCm (AMD GPU)
+- `"fp8_e5m2"`: Supported on CUDA 11.8+
 
----
+## Calibrated Scales for Better Accuracy
 
-### 3. **[Recommended] Calibration Using a Dataset (with `llm-compressor`)**
+For optimal model quality when using FP8 KV Cache, we recommend using calibrated scales tuned to representative inference data. [LLM Compressor](https://github.com/vllm-project/llm-compressor/) is the recommended tool for this process.
 
-For the highest-quality quantization, we recommend calibrating against a dataset using `llm-compressor`. This enables advanced strategies such as per-attention-head quantization.
+### Installation
 
-#### Install the required package
+First, install the required dependencies:
 
 ```bash
 pip install llmcompressor
 ```
 
-#### Example: Quantize Llama Attention & KV Cache to FP8
+### Example Usage
 
-```python
-"""
-Quantize Llama attention + KV cache to FP8 (choose either 'tensor' or 'attn_head' strategy)
-using llm-compressor one-shot calibration.
-"""
+Here's a complete example using `meta-llama/Llama-3.1-8B-Instruct` (most models can use this same pattern):
 
-from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoTokenizer
+??? code
 
-from llmcompressor import oneshot
-from llmcompressor.modifiers.quantization import QuantizationModifier
-from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs
+    ```python
+    from datasets import load_dataset
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from llmcompressor import oneshot
 
-# -----------------------------
-# Config
-# -----------------------------
-MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"
-DATASET_ID = "HuggingFaceH4/ultrachat_200k"
-DATASET_SPLIT = "train_sft"
-STRATEGY = "tensor"       # or "attn_head"
-NUM_CALIB_SAMPLES = 512   # Good starting value
-MAX_SEQ_LEN = 2048
-
-# -----------------------------
-# Helpers
-# -----------------------------
-def process_and_tokenize(example, tokenizer: AutoTokenizer):
-    """Convert chat messages to tokens."""
-    text = tokenizer.apply_chat_template(example["messages"], tokenize=False)
-    return tokenizer(
-        text,
-        padding=False,
-        max_length=MAX_SEQ_LEN,
-        truncation=True,
-        add_special_tokens=False,
-    )
-
-def build_recipe(strategy: str) -> QuantizationModifier:
-    fp8_args = QuantizationArgs(num_bits=8, type="float", strategy=strategy)
-    return QuantizationModifier(
-        config_groups={
-            "attention": QuantizationScheme(
-                targets=["LlamaAttention"],  # Quantize queries: q_scale
-                input_activations=fp8_args,
-            )
-        },
-        kv_cache_scheme=fp8_args,           # Quantize KV cache: k/v_scale
-    )
-
-# -----------------------------
-# Main
-# -----------------------------
-def main():
-    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+    # Select model and load it
+    MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"
+    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto", dtype="auto")
     tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-    ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIB_SAMPLES}]")
-    ds = ds.shuffle(seed=42)
-    ds = ds.map(
-        lambda ex: process_and_tokenize(ex, tokenizer),
-        remove_columns=ds.column_names,
-    )
 
-    recipe = build_recipe(STRATEGY)
+    # Select calibration dataset
+    DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+    DATASET_SPLIT = "train_sft"
+
+    # Configure calibration parameters
+    NUM_CALIBRATION_SAMPLES = 512  # 512 samples is a good starting point
+    MAX_SEQUENCE_LENGTH = 2048
+
+    # Load and preprocess dataset
+    ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+    ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+    def process_and_tokenize(example):
+        text = tokenizer.apply_chat_template(example["messages"], tokenize=False)
+        return tokenizer(
+            text,
+            padding=False,
+            max_length=MAX_SEQUENCE_LENGTH,
+            truncation=True,
+            add_special_tokens=False,
+        )
+
+    ds = ds.map(process_and_tokenize, remove_columns=ds.column_names)
+
+    # Configure quantization settings
+    recipe = """
+    quant_stage:
+        quant_modifiers:
+            QuantizationModifier:
+                kv_cache_scheme:
+                    num_bits: 8
+                    type: float
+                    strategy: tensor
+                    dynamic: false
+                    symmetric: true
+    """
+
+    # Apply quantization
     oneshot(
         model=model,
         dataset=ds,
         recipe=recipe,
-        max_seq_length=MAX_SEQ_LEN,
-        num_calibration_samples=NUM_CALIB_SAMPLES,
+        max_seq_length=MAX_SEQUENCE_LENGTH,
+        num_calibration_samples=NUM_CALIBRATION_SAMPLES,
     )
 
-    save_dir = f"{MODEL_ID.rstrip('/').split('/')[-1]}-kvattn-fp8-{STRATEGY}"
-    model.save_pretrained(save_dir, save_compressed=True)
-    tokenizer.save_pretrained(save_dir)
+    # Save quantized model: Llama-3.1-8B-Instruct-FP8-KV
+    SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-KV"
+    model.save_pretrained(SAVE_DIR, save_compressed=True)
+    tokenizer.save_pretrained(SAVE_DIR)
+    ```
 
-if __name__ == "__main__":
-    main()
+The above script will create a folder in your current directory containing your quantized model (e.g., `Llama-3.1-8B-Instruct-FP8-KV`) with calibrated scales.
+
+When running the model you must specify `kv_cache_dtype="fp8"` in order to enable the kv cache quantization and use the scales.
+
+```python
+from vllm import LLM, SamplingParams
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8)
+llm = LLM(model="Llama-3.1-8B-Instruct-FP8-KV", kv_cache_dtype="fp8")
+prompt = "London is the capital of"
+out = llm.generate(prompt, sampling_params)[0].outputs[0].text
+print(out)
 ```
-
-For more detailed and up-to-date examples, see the [`llm-compressor` official examples](https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_kv_cache).

docs/features/reasoning_outputs.md

@@ -254,8 +254,7 @@ You can add a new `ReasoningParser` similar to [vllm/reasoning/deepseek_r1_reaso
     # import the required packages
 
     from vllm.reasoning import ReasoningParser, ReasoningParserManager
-    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
-    from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+    from vllm.entrypoints.openai.protocol import ChatCompletionRequest, DeltaMessage
 
     # define a reasoning parser and register it to vllm
     # the name list in register_module can be used

docs/features/tool_calling.md

@@ -369,7 +369,6 @@ Flags: `--tool-call-parser glm45`
 Supported models:
 
 * `zai-org/GLM-4.7`
-* `zai-org/GLM-4.7-Flash`
 
 Flags: `--tool-call-parser glm47`
 

docs/getting_started/installation/cpu.md

@@ -131,7 +131,7 @@ VLLM_USE_PRECOMPILED=1 VLLM_PRECOMPILED_WHEEL_VARIANT=cpu VLLM_TARGET_DEVICE=cpu
 
 === "Apple silicon"
 
-    --8<-- "docs/getting_started/installation/cpu.apple.inc.md:build-image-from-source"
+    --8<-- "docs/getting_started/installation/cpu.arm.inc.md:build-image-from-source"
 
 === "IBM Z (S390X)"
     --8<-- "docs/getting_started/installation/cpu.s390x.inc.md:build-image-from-source"