Done

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

.buildkite/hardware_tests/cpu.yaml

@@ -69,11 +69,11 @@ steps:
       pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
       pytest -x -v -s tests/quantization/test_cpu_wna16.py"
       
-- label: CPU-Distributed Tests (PP+TP)
+- label: CPU-Distributed Tests
   depends_on: []
   device: intel_cpu
   no_plugin: true
-  source_file_dependencies: &cpu_distributed_deps
+  source_file_dependencies:
   - csrc/cpu/shm.cpp
   - vllm/v1/worker/cpu_worker.py
   - vllm/v1/worker/gpu_worker.py
@@ -82,21 +82,10 @@ steps:
   - vllm/platforms/cpu.py
   - vllm/distributed/parallel_state.py
   - vllm/distributed/device_communicators/cpu_communicator.py
-  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
-
-- label: CPU-Distributed Tests (DP+TP)
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies: *cpu_distributed_deps
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
 
 - label: CPU-Multi-Modal Model Tests %N
   depends_on: []

.buildkite/image_build/image_build.sh

@@ -192,7 +192,6 @@ export BUILDKITE_COMMIT
 export PARENT_COMMIT
 export IMAGE_TAG
 export IMAGE_TAG_LATEST
-export COMMIT="${COMMIT:-${BUILDKITE_COMMIT}}"
 export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN

.buildkite/intel_jobs/lora_intel.yaml

@@ -126,4 +126,5 @@ steps:
       'cd tests &&
       pytest -v -s lora/test_default_mm_loras.py && 
       (pytest -v -s lora/test_qwen3_unembed.py || true) &&
+      (pytest -v -s lora/test_qwenvl.py || true) &&
       pytest -v -s lora/test_whisper.py'

.buildkite/release-pipeline.yaml

@@ -27,7 +27,7 @@ steps:
           - "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=\"${CUDA_ARCH_AARCH64_CU129}\" --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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -37,10 +37,10 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         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=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -53,7 +53,7 @@ steps:
           - "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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -66,7 +66,7 @@ steps:
           - "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=\"${CUDA_ARCH_X86_CU129}\" --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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -76,10 +76,10 @@ steps:
         agents:
           queue: cpu_queue_release
         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=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -92,7 +92,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=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"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -121,19 +121,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$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"
@@ -146,19 +134,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
 
       - label: "Build release image - x86_64 - CUDA 12.9"
@@ -168,18 +144,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
           # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
@@ -192,18 +157,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
 
       - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
@@ -213,21 +167,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
@@ -239,21 +179,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
 
       - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
@@ -263,20 +189,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
@@ -288,20 +201,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
 
       - block: "Build release image for x86_64 CPU"
@@ -309,7 +209,6 @@ steps:
         depends_on: ~
 
       - label: "Build release image - x86_64 - CPU"
-        key: build-cpu-release-image-x86
         depends_on:
           - block-cpu-release-image-build
           - input-release-version
@@ -328,8 +227,7 @@ steps:
         depends_on: ~
 
       - label: "Build release image - arm64 - CPU"
-        key: build-cpu-release-image-arm64
-        depends_on:
+        depends_on: 
           - block-arm64-cpu-release-image-build
           - input-release-version
         agents:
@@ -438,41 +336,6 @@ steps:
           DOCKER_BUILDKIT: "1"
           DOCKERHUB_USERNAME: "vllmbot"
 
-      - block: "Publish release images to DockerHub"
-        key: block-publish-release-images
-        depends_on:
-          - create-multi-arch-manifest
-          - create-multi-arch-manifest-cuda-12-9
-          - create-multi-arch-manifest-ubuntu2404
-          - create-multi-arch-manifest-cuda-12-9-ubuntu2404
-          - build-rocm-release-image
-          - input-release-version
-          # Wait for CPU builds if their block steps were unblocked, so publish
-          # doesn't race the in-progress CPU build. allow_failure lets publish
-          # proceed when the operator legitimately leaves the CPU block steps
-          # unblocked or the CPU build fails.
-          - step: build-cpu-release-image-x86
-            allow_failure: true
-          - step: build-cpu-release-image-arm64
-            allow_failure: true
-        if: build.env("NIGHTLY") != "1"
-
-      - label: "Publish release images to DockerHub"
-        depends_on:
-          - block-publish-release-images
-        key: publish-release-images-dockerhub
-        agents:
-          queue: small_cpu_queue_release
-        commands:
-          - "bash .buildkite/scripts/publish-release-images.sh"
-        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:
@@ -760,7 +623,7 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm722"
+      VARIANT: "rocm721"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"

.buildkite/scripts/annotate-release.sh

@@ -8,6 +8,8 @@ if [ -z "${RELEASE_VERSION}" ]; then
   RELEASE_VERSION="1.0.0.dev"
 fi
 
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
@@ -23,5 +25,95 @@ aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-Docker images are published automatically by the "Publish release images to DockerHub" pipeline step.
+
+To download and upload the image:
+
+\`\`\`
+# Download images:
+
+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 pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+# Tag and push images:
+
+## CUDA
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu129 vllm/vllm-openai:x86_64-cu129
+docker tag vllm/vllm-openai:x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
+docker tag vllm/vllm-openai:x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+docker push vllm/vllm-openai:latest-x86_64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu129 vllm/vllm-openai:aarch64-cu129
+docker tag vllm/vllm-openai:aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker tag vllm/vllm-openai:aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker push vllm/vllm-openai:latest-aarch64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+
+## ROCm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+## CPU
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-cpu:latest-x86_64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker push vllm/vllm-openai-cpu:latest-arm64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+# Create multi-arch manifest:
+
+docker manifest rm vllm/vllm-openai:latest
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+docker manifest rm vllm/vllm-openai:latest-cu129
+docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker manifest push vllm/vllm-openai:latest-cu129
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
+
+docker manifest rm vllm/vllm-openai-cpu:latest || true
+docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker manifest push vllm/vllm-openai-cpu:latest
+docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+\`\`\`
 EOF

.buildkite/scripts/ci-fetch-log.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
-#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
-#
-# Downloads the raw log for a Buildkite job from the public, unauthenticated
-# /organizations/<org>/pipelines/<pipeline>/builds/<n>/jobs/<uuid>/download
-# endpoint, then strips ANSI/timestamps via ci-clean-log.sh.
-#
-# Find <build_number> and <job_uuid> via:
-#   gh pr checks <PR> --repo vllm-project/vllm
-# Each failing row's URL is .../builds/<build_number>#<job_uuid>.
-
-set -euo pipefail
-
-ORG="vllm"
-PIPELINE="ci"
-
-usage() {
-    echo "Usage: $0 <buildkite_job_url> [output_file]"
-    echo "       $0 <build_number> <job_uuid> [output_file]"
-    exit 1
-}
-
-if [ $# -lt 1 ]; then usage; fi
-
-if [[ "$1" == https://* ]]; then
-    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
-    OUT="${2:-ci-${BUILD}-${JOB:0:8}.log}"
-else
-    if [ $# -lt 2 ]; then usage; fi
-    BUILD="$1"
-    JOB="$2"
-    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
-fi
-
-if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
-    echo "Could not parse build number or job UUID from: $1" >&2
-    usage
-fi
-
-COOKIES=$(mktemp)
-trap 'rm -f "$COOKIES"' EXIT
-
-# Buildkite issues a session cookie on first hit; subsequent /download needs it.
-curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
-
-curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
-    -o "$OUT"
-
-bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"
-
-echo "$OUT"

.buildkite/scripts/detect-manylinux-tag.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Detect the manylinux platform tag for a wheel and rename it in place.
-
-vLLM's build images produce wheels with the generic ``linux_<arch>`` platform
-tag, which installers like ``pip`` won't accept off PyPI/our index. We need to
-rewrite the platform tag to the appropriate ``manylinux_<major>_<minor>_<arch>``
-before uploading.
-
-Historically the tag was hard-coded per build (``manylinux_2_31`` for the
-Ubuntu 20.04-based image, ``manylinux_2_35`` for the Ubuntu 22.04-based
-images). That is brittle: bumping the base image silently produces wheels
-labelled with the wrong glibc requirement. This script asks ``auditwheel``
-to derive the tag from the symbol versions actually referenced by the
-binaries inside the wheel, so the label tracks reality.
-
-We can't simply call ``auditwheel repair`` -- it tries to graft external
-shared libraries into the wheel and fails on vLLM's CUDA/cuBLAS dependencies.
-Instead we use ``auditwheel.wheel_abi.analyze_wheel_abi`` directly, which is
-the same call that powers ``auditwheel show``, and read off
-``winfo.sym_policy.name``.
-
-Usage:
-    detect-manylinux-tag.py <wheel_path>
-
-The wheel is renamed in place; the new path is printed on stdout. All
-diagnostics go to stderr so callers can capture stdout safely.
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-from auditwheel.error import (
-    AuditwheelError,
-    NonPlatformWheelError,
-    WheelToolsError,
-)
-from auditwheel.wheel_abi import analyze_wheel_abi
-from auditwheel.wheeltools import get_wheel_architecture, get_wheel_libc
-
-
-def detect_platform_tag(wheel_path: Path) -> str:
-    """Return the most precise platform tag the wheel is consistent with.
-
-    Mirrors ``auditwheel show`` but returns ``sym_policy`` rather than
-    ``overall_policy``: we only care about the glibc symbol versions used,
-    not about other policy axes (ISA extensions, blacklist, etc.) that
-    ``overall_policy`` folds in.
-    """
-    fn = wheel_path.name
-
-    try:
-        arch = get_wheel_architecture(fn)
-    except (WheelToolsError, NonPlatformWheelError):
-        # Architecture isn't deducible from the filename; let auditwheel
-        # infer it from the ELF binaries inside the wheel.
-        arch = None
-
-    try:
-        libc = get_wheel_libc(fn)
-    except WheelToolsError:
-        # An unrepaired wheel uses ``linux_<arch>``, which doesn't encode
-        # libc. Let auditwheel infer it from the ELF binaries.
-        libc = None
-
-    winfo = analyze_wheel_abi(
-        libc,
-        arch,
-        wheel_path,
-        frozenset(),
-        disable_isa_ext_check=False,
-        allow_graft=False,
-    )
-    return winfo.sym_policy.name
-
-
-def rename_wheel(wheel_path: Path, new_platform_tag: str) -> Path:
-    """Rename the wheel in place, replacing only its platform tag."""
-    # Wheel filename per PEP 427:
-    #   {distribution}-{version}(-{build})?-{python}-{abi}-{platform}.whl
-    # The platform tag is always the last ``-``-separated token before
-    # ``.whl``. Compound tags like ``manylinux_2_31_x86_64`` use ``_`` as the
-    # internal separator, so ``-``-splitting is unambiguous.
-    parts = wheel_path.stem.split("-")
-    if len(parts) < 5:
-        raise ValueError(f"Unrecognised wheel filename: {wheel_path.name}")
-    parts[-1] = new_platform_tag
-    new_path = wheel_path.with_name("-".join(parts) + ".whl")
-    if new_path != wheel_path:
-        wheel_path.rename(new_path)
-    return new_path
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Detect a wheel's manylinux platform tag with "
-        "auditwheel and rename the wheel in place."
-    )
-    parser.add_argument(
-        "wheel",
-        type=Path,
-        help="Path to the wheel to inspect and rename.",
-    )
-    args = parser.parse_args()
-
-    wheel_path: Path = args.wheel
-    if not wheel_path.is_file():
-        print(f"error: {wheel_path} is not a file", file=sys.stderr)
-        return 1
-
-    # Catch the things that ``analyze_wheel_abi`` and ``rename_wheel`` can
-    # raise: any subclass of ``AuditwheelError`` (pure-Python wheels,
-    # invalid libc, malformed wheels), filesystem errors, or our own
-    # ``ValueError`` for an unrecognised wheel filename. Print a single
-    # ``ERROR_TYPE: message`` line to stderr instead of a Python
-    # traceback, which is much friendlier in CI logs.
-    try:
-        new_tag = detect_platform_tag(wheel_path)
-        print(f"detected platform tag: {new_tag}", file=sys.stderr)
-        new_path = rename_wheel(wheel_path, new_tag)
-    except (AuditwheelError, ValueError, OSError) as e:
-        print(
-            f"error: failed to retag {wheel_path.name}: {type(e).__name__}: {e}",
-            file=sys.stderr,
-        )
-        return 2
-
-    if new_path != wheel_path:
-        print(f"renamed {wheel_path.name} -> {new_path.name}", file=sys.stderr)
-    else:
-        print(f"wheel already tagged {new_tag}", file=sys.stderr)
-
-    print(new_path)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

.buildkite/scripts/docker-build-metadata-args.sh

@@ -1,54 +0,0 @@
-#!/bin/bash
-# Emit docker build flags for release image provenance metadata.
-# Keep this helper best-effort: missing Buildkite metadata should fall back to
-# local/default values instead of blocking the Docker build.
-
-# Variant examples: "", "cu129", "ubuntu2404", "cu129-ubuntu2404".
-variant="${1:-}"
-variant_suffix="${variant:+-${variant}}"
-
-image_name="${VLLM_DOCKER_IMAGE_NAME:-vllm/vllm-openai}"
-staging_repo="${VLLM_STAGING_IMAGE_REPO:-public.ecr.aws/q9t5s3a7/vllm-release-repo}"
-build_commit="${VLLM_BUILD_COMMIT:-${BUILDKITE_COMMIT:-unknown}}"
-build_pipeline="${VLLM_BUILD_PIPELINE:-${BUILDKITE_PIPELINE_ID:-${BUILDKITE_PIPELINE_SLUG:-local}}}"
-build_url="${VLLM_BUILD_URL:-${BUILDKITE_BUILD_URL:-}}"
-tag_commit="${BUILDKITE_COMMIT:-${build_commit}}"
-
-if [[ -n "${BUILDKITE:-}" || -n "${BUILDKITE_COMMIT:-}" ]]; then
-  release_version="${RELEASE_VERSION:-}"
-  if command -v buildkite-agent >/dev/null 2>&1; then
-    release_version="${release_version:-$(buildkite-agent meta-data get release-version 2>/dev/null)}"
-  fi
-  release_version="${release_version#v}"
-  release_version="${release_version:-${tag_commit}}"
-
-  staging_image_ref="${staging_repo}:${tag_commit}-$(uname -m)${variant_suffix}"
-
-  if [[ "${NIGHTLY:-}" == "1" ]]; then
-    if [[ -z "${variant}" ]]; then
-      image_tag="${image_name}:nightly-${tag_commit}"
-    elif [[ "${variant}" == cu* ]]; then
-      cuda_variant="${variant%%-*}"
-      remaining_variant="${variant#${cuda_variant}}"
-      image_tag="${image_name}:${cuda_variant}-nightly-${tag_commit}${remaining_variant}"
-    else
-      image_tag="${image_name}:nightly-${tag_commit}${variant_suffix}"
-    fi
-  else
-    image_tag="${image_name}:v${release_version}${variant_suffix}"
-  fi
-else
-  image_tag="${VLLM_IMAGE_TAG:-local/vllm-openai:dev}"
-  staging_image_ref="${image_tag}"
-fi
-
-emit_arg() {
-  printf -- "--build-arg %s=%s " "$1" "$2"
-}
-
-emit_arg VLLM_BUILD_COMMIT "${build_commit}"
-emit_arg VLLM_BUILD_PIPELINE "${build_pipeline}"
-emit_arg VLLM_BUILD_URL "${build_url}"
-# This is the intended public tag. The final digest is only known after push.
-emit_arg VLLM_IMAGE_TAG "${image_tag}"
-printf -- "--tag %s " "${staging_image_ref}"

.buildkite/scripts/generate-and-upload-nightly-index.sh

@@ -10,13 +10,20 @@ set -ex
 BUCKET="vllm-wheels"
 INDICES_OUTPUT_DIR="indices"
 DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+PYTHON="${PYTHON_PROG:-python3}" # try to read from env var, otherwise use python3
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# Select python3 (>= 3.12) -- local if available, else a docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
+# detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
+if [[ "$has_new_python" -eq 0 ]]; then
+    # use new python from docker
+    docker pull python:3-slim
+    PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
 
 # ======== generate and upload indices ========
 

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

@@ -3,37 +3,42 @@ set -euox pipefail
 export VLLM_CPU_CI_ENV=0
 export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
 
-MODE=${1:-all}
+echo "--- PP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 --max-model-len=4096 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+vllm bench serve \
+    --backend vllm \
+    --dataset-name random \
+    --model meta-llama/Llama-3.2-3B-Instruct \
+    --num-prompts 20 \
+    --result-dir ./test_results \
+    --result-filename tp_pp.json \
+    --save-result \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/tp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+  echo "Some requests were failed!"
+  exit 1
+fi
 
-run_scenario() {
-    local label="$1" result_file="$2"
-    shift 2
-    echo "--- $label"
-    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
-    local server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-    vllm bench serve \
-        --backend vllm \
-        --dataset-name random \
-        --model meta-llama/Llama-3.2-3B-Instruct \
-        --num-prompts 20 \
-        --result-dir ./test_results \
-        --result-filename "$result_file" \
-        --save-result \
-        --endpoint /v1/completions
-    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
-    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
-        echo "Some requests were failed in $label!"
-        exit 1
-    fi
-}
-
-case "$MODE" in
-    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
-    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
-    all)
-        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
-        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
-        ;;
-    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
-esac
+echo "--- DP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 --max-model-len=4096 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+vllm bench serve \
+   --backend vllm \
+   --dataset-name random \
+   --model meta-llama/Llama-3.2-3B-Instruct \
+   --num-prompts 20 \
+   --result-dir ./test_results \
+   --result-filename dp_pp.json \
+   --save-result \
+   --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/dp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+ echo "Some requests were failed!"
+ exit 1
+fi

.buildkite/scripts/lib/manylinux.sh

@@ -1,127 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Shared helper for rewriting a wheel's platform tag from the generic
-# ``linux_<arch>`` to the correct ``manylinux_<major>_<minor>_<arch>``.
-# After sourcing, call ``apply_manylinux_tag <wheel>`` on each wheel
-# that still carries the generic tag; the renamed path is printed on
-# stdout (logs go to stderr).
-#
-# Why a pinned Docker container instead of using whatever Python
-# happens to be on the agent:
-#   - vLLM's release agents are heterogeneous -- they don't agree on
-#     a Python minor version, and we can't rely on a particular
-#     ``auditwheel`` being installed.
-#   - ``detect-manylinux-tag.py`` reads ``auditwheel.wheel_abi`` and
-#     ``Policy.sym_policy``, which are *internal* APIs without a
-#     stability promise. Pinning both Python and auditwheel makes the
-#     detected tag a function of the inputs alone, and shifts version
-#     bumps from "implicit drift" to "deliberate, retested change".
-#   - Other release scripts (``generate-and-upload-nightly-index.sh``,
-#     ``upload-rocm-wheels.sh``) already use the python:3-slim image
-#     when the agent's interpreter is too old; this is the same idea
-#     made stricter.
-#
-# To keep the per-wheel cost down (the ROCm upload retags ~10 wheels
-# each run), we install auditwheel into a long-lived helper container
-# once on source, then ``docker exec`` into it for each call.
-#
-# Trap behaviour:
-# - Sourcing installs an EXIT trap that calls ``manylinux_cleanup`` to
-#   tear down the helper container. Any EXIT trap that was already in
-#   place when this file was sourced is captured and run AFTER our
-#   cleanup, so we don't silently clobber it.
-# - If a caller sets a new EXIT trap *after* sourcing, that trap will
-#   replace ours; in that case the caller should call
-#   ``manylinux_cleanup`` from their own handler.
-
-if [[ -n "${_MANYLINUX_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_MANYLINUX_LIB_SOURCED=1
-
-# Pin both sides. Bump these deliberately and re-run a representative
-# wheel from each build target through the detection.
-_MANYLINUX_PYTHON_IMAGE="python:3.12-slim"
-_MANYLINUX_AUDITWHEEL_VERSION="6.6.0"
-
-# Resolve our own directory (and the sibling detect script) using the
-# canonical, symlink-resolved path. The container mounts cwd at the
-# same absolute path on both sides, so all paths we hand to it -- the
-# script, the wheel -- must canonicalise to a location under cwd.
-_MANYLINUX_LIB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
-_MANYLINUX_DETECT_SCRIPT="$(cd "${_MANYLINUX_LIB_DIR}/.." && pwd -P)/detect-manylinux-tag.py"
-_MANYLINUX_CWD="$(pwd -P)"
-
-docker pull --quiet "$_MANYLINUX_PYTHON_IMAGE" >/dev/null
-
-# Spin up a long-lived helper container so we install auditwheel once
-# and then ``docker exec`` into it for each wheel.
-#
-# The container runs as root so ``pip install`` can write into the
-# system site-packages; individual ``docker exec`` calls below pin
-# themselves to the host UID so any file rename happens with host
-# ownership, not root.
-_MANYLINUX_CONTAINER="$(docker run -d --rm \
-    -v "$_MANYLINUX_CWD:$_MANYLINUX_CWD" \
-    -w "$_MANYLINUX_CWD" \
-    "$_MANYLINUX_PYTHON_IMAGE" \
-    sleep infinity)"
-docker exec "$_MANYLINUX_CONTAINER" \
-    pip install --quiet --disable-pip-version-check \
-    --root-user-action=ignore \
-    "auditwheel==${_MANYLINUX_AUDITWHEEL_VERSION}"
-
-# Public cleanup -- safe to call multiple times.
-manylinux_cleanup() {
-    if [[ -n "${_MANYLINUX_CONTAINER:-}" ]]; then
-        docker rm -f "$_MANYLINUX_CONTAINER" >/dev/null 2>&1 || true
-        _MANYLINUX_CONTAINER=""
-    fi
-}
-
-# Capture any EXIT trap that was already in place so we can chain to
-# it rather than overwrite it. ``trap -p EXIT`` prints the handler in
-# eval-able form (``trap -- 'CMD' EXIT``) or nothing if unset; we
-# strip the wrapper to recover ``CMD``. Handles the common case --
-# CMDs without embedded single quotes -- and degrades gracefully (we
-# still run our own cleanup) for the pathological case.
-_manylinux_prev_exit_trap_cmd=""
-_manylinux_existing_exit_trap="$(trap -p EXIT)"
-if [[ -n "$_manylinux_existing_exit_trap" ]]; then
-    _tmp="${_manylinux_existing_exit_trap#trap -- \'}"
-    _manylinux_prev_exit_trap_cmd="${_tmp%\' EXIT}"
-    unset _tmp
-fi
-unset _manylinux_existing_exit_trap
-
-_manylinux_run_exit_chain() {
-    manylinux_cleanup
-    if [[ -n "$_manylinux_prev_exit_trap_cmd" ]]; then
-        eval "$_manylinux_prev_exit_trap_cmd"
-    fi
-}
-trap _manylinux_run_exit_chain EXIT
-
-# Detect the manylinux platform tag for a single wheel and rename it
-# in place, printing the renamed wheel path on stdout. Returns
-# non-zero on failure (which under ``set -e`` propagates to caller).
-#
-# The wheel must be reachable via a path under the host cwd so it's
-# visible inside the helper container; in CI the wheels always live
-# under ``artifacts/`` so this is fine.
-apply_manylinux_tag() {
-    local wheel="$1"
-    local abs_wheel
-    abs_wheel="$(realpath "$wheel")"
-    local new_wheel
-    new_wheel="$(docker exec -u "$(id -u):$(id -g)" \
-        "$_MANYLINUX_CONTAINER" \
-        python "$_MANYLINUX_DETECT_SCRIPT" "$abs_wheel")"
-    if [[ -z "$new_wheel" || ! -f "$new_wheel" ]]; then
-        echo "apply_manylinux_tag: detect-manylinux-tag.py did not produce a valid wheel path for $wheel" >&2
-        return 1
-    fi
-    printf '%s\n' "$new_wheel"
-}

.buildkite/scripts/lib/select-python.sh

@@ -1,41 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Pick a Python interpreter for buildkite scripts: prefer a local
-# ``python3`` if it is recent enough (>= 3.12), otherwise fall back to
-# a one-shot Docker container running ``python:3-slim``. After
-# ``select_python`` returns, ``$PYTHON`` is set in the caller's shell
-# and is safe to use as a command (e.g. ``$PYTHON some_script.py``).
-#
-# The 3.12 threshold matches what the existing nightly-index work
-# expects -- typing features used by ``generate-nightly-index.py``.
-# This helper does not pin the *minor* version; if you need stricter
-# reproducibility (e.g. relying on auditwheel internals), invoke
-# Docker yourself with a pinned tag rather than calling this.
-
-if [[ -n "${_SELECT_PYTHON_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_SELECT_PYTHON_LIB_SOURCED=1
-
-# Sets ``PYTHON`` in the caller's shell and exports it. Idempotent --
-# calling twice is safe and the second call simply re-runs the probe.
-select_python() {
-    local py="${PYTHON_PROG:-python3}"
-    local has_new_python
-    has_new_python=$("$py" -c \
-        "print(1 if __import__('sys').version_info >= (3,12) else 0)" \
-        2>/dev/null || echo 0)
-    if [[ "$has_new_python" -eq 0 ]]; then
-        # ``-u $(id -u):$(id -g)`` so files created via the container
-        # end up owned by the host user, not root.
-        docker pull python:3-slim
-        PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-    else
-        PYTHON="$py"
-    fi
-    export PYTHON
-    echo "Using python interpreter: $PYTHON"
-    echo "Python version: $($PYTHON --version)"
-}

.buildkite/scripts/publish-release-images.sh

@@ -1,180 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Publish release Docker images from ECR to DockerHub.
-# Pulls per-arch images, tags with latest and versioned tags, pushes them,
-# then creates and pushes multi-arch manifests.
-
-set -euo pipefail
-
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
-if [ -z "${RELEASE_VERSION}" ]; then
-  echo "ERROR: release-version metadata not set"
-  exit 1
-fi
-
-COMMIT="$BUILDKITE_COMMIT"
-ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-
-echo "========================================"
-echo "Publishing release images v${RELEASE_VERSION}"
-echo "  Commit: ${COMMIT}"
-echo "  ROCm base cache key: ${ROCM_BASE_CACHE_KEY}"
-echo "========================================"
-
-# Login to ECR to pull staging images
-aws ecr-public get-login-password --region us-east-1 | \
-  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-
-# ---- CUDA (default: 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:latest-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai:latest-x86_64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:latest-aarch64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker push vllm/vllm-openai:latest-aarch64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-
-docker manifest rm vllm/vllm-openai:latest || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION} || true
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker manifest push vllm/vllm-openai:latest
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
-
-# ---- CUDA 12.9 ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-docker push vllm/vllm-openai:latest-x86_64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker push vllm/vllm-openai:latest-aarch64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-
-docker manifest rm vllm/vllm-openai:latest-cu129 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129 || true
-docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker manifest push vllm/vllm-openai:latest-cu129
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
-
-# ---- Ubuntu 24.04 (CUDA 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404
-
-# ---- Ubuntu 24.04 (CUDA 12.9) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-cu129-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404
-
-# ---- ROCm ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:latest
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-docker push vllm/vllm-openai-rocm:latest
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:latest-base
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-docker push vllm/vllm-openai-rocm:latest-base
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-
-# ---- CPU ----
-# CPU images are behind separate block steps and may not have been built.
-# All-or-nothing: inspect both arches first, then either publish everything
-# (per-arch + multi-arch manifest) or skip everything. Publishing only one
-# arch would leave `:latest-x86_64` pointing at the new release while the
-# `:latest` multi-arch manifest still resolves to the previous release.
-
-CPU_X86_TAG=public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
-CPU_ARM_TAG=public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
-
-CPU_X86_AVAILABLE=false
-CPU_ARM_AVAILABLE=false
-docker manifest inspect "${CPU_X86_TAG}" >/dev/null 2>&1 && CPU_X86_AVAILABLE=true
-docker manifest inspect "${CPU_ARM_TAG}" >/dev/null 2>&1 && CPU_ARM_AVAILABLE=true
-
-if [ "$CPU_X86_AVAILABLE" = "true" ] && [ "$CPU_ARM_AVAILABLE" = "true" ]; then
-  docker pull "${CPU_X86_TAG}"
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:latest-x86_64
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-  docker push vllm/vllm-openai-cpu:latest-x86_64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-
-  docker pull "${CPU_ARM_TAG}"
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:latest-arm64
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker push vllm/vllm-openai-cpu:latest-arm64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-
-  docker manifest rm vllm/vllm-openai-cpu:latest || true
-  docker manifest rm vllm/vllm-openai-cpu:v${RELEASE_VERSION} || true
-  docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
-  docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker manifest push vllm/vllm-openai-cpu:latest
-  docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
-elif [ "$CPU_X86_AVAILABLE" = "false" ] && [ "$CPU_ARM_AVAILABLE" = "false" ]; then
-  echo "WARNING: Neither CPU image found in ECR, skipping CPU publish (ensure block-cpu-release-image-build and block-arm64-cpu-release-image-build were unblocked and the builds finished pushing)"
-else
-  # Partial state: one arch built, the other did not. Fail loudly rather than
-  # ship a Docker Hub state where `:latest-${arch}` and `:latest` (multi-arch)
-  # disagree on which release they point at.
-  echo "ERROR: Partial CPU build detected (x86_64=${CPU_X86_AVAILABLE}, arm64=${CPU_ARM_AVAILABLE})."
-  echo "       Refusing to publish to avoid split-tag drift between per-arch and multi-arch tags."
-  echo "       Re-run the missing CPU build and retry, or manually publish if a single-arch release is intended."
-  exit 1
-fi
-
-echo ""
-echo "Successfully published release images for v${RELEASE_VERSION}"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -51,7 +51,6 @@ vllm serve "$MODEL" \
   --offload-num-in-group 2 \
   --offload-prefetch-step 1 \
   --offload-params w13_weight w2_weight \
-  --generation-config vllm \
   --port "$PORT" \
   ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
 SERVER_PID=$!

.buildkite/scripts/tool_call/run-bfcl-eval.sh

@@ -28,7 +28,6 @@
 #   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
 #   BFCL_PORT           - Server port (default: 8000)
 #   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
-#   BFCL_TEMPERATURE    - Temperature (default: 0.0)
 #   BFCL_EXTRA_ARGS     - Additional vLLM server args
 
 set -euo pipefail
@@ -44,7 +43,6 @@ TP_SIZE="${BFCL_TP_SIZE:-1}"
 MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
 PORT="${BFCL_PORT:-8000}"
 REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
-TEMPERATURE="${BFCL_TEMPERATURE:-0.0}"
 EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
 
 # Set up output directory
@@ -141,7 +139,7 @@ echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
 # be patched in-process so BFCL knows to use the OpenAI-compatible handler
 # against our local vLLM server.
 bfcl_exit_code=0
-python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
 import os
 import sys
 
@@ -150,8 +148,7 @@ test_category = sys.argv[2]
 num_threads = int(sys.argv[3])
 port = sys.argv[4]
 api_type = sys.argv[5]
-temperature = float(sys.argv[6])
-output_dir = sys.argv[7] if len(sys.argv) > 7 and sys.argv[7] else os.getcwd()
+output_dir = sys.argv[6] if len(sys.argv) > 6 and sys.argv[6] else os.getcwd()
 
 os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
 os.environ["OPENAI_API_KEY"] = "dummy"
@@ -207,7 +204,6 @@ gen_kwargs["model"] = [model]
 gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
 gen_kwargs["skip_server_setup"] = True
 gen_kwargs["num_threads"] = num_threads
-gen_kwargs["temperature"] = temperature
 generate(**gen_kwargs)
 
 # ---- evaluate ----

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

@@ -2,18 +2,14 @@
 
 set -ex
 
-# Upload a single wheel to S3, after detecting and applying the appropriate
-# manylinux platform tag with auditwheel.
+# Upload a single wheel to S3 (rename linux -> manylinux).
 # Index generation is handled separately by generate-and-upload-nightly-index.sh.
 
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
-
 BUCKET="vllm-wheels"
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# ========= locate the wheel ==========
+# ========= collect, rename & upload the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -25,9 +21,19 @@ if [[ ${#wheel_files[@]} -ne 1 ]]; then
 fi
 wheel="${wheel_files[0]}"
 
-# ========= detect manylinux tag and rename ==========
+# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
+# we also accept params as manylinux tag
+# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
+manylinux_version="${1:-manylinux_2_31}"
 
-wheel="$(apply_manylinux_tag "$wheel")"
+# Rename 'linux' to the appropriate manylinux version in the wheel filename
+if [[ "$wheel" != *"linux"* ]]; then
+  echo "Error: Wheel filename does not contain 'linux': $wheel"
+  exit 1
+fi
+new_wheel="${wheel/linux/$manylinux_version}"
+mv -- "$wheel" "$new_wheel"
+wheel="$new_wheel"
 echo "Renamed wheel to: $wheel"
 
 # Extract the version from the wheel

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

@@ -39,11 +39,10 @@ fi
 
 set -x # avoid printing secrets above
 
-# install twine and sdist build prerequisites from pypi
+# install twine from pypi
 python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
 pip install twine
-pip install -r requirements/build/cuda.txt
 python3 -m twine --version
 
 # copy release wheels to local directory

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

@@ -20,6 +20,10 @@ BUCKET="${S3_BUCKET:-vllm-wheels}"
 ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
 S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
 INDICES_OUTPUT_DIR="rocm-indices"
+PYTHON="${PYTHON_PROG:-python3}"
+
+# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
+MANYLINUX_VERSION="manylinux_2_35"
 
 echo "========================================"
 echo "ROCm Wheel Upload Configuration"
@@ -30,21 +34,19 @@ echo "Commit: $BUILDKITE_COMMIT"
 echo "Branch: $BUILDKITE_BRANCH"
 echo "========================================"
 
-# ======== Part 0: Setup Python and helpers ========
+# ======== Part 0: Setup Python ========
 
-# Pick a Python interpreter for index generation -- local if recent
-# enough, else a one-shot docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
+# Detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
+if [[ "$has_new_python" -eq 0 ]]; then
+    # Use new python from docker
+    # Use --user to ensure files are created with correct ownership (not root)
+    docker pull python:3-slim
+    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
 
-# Set up auditwheel-in-a-container for the manylinux retagging step.
-# Distinct from select_python: ``manylinux.sh`` deliberately pins both
-# the Python and auditwheel versions (the script reads auditwheel
-# internals) and so always runs in a known-good container regardless
-# of what's on the agent.
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
 
 # ======== Part 1: Collect and prepare wheels ========
 
@@ -61,18 +63,11 @@ if [ "$WHEEL_COUNT" -eq 0 ]; then
     exit 1
 fi
 
-# Detect the appropriate manylinux platform tag for any wheel that still
-# carries the generic ``linux_<arch>`` tag, and rename it in place. We use
-# auditwheel via ``apply_manylinux_tag`` (see lib/manylinux.sh) rather than
-# a hard-coded ``manylinux_2_35`` string so that the label tracks the actual
-# glibc symbol versions used by the binaries (and stays correct if the
-# rocm_base image is rebased).
-#
-# The ``linux``/``manylinux`` filter below skips both pre-tagged wheels
-# (e.g. upstream torch) and pure-Python ``-any.whl`` wheels.
+# Rename linux to manylinux in wheel filenames
 for wheel in all-rocm-wheels/*.whl; do
     if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="$(apply_manylinux_tag "$wheel")"
+        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
+        mv -- "$wheel" "$new_wheel"
         echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
     fi
 done

.buildkite/test-amd.yaml

@@ -388,18 +388,18 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
     - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi250 · kernels  ----------------------------------------------------------#
 
@@ -1108,7 +1108,6 @@ steps:
   - export VLLM_TEST_CLEAN_GPU_MEMORY=1
   - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
   - pytest -v -s tests/compile/passes/distributed/test_sequence_parallelism.py
-  - pytest -v -s tests/compile/passes/distributed/test_tp2_ar_rms.py::test_tp2_ar_rms_fusions
 
 #-----------------------------------------------------------  mi300 · cuda  ------------------------------------------------------------#
 
@@ -1169,13 +1168,13 @@ steps:
   - vllm/v1/attention/backends/
   - vllm/v1/attention/selector.py
   - tests/distributed/test_context_parallel.py
-  - examples/features/data_parallel/data_parallel_offline.py
+  - examples/offline_inference/data_parallel.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
   - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s tests/distributed/test_context_parallel.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
+  - 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=allgather_reducescatter --disable-nccl-for-dp-synchronization
 
 - label: Distributed Tests (4xA100-4xMI300) # TBD
   timeout_in_minutes: 180
@@ -1204,7 +1203,7 @@ steps:
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
   - examples/rl/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   - vllm/platforms/rocm.py
   commands:
   - export TORCH_NCCL_BLOCKING_WAIT=1
@@ -1214,7 +1213,7 @@ steps:
   - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - python3 ../examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_ipc.py
@@ -1267,7 +1266,7 @@ steps:
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - examples/offline_inference/torchrun_dp_example.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
@@ -1276,7 +1275,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - export TORCH_NCCL_BLOCKING_WAIT=1
-  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 #--------------------------------------------------------  mi300 · entrypoints  --------------------------------------------------------#
 
@@ -1648,18 +1647,18 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
     - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi300 · kernels  ----------------------------------------------------------#
 
@@ -1952,8 +1951,8 @@ steps:
   - pytest -v -s tests/models/multimodal/processing/
   - pytest -v -s tests/models/multimodal/test_mapping.py
   - python3 examples/basic/offline_inference/chat.py
-  - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+  - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 #-------------------------------------------------------  mi300 · quantization  --------------------------------------------------------#
 
@@ -2303,7 +2302,7 @@ steps:
   commands:
   - export TORCH_NCCL_BLOCKING_WAIT=1
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+  - 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
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
   - pytest -v -s tests/distributed/test_packed_tensor.py
@@ -2714,7 +2713,7 @@ steps:
   - vllm/v1/attention/selector.py
   - tests/distributed/test_context_parallel.py
   - tests/v1/distributed/test_dbo.py
-  - examples/features/data_parallel/data_parallel_offline.py
+  - examples/offline_inference/data_parallel.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
@@ -2931,18 +2930,18 @@ steps:
   - python3 basic/offline_inference/embed.py
   - python3 basic/offline_inference/score.py
   # Multi-modal models
-  - python3 generate/multimodal/audio_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-  - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+  - python3 offline_inference/audio_language.py --seed 0
+  - python3 offline_inference/vision_language.py --seed 0
+  - python3 offline_inference/vision_language_multi_image.py --seed 0
+  - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
   # Pooling models
   - python3 pooling/embed/vision_embedding_offline.py --seed 0
   # Features demo
-  - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+  - python3 offline_inference/prefix_caching.py
   - python3 offline_inference/llm_engine_example.py
   - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+  - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+  - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#
 

.buildkite/test_areas/attention.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: V1 attention (H100)
-  key: v1-attention-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -15,9 +14,8 @@ steps:
     - pytest -v -s v1/attention
 
 - label: V1 attention (B200)
-  key: v1-attention-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention

.buildkite/test_areas/basic_correctness.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
-  key: basic-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/benchmarks.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Benchmarks CLI Test
-  key: benchmarks-cli-test
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
@@ -13,8 +12,7 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Attention Benchmarks Smoke Test (B200)
-  key: attention-benchmarks-smoke-test-b200
-  device: b200-k8s
+  device: b200
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"

.buildkite/test_areas/compile.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
-  key: sequence-parallel-correctness-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -18,7 +17,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: Sequence Parallel Correctness Tests (2xH100)
-  key: sequence-parallel-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -29,7 +27,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: AsyncTP Correctness Tests (2xH100)
-  key: asynctp-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -40,10 +37,9 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: AsyncTP Correctness Tests (B200)
-  key: asynctp-correctness-tests-b200
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
@@ -51,7 +47,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: Distributed Compile Unit Tests (2xH100)
-  key: distributed-compile-unit-tests-2xh100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -65,10 +60,9 @@ steps:
   - pytest -s -v tests/compile/passes/distributed
 
 - label: Fusion and Compile Unit Tests (2xB200)
-  key: fusion-and-compile-unit-tests-2xb200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - csrc/quantization/fp4/
   - vllm/model_executor/layers/quantization/
@@ -95,7 +89,6 @@ steps:
     - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Fusion E2E Quick (H100)
-  key: fusion-e2e-quick-h100
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/"
   device: h100
@@ -114,7 +107,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
 
 - label: Fusion E2E Config Sweep (H100)
-  key: fusion-e2e-config-sweep-h100
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
@@ -134,10 +126,9 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
 
 - label: Fusion E2E Config Sweep (B200)
-  key: fusion-e2e-config-sweep-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -148,7 +139,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
 
 - label: Fusion E2E TP2 Quick (H100)
-  key: fusion-e2e-tp2-quick-h100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -166,7 +156,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
 
 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
-  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -186,7 +175,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
 
 - label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
-  key: fusion-e2e-tp2-asynctp-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -206,10 +194,9 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
 
 - label: Fusion E2E TP2 (B200)
-  key: fusion-e2e-tp2-b200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   num_devices: 2
   source_file_dependencies:
     - csrc/quantization/

.buildkite/test_areas/cuda.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Platform Tests (CUDA)
-  key: platform-tests-cuda
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
@@ -14,7 +13,6 @@ steps:
     - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
-  key: cudagraph
   timeout_in_minutes: 20
   source_file_dependencies:
   - tests/v1/cudagraph

.buildkite/test_areas/disaggregated.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
-  key: distributed-nixlconnector-pd-accuracy-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -14,7 +13,6 @@ steps:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 - label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
-  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -26,7 +24,6 @@ steps:
     - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -38,7 +35,6 @@ steps:
     - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -50,7 +46,6 @@ steps:
     - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
-  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -62,7 +57,6 @@ steps:
     - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -77,7 +71,6 @@ steps:
     - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
 
 - label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
-  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
   timeout_in_minutes: 30
   device: a100
   working_dir: "/vllm-workspace/tests"
@@ -91,7 +84,6 @@ steps:
     - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Comm Ops
-  key: distributed-comm-ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -17,7 +16,6 @@ steps:
   - pytest -v -s distributed/test_shm_storage.py
 
 - label: Distributed DP Tests (2 GPUs)
-  key: distributed-dp-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -39,7 +37,6 @@ steps:
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
-  key: distributed-compile-rpc-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -62,7 +59,6 @@ steps:
   - pytest -v -s ./compile/test_wrapper.py
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
-  key: distributed-torchrun-shutdown-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -85,7 +81,6 @@ steps:
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Distributed Torchrun + Examples (4 GPUs)
-  key: distributed-torchrun-examples-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace"
   num_devices: 4
@@ -93,8 +88,9 @@ steps:
   - vllm/distributed/
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
+  - examples/offline_inference/rlhf_colocate.py
   - examples/rl/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
@@ -111,13 +107,12 @@ steps:
   # test with torchrun tp=2 and dp=2 with ep
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - python3 examples/offline_inference/data_parallel.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
 
 - label: Distributed DP Tests (4 GPUs)
-  key: distributed-dp-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -138,7 +133,6 @@ steps:
   - pytest -v -s distributed/test_utils.py
 
 - label: Distributed Compile + Comm (4 GPUs)
-  key: distributed-compile-comm-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -160,28 +154,24 @@ steps:
   - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
-  key: distributed-tests-8-gpus-h100
   timeout_in_minutes: 10
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - examples/offline_inference/torchrun_dp_example.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
-  - tests/distributed/test_mnnvl_alltoall.py
-
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
-  key: distributed-tests-4-gpus-a100
   device: a100
   optional: true
   num_devices: 4
@@ -196,7 +186,6 @@ steps:
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: Distributed Tests (2 GPUs)(H100)
-  key: distributed-tests-2-gpus-h100
   timeout_in_minutes: 15
   device: h100
   optional: true
@@ -205,14 +194,13 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
     - pytest -v -s tests/distributed/test_packed_tensor.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  key: distributed-tests-2-gpus-b200
-  device: b200-k8s
+  device: b200
   optional: true
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -220,12 +208,8 @@ steps:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
     - pytest -v -s tests/v1/distributed/test_dbo.py
-    - pytest -v -s tests/distributed/test_mnnvl_alltoall.py
-
-    
 
 - label: 2 Node Test (4 GPUs)
-  key: 2-node-test-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -238,12 +222,11 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
 - label: Pipeline + Context Parallelism (4 GPUs)
-  key: pipeline-context-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -258,7 +241,6 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: RayExecutorV2 (4 GPUs)
-  key: rayexecutorv2-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/docker.yaml

@@ -1,16 +0,0 @@
-group: Docker
-depends_on:
-  - image-build-cpu
-steps:
-- label: Docker Build Metadata
-  timeout_in_minutes: 10
-  device: cpu-small
-  source_file_dependencies:
-    - .buildkite/release-pipeline.yaml
-    - .buildkite/scripts/docker-build-metadata-args.sh
-    - docker/Dockerfile
-    - docker/Dockerfile.cpu
-    - docker/docker-bake.hcl
-    - tests/tools/test_docker_build_metadata_args.py
-  commands:
-    - pytest -v -s tools/test_docker_build_metadata_args.py

.buildkite/test_areas/e2e_integration.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: DeepSeek V2-Lite Accuracy
-  key: deepseek-v2-lite-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -13,7 +12,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
-  key: qwen3-30b-a3b-fp8-block-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -23,9 +21,8 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  key: qwen3-30b-a3b-fp8-block-accuracy-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   working_dir: "/vllm-workspace"
@@ -33,7 +30,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
 - label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
-  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -43,7 +39,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
 
 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
-  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
   timeout_in_minutes: 60
   device: h100
   optional: true

.buildkite/test_areas/engine.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Engine
-  key: engine
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
@@ -13,12 +12,10 @@ steps:
   - tests/test_config
   - tests/test_logger
   - tests/test_vllm_port
-  - tests/test_jit_monitor.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
 - label: Engine (1 GPU)
-  key: engine-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/engine/
@@ -28,7 +25,6 @@ steps:
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
 
 - label: e2e Scheduling (1 GPU)
-  key: e2e-scheduling-1-gpu
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -38,7 +34,6 @@ steps:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
 
 - label: e2e Core (1 GPU)
-  key: e2e-core-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/
@@ -47,7 +42,6 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
 - label: V1 e2e (2 GPUs)
-  key: v1-e2e-2-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 2
@@ -64,7 +58,6 @@ steps:
       - image-build-amd
 
 - label: V1 e2e (4 GPUs)
-  key: v1-e2e-4-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 4
@@ -81,7 +74,6 @@ steps:
       - image-build-amd
 
 - label: V1 e2e (4xH100)
-  key: v1-e2e-4xh100
   timeout_in_minutes: 60
   device: h100
   num_devices: 4

.buildkite/test_areas/entrypoints.yaml

@@ -2,8 +2,7 @@ group: Entrypoints
 depends_on: 
   - image-build
 steps:
-- label: Entrypoints Unit Tests
-  key: entrypoints-unit-tests
+- label: Entrypoints Unit Tests  
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -14,7 +13,6 @@ steps:
   - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
-  key: entrypoints-integration-llm
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -28,7 +26,6 @@ steps:
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
 - label: Entrypoints Integration (API Server openai - Part 1)
-  key: entrypoints-integration-api-server-openai-part-1
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -46,7 +43,6 @@ steps:
 
 
 - label: Entrypoints Integration (API Server openai - Part 2)
-  key: entrypoints-integration-api-server-openai-part-2
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -64,7 +60,6 @@ steps:
       - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 3)
-  key: entrypoints-integration-api-server-openai-part-3
   timeout_in_minutes: 50
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -77,7 +72,6 @@ steps:
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (API Server 2)
-  key: entrypoints-integration-api-server-2
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -92,7 +86,6 @@ steps:
   - pytest -v -s tool_use
 
 - label: Entrypoints Integration (Pooling)
-  key: entrypoints-integration-pooling
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -103,7 +96,6 @@ steps:
   - pytest -v -s entrypoints/pooling
 
 - label: Entrypoints Integration (Responses API)
-  key: entrypoints-integration-responses-api
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -113,7 +105,6 @@ steps:
   - pytest -v -s entrypoints/openai/responses
 
 - label: OpenAI API Correctness
-  key: openai-api-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/expert_parallelism.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: EPLB Algorithm
-  key: eplb-algorithm
   timeout_in_minutes: 15
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -16,7 +15,6 @@ steps:
   - pytest -v -s distributed/test_eplb_utils.py
 
 - label: EPLB Execution # 17min
-  key: eplb-execution
   timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -28,7 +26,6 @@ steps:
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Elastic EP Scaling Test
-  key: elastic-ep-scaling-test
   timeout_in_minutes: 20
   device: h100
   working_dir: "/vllm-workspace/tests"

.buildkite/test_areas/kernels.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: vLLM IR Tests
-  key: vllm-ir-tests
   timeout_in_minutes: 10
   device: h200_18gb
   working_dir: "/vllm-workspace/"
@@ -15,7 +14,6 @@ steps:
     - pytest -v -s tests/kernels/ir
 
 - label: Kernels Core Operation Test
-  key: kernels-core-operation-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -25,7 +23,6 @@ steps:
     - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
-  key: kernels-minimax-reduce-rms-test-2-gpus
   timeout_in_minutes: 15
   num_devices: 2
   device: h100
@@ -39,7 +36,6 @@ steps:
     - pytest -v -s kernels/core/test_minimax_reduce_rms.py
 
 - label: Kernels Attention Test %N
-  key: kernels-attention-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
@@ -53,7 +49,6 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  key: kernels-quantization-test
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/quantization/
@@ -64,7 +59,6 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test %N
-  key: kernels-moe-test
   timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
@@ -80,7 +74,6 @@ steps:
   parallelism: 5
 
 - label: Kernels Mamba Test
-  key: kernels-mamba-test
   timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/mamba/
@@ -89,18 +82,7 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
-- label: Kernels KDA Test
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - vllm/model_executor/layers/fla/ops/kda.py
-  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
-  - vllm/model_executor/layers/fla/ops/l2norm.py
-  - tests/kernels/test_kda.py
-  commands:
-    - pytest -v -s kernels/test_kda.py
-
 - label: Kernels DeepGEMM Test (H100)
-  key: kernels-deepgemm-test-h100
   timeout_in_minutes: 45
   device: h100
   num_devices: 1
@@ -113,19 +95,16 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
-  - tests/quantization/test_cutlass_w4a16.py
   commands:
     - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
-    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
-  key: kernels-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
@@ -171,7 +150,6 @@ steps:
     - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Kernels Helion Test
-  key: kernels-helion-test
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -183,7 +161,6 @@ steps:
 
  
 - label: Kernels FP8 MoE Test (1 H100)
-  key: kernels-fp8-moe-test-1-h100
   timeout_in_minutes: 90
   device: h100
   num_devices: 1
@@ -200,7 +177,6 @@ steps:
     - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
 
 - label: Kernels FP8 MoE Test (2 H100s)
-  key: kernels-fp8-moe-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -210,9 +186,8 @@ steps:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
 - label: Kernels Fp4 MoE Test (B200)
-  key: kernels-fp4-moe-test-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -223,7 +198,6 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 H100s)
-  key: kernels-fusedmoe-layer-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -240,7 +214,6 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 B200s)
-  key: kernels-fusedmoe-layer-test-2-b200s
   timeout_in_minutes: 90
   device: b200
   num_devices: 2

.buildkite/test_areas/lm_eval.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: LM Eval Small Models
-  key: lm-eval-small-models
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -25,7 +24,6 @@ steps:
 #   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
-  key: lm-eval-large-models-4-gpus-h100
   device: h100
   optional: true
   num_devices: 4
@@ -38,7 +36,6 @@ steps:
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
 - label: LM Eval Small Models (B200)
-  key: lm-eval-small-models-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -49,9 +46,8 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
 
 - label: LM Eval Qwen3.5 Models (B200)
-  key: lm-eval-qwen3-5-models-b200
   timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -66,7 +62,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
 
 - label: LM Eval Large Models (H200)
-  key: lm-eval-large-models-h200
   timeout_in_minutes: 60
   device: h200
   optional: true
@@ -75,7 +70,6 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
-  key: moe-refactor-integration-test-h100-temporary
   device: h100
   optional: true
   num_devices: 2
@@ -83,15 +77,13 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
   
 - label: MoE Refactor Integration Test (B200 - TEMPORARY)
-  key: moe-refactor-integration-test-b200-temporary
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
 
 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
-  key: moe-refactor-integration-test-b200-dp-temporary
   device: b200
   optional: true
   num_devices: 2
@@ -100,7 +92,6 @@ steps:
 
 
 - label: LM Eval TurboQuant KV Cache
-  key: lm-eval-turboquant-kv-cache
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers/quantization/turboquant/
@@ -111,7 +102,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/models-turboquant.txt
 
 - label: GPQA Eval (GPT-OSS) (H100)
-  key: gpqa-eval-gpt-oss-h100
   timeout_in_minutes: 120
   device: h100
   optional: true
@@ -125,7 +115,6 @@ steps:
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
 
 - label: GPQA Eval (GPT-OSS) (B200)
-  key: gpqa-eval-gpt-oss-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -137,10 +126,3 @@ steps:
   commands:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt
-
-- label: MRCR Eval Small Models
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - tests/evals/mrcr/
-  commands:
-    - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt

.buildkite/test_areas/lora.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
-  key: lora
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/lora
@@ -14,7 +13,6 @@ steps:
 
 
 - label: LoRA TP (Distributed)
-  key: lora-tp-distributed
   timeout_in_minutes: 30
   num_devices: 4
   source_file_dependencies:

.buildkite/test_areas/misc.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: V1 Spec Decode
-  key: v1-spec-decode
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/
@@ -19,7 +18,6 @@ steps:
       - image-build-amd
 
 - label: V1 Sample + Logits
-  key: v1-sample-logits
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -43,7 +41,6 @@ steps:
       - image-build-amd
 
 - label: V1 Core + KV + Metrics
-  key: v1-core-kv-metrics
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/
@@ -74,7 +71,6 @@ steps:
       - image-build-amd
 
 - label: V1 Others (CPU)
-  key: v1-others-cpu
   depends_on:
     - image-build-cpu
   source_file_dependencies:
@@ -90,7 +86,6 @@ steps:
     - pytest -v -s -m 'cpu_test' v1/metrics
 
 - label: Regression
-  key: regression
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
@@ -102,7 +97,6 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
-  key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -119,22 +113,21 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
     - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Metrics, Tracing (2 GPUs)
-  key: metrics-tracing-2-gpus
   timeout_in_minutes: 20
   num_devices: 2
   source_file_dependencies:
@@ -149,7 +142,6 @@ steps:
   - pytest -v -s v1/tracing
 
 - label: Python-only Installation
-  key: python-only-installation
   depends_on: ~
   timeout_in_minutes: 20
   source_file_dependencies:
@@ -159,7 +151,6 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Async Engine, Inputs, Utils, Worker
-  key: async-engine-inputs-utils-worker
   timeout_in_minutes: 50
   source_file_dependencies:
   - vllm/
@@ -172,8 +163,7 @@ steps:
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  key: async-engine-inputs-utils-worker-config-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
@@ -206,7 +196,6 @@ steps:
   - pytest -v -s config
 
 - label: Batch Invariance (H100)
-  key: batch-invariance-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -222,9 +211,8 @@ steps:
     - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
 
 - label: Batch Invariance (B200)
-  key: batch-invariance-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/v1/attention
     - vllm/model_executor/layers
@@ -239,7 +227,6 @@ steps:
     - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
   
 - label: Acceptance Length Test (Large Models) # optional
-  key: acceptance-length-test-large-models
   timeout_in_minutes: 25
   gpu: h100
   optional: true

.buildkite/test_areas/model_executor.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Executor
-  key: model-executor
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/engine/arg_utils.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Runner V2 Core Tests
-  key: model-runner-v2-core-tests
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/v1/worker/gpu/
@@ -26,16 +25,14 @@ steps:
   - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
 
 - label: Model Runner V2 Examples
-  key: model-runner-v2-examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
     - vllm/v1/worker/gpu/
     - vllm/v1/core/sched/
     - vllm/v1/worker/gpu_worker.py
+    - examples/offline_inference/
     - examples/basic/offline_inference/
-    - examples/generate/multimodal/
-    - examples/features/
     - examples/pooling/embed/vision_embedding_offline.py
     - examples/others/tensorize_vllm_model.py
   commands:
@@ -47,22 +44,21 @@ steps:
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
     #- python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
     - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs)
-  key: model-runner-v2-distributed-2-gpus
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -83,7 +79,6 @@ steps:
     - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
-  key: model-runner-v2-pipeline-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -99,7 +94,6 @@ steps:
     - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
 
 - label: Model Runner V2 Spec Decode
-  key: model-runner-v2-spec-decode
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:

.buildkite/test_areas/models_basic.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Basic Models Tests (Initialization)
-  key: basic-models-tests-initialization
   timeout_in_minutes: 45
   torch_nightly: true
   source_file_dependencies:
@@ -17,7 +16,6 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Extra Initialization) %N
-  key: basic-models-tests-extra-initialization
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -33,7 +31,6 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Other)
-  key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -50,7 +47,6 @@ steps:
 
 
 - label: Basic Models Test (Other CPU) # 5min
-  key: basic-models-test-other-cpu
   depends_on:
   - image-build-cpu
   timeout_in_minutes: 10
@@ -63,7 +59,6 @@ steps:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
 - label: Transformers Nightly Models
-  key: transformers-nightly-models
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -74,12 +69,11 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 - label: Transformers Backward Compatibility Models Test
-  key: transformers-backward-compatibility-models-test
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -89,7 +83,7 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

.buildkite/test_areas/models_distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Model Tests (2 GPUs)
-  key: distributed-model-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/models_language.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Language Models Tests (Standard)
-  key: language-models-tests-standard
   timeout_in_minutes: 25
   source_file_dependencies:
   - vllm/
@@ -16,7 +15,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Extra Standard) %N
-  key: language-models-tests-extra-standard
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -33,7 +31,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Hybrid) %N
-  key: language-models-tests-hybrid
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/
@@ -50,7 +47,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Test (Extended Generation) # 80min
-  key: language-models-test-extended-generation
   timeout_in_minutes: 110
   optional: true
   source_file_dependencies:
@@ -73,7 +69,6 @@ steps:
       - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
-  key: language-models-test-ppl
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true
@@ -84,7 +79,6 @@ steps:
     - pytest -v -s models/language/generation_ppl_test
 
 - label: Language Models Test (Extended Pooling)  # 36min
-  key: language-models-test-extended-pooling
   timeout_in_minutes: 50
   optional: true
   source_file_dependencies:
@@ -99,7 +93,6 @@ steps:
       - image-build-amd
 
 - label: Language Models Test (MTEB)
-  key: language-models-test-mteb
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true

.buildkite/test_areas/models_multimodal.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
-  key: multi-modal-models-standard-1-qwen2
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -20,7 +19,6 @@ steps:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
-  key: multi-modal-models-standard-2-qwen3-gemma
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -38,7 +36,6 @@ steps:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  key: multi-modal-models-standard-3-llava-qwen2-vl
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -54,7 +51,6 @@ steps:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
-  key: multi-modal-models-standard-4-other-whisper
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -71,8 +67,7 @@ steps:
       - image-build-amd
 
 - label: Multi-Modal Processor (CPU)
-  key: multi-modal-processor-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 60
   source_file_dependencies:
@@ -85,7 +80,6 @@ steps:
     - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Processor # 44min
-  key: multi-modal-processor
   timeout_in_minutes: 60
   device: h200_18gb
   source_file_dependencies:
@@ -97,7 +91,6 @@ steps:
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  key: multi-modal-accuracy-eval-small-models
   timeout_in_minutes: 70
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
@@ -108,7 +101,6 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
 - label: Multi-Modal Models (Extended Generation 1)
-  key: multi-modal-models-extended-generation-1
   optional: true
   source_file_dependencies:
   - vllm/
@@ -125,7 +117,6 @@ steps:
       - image-build-amd
 
 - label: Multi-Modal Models (Extended Generation 2)
-  key: multi-modal-models-extended-generation-2
   optional: true
   source_file_dependencies:
   - vllm/
@@ -135,7 +126,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models (Extended Generation 3)
-  key: multi-modal-models-extended-generation-3
   optional: true
   source_file_dependencies:
   - vllm/
@@ -145,7 +135,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Multi-Modal Models (Extended Pooling)
-  key: multi-modal-models-extended-pooling
   optional: true
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/plugins.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Plugin Tests (2 GPUs)
-  key: plugin-tests-2-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/pytorch.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
-  key: pytorch-compilation-unit-tests
   timeout_in_minutes: 10
   source_file_dependencies:
     - vllm/
@@ -19,7 +18,6 @@ steps:
   - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Unit Tests (H100)
-  key: pytorch-compilation-unit-tests-h100
   timeout_in_minutes: 30
   device: h100
   num_devices: 1
@@ -30,7 +28,6 @@ steps:
   - "find compile/h100/ -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Passes Unit Tests
-  key: pytorch-compilation-passes-unit-tests
   timeout_in_minutes: 20
   source_file_dependencies:
     - vllm/
@@ -39,7 +36,6 @@ steps:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
 
 - label: PyTorch Fullgraph Smoke Test
-  key: pytorch-fullgraph-smoke-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/
@@ -52,7 +48,6 @@ steps:
   - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph
-  key: pytorch-fullgraph
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -63,7 +58,6 @@ steps:
   - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
 - label: Pytorch Nightly Dependency Override Check # 2min
-  key: pytorch-nightly-dependency-override-check
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py

.buildkite/test_areas/quantization.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Quantization
-  key: quantization
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/
@@ -22,10 +21,9 @@ steps:
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: Quantized MoE Test (B200)
-  key: quantized-moe-test-b200
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - tests/quantization/test_blackwell_moe.py
   - vllm/model_executor/models/deepseek_v2.py
@@ -40,7 +38,6 @@ steps:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
 - label: Quantized Models Test
-  key: quantized-models-test
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/model_executor/layers/quantization

.buildkite/test_areas/ray_compat.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Ray Dependency Compatibility Check
-  key: ray-dependency-compatibility-check
   # Informational only — does not block the pipeline.
   # If this fails, it means the PR introduces a dependency that
   # conflicts with Ray's dependency constraints.

.buildkite/test_areas/samplers.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
-  key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers
@@ -11,9 +10,7 @@ steps:
   - tests/samplers
   - tests/conftest.py
   commands:
-    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
-    # values explicitly to still cover the PyTorch-native (Triton) path.
-    - VLLM_USE_FLASHINFER_SAMPLER=0 pytest -v -s samplers
+    - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:

.buildkite/test_areas/spec_decode.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Spec Decode Eagle
-  key: spec-decode-eagle
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -14,7 +13,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
 - label: Spec Decode Eagle Nightly B200
-  key: spec-decode-eagle-nightly-b200
   timeout_in_minutes: 30
   device: b200
   optional: true
@@ -26,7 +24,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
 - label: Spec Decode Speculators + MTP
-  key: spec-decode-speculators-mtp
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -38,7 +35,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
 - label: Spec Decode Speculators + MTP Nightly B200
-  key: spec-decode-speculators-mtp-nightly-b200
   timeout_in_minutes: 30
   device: b200
   optional: true
@@ -51,7 +47,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
   
 - label: Spec Decode Ngram + Suffix
-  key: spec-decode-ngram-suffix
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -62,7 +57,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
 
 - label: Spec Decode Draft Model
-  key: spec-decode-draft-model
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -73,9 +67,8 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: Spec Decode Draft Model Nightly B200
-  key: spec-decode-draft-model-nightly-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   optional: true
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -85,7 +78,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: DFlash Speculators Correctness
-  key: dflash-speculators-correctness
   timeout_in_minutes: 30
   device: h100
   optional: true
@@ -97,16 +89,3 @@ steps:
   commands:
     - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
     - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test
-
-- label: Spec Decode MTP hybrid (B200)
-  timeout_in_minutes: 30
-  device: b200
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - vllm/model_executor/models/qwen3_5.py
-    - vllm/model_executor/models/qwen3_5_mtp.py
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"

.buildkite/test_areas/weight_loading.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Weight Loading Multiple GPU  # 33min
-  key: weight-loading-multiple-gpu
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.github/mergify.yml

@@ -308,7 +308,8 @@ pull_request_rules:
       - files=benchmarks/benchmark_serving_structured_output.py
       - files=benchmarks/run_structured_output_benchmark.sh
       - files=docs/features/structured_outputs.md
-      - files=^examples/features/structured_outputs/
+      - files=examples/offline_inference/structured_outputs.py
+      - files=examples/online_serving/structured_outputs/structured_outputs.py
       - files~=^tests/v1/structured_output/
       - files=tests/entrypoints/llm/test_struct_output_generate.py
       - files~=^vllm/v1/structured_output/
@@ -324,7 +325,7 @@ pull_request_rules:
     - or:
       - files~=^vllm/v1/spec_decode/
       - files~=^tests/v1/spec_decode/
-      - files=^examples/features/speculative_decoding/
+      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
       - files~=^vllm/model_executor/models/.*eagle.*\.py
       - files=vllm/model_executor/models/mlp_speculator.py
       - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
@@ -388,7 +389,11 @@ pull_request_rules:
       - files~=^tests/entrypoints/anthropic/.*tool.*
       - files~=^vllm/tool_parsers/
       - files=docs/features/tool_calling.md
-      - files~=^examples/tool_calling/
+      - files~=^examples/tool_chat_*
+      - files=examples/offline_inference/chat_with_tools.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
+      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
   actions:
     label:
       add:

.github/workflows/pre-commit.yml

@@ -16,7 +16,11 @@ permissions:
 
 jobs:
   pre-run-check:
-    if: github.event_name == 'pull_request'
+    if: >-
+      github.event_name == 'pull_request' &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified')
     runs-on: ubuntu-latest
     steps:
     - name: Check PR label and author merge count
@@ -45,7 +49,12 @@ jobs:
 
   pre-commit:
     needs: pre-run-check
-    if: always() && (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
+    if: >-
+      always() &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified') &&
+      (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1

.gitignore

@@ -237,7 +237,6 @@ ep_kernels_workspace/
 
 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
-!.buildkite/scripts/lib/
 
 # Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
 vllm/grpc/vllm_engine_pb2.py

CMakeLists.txt

@@ -307,12 +307,10 @@ set(VLLM_EXT_SRC
   "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/custom_all_reduce.cu"
-  "csrc/torch_bindings.cpp"
-  "csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
+  "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  list(APPEND VLLM_EXT_SRC
-    "csrc/minimax_reduce_rms_kernel.cu")
+  list(APPEND VLLM_EXT_SRC "csrc/minimax_reduce_rms_kernel.cu")
 
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
@@ -1047,13 +1045,13 @@ endif()
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/topk_softmax_kernels.cu"
-  "csrc/moe/topk_softplus_sqrt_kernels.cu")
+  "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC
     "csrc/moe/moe_wna16.cu"
-    "csrc/moe/grouped_topk_kernels.cu")
+    "csrc/moe/grouped_topk_kernels.cu"
+    "csrc/moe/router_gemm.cu")
 endif()
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")

benchmarks/kernels/benchmark_vit_fp8_attn.py

@@ -1,324 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Benchmarks FP8 vs BF16 ViT attention via FlashInfer cuDNN backend.
-#
-# == Usage Examples ==
-#
-# Benchmark mode (default, FlashInfer CUDAGraph Bench)
-#   python3 benchmark_vit_fp8_attn.py
-#
-# Profile mode (PyTorch profiler, saves TensorBoard traces):
-#   python3 benchmark_vit_fp8_attn.py --profile
-#   python3 benchmark_vit_fp8_attn.py --profile --profile-output-dir ./profile_traces
-#
-# Custom seq_lens:
-#   python3 benchmark_vit_fp8_attn.py --seq-lens 4096 8192 16384
-
-from functools import partial
-
-import numpy as np
-import torch
-from torch.profiler import ProfilerActivity, profile, record_function
-
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-
-# Qwen3-VL defaults
-NUM_HEADS = 16
-HEAD_DIM = 72
-DEFAULT_SEQ_LENS = [2304, 4096, 8192, 16384]
-
-
-def _setup_fp8_attention(num_heads: int, head_dim: int) -> tuple:
-    """Create FP8 and BF16 attention modules + workspace."""
-    from types import SimpleNamespace
-    from unittest.mock import patch
-
-    from vllm.config import VllmConfig, set_current_vllm_config
-    from vllm.config.multimodal import MultiModalConfig
-    from vllm.model_executor.layers.attention.mm_encoder_attention import (
-        MMEncoderAttention,
-        _get_flashinfer_workspace_buffer,
-    )
-    from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-    old_dtype = torch.get_default_dtype()
-    torch.set_default_dtype(torch.bfloat16)
-
-    backend_patch = patch(
-        "vllm.model_executor.layers.attention.mm_encoder_attention"
-        ".get_vit_attn_backend",
-        return_value=AttentionBackendEnum.FLASHINFER,
-    )
-
-    # FP8 attention
-    mm_config_fp8 = MultiModalConfig(mm_encoder_attn_dtype="fp8")
-    vllm_config_fp8 = VllmConfig()
-    vllm_config_fp8.model_config = SimpleNamespace(multimodal_config=mm_config_fp8)
-    with set_current_vllm_config(vllm_config_fp8), backend_patch:
-        attn_fp8 = MMEncoderAttention(
-            num_heads=num_heads,
-            head_size=head_dim,
-            prefix="visual.blocks.0.attn",
-        ).to("cuda")
-
-    # BF16 attention (no FP8)
-    with set_current_vllm_config(VllmConfig()), backend_patch:
-        attn_bf16 = MMEncoderAttention(
-            num_heads=num_heads,
-            head_size=head_dim,
-            prefix="visual.blocks.0.attn",
-        ).to("cuda")
-
-    torch.set_default_dtype(old_dtype)
-
-    workspace = _get_flashinfer_workspace_buffer()
-    return attn_fp8, attn_bf16, workspace
-
-
-def _build_meta(
-    seq_len: int,
-    num_heads: int,
-    head_dim: int,
-    fp8: bool,
-):
-    """Build cu_seqlens, max_seqlen, sequence_lengths."""
-    from vllm.model_executor.layers.attention.mm_encoder_attention import (
-        MMEncoderAttention,
-    )
-    from vllm.utils.math_utils import round_up
-    from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-    cu_np = np.array([0, seq_len], dtype=np.int32)
-    fp8_padded = num_heads * round_up(head_dim, 16) if fp8 else None
-
-    seq_lengths = MMEncoderAttention.maybe_compute_seq_lens(
-        AttentionBackendEnum.FLASHINFER, cu_np, torch.device("cuda")
-    )
-    max_seqlen = torch.tensor(
-        MMEncoderAttention.compute_max_seqlen(AttentionBackendEnum.FLASHINFER, cu_np),
-        dtype=torch.int32,
-    )
-    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
-        AttentionBackendEnum.FLASHINFER,
-        cu_np,
-        num_heads * head_dim,
-        1,
-        torch.device("cuda"),
-        fp8_padded_hidden_size=fp8_padded,
-    )
-    return cu_seqlens, max_seqlen, seq_lengths
-
-
-def run_benchmark(
-    seq_lens: list[int],
-    num_heads: int,
-    head_dim: int,
-    method: str,
-):
-    """Benchmark FP8 vs BF16 attention across seq_lens.
-
-    Uses FlashInfer GPU-level timing to measure pure kernel time,
-    excluding CPU launch overhead.
-    """
-    if method == "cupti":
-        from flashinfer.testing import bench_gpu_time_with_cupti as bench_fn
-
-        bench_fn = partial(bench_fn, use_cuda_graph=True, cold_l2_cache=False)
-    elif method == "cudagraph":
-        from flashinfer.testing import (
-            bench_gpu_time_with_cudagraph as bench_fn,
-        )
-
-        bench_fn = partial(bench_fn, cold_l2_cache=False)
-    else:
-        raise ValueError(f"Invalid method: {method}")
-
-    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
-
-    print(f"Timing method: {method}")
-    print(f"{'seq_len':>8} {'BF16 (us)':>12} {'FP8 (us)':>12} {'Speedup':>10}")
-    print("-" * 46)
-
-    for seq_len in seq_lens:
-        torch.manual_seed(42)
-
-        q = torch.randn(
-            seq_len,
-            num_heads,
-            head_dim,
-            device="cuda",
-            dtype=torch.bfloat16,
-        )
-        k = torch.randn_like(q)
-        v = torch.randn_like(q)
-
-        cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
-        # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
-        cu_bf16 = cu_fp8.clone()
-
-        def bf16_fn(q=q, k=k, v=v, cu=cu_bf16, ms=max_s, sl=seq_l):
-            attn_bf16._forward_flashinfer(q, k, v, cu, ms, sl)
-
-        def fp8_fn(q=q, k=k, v=v, cu=cu_fp8, ms=max_s, sl=seq_l):
-            attn_fp8._forward_flashinfer(q, k, v, cu, ms, sl)
-
-        # bench_fn returns List[float] of per-iteration times in ms
-        bf16_times = bench_fn(bf16_fn)
-        fp8_times = bench_fn(fp8_fn)
-
-        bf16_us = np.median(bf16_times) * 1e3  # ms -> us
-        fp8_us = np.median(fp8_times) * 1e3
-        speedup = bf16_us / fp8_us if fp8_us > 0 else float("inf")
-
-        print(f"{seq_len:>8} {bf16_us:>12.1f} {fp8_us:>12.1f} {speedup:>9.2f}x")
-
-
-def _make_trace_handler(output_dir: str, worker_name: str, label: str):
-    """Create a trace handler that saves to TensorBoard and prints summary."""
-
-    def handler(prof):
-        torch.profiler.tensorboard_trace_handler(output_dir, worker_name)(prof)
-        print(f"\n{'=' * 80}")
-        print(label)
-        print(f"{'=' * 80}")
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
-
-    return handler
-
-
-def run_profile(
-    seq_len: int,
-    num_heads: int,
-    head_dim: int,
-    warmup: int,
-    output_dir: str,
-):
-    """Profile FP8 vs BF16 attention with PyTorch profiler."""
-    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
-
-    torch.manual_seed(42)
-    q = torch.randn(
-        seq_len,
-        num_heads,
-        head_dim,
-        device="cuda",
-        dtype=torch.bfloat16,
-    )
-    k = torch.randn_like(q)
-    v = torch.randn_like(q)
-
-    cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
-    # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
-    cu_bf16 = cu_fp8.clone()
-
-    sched = torch.profiler.schedule(wait=0, warmup=warmup, active=1)
-
-    # Profile BF16 (warmup handled by profiler schedule)
-    with profile(
-        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-        schedule=sched,
-        on_trace_ready=_make_trace_handler(
-            output_dir,
-            f"bf16_h{head_dim}_s{seq_len}",
-            f"BF16 Attention (seq_len={seq_len}, heads={num_heads}, "
-            f"head_dim={head_dim})",
-        ),
-    ) as prof_bf16:
-        for _ in range(warmup + 1):
-            with record_function("bf16_attention"):
-                attn_bf16._forward_flashinfer(
-                    q.clone(), k.clone(), v.clone(), cu_bf16, max_s, seq_l
-                )
-                torch.accelerator.synchronize()
-            prof_bf16.step()
-
-    # Profile FP8 (warmup handled by profiler schedule)
-    with profile(
-        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-        schedule=sched,
-        on_trace_ready=_make_trace_handler(
-            output_dir,
-            f"fp8_h{head_dim}_s{seq_len}",
-            f"FP8 Attention (seq_len={seq_len}, heads={num_heads}, "
-            f"head_dim={head_dim})",
-        ),
-    ) as prof_fp8:
-        for _ in range(warmup + 1):
-            with record_function("fp8_attention"):
-                attn_fp8._forward_flashinfer(
-                    q.clone(), k.clone(), v.clone(), cu_fp8, max_s, seq_l
-                )
-                torch.accelerator.synchronize()
-            prof_fp8.step()
-
-    print(f"\nTensorBoard traces saved to: {output_dir}")
-    print(f"View with: tensorboard --logdir={output_dir}")
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(description="Benchmark FP8 vs BF16 ViT attention.")
-    parser.add_argument(
-        "--seq-lens",
-        type=int,
-        nargs="+",
-        default=DEFAULT_SEQ_LENS,
-        help="Sequence lengths to benchmark",
-    )
-    parser.add_argument(
-        "--num-heads",
-        type=int,
-        default=NUM_HEADS,
-    )
-    parser.add_argument(
-        "--head-dim",
-        type=int,
-        default=HEAD_DIM,
-    )
-    parser.add_argument(
-        "--method",
-        choices=["cupti", "cudagraph"],
-        default="cudagraph",
-        help="GPU timing method: cupti (CUPTI kernel timing) or "
-        "cudagraph (CUDA graph capture/replay). Default: cudagraph",
-    )
-    parser.add_argument(
-        "--warmup",
-        type=int,
-        default=10,
-        help="Warmup iterations (profile mode only)",
-    )
-    parser.add_argument(
-        "--profile",
-        action="store_true",
-        help="Run PyTorch profiler instead of benchmark",
-    )
-    parser.add_argument(
-        "--profile-seq-len",
-        type=int,
-        default=8192,
-        help="Sequence length for profiling (default: 8192)",
-    )
-    parser.add_argument(
-        "--profile-output-dir",
-        type=str,
-        default="./profile_traces",
-        help="Output directory for TensorBoard traces (default: ./profile_traces)",
-    )
-    args = parser.parse_args()
-
-    if args.profile:
-        run_profile(
-            args.profile_seq_len,
-            args.num_heads,
-            args.head_dim,
-            args.warmup,
-            args.profile_output_dir,
-        )
-    else:
-        run_benchmark(
-            args.seq_lens,
-            args.num_heads,
-            args.head_dim,
-            args.method,
-        )

benchmarks/multi_turn/benchmark_serving_multi_turn.py

@@ -217,7 +217,6 @@ async def send_request(
     min_tokens: int | None = None,
     max_tokens: int | None = None,
     timeout_sec: int = 120,
-    conversation_id: str | None = None,
 ) -> ServerResponse:
     payload = {
         "model": model,
@@ -226,9 +225,6 @@ async def send_request(
         "temperature": 0.0,
     }
 
-    if conversation_id is not None:
-        payload["conversation_id"] = conversation_id
-
     if stream:
         payload["stream"] = True
         payload["stream_options"] = {"include_usage": False}
@@ -423,7 +419,6 @@ async def send_turn(
         min_tokens,
         max_tokens,
         req_args.timeout_sec,
-        conversation_id=conv_id,
     )
 
     if response.valid is False:
@@ -1473,12 +1468,6 @@ async def main() -> None:
         "(for example: --warmup-percentages=0%%,50%%)",
     )
 
-    parser.add_argument(
-        "--trust-remote-code",
-        action="store_true",
-        help="Trust remote code when loading the tokenizer.",
-    )
-
     args = parser.parse_args()
 
     logger.info(args)
@@ -1521,9 +1510,7 @@ async def main() -> None:
     np.random.seed(args.seed)
 
     logger.info("Loading tokenizer")
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.model, trust_remote_code=args.trust_remote_code
-    )
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
 
     await get_server_info(args.url)
 

cmake/external_projects/deepgemm.cmake

@@ -20,7 +20,7 @@ else()
   FetchContent_Declare(
     deepgemm
     GIT_REPOSITORY https://github.com/deepseek-ai/DeepGEMM.git
-    GIT_TAG 891d57b4db1071624b5c8fa0d1e51cb317fa709f
+    GIT_TAG 477618cd51baffca09c4b0b87e97c03fe827ef03
     GIT_SUBMODULES "third-party/cutlass" "third-party/fmt"
     GIT_PROGRESS TRUE
     CONFIGURE_COMMAND ""
@@ -120,11 +120,6 @@ if(DEEPGEMM_ARCHS)
     COMPONENT _deep_gemm_C
     FILES_MATCHING PATTERN "*.py")
 
-  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/mega/"
-    DESTINATION vllm/third_party/deep_gemm/mega
-    COMPONENT _deep_gemm_C
-    FILES_MATCHING PATTERN "*.py")
-
   # Generate envs.py (normally generated by DeepGEMM's setup.py build step)
   file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/deep_gemm_envs.py"
     "# Pre-installed environment variables\npersistent_envs = dict()\n")

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG a6ec2ba7bd0a7dff98b3f4d3e6b52b159c48d78b
+        GIT_TAG 692917b1cda61b93ac9ee2d846ec54e75afe87b1
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""

csrc/activation_kernels.cu

@@ -11,74 +11,29 @@
 namespace vllm {
 
 template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
-          bool act_first, bool HAS_CLAMP>
+          bool act_first>
 __device__ __forceinline__ scalar_t compute(const scalar_t& x,
-                                            const scalar_t& y,
-                                            const float limit) {
-  if constexpr (act_first) {
-    scalar_t gate = x;
-    scalar_t up = y;
-    if constexpr (HAS_CLAMP) {
-      gate = (scalar_t)fminf((float)gate, limit);
-      up = (scalar_t)fmaxf(fminf((float)up, limit), -limit);
-    }
-    return ACT_FN(gate) * up;
-  } else {
-    scalar_t gate = x;
-    scalar_t up = y;
-    if constexpr (HAS_CLAMP) {
-      gate = (scalar_t)fmaxf(fminf((float)gate, limit), -limit);
-      up = (scalar_t)fminf((float)up, limit);
-    }
-    return gate * ACT_FN(up);
-  }
+                                            const scalar_t& y) {
+  return act_first ? ACT_FN(x) * y : x * ACT_FN(y);
 }
 
 template <typename packed_t, packed_t (*PACKED_ACT_FN)(const packed_t&),
-          bool act_first, bool HAS_CLAMP>
+          bool act_first>
 __device__ __forceinline__ packed_t packed_compute(const packed_t& x,
-                                                   const packed_t& y,
-                                                   const float limit) {
-  if constexpr (act_first) {
-    packed_t gate = x;
-    packed_t up = y;
-    if constexpr (HAS_CLAMP) {
-      float2 g = cast_to_float2(gate);
-      float2 u = cast_to_float2(up);
-      g.x = fminf(g.x, limit);
-      g.y = fminf(g.y, limit);
-      u.x = fmaxf(fminf(u.x, limit), -limit);
-      u.y = fmaxf(fminf(u.y, limit), -limit);
-      gate = cast_to_packed<packed_t>(g);
-      up = cast_to_packed<packed_t>(u);
-    }
-    return packed_mul(PACKED_ACT_FN(gate), up);
-  } else {
-    packed_t gate = x;
-    packed_t up = y;
-    if constexpr (HAS_CLAMP) {
-      float2 g = cast_to_float2(gate);
-      float2 u = cast_to_float2(up);
-      g.x = fmaxf(fminf(g.x, limit), -limit);
-      g.y = fmaxf(fminf(g.y, limit), -limit);
-      u.x = fminf(u.x, limit);
-      u.y = fminf(u.y, limit);
-      gate = cast_to_packed<packed_t>(g);
-      up = cast_to_packed<packed_t>(u);
-    }
-    return packed_mul(gate, PACKED_ACT_FN(up));
-  }
+                                                   const packed_t& y) {
+  return act_first ? packed_mul(PACKED_ACT_FN(x), y)
+                   : packed_mul(x, PACKED_ACT_FN(y));
 }
 
 // Activation and gating kernel template.
 template <typename scalar_t, typename packed_t,
           scalar_t (*ACT_FN)(const scalar_t&),
           packed_t (*PACKED_ACT_FN)(const packed_t&), bool act_first,
-          bool use_vec, bool HAS_CLAMP, bool use_256b = false>
+          bool use_vec, bool use_256b = false>
 __global__ void act_and_mul_kernel(
     scalar_t* __restrict__ out,          // [..., d]
     const scalar_t* __restrict__ input,  // [..., 2, d]
-    const int d, const float limit) {
+    const int d) {
   const scalar_t* x_ptr = input + blockIdx.x * 2 * d;
   const scalar_t* y_ptr = x_ptr + d;
   scalar_t* out_ptr = out + blockIdx.x * d;
@@ -103,9 +58,8 @@ __global__ void act_and_mul_kernel(
       }
 #pragma unroll
       for (int j = 0; j < pvec_t::NUM_ELTS; j++) {
-        x.elts[j] =
-            packed_compute<packed_t, PACKED_ACT_FN, act_first, HAS_CLAMP>(
-                x.elts[j], y.elts[j], limit);
+        x.elts[j] = packed_compute<packed_t, PACKED_ACT_FN, act_first>(
+            x.elts[j], y.elts[j]);
       }
       if constexpr (use_256b) {
         st256(x, &out_vec[i]);
@@ -118,8 +72,7 @@ __global__ void act_and_mul_kernel(
     for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
       const scalar_t x = VLLM_LDG(&x_ptr[idx]);
       const scalar_t y = VLLM_LDG(&y_ptr[idx]);
-      out_ptr[idx] =
-          compute<scalar_t, ACT_FN, act_first, HAS_CLAMP>(x, y, limit);
+      out_ptr[idx] = compute<scalar_t, ACT_FN, act_first>(x, y);
     }
   }
 }
@@ -198,11 +151,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
 
 // Launch activation and gating kernel.
 // Use ACT_FIRST (bool) indicating whether to apply the activation function
-// first. HAS_CLAMP (bool) enables pre-activation clamping: gate input is
-// clamped (max only) and up input is clamped (both sides) before the
-// activation function is applied.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST,        \
-                                      HAS_CLAMP, LIMIT)                        \
+// first.
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST)        \
   auto dtype = input.scalar_type();                                            \
   int d = input.size(-1) / 2;                                                  \
   int64_t num_tokens = input.numel() / input.size(-1);                         \
@@ -227,8 +177,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
             scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
             KERNEL<scalar_t>,                                                  \
             PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, HAS_CLAMP, true><<<grid, block, 0, stream>>>(     \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);   \
+            ACT_FIRST, true, true><<<grid, block, 0, stream>>>(                \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
       });                                                                      \
     } else {                                                                   \
       VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {          \
@@ -236,8 +186,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
             scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
             KERNEL<scalar_t>,                                                  \
             PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, HAS_CLAMP, false><<<grid, block, 0, stream>>>(    \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);   \
+            ACT_FIRST, true, false><<<grid, block, 0, stream>>>(               \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
       });                                                                      \
     }                                                                          \
   } else {                                                                     \
@@ -247,8 +197,8 @@ packed_gelu_tanh_kernel(const packed_t& val) {
           scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,        \
           KERNEL<scalar_t>,                                                    \
           PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>,   \
-          ACT_FIRST, false, HAS_CLAMP><<<grid, block, 0, stream>>>(            \
-          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, LIMIT);     \
+          ACT_FIRST, false><<<grid, block, 0, stream>>>(                       \
+          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);            \
     });                                                                        \
   }
 
@@ -256,14 +206,7 @@ void silu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
-                                true, false, 0.0f);
-}
-
-void silu_and_mul_clamp(torch::Tensor& out,    // [..., d]
-                        torch::Tensor& input,  // [..., 2 * d]
-                        double limit) {
-  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
-                                true, true, (float)limit);
+                                true);
 }
 
 void mul_and_silu(torch::Tensor& out,    // [..., d]
@@ -272,21 +215,21 @@ void mul_and_silu(torch::Tensor& out,    // [..., d]
   // The difference between mul_and_silu and silu_and_mul is that mul_and_silu
   // applies the silu to the latter half of the input.
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, vllm::packed_silu_kernel,
-                                false, false, 0.0f);
+                                false);
 }
 
 void gelu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel, vllm::packed_gelu_kernel,
-                                true, false, 0.0f);
+                                true);
 }
 
 void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
                        torch::Tensor& input)  // [..., 2 * d]
 {
-  LAUNCH_ACTIVATION_GATE_KERNEL(
-      vllm::gelu_tanh_kernel, vllm::packed_gelu_tanh_kernel, true, false, 0.0f);
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel,
+                                vllm::packed_gelu_tanh_kernel, true);
 }
 
 namespace vllm {

csrc/cpu/cpu_attn.cpp

@@ -1,16 +1,5 @@
 #include "cpu_attn_dispatch_generated.h"
 
-// Maps kv_cache_dtype string to Fp8KVCacheDataType enum.
-// "auto" -> kAuto(0); "fp8"/"fp8_e4m3" -> kFp8E4M3; "fp8_e5m2" -> kFp8E5M2.
-static inline cpu_attention::Fp8KVCacheDataType parse_fp8_kv_dtype(
-    const std::string& kv_cache_dtype) {
-  if (kv_cache_dtype == "fp8_e5m2")
-    return cpu_attention::Fp8KVCacheDataType::kFp8E5M2;
-  if (kv_cache_dtype == "fp8_e4m3" || kv_cache_dtype == "fp8")
-    return cpu_attention::Fp8KVCacheDataType::kFp8E4M3;
-  return cpu_attention::Fp8KVCacheDataType::kAuto;
-}
-
 torch::Tensor get_scheduler_metadata(
     const int64_t num_req, const int64_t num_heads_q,
     const int64_t num_heads_kv, const int64_t head_dim,
@@ -29,8 +18,6 @@ torch::Tensor get_scheduler_metadata(
     isa = cpu_attention::ISA::NEON;
   } else if (isa_hint == "vxe") {
     isa = cpu_attention::ISA::VXE;
-  } else if (isa_hint == "vsx") {
-    isa = cpu_attention::ISA::VSX;
   } else {
     TORCH_CHECK(false, "Unsupported CPU attention ISA hint: " + isa_hint);
   }
@@ -62,7 +49,7 @@ torch::Tensor get_scheduler_metadata(
   input.enable_kv_split = enable_kv_split;
 
   VLLM_DISPATCH_FLOATING_TYPES(dtype, "get_scheduler_metadata", [&]() {
-    CPU_ATTN_DISPATCH(head_dim, isa, 0, [&]() {
+    CPU_ATTN_DISPATCH(head_dim, isa, [&]() {
       input.elem_size = sizeof(scalar_t);
       input.q_buffer_elem_size = sizeof(attn_impl::q_buffer_t);
       input.logits_buffer_elem_size = sizeof(attn_impl::logits_buffer_t);
@@ -85,9 +72,7 @@ void cpu_attn_reshape_and_cache(
         key_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
     torch::Tensor&
         value_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
-    const torch::Tensor& slot_mapping, const std::string& isa,
-    const double k_scale = 1.0, const double v_scale = 1.0,
-    const std::string& kv_cache_dtype = "auto") {
+    const torch::Tensor& slot_mapping, const std::string& isa) {
   TORCH_CHECK_EQ(key.dim(), 3);
   TORCH_CHECK_EQ(value.dim(), 3);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
@@ -95,30 +80,18 @@ void cpu_attn_reshape_and_cache(
   TORCH_CHECK_EQ(key.stride(2), 1);
   TORCH_CHECK_EQ(value.stride(2), 1);
 
-  const int64_t kv_cache_idx =
-      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
-  const bool is_fp8 = (kv_cache_idx != 0);
-
-  if (is_fp8) {
-    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
-                "key_cache must be uint8 for FP8 path");
-    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
-                "value_cache must be uint8 for FP8 path");
-    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
-    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
-  }
-
-  const float k_inv = is_fp8 ? 1.0f / static_cast<float>(k_scale) : 0.0f;
-  const float v_inv = is_fp8 ? 1.0f / static_cast<float>(v_scale) : 0.0f;
-
   const int64_t token_num = key.size(0);
-  const int64_t head_num = key.size(1);
-  const int64_t head_dim = key.size(2);
+  const int64_t key_token_num_stride = key.stride(0);
+  const int64_t value_token_num_stride = value.stride(0);
+  const int64_t head_num = value.size(1);
+  const int64_t key_head_num_stride = key.stride(1);
+  const int64_t value_head_num_stride = value.stride(1);
   const int64_t num_blocks = key_cache.size(0);
   const int64_t num_blocks_stride = key_cache.stride(0);
   const int64_t cache_head_num_stride = key_cache.stride(1);
   const int64_t block_size = key_cache.size(2);
   const int64_t block_size_stride = key_cache.stride(2);
+  const int64_t head_dim = key.size(-1);
 
   cpu_attention::ISA isa_tag = [&]() {
     if (isa == "amx") {
@@ -131,31 +104,21 @@ void cpu_attn_reshape_and_cache(
       return cpu_attention::ISA::NEON;
     } else if (isa == "vxe") {
       return cpu_attention::ISA::VXE;
-    } else if (isa == "vsx") {
-      return cpu_attention::ISA::VSX;
     } else {
       TORCH_CHECK(false, "Invalid ISA type: " + isa);
     }
   }();
 
-  if (is_fp8) {
-    TORCH_CHECK(isa_tag == cpu_attention::ISA::AMX ||
-                    isa_tag == cpu_attention::ISA::VEC,
-                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
-  }
-
   VLLM_DISPATCH_FLOATING_TYPES(
       key.scalar_type(), "cpu_attn_reshape_and_cache", [&]() {
-        CPU_ATTN_DISPATCH(head_dim, isa_tag, kv_cache_idx, [&]() {
-          using kv_t = typename attn_impl::kv_cache_t;
+        CPU_ATTN_DISPATCH(head_dim, isa_tag, [&]() {
           attn_impl::reshape_and_cache(
               key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
-              reinterpret_cast<kv_t*>(key_cache.data_ptr()),
-              reinterpret_cast<kv_t*>(value_cache.data_ptr()),
-              slot_mapping.data_ptr<int64_t>(), token_num, key.stride(0),
-              value.stride(0), head_num, key.stride(1), value.stride(1),
-              num_blocks, num_blocks_stride, cache_head_num_stride, block_size,
-              block_size_stride, k_inv, v_inv);
+              key_cache.data_ptr<scalar_t>(), value_cache.data_ptr<scalar_t>(),
+              slot_mapping.data_ptr<int64_t>(), token_num, key_token_num_stride,
+              value_token_num_stride, head_num, key_head_num_stride,
+              value_head_num_stride, num_blocks, num_blocks_stride,
+              cache_head_num_stride, block_size, block_size_stride);
         });
       });
 }
@@ -174,26 +137,13 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table,  // [num_tokens, max_block_num]
     const double softcap, const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux,  // [num_heads]
-    const double k_scale = 1.0, const double v_scale = 1.0,
-    const std::string& kv_cache_dtype = "auto") {
+    const std::optional<torch::Tensor>& s_aux  // [num_heads]
+) {
   TORCH_CHECK_EQ(query.dim(), 3);
   TORCH_CHECK_EQ(query.stride(2), 1);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
   TORCH_CHECK_EQ(value_cache.dim(), 4);
 
-  const int64_t kv_cache_idx =
-      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
-  const bool is_fp8 = (kv_cache_idx != 0);
-  if (is_fp8) {
-    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
-                "key_cache must be uint8 for FP8 path");
-    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
-                "value_cache must be uint8 for FP8 path");
-    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
-    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
-  }
-
   cpu_attention::AttentionInput input;
   input.metadata = reinterpret_cast<cpu_attention::AttentionMetadata*>(
       scheduler_metadata.data_ptr());
@@ -215,32 +165,25 @@ void cpu_attention_with_kv_cache(
   input.block_table = block_table.data_ptr<int32_t>();
   input.alibi_slopes =
       alibi_slopes.has_value() ? alibi_slopes->data_ptr<float>() : nullptr;
+  // For now sink must be bf16
   input.s_aux = s_aux.has_value() ? s_aux->data_ptr<c10::BFloat16>() : nullptr;
   input.scale = scale;
   input.causal = causal;
   input.sliding_window_left = sliding_window_left;
   input.sliding_window_right = sliding_window_right;
   if (input.causal) {
+    // to make boundary calculation easier
     input.sliding_window_right = 0;
   }
-  input.softcap = static_cast<float>(softcap);
-
-  if (is_fp8) {
-    input.k_scale_fp8 = static_cast<float>(k_scale);
-    input.v_scale_fp8 = static_cast<float>(v_scale);
-    TORCH_CHECK(input.metadata->isa == cpu_attention::ISA::AMX ||
-                    input.metadata->isa == cpu_attention::ISA::VEC,
-                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
-  }
+  float softcap_fp32 = softcap;
+  input.softcap = softcap_fp32;
 
   VLLM_DISPATCH_FLOATING_TYPES(
       query.scalar_type(), "cpu_attention_with_kv_cache", [&]() {
-        CPU_ATTN_DISPATCH(
-            query.size(2), input.metadata->isa, kv_cache_idx, [&]() {
-              TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment,
-                             0);
-              cpu_attention::AttentionMainLoop<attn_impl> mainloop;
-              mainloop(&input);
-            });
+        CPU_ATTN_DISPATCH(query.size(2), input.metadata->isa, [&]() {
+          TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment, 0);
+          cpu_attention::AttentionMainLoop<attn_impl> mainloop;
+          mainloop(&input);
+        });
       });
 }

csrc/cpu/cpu_attn_amx.hpp

@@ -1,7 +1,6 @@
 #ifndef CPU_ATTN_AMX_HPP
 #define CPU_ATTN_AMX_HPP
 
-#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
@@ -22,10 +21,9 @@ typedef struct __tile_config {
 // 2-2-4 pattern, for 16 < m <= 32
 // TILE 0, 1: load A matrix, row num should be 16, m - 16
 // TILE 2, 3: load B matrix, row num should be 16
-// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16,
-// m - 16, m - 16
-// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
-template <typename q_buffer_t, typename kv_cache_t>
+// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16, m - 16, m
+// - 16
+template <typename kv_cache_t>
 class TileGemm224 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -44,56 +42,13 @@ class TileGemm224 {
   }
 };
 
-// Dequantize one FP8 tile (AMX_TILE_ROW_NUM rows x 32 cols) to BF16.
-template <typename kv_cache_t>
-FORCE_INLINE void deq_tile_amx(const uint8_t* src, c10::BFloat16* dst) {
-  for (int r = 0; r < AMX_TILE_ROW_NUM; ++r) {
-    if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
-      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e4m3_tag{})
-          .save(dst + r * 32);
-    } else {
-      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e5m2_tag{})
-          .save(dst + r * 32);
-    }
-  }
-}
-
-// For FP8: dequant src into scratch and return scratch.
-// For BF16: return src directly (scratch is unused; the compiler elides it).
-template <typename kv_cache_t>
-FORCE_INLINE const c10::BFloat16* prepare_b_tile(const kv_cache_t* src,
-                                                 c10::BFloat16* scratch) {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-    deq_tile_amx<kv_cache_t>(reinterpret_cast<const uint8_t*>(src), scratch);
-    return scratch;
-  } else {
-    return reinterpret_cast<const c10::BFloat16*>(src);
-  }
-}
-
-// Handles both BF16 and FP8 KV cache (2-2-4 pattern).
-template <typename kv_cache_t>
-class TileGemm224<c10::BFloat16, kv_cache_t> {
-  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
-                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
-
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-
-  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
-  // BF16 path: scratch_elems=1 so the scratch array is eliminated by the
-  // compiler.
-  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
-
+template <>
+class TileGemm224<c10::BFloat16> {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
+                                c10::BFloat16* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -101,7 +56,6 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
                                 const bool accum_c) {
     const int32_t k_times =
         dynamic_k_size / (AMX_TILE_ROW_NUM * 4 / sizeof(c10::BFloat16));
-
     c10::BFloat16* __restrict__ a_tile_0 = a_tile;
     c10::BFloat16* __restrict__ a_tile_1 = a_tile + lda * AMX_TILE_ROW_NUM;
     const int64_t a_tile_stride = [&]() {
@@ -116,8 +70,8 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
       }
     }();
 
-    kv_cache_t* __restrict__ b_tile_2 = b_tile;
-    kv_cache_t* __restrict__ b_tile_3 = [&]() {
+    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
+    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
         // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
@@ -152,16 +106,11 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
       _tile_zero(7);
     }
 
-    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
     for (int32_t k = 0; k < k_times; ++k) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_tile_stride);
+      _tile_stream_loadd(2, b_tile_2, b_tile_stride);
       _tile_dpbf16ps(4, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_tile_stride);
+      _tile_stream_loadd(3, b_tile_3, b_tile_stride);
       _tile_dpbf16ps(5, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
       _tile_dpbf16ps(6, 1, 2);
@@ -205,13 +154,13 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
 };
 
 // 1-2-2 pattern, for 0 < m <= 16
-// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should
-// be m, m
-// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row num
-// should be 16
-// TILE 6, 7: store results C matrix, row num should be m
-// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
-template <typename q_buffer_t, typename kv_cache_t>
+// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should be
+// m, m
+// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row
+// num should be 16
+// TILE 6, 7, (6, 7): store results C matrix, row num should be
+// m
+template <typename kv_cache_t>
 class TileGemm122 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -230,26 +179,13 @@ class TileGemm122 {
   }
 };
 
-// Handles both BF16 and FP8 KV cache (1-2-2 pattern).
-template <typename kv_cache_t>
-class TileGemm122<c10::BFloat16, kv_cache_t> {
-  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
-                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
-
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-
-  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
-  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
-
+template <>
+class TileGemm122<c10::BFloat16> {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
+                                c10::BFloat16* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -279,19 +215,21 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
       }
     }();
 
-    kv_cache_t* __restrict__ b_tile_2 = b_tile;
-    kv_cache_t* __restrict__ b_tile_3 = [&]() {
+    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
+    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
+        // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
       } else if constexpr (phase == AttentionGemmPhase::PV) {
+        // v_cache is prepacked
         return b_tile + (block_size * AMX_TILE_ROW_BYTES / 4);
       } else {
         TORCH_CHECK(false, "Unreachable");
       }
     }();
-    kv_cache_t* __restrict__ b_tile_4 =
+    c10::BFloat16* __restrict__ b_tile_4 =
         b_tile_2 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
-    kv_cache_t* __restrict__ b_tile_5 =
+    c10::BFloat16* __restrict__ b_tile_5 =
         b_tile_3 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
     int64_t b_stride = AMX_TILE_ROW_BYTES;
 
@@ -312,25 +250,16 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
       _tile_zero(7);
     }
 
-    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_4[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_5[scratch_elems];
     for (int32_t k = 0; k < k_group_times; ++k) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-      const c10::BFloat16* load_4 = prepare_b_tile(b_tile_4, scratch_4);
-      const c10::BFloat16* load_5 = prepare_b_tile(b_tile_5, scratch_5);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
+      _tile_stream_loadd(2, b_tile_2, b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
+      _tile_stream_loadd(3, b_tile_3, b_stride);
       _tile_dpbf16ps(7, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
-      _tile_stream_loadd(4, const_cast<c10::BFloat16*>(load_4), b_stride);
+      _tile_stream_loadd(4, b_tile_4, b_stride);
       _tile_dpbf16ps(6, 1, 4);
-      _tile_stream_loadd(5, const_cast<c10::BFloat16*>(load_5), b_stride);
+      _tile_stream_loadd(5, b_tile_5, b_stride);
       _tile_dpbf16ps(7, 1, 5);
 
       // update ptrs
@@ -350,13 +279,10 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
     }
 
     if (has_tail) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
+      _tile_stream_loadd(2, b_tile_2, b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
+      _tile_stream_loadd(3, b_tile_3, b_stride);
       _tile_dpbf16ps(7, 0, 3);
     }
 
@@ -376,25 +302,21 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
     _tile_loadconfig(&config);
   }
 };
-
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
-
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = scalar_t;
-  using kv_cache_t = kv_cache_scalar_t;
+  using kv_cache_t = scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = scalar_t;
 
   constexpr static int64_t BlockSizeAlignment =
-      32;  // AMX_TILE_ROW_NUM = 16 tokens/tile; 32 = 2 tiles
+      AMX_TILE_ROW_BYTES /
+      sizeof(kv_cache_t);  // KV token num unit of QK and PV phases
   constexpr static int64_t HeadDimAlignment =
       2 * (AMX_TILE_ROW_BYTES / 4);  // headdim num unit of PV phase
   constexpr static int64_t MaxQHeadNumPerIteration = 32;
@@ -402,9 +324,6 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
   constexpr static ISA ISAType = ISA::AMX;
   constexpr static bool scale_on_logits = true;
 
-  float k_scale = 1.0f;
-  float v_scale = 1.0f;
-
  public:
   AttentionImpl() : current_q_head_num_(0) {
     // Use all columns in AMX tiles
@@ -413,50 +332,21 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
 
   ~AttentionImpl() { _tile_release(); }
 
-  void init_from_input(const AttentionInput* input) {
-    if constexpr (fp8_kv) {
-      k_scale = input->k_scale_fp8;
-      v_scale = input->v_scale_fp8;
-    }
-  }
-
-  float get_output_v_scale() const noexcept {
-    if constexpr (fp8_kv) {
-      // AMX dequant places FP8 payload into a BF16 field (exponent bias 127).
-      // Correction = 2^(127 - FP8_bias): E4M3 bias=7 → 2^120, E5M2 bias=15 →
-      // 2^112.
-      constexpr float bias =
-          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
-      return v_scale * bias;
-    }
-    return 1.0f;
-  }
-
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    if constexpr (fp8_kv) {
-      // Same bias correction as get_output_v_scale: AMX FP8→BF16 dequant
-      // shifts the exponent bias from FP8 to BF16 (127), so we multiply by
-      // 2^(127-FP8_bias) to recover the true value. E4M3: 2^120, E5M2: 2^112.
-      const float bias =
-          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
-      scale *= k_scale * bias;
-    }
     if (q_head_num > AMX_TILE_ROW_NUM) {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm224<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
-                                                              amx_tile_config_);
+        TileGemm224<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
       }
-      attention<TileGemm224<q_buffer_t, kv_cache_t>> attention_iteration;
+      attention<TileGemm224<kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     } else {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm122<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
-                                                              amx_tile_config_);
+        TileGemm122<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
       }
-      attention<TileGemm122<q_buffer_t, kv_cache_t>> attention_iteration;
+      attention<TileGemm122<kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     }
   }
@@ -521,26 +411,13 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
   // reshape KV to AMX friendly layout
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
+      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    if constexpr (fp8_kv) {
-      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
-      reshape_and_cache_fp8_amx_impl<scalar_t, qfn>(
-          key, value, reinterpret_cast<uint8_t*>(key_cache),
-          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
-          head_num, head_dim, block_size, key_token_num_stride,
-          key_head_num_stride, value_token_num_stride, value_head_num_stride,
-          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
-          cache_head_num_stride, k_inv, v_inv);
-      return;
-    }
-
+      const int64_t block_size, const int64_t block_size_stride) {
     // For AMX 2D tiles, size of each line is 64 bytes
     constexpr int64_t amx_tile_row_size = AMX_TILE_ROW_BYTES;
     // For AMX B matrix, N always is 16
@@ -549,9 +426,6 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
     // For now suppose block_size is divisible by amx_tile_column_num
     TORCH_CHECK_EQ(block_size % amx_b_tile_k_size, 0);
 
-    scalar_t* __restrict__ kc = reinterpret_cast<scalar_t*>(key_cache);
-    scalar_t* __restrict__ vc = reinterpret_cast<scalar_t*>(value_cache);
-
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -579,7 +453,8 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
           constexpr int64_t quadword_num_per_group =
               token_num_per_group * quadword_num;
           int32_t* key_cache_start_ptr =
-              reinterpret_cast<int32_t*>(kc + block_idx * num_blocks_stride +
+              reinterpret_cast<int32_t*>(key_cache +
+                                         block_idx * num_blocks_stride +
                                          head_idx * cache_head_num_stride) +
               group_idx * quadword_num_per_group + group_offset;
 
@@ -608,7 +483,7 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              vc + block_idx * num_blocks_stride +
+              value_cache + block_idx * num_blocks_stride +
               head_idx * cache_head_num_stride +
               sub_group_idx * token_num_per_sub_group * amx_b_tile_n_size +
               sub_group_offset;

csrc/cpu/cpu_attn_fp8.hpp

@@ -1,214 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#pragma once
-#include <algorithm>
-#include <cmath>
-#include <cstdint>
-#include <limits>
-#include <type_traits>
-
-#include "cpu/utils.hpp"
-
-typedef uint32_t __attribute__((__may_alias__)) u32_alias_t;
-typedef uint16_t __attribute__((__may_alias__)) u16_alias_t;
-typedef float __attribute__((__may_alias__)) f32_alias_t;
-
-// Reference scalar dequant — used to verify vectorized AMX dequant.
-inline float fp8e4m3_to_float_scalar(uint8_t b, float scale) noexcept {
-  // NaN encoding in E4M3
-  if ((b & 0x7F) == 0x7F) return std::numeric_limits<float>::quiet_NaN();
-  uint32_t b_u32 = static_cast<uint32_t>(b);
-  uint32_t sign = (b_u32 & 0x80) << 24;
-  uint32_t payload = (b_u32 & 0x7F) << 20;
-  uint32_t bits = sign | payload;
-  float b_f32_unscaled = *reinterpret_cast<const f32_alias_t*>(&bits);
-  float b_f32_scaled = b_f32_unscaled * scale * 0x1p120f;
-  return b_f32_scaled;
-}
-
-inline uint8_t float_to_fp8e4m3_scalar(float v, float inv_scale) noexcept {
-  v *= inv_scale;
-  constexpr float fp8_max = 448.0f;
-  v = std::max(-fp8_max, std::min(fp8_max, v));
-  if (v == 0.0f) return 0;
-
-  // Inverse mapping of fp8e4m3_to_float_scalar: shift the effective exponent
-  // bias from fp32 (127) back to fp8 e4m3 (7), then pack sign|payload.
-  float v_f32_unscaled = v * 0x1p-120f;
-  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v_f32_unscaled);
-  uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
-  uint8_t payload = static_cast<uint8_t>((bits >> 20) & 0x7F);
-  if (payload == 0) return sign;
-  payload = std::min<uint8_t>(payload, 0x7E);  // keep 0x7F as NaN encoding
-  return static_cast<uint8_t>(sign | payload);
-}
-
-// ---------------------------------------------------------------------------
-// AMX reshape impl — parameterised on the quantisation function.
-// Writes key/value into uint8 FP8 KV cache using the AMX tile-friendly layout.
-// K: halfword-packed (2 FP8 per uint16, token_num_per_group=16).
-// V: sub-group packing (token_num_per_sub_group=2, head_elems_per_group=16).
-// block_size must be divisible by 32.
-// ---------------------------------------------------------------------------
-template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
-inline void reshape_and_cache_fp8_amx_impl(
-    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
-    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
-    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
-    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
-    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
-    float v_inv) {
-  constexpr int64_t token_num_per_group = 16;  // AMX_TILE_ROW_NUM
-  const int64_t halfword_num = head_dim / 2;   // 2 FP8 per uint16
-  const int64_t halfword_num_per_group = token_num_per_group * halfword_num;
-  constexpr int64_t head_elems_per_group = 16;
-  constexpr int64_t token_num_per_sub_group = 2;  // = 4 / sizeof(BF16)
-  const int64_t group_num = head_dim / head_elems_per_group;
-  const int64_t group_size = block_size * head_elems_per_group;
-
-#pragma omp parallel for collapse(2) schedule(static)
-  for (int64_t tok = 0; tok < token_num; ++tok) {
-    for (int64_t h = 0; h < head_num; ++h) {
-      const int64_t slot = slot_ptr[tok];
-      if (slot < 0) continue;
-      const int64_t block_idx = slot / block_size;
-      const int64_t block_offset = slot % block_size;
-
-      // Key: halfword-packed, 2 FP8 per uint16
-      {
-        const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
-        const int64_t group_idx = block_offset / token_num_per_group;
-        const int64_t group_offset = block_offset % token_num_per_group;
-        uint16_t* kdst =
-            reinterpret_cast<uint16_t*>(key_cache_ptr + block_idx * kc_stride0 +
-                                        h * kc_stride1) +
-            group_idx * halfword_num_per_group + group_offset;
-        for (int64_t j = 0; j < halfword_num; ++j) {
-          uint8_t fp8_0 = quant_fn(static_cast<float>(ksrc[j * 2]), k_inv);
-          uint8_t fp8_1 = quant_fn(static_cast<float>(ksrc[j * 2 + 1]), k_inv);
-          uint8_t bytes[2] = {fp8_0, fp8_1};
-          uint16_t hw = *reinterpret_cast<const u16_alias_t*>(bytes);
-          kdst[j * token_num_per_group] = hw;
-        }
-      }
-
-      // Value: sub-group packing (token_num_per_sub_group = 2)
-      {
-        const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
-        const int64_t sub_group_idx = block_offset / token_num_per_sub_group;
-        const int64_t sub_group_offset = block_offset % token_num_per_sub_group;
-        uint8_t* vdst =
-            value_cache_ptr + block_idx * vc_stride0 + h * vc_stride1 +
-            sub_group_idx * token_num_per_sub_group * head_elems_per_group +
-            sub_group_offset;
-        for (int64_t i = 0; i < group_num; ++i) {
-          for (int64_t j = 0; j < head_elems_per_group; ++j)
-            vdst[j * token_num_per_sub_group] =
-                quant_fn(static_cast<float>(vsrc[j]), v_inv);
-          vsrc += head_elems_per_group;
-          vdst += group_size;
-        }
-      }
-    }
-  }
-}
-
-// ---------------------------------------------------------------------------
-// FP8 E5M2 scalar helpers
-// ---------------------------------------------------------------------------
-
-// Reference scalar dequant — used to verify vectorized AMX dequant.
-// FP8 E5M2: s[7] e[6:2] m[1:0], exponent bias = 15 (same as FP16).
-// Byte b → FP16 bits = b << 8 (no bias correction needed).
-inline float fp8e5m2_to_float_scalar(uint8_t b, float scale) noexcept {
-  const uint8_t exp_bits = (b >> 2) & 0x1F;
-  const uint8_t mant_bits = b & 0x03;
-  // NaN: exp=11111, mant!=00
-  if (exp_bits == 0x1F && mant_bits != 0)
-    return std::numeric_limits<float>::quiet_NaN();
-  const uint32_t sign = static_cast<uint32_t>(b & 0x80) << 24;
-  if (exp_bits == 0x1F)
-    return sign ? -std::numeric_limits<float>::infinity()
-                : std::numeric_limits<float>::infinity();
-  if (exp_bits == 0) {  // subnormal: (-1)^s * 2^-14 * mant/4
-    if (mant_bits == 0) return 0.0f;
-    float v = mant_bits * 0x1p-16f;
-    return (sign ? -v : v) * scale;
-  }
-  // Normal: FP32 exp = exp5 - 15 + 127, mantissa top 2 bits
-  uint32_t fp32_bits = sign |
-                       ((static_cast<uint32_t>(exp_bits) - 15 + 127) << 23) |
-                       (static_cast<uint32_t>(mant_bits) << 21);
-  float val = *reinterpret_cast<const f32_alias_t*>(&fp32_bits);
-  return val * scale;
-}
-
-inline uint8_t float_to_fp8e5m2_scalar(float v, float inv_scale) noexcept {
-  v *= inv_scale;
-  constexpr float fp8_e5m2_max = 57344.0f;
-  v = std::max(-fp8_e5m2_max, std::min(fp8_e5m2_max, v));
-  if (v == 0.0f) return 0;
-  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v);
-  const uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
-  const int32_t exp_fp32 = static_cast<int32_t>((bits >> 23) & 0xFF) - 127;
-  const uint8_t mant2 = static_cast<uint8_t>((bits >> 21) & 0x03);
-  if (exp_fp32 < -14) {  // subnormal in E5M2
-    const int shift = -14 - exp_fp32;
-    if (shift + 21 >= 32)
-      return sign;  // underflow: too small for E5M2 subnormal
-    const uint32_t m = (0x800000u | (bits & 0x7FFFFFu)) >> (shift + 21);
-    return sign | static_cast<uint8_t>(std::min<uint32_t>(m, 3u));
-  }
-  const uint8_t exp5 = static_cast<uint8_t>(exp_fp32 + 15);
-  return sign | (exp5 << 2) | mant2;
-}
-
-// ---------------------------------------------------------------------------
-// Select the FP8 quant function at compile time based on kv_cache_t.
-// ---------------------------------------------------------------------------
-template <typename kv_cache_t>
-constexpr auto select_fp8_quant_fn() {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>)
-    return float_to_fp8e5m2_scalar;
-  else
-    return float_to_fp8e4m3_scalar;
-}
-
-// ---------------------------------------------------------------------------
-// VEC reshape impl — parameterised on the quantisation function.
-// Writes key (column-major) and value (row-major) into uint8 FP8 KV cache.
-// The pragma omp must live outside VLLM_DISPATCH_FLOATING_TYPES because
-// #pragma cannot appear inside variadic macro arguments.
-// ---------------------------------------------------------------------------
-template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
-inline void reshape_and_cache_fp8_vec_impl(
-    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
-    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
-    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
-    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
-    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
-    float v_inv) {
-#pragma omp parallel for collapse(2) schedule(static)
-  for (int64_t tok = 0; tok < token_num; ++tok) {
-    for (int64_t h = 0; h < head_num; ++h) {
-      const int64_t slot = slot_ptr[tok];
-      if (slot < 0) continue;
-      const int64_t block_idx = slot / block_size;
-      const int64_t block_offset = slot % block_size;
-
-      // Key layout: column-major within block
-      const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
-      uint8_t* kdst = key_cache_ptr + block_idx * kc_stride0 + h * kc_stride1 +
-                      block_offset;
-      for (int64_t i = 0; i < head_dim; ++i)
-        kdst[i * block_size] = quant_fn(static_cast<float>(ksrc[i]), k_inv);
-
-      // Value layout: row-major within block (contiguous head_dim bytes)
-      const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
-      uint8_t* vdst = value_cache_ptr + block_idx * vc_stride0 +
-                      h * vc_stride1 + block_offset * head_dim;
-      for (int64_t i = 0; i < head_dim; ++i)
-        vdst[i] = quant_fn(static_cast<float>(vsrc[i]), v_inv);
-    }
-  }
-}

csrc/cpu/cpu_attn_impl.hpp

@@ -12,24 +12,10 @@
 #include "cpu/utils.hpp"
 
 namespace cpu_attention {
-enum class ISA { AMX, VEC, VEC16, NEON, VXE, VSX };
+enum class ISA { AMX, VEC, VEC16, NEON, VXE };
 
-// Mirrors csrc/attention/dtype_fp8.cuh Fp8KVCacheDataType exactly.
-enum class Fp8KVCacheDataType {
-  kAuto = 0,
-  kFp8E4M3 = 1,
-  kFp8E5M2 = 2,
-};
-
-struct AttentionInput;
-
-template <ISA isa, typename scalar_t, int64_t head_dim,
-          typename kv_cache_scalar_t = scalar_t>
-class AttentionImpl {
- public:
-  void init_from_input(const AttentionInput*) {}
-  float get_output_v_scale() const noexcept { return 1.0f; }
-};
+template <ISA isa, typename scalar_t, int64_t head_dim>
+class AttentionImpl {};
 
 struct AttentionWorkItemGroup {
   int32_t req_id;
@@ -164,9 +150,6 @@ struct AttentionMetadata {
       case ISA::VXE:
         ss << "VXE, ";
         break;
-      case ISA::VSX:
-        ss << "VSX, ";
-        break;
     }
     ss << "workitem_group_num: " << workitem_group_num
        << ", reduction_item_num: " << reduction_item_num
@@ -797,9 +780,6 @@ struct AttentionInput {
   int32_t sliding_window_left;
   int32_t sliding_window_right;
   float softcap;
-  // FP8 KV cache scales (used by FP8 attention implementations)
-  float k_scale_fp8 = 1.0f;
-  float v_scale_fp8 = 1.0f;
 };
 
 #define DEFINE_CPU_ATTENTION_PARAMS                                         \
@@ -1394,13 +1374,6 @@ class AttentionMainLoop {
       }
 
       attention_impl_t attn_impl;
-      constexpr bool fp8_kv = std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                              std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-      float output_v_scale = 1.0f;
-      if constexpr (fp8_kv) {
-        attn_impl.init_from_input(input);
-        output_v_scale = attn_impl.get_output_v_scale();
-      }
 
       // general information
       const int32_t q_head_num = input->num_heads;
@@ -1780,7 +1753,7 @@ class AttentionMainLoop {
                                reinterpret_cast<query_t*>(input->output) +
                                    output_buffer_offset,
                                sum_buffer, actual_q_heads_per_kv,
-                               actual_q_token_num, q_head_num, output_v_scale);
+                               actual_q_token_num, q_head_num);
                 } else {
                   const int32_t stride =
                       actual_q_heads_per_kv * split_kv_q_token_num_threshold;
@@ -1850,7 +1823,7 @@ class AttentionMainLoop {
               split_output_buffer,
               reinterpret_cast<query_t*>(input->output) + output_buffer_offset,
               split_sum_buffer, actual_q_heads_per_kv, curr_output_token_num,
-              q_head_num, output_v_scale);
+              q_head_num);
         }
       }
     }
@@ -1974,8 +1947,8 @@ class AttentionMainLoop {
                     query_t* __restrict__ curr_output_buffer,
                     float* __restrict__ sum_buffer,
                     const int32_t q_heads_per_kv,
-                    const int32_t actual_q_token_num, const int32_t q_head_num,
-                    const float v_scale = 1.0f) {
+                    const int32_t actual_q_token_num,
+                    const int32_t q_head_num) {
     // final output
     using output_vec_t = typename VecTypeTrait<query_t>::vec_t;
 
@@ -1989,7 +1962,7 @@ class AttentionMainLoop {
           curr_partial_output_buffer;
       query_t* __restrict__ curr_output_buffer_iter = curr_output_buffer;
       for (int32_t head_idx = 0; head_idx < q_heads_per_kv; ++head_idx) {
-        vec_op::FP32Vec16 inv_sum_scale_vec(v_scale / *curr_sum_buffer);
+        vec_op::FP32Vec16 inv_sum_scale_vec(1.0 / *curr_sum_buffer);
 
         for (int32_t i = 0; i < group_num_per_head; ++i) {
           vec_op::FP32Vec16 vec(curr_partial_output_buffer_iter);

csrc/cpu/cpu_attn_neon.hpp

@@ -248,8 +248,8 @@ class TileGemmNeonFMLA {
 }  // namespace
 
 // this is similar to "ISA::VEC" at the moment
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -343,8 +343,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -389,7 +388,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
 #ifdef ARM_BF16_SUPPORT
 // For BF16 on Arm, reuse the BFMMLA kernels with 32-token alignment.
 template <int64_t head_dim>
-class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim, c10::BFloat16>
+class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim>
     : public AttentionImplNEONBFMMLA<BLOCK_SIZE_ALIGNMENT, ISA::NEON,
                                      head_dim> {};
 #endif

csrc/cpu/cpu_attn_neon_bfmmla.hpp

@@ -602,8 +602,7 @@ class AttentionImplNEONBFMMLA {
       [[maybe_unused]] const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
       const int64_t block_size,
-      [[maybe_unused]] const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      [[maybe_unused]] const int64_t block_size_stride) {
     const int64_t k_block_stride = (head_dim / TILE_K) * K_INNER_STRIDE;
     const int64_t v_pair_stride =
         (block_size / V_TOKENS_PER_ROW_BLOCK) * V_INNER_STRIDE;

csrc/cpu/cpu_attn_vec.hpp

@@ -1,37 +1,11 @@
 #ifndef CPU_ATTN_VEC_HPP
 #define CPU_ATTN_VEC_HPP
 
-#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
 
 namespace {
-
-// Load 32 kv_cache_t elements starting at ptr and return them as two FP32Vec16s
-// covering the lower 16 and upper 16 positions.
-// For FP8: both halves come from a single BF16Vec32 dequant of 32 bytes.
-// For BF16/FP16/FP32: two separate vector loads at ptr and ptr+16.
-template <typename kv_cache_t>
-FORCE_INLINE std::pair<vec_op::FP32Vec16, vec_op::FP32Vec16> load_b_pair_vec(
-    const kv_cache_t* ptr) {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
-    // BF16 container, but values are in the FP16 exponent range (bias 15 not
-    // 127).
-    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
-                                 vec_op::fp8_e4m3_tag{});
-    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
-  } else if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
-                                 vec_op::fp8_e5m2_tag{});
-    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
-  } else {
-    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
-    return std::make_pair(vec_op::FP32Vec16(load_vec_t(ptr)),
-                          vec_op::FP32Vec16(load_vec_t(ptr + 16)));
-  }
-}
-
 // 8-2-16 pattern, 8 regs for A, 2 regs for B, 16 regs for C, [8, K] @ [k, 32]
 template <typename kv_cache_t>
 class TileGemm82 {
@@ -80,7 +54,10 @@ class TileGemm82 {
                          const int32_t block_size, const int32_t dynamic_k_size,
                          const bool accum_c) {
     static_assert(0 < M && M <= 8);
+    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
 
+    kv_cache_t* __restrict__ curr_b_0 = b_tile;
+    kv_cache_t* __restrict__ curr_b_1 = b_tile + 16;
     float* __restrict__ curr_c_0 = c_tile;
     float* __restrict__ curr_c_1 = c_tile + 16;
 
@@ -99,14 +76,16 @@ class TileGemm82 {
     }
 
     float* __restrict__ curr_a = a_tile;
-    kv_cache_t* __restrict__ curr_b = b_tile;
-
     for (int32_t k = 0; k < dynamic_k_size; ++k) {
-      auto [fp32_b_0_reg, fp32_b_1_reg] = load_b_pair_vec(curr_b);
+      load_vec_t b_0_reg(curr_b_0);
+      vec_op::FP32Vec16 fp32_b_0_reg(b_0_reg);
+      load_vec_t b_1_reg(curr_b_1);
+      vec_op::FP32Vec16 fp32_b_1_reg(b_1_reg);
 
       float* __restrict__ curr_m_a = curr_a;
       vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
-        vec_op::FP32Vec16 a_reg(*curr_m_a);
+        float v = *curr_m_a;
+        vec_op::FP32Vec16 a_reg(v);
         c_regs[i * 2] = c_regs[i * 2] + a_reg * fp32_b_0_reg;
         c_regs[i * 2 + 1] = c_regs[i * 2 + 1] + a_reg * fp32_b_1_reg;
 
@@ -116,7 +95,8 @@ class TileGemm82 {
 
       // update
       curr_a += 1;
-      curr_b += ldb;
+      curr_b_0 += ldb;
+      curr_b_1 += ldb;
     }
 
     vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
@@ -129,20 +109,15 @@ class TileGemm82 {
     });
   }
 };
-
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
-
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
-  using kv_cache_t = kv_cache_scalar_t;
+  using kv_cache_t = scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = float;
@@ -154,45 +129,11 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
   constexpr static int64_t MaxQHeadNumPerIteration = 8;
   constexpr static int64_t HeadDim = head_dim;
   constexpr static ISA ISAType = ISA::VEC;
-  constexpr static bool scale_on_logits = fp8_kv;
-
-  float k_scale = 1.0f;
-  float v_scale = 1.0f;
+  constexpr static bool scale_on_logits = false;  // apply scale on q_buffer
 
  public:
-  void init_from_input(const AttentionInput* input) {
-    if constexpr (fp8_kv) {
-      k_scale = input->k_scale_fp8;
-      v_scale = input->v_scale_fp8;
-    }
-  }
-
-  float get_output_v_scale() const noexcept {
-    if constexpr (fp8_kv) {
-      // VEC dequant unpacks FP8 into a pseudo-FP16 layout (exponent bias 15).
-      // E4M3 (bias=7) needs correction 2^(15-7) = 2^8; E5M2 bias matches FP16
-      // so no correction.
-      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-        return v_scale;
-      } else {
-        return v_scale * 0x1p8f;
-      }
-    }
-    return 1.0f;
-  }
-
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    if constexpr (fp8_kv) {
-      // Same bias correction as get_output_v_scale: VEC FP8→pseudo-FP16 dequant
-      // uses bias 15; E4M3 (bias=7) needs ×2^8, E5M2 (bias=15) needs no
-      // correction.
-      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-        scale *= k_scale;
-      } else {
-        scale *= k_scale * 0x1p8f;
-      }
-    }
     attention<TileGemm82<kv_cache_t>> attention_iteration;
     attention_iteration(CPU_ATTENTION_PARAMS);
   }
@@ -220,19 +161,17 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                               // row-major
   }
 
-  // Copy q to q_buffer and cast it to fp32.
-  // FP8: QK scale is folded into execute_attention; copy Q unscaled here.
-  void copy_q_heads_tile(scalar_t* __restrict__ src,
-                         float* __restrict__ q_buffer, const int32_t q_num,
-                         const int32_t q_heads_per_kv,
-                         const int64_t q_num_stride,
-                         const int64_t q_head_stride, float scale) {
+  // Copy q to q_buffer and cast it to fp32
+  static void copy_q_heads_tile(
+      scalar_t* __restrict__ src,  // [q_num, q_heads_per_kv, head_size]
+      float* __restrict__ q_buffer, const int32_t q_num,
+      const int32_t q_heads_per_kv, const int64_t q_num_stride,
+      const int64_t q_head_stride, float scale) {
     static_assert(head_dim % 16 == 0);
     constexpr int32_t unroll_size = head_dim / 16;
     using load_vec_t = typename VecTypeTrait<scalar_t>::vec_t;
 
-    const float effective_scale = fp8_kv ? 1.0f : scale;
-    vec_op::FP32Vec16 scale_vec(effective_scale);
+    vec_op::FP32Vec16 scale_vec(scale);
     for (int32_t q_num_idx = 0; q_num_idx < q_num; ++q_num_idx) {
       for (int32_t q_head_idx = 0; q_head_idx < q_heads_per_kv; ++q_head_idx) {
         scalar_t* __restrict__ curr_q =
@@ -257,26 +196,13 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
   // reshape K as column-major and V as row-major
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
+      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    if constexpr (fp8_kv) {
-      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
-      reshape_and_cache_fp8_vec_impl<scalar_t, qfn>(
-          key, value, reinterpret_cast<uint8_t*>(key_cache),
-          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
-          head_num, head_dim, block_size, key_token_num_stride,
-          key_head_num_stride, value_token_num_stride, value_head_num_stride,
-          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
-          cache_head_num_stride, k_inv, v_inv);
-      return;
-    }
-
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -294,9 +220,8 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                                           token_idx * key_token_num_stride +
                                           head_idx * key_head_num_stride;
           scalar_t* key_cache_start_ptr =
-              reinterpret_cast<scalar_t*>(key_cache) +
-              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
-              block_offset;
+              key_cache + block_idx * num_blocks_stride +
+              head_idx * cache_head_num_stride + block_offset;
 
 #pragma GCC unroll 8
           for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
@@ -309,9 +234,8 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              reinterpret_cast<scalar_t*>(value_cache) +
-              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
-              block_offset * head_dim;
+              value_cache + block_idx * num_blocks_stride +
+              head_idx * cache_head_num_stride + block_offset * head_dim;
           std::memcpy(value_cache_start_ptr, value_start_ptr,
                       sizeof(scalar_t) * head_dim);
         }
@@ -319,7 +243,6 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
     }
   }
 };
-
 }  // namespace cpu_attention
 
 #endif

csrc/cpu/cpu_attn_vec16.hpp

@@ -116,9 +116,9 @@ class TileGemm161 {
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VEC16, scalar_t, head_dim, kv_cache_scalar_t>
-    : public AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VEC16, scalar_t, head_dim>
+    : public AttentionImpl<ISA::VEC, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;

csrc/cpu/cpu_attn_vsx.hpp

@@ -1,359 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#ifndef CPU_ATTN_VSX_HPP
-#define CPU_ATTN_VSX_HPP
-
-#include "cpu_attn_impl.hpp"
-#include <altivec.h>
-#include <type_traits>
-
-namespace cpu_attention {
-
-namespace {
-
-// ppc64le Vector = 16 bytes (128 bits)
-#define BLOCK_SIZE_ALIGNMENT 32
-#define HEAD_SIZE_ALIGNMENT 32
-#define MAX_Q_HEAD_NUM_PER_ITER 16
-
-template <typename kv_cache_t>
-FORCE_INLINE void load_row8_B_as_f32(const kv_cache_t* p, __vector float& b0,
-                                     __vector float& b1);
-
-// [1] Float Specialization
-template <>
-FORCE_INLINE void load_row8_B_as_f32<float>(const float* p, __vector float& b0,
-                                            __vector float& b1) {
-  b0 = vec_xl(0, const_cast<float*>(p));
-  b1 = vec_xl(0, const_cast<float*>(p + 4));
-}
-
-// [2] BFloat16 Specialization (Little Endian ppc64le)
-// On ppc64le (LE): BF16 bits should land in the HIGH 16 bits of each float32.
-// Byte layout of float32 on LE: [byte0(LSB), byte1, byte2, byte3(MSB)]
-// We need BF16 in bytes2-3 (high half) with bytes0-1 zeroed.
-// vec_mergeh on LE interleaves elements 0..3: result_i = {a[i], b[i]}
-// So vec_mergeh(zeros_u16, raw_u16) gives for each uint16 pair:
-//   uint16[2i]   = zeros[i]  -> low 16 bits of uint32  -> zeroed mantissa LSBs
-//   uint16[2i+1] = raw[i]    -> high 16 bits of uint32 -> BF16 bits
-// Cast to float32 gives exactly (bf16_bits << 16) per element.
-template <>
-FORCE_INLINE void load_row8_B_as_f32<c10::BFloat16>(const c10::BFloat16* p,
-                                                    __vector float& b0,
-                                                    __vector float& b1) {
-  __vector unsigned short raw = vec_xl(
-      0, reinterpret_cast<unsigned short*>(const_cast<c10::BFloat16*>(p)));
-  __vector unsigned short zeros = vec_splat_u16(0);
-
-  // LE: zeros in low 16 bits, raw in high 16 bits → bf16 << 16 == float32
-  b0 = (__vector float)vec_mergeh(zeros, raw);
-  b1 = (__vector float)vec_mergel(zeros, raw);
-}
-
-// Note: c10::Half (FP16) is not supported on PowerPC architecture
-
-template <int32_t M, typename kv_cache_t>
-FORCE_INLINE void gemm_micro_ppc64le_Mx8_Ku4(
-    const float* __restrict A,       // [M x K]
-    const kv_cache_t* __restrict B,  // [K x 8]
-    float* __restrict C,             // [M x 8]
-    int64_t lda, int64_t ldb, int64_t ldc, int32_t K, bool accumulate) {
-  static_assert(1 <= M && M <= 8, "M must be in [1,8]");
-
-#define ROWS_APPLY(OP) OP(0) OP(1) OP(2) OP(3) OP(4) OP(5) OP(6) OP(7)
-#define IF_M(i) if constexpr (M > (i))
-
-  // 1. Define A pointers
-#define DECL_A(i) const float* a##i = A + (i) * lda;
-  ROWS_APPLY(DECL_A)
-#undef DECL_A
-
-  // 2. Define Accumulators (2 vectors covers 8 columns)
-#define DECL_ACC(i) __vector float acc##i##_0, acc##i##_1;
-  ROWS_APPLY(DECL_ACC)
-#undef DECL_ACC
-
-  // 3. Initialize Accumulators (Load C or Zero)
-#define INIT_ACC(i)                                                  \
-  IF_M(i) {                                                          \
-    if (accumulate) {                                                \
-      acc##i##_0 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 0)); \
-      acc##i##_1 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 4)); \
-    } else {                                                         \
-      acc##i##_0 = vec_splats(0.0f);                                 \
-      acc##i##_1 = vec_splats(0.0f);                                 \
-    }                                                                \
-  }
-  ROWS_APPLY(INIT_ACC)
-#undef INIT_ACC
-
-  int32_t k = 0;
-
-  for (; k + 3 < K; k += 4) {
-    // Load 4 values of A for each Row M: A[k...k+3]
-#define LOAD_A4(i)        \
-  __vector float a##i##v; \
-  IF_M(i) a##i##v = vec_xl(0, const_cast<float*>(a##i + k));
-    ROWS_APPLY(LOAD_A4)
-#undef LOAD_A4
-
-    // FMA for specific lane L of A
-    // ppc64le: vec_madd(b, vec_splat(a, lane), acc)
-#define FMAS_LANE(i, aiv, L)                        \
-  IF_M(i) {                                         \
-    __vector float a_broad = vec_splat(aiv, L);     \
-    acc##i##_0 = vec_madd(b0, a_broad, acc##i##_0); \
-    acc##i##_1 = vec_madd(b1, a_broad, acc##i##_1); \
-  }
-
-    // Unroll K=0..3
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 0) * ldb, b0, b1);
-#define STEP_K0(i) FMAS_LANE(i, a##i##v, 0)
-      ROWS_APPLY(STEP_K0)
-#undef STEP_K0
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 1) * ldb, b0, b1);
-#define STEP_K1(i) FMAS_LANE(i, a##i##v, 1)
-      ROWS_APPLY(STEP_K1)
-#undef STEP_K1
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 2) * ldb, b0, b1);
-#define STEP_K2(i) FMAS_LANE(i, a##i##v, 2)
-      ROWS_APPLY(STEP_K2)
-#undef STEP_K2
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 3) * ldb, b0, b1);
-#define STEP_K3(i) FMAS_LANE(i, a##i##v, 3)
-      ROWS_APPLY(STEP_K3)
-#undef STEP_K3
-    }
-#undef FMAS_LANE
-  }
-
-  for (; k < K; ++k) {
-    __vector float b0, b1;
-    load_row8_B_as_f32<kv_cache_t>(B + (int64_t)k * ldb, b0, b1);
-#define TAIL_ROW(i)                              \
-  IF_M(i) {                                      \
-    __vector float ai = vec_splats(*(a##i + k)); \
-    acc##i##_0 = vec_madd(b0, ai, acc##i##_0);   \
-    acc##i##_1 = vec_madd(b1, ai, acc##i##_1);   \
-  }
-    ROWS_APPLY(TAIL_ROW)
-#undef TAIL_ROW
-  }
-
-#define STORE_ROW(i)                           \
-  IF_M(i) {                                    \
-    vec_xst(acc##i##_0, 0, C + (i) * ldc + 0); \
-    vec_xst(acc##i##_1, 0, C + (i) * ldc + 4); \
-  }
-  ROWS_APPLY(STORE_ROW)
-#undef STORE_ROW
-
-#undef ROWS_APPLY
-#undef IF_M
-}
-
-template <int32_t N, typename kv_cache_t>
-FORCE_INLINE void gemm_macro_ppc64le_Mx8_Ku4(const float* __restrict A,
-                                             const kv_cache_t* __restrict B,
-                                             float* __restrict C, int32_t M,
-                                             int32_t K, int64_t lda,
-                                             int64_t ldb, int64_t ldc,
-                                             bool accumulate) {
-  static_assert(N % 8 == 0, "N must be a multiple of 8");
-  for (int32_t m = 0; m < M;) {
-    int32_t mb = (M - m >= 8) ? 8 : (M - m >= 4) ? 4 : (M - m >= 2) ? 2 : 1;
-    const float* Ab = A + m * lda;
-    float* Cb = C + m * ldc;
-
-    for (int32_t n = 0; n < N; n += 8) {
-      const kv_cache_t* Bn = B + n;
-      float* Cn = Cb + n;
-      switch (mb) {
-        case 8:
-          gemm_micro_ppc64le_Mx8_Ku4<8, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        case 4:
-          gemm_micro_ppc64le_Mx8_Ku4<4, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        case 2:
-          gemm_micro_ppc64le_Mx8_Ku4<2, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        default:
-          gemm_micro_ppc64le_Mx8_Ku4<1, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-      }
-    }
-    m += mb;
-  }
-}
-
-template <typename kv_cache_t>
-class TileGemmPPC64 {
- public:
-  template <AttentionGemmPhase phase, int32_t k_size>
-  FORCE_INLINE static void gemm(const int32_t m_size,
-                                float* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
-                                float* __restrict__ c_tile, const int64_t lda,
-                                const int64_t ldb, const int64_t ldc,
-                                const int32_t block_size,
-                                const int32_t dynamic_k_size,
-                                const bool accum_c) {
-    if constexpr (phase == AttentionGemmPhase::QK) {
-      gemm_macro_ppc64le_Mx8_Ku4<BLOCK_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, k_size, lda, ldb, ldc, accum_c);
-    } else {
-      gemm_macro_ppc64le_Mx8_Ku4<HEAD_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, dynamic_k_size, lda, ldb, ldc,
-          accum_c);
-    }
-  }
-};
-
-}  // namespace
-
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VSX, scalar_t, head_dim> {
- public:
-  using query_t = scalar_t;
-  using q_buffer_t = float;
-  using kv_cache_t = scalar_t;
-  using logits_buffer_t = float;
-  using partial_output_buffer_t = float;
-  using prob_buffer_t = float;
-
-  constexpr static int64_t BlockSizeAlignment = BLOCK_SIZE_ALIGNMENT;
-  constexpr static int64_t HeadDimAlignment = HEAD_SIZE_ALIGNMENT;
-  constexpr static int64_t MaxQHeadNumPerIteration = MAX_Q_HEAD_NUM_PER_ITER;
-  constexpr static int64_t HeadDim = head_dim;
-  constexpr static ISA ISAType = ISA::VSX;
-  constexpr static bool scale_on_logits =
-      false;  // Scale is applied to Q during copy
-
- public:
-  AttentionImpl() {}
-
-  template <template <typename tile_gemm_t> typename attention>
-  FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    attention<TileGemmPPC64<kv_cache_t>> attention_iteration;
-    attention_iteration(CPU_ATTENTION_PARAMS);
-  }
-
-  // Strides for Memory Layout
-  constexpr static int64_t k_cache_token_group_stride(
-      const int32_t block_size) {
-    return BlockSizeAlignment;  // [head_dim, block_size] layout
-  }
-
-  constexpr static int64_t v_cache_token_group_stride(
-      const int32_t block_size) {
-    return head_dim * BlockSizeAlignment;
-  }
-
-  constexpr static int64_t v_cache_head_group_stride(const int32_t block_size) {
-    return HeadDimAlignment;
-  }
-
-  static void copy_q_heads_tile(scalar_t* __restrict__ src,
-                                float* __restrict__ q_buffer,
-                                const int32_t q_num,
-                                const int32_t q_heads_per_kv,
-                                const int64_t q_num_stride,
-                                const int64_t q_head_stride, float scale) {
-    __vector float scale_vec = vec_splats(scale);
-    constexpr bool is_bf16 = std::is_same<scalar_t, c10::BFloat16>::value;
-
-    for (int32_t i = 0; i < q_num; ++i) {
-      for (int32_t h = 0; h < q_heads_per_kv; ++h) {
-        scalar_t* curr_src = src + i * q_num_stride + h * q_head_stride;
-        float* curr_dst =
-            q_buffer + i * q_heads_per_kv * head_dim + h * head_dim;
-
-        int32_t d = 0;
-        for (; d <= head_dim - 8; d += 8) {
-          __vector float v0, v1;
-          load_row8_B_as_f32<scalar_t>(curr_src + d, v0, v1);
-
-          v0 = vec_mul(v0, scale_vec);
-          v1 = vec_mul(v1, scale_vec);
-
-          vec_xst(v0, 0, curr_dst + d);
-          vec_xst(v1, 0, curr_dst + d + 4);
-        }
-
-        for (; d < head_dim; ++d) {
-          float val = static_cast<float>(curr_src[d]);
-          curr_dst[d] = val * scale;
-        }
-      }
-    }
-  }
-
-  static void reshape_and_cache(
-      const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
-      const int64_t* __restrict__ slot_mapping, const int64_t token_num,
-      const int64_t key_token_num_stride, const int64_t value_token_num_stride,
-      const int64_t head_num, const int64_t key_head_num_stride,
-      const int64_t value_head_num_stride, const int64_t num_blocks,
-      const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    // k_inv and v_inv are unused on VSX: FP8 KV cache is not supported on
-    // PowerPC. The parameters are present to match the common interface.
-#pragma omp parallel for collapse(2)
-    for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
-      for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
-        const int64_t pos = slot_mapping[token_idx];
-        if (pos < 0) continue;
-
-        const int64_t block_idx = pos / block_size;
-        const int64_t block_offset = pos % block_size;
-
-        {
-          const scalar_t* key_src = key + token_idx * key_token_num_stride +
-                                    head_idx * key_head_num_stride;
-          scalar_t* key_dst = key_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride + block_offset;
-
-          for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
-            key_dst[j] = key_src[i];
-          }
-        }
-
-        {
-          const scalar_t* val_src = value + token_idx * value_token_num_stride +
-                                    head_idx * value_head_num_stride;
-          scalar_t* val_dst = value_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride +
-                              block_offset * head_dim;
-
-          std::memcpy(val_dst, val_src, sizeof(scalar_t) * head_dim);
-        }
-      }
-    }
-  }
-};
-
-}  // namespace cpu_attention
-
-#undef BLOCK_SIZE_ALIGNMENT
-#undef HEAD_SIZE_ALIGNMENT
-#undef MAX_Q_HEAD_NUM_PER_ITER
-
-#endif  // CPU_ATTN_VSX_HPP

csrc/cpu/cpu_attn_vxe.hpp

@@ -244,8 +244,8 @@ class TileGemmS390X {
 
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VXE, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VXE, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -342,8 +342,7 @@ class AttentionImpl<ISA::VXE, scalar_t, head_dim, kv_cache_scalar_t> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {

csrc/cpu/cpu_types_arm.hpp

@@ -15,9 +15,6 @@ using namespace at::vec;
 
 namespace vec_op {
 
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
   AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
@@ -325,9 +322,6 @@ struct BF16Vec32 : public VectorizedRegWrapper<BF16Vec32, 4, c10::BFloat16> {
     reg.val[2] = vec8_data.reg.val[0];
     reg.val[3] = vec8_data.reg.val[0];
   };
-
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : Base() {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : Base() {}
 };
 
 struct FP32Vec4 : public VectorizedRegWrapper<FP32Vec4, 1, float> {

csrc/cpu/cpu_types_vsx.hpp

@@ -9,10 +9,6 @@
 
 namespace vec_op {
 
-// FP8 tag types for tag dispatch (see cpu_attn_vec.hpp)
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 // FIXME: FP16 is not fully supported in Torch-CPU
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \

csrc/cpu/cpu_types_vxe.hpp

@@ -8,9 +8,6 @@
 #include <torch/all.h>
 namespace vec_op {
 
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 #define vec_neg(a) (-(a))
 #define vec_add(a, b) ((a) + (b))
 #define vec_sub(a, b) ((a) - (b))
@@ -244,9 +241,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   explicit BF16Vec32(const BF16Vec8& vec8_data)
       : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
 
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
-
   void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
 };
 

csrc/cpu/cpu_types_x86.hpp

@@ -11,17 +11,6 @@ static_assert(false, "AVX2 must be supported for the current implementation.");
 
 namespace vec_op {
 
-// Tags for FP8 BF16Vec32 constructors (avoid overload collision with
-// BF16Vec32(void*)).
-// VEC path (FP8 → pseudo-FP16 layout, scale correction applied later):
-struct fp8_e4m3_tag {};  // E4M3 → pseudo-FP16; BF16 value = true_E4M3 * 2^-8
-struct fp8_e5m2_tag {};  // E5M2 → FP16 bits directly (same exponent bias=15)
-// AMX path (FP8 → unscaled BF16, no FP32 round-trip):
-// BF16 value = true_E4M3 * 2^-120 (E4M3) or true_E5M2 * 2^-112 (E5M2).
-// Exponent rebiasing is folded into k/v scales by the caller.
-struct fp8_bf16_e4m3_tag {};
-struct fp8_bf16_e5m2_tag {};
-
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
@@ -187,50 +176,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
                                (__m128i)vec8_data.reg, 2),
             (__m128i)vec8_data.reg, 3)) {}
 
-  // Decode 32 FP8-E4M3 bytes to pseudo-FP16 layout (stored in the BF16
-  // register).  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 7);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
-  // Decode 32 FP8-E5M2 bytes to FP16 layout.
-  // E5M2 and FP16 share the same 5-bit exponent bias (15), so FP8 byte b maps
-  // directly to FP16 bits by shifting left 8 — no sign/payload reconstruction.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    reg = _mm512_slli_epi16(_mm512_cvtepu8_epi16(b8), 8);
-  }
-
-  // Direct FP8-E4M3 → unscaled BF16 for AMX (no FP32 round-trip).
-  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 4);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
-  // Direct FP8-E5M2 → unscaled BF16 for AMX (no FP32 round-trip).
-  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 5);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
   void save(void* ptr) const { *reinterpret_cast<__m512i*>(ptr) = reg; }
 };
 #else
@@ -255,77 +200,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
             _mm256_castsi128_si256((__m128i)vec8_data.reg),
             (__m128i)vec8_data.reg, 1)) {}
 
-  // E4M3 decode (AVX2 path) — same bit-layout trick as the AVX512 variant
-  // above.  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-
-    __m256i sign_low = _mm256_slli_epi16(
-        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)), 8);
-    __m256i payload_low = _mm256_slli_epi16(
-        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)), 7);
-    __m256i sign_high = _mm256_slli_epi16(
-        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)), 8);
-    __m256i payload_high = _mm256_slli_epi16(
-        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)), 7);
-    reg_low = _mm256_or_si256(sign_low, payload_low);
-    reg_high = _mm256_or_si256(sign_high, payload_high);
-  }
-
-  // E5M2 decode (AVX2 path) — b << 8 maps to FP16 bits; see AVX512 variant
-  // above.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    reg_low = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_low), 8);
-    reg_high = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_high), 8);
-  }
-
-  // Direct FP8-E4M3 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
-  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-    reg_low = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
-                          4));
-    reg_high = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
-                          4));
-  }
-
-  // Direct FP8-E5M2 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
-  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-    reg_low = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
-                          5));
-    reg_high = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
-                          5));
-  }
-
   void save(void* ptr) const {
     _mm256_storeu_si256((__m256i*)ptr, reg_low);
     _mm256_storeu_si256((__m256i*)ptr + 1, reg_high);
@@ -516,11 +390,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
       : reg(_mm512_castsi512_ps(
             _mm512_bslli_epi128(_mm512_cvtepu16_epi32(v.reg), 2))) {}
 
-  explicit FP32Vec16(const BF16Vec32& v, int upper) {
-    __m256i v_half_i = _mm512_extracti32x8_epi32(v.reg, upper);
-    reg = _mm512_cvtph_ps(v_half_i);
-  }
-
   explicit FP32Vec16(const FP16Vec16& v) : reg(_mm512_cvtph_ps(v.reg)) {}
 
   explicit FP32Vec16(const FP16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
@@ -625,14 +494,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
   explicit FP32Vec16(const FP32Vec8& data)
       : reg_low(data.reg), reg_high(data.reg) {}
 
-  explicit FP32Vec16(const BF16Vec32& v, int upper) {
-    const __m256i& half = upper ? v.reg_high : v.reg_low;
-    __m128i lo = _mm256_extractf128_si256(half, 0);
-    __m128i hi = _mm256_extractf128_si256(half, 1);
-    reg_low = _mm256_cvtph_ps(lo);
-    reg_high = _mm256_cvtph_ps(hi);
-  }
-
   explicit FP32Vec16(const FP16Vec16& v) {
     __m128i low = _mm256_extractf128_si256(v.reg, 0);
     __m128i high = _mm256_extractf128_si256(v.reg, 1);

csrc/cpu/generate_cpu_attn_dispatch.py

@@ -20,98 +20,73 @@ ISA_TYPES = {
     "VEC16": 2,
     "NEON": 3,
     "VXE": 4,
-    "VSX": 5,
-}
-
-# KV cache index: 0 = auto (same as scalar_t), 1 = fp8_e4m3, 2 = fp8_e5m2
-KV_CACHE_IDX = {
-    "auto": 0,
-    "fp8_e4m3": 1,
-    "fp8_e5m2": 2,
-}
-
-# C++ type for each kv_cache index
-KV_CACHE_CPP_TYPES = {
-    "auto": "scalar_t",
-    "fp8_e4m3": "c10::Float8_e4m3fn",
-    "fp8_e5m2": "c10::Float8_e5m2",
 }
 
 # ISAs supported for head_dims divisible by 32
-ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE", "VSX"]
+ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE"]
 
 # ISAs supported for head_dims divisible by 16 only
 ISA_FOR_16 = ["VEC16"]
 
-# ISAs that support FP8 KV cache (x86 AVX2/AVX-512 required)
-ISA_FOR_FP8 = ["AMX", "VEC"]
 
-
-def encode_params(head_dim: int, isa_type: str, kv_cache: str = "auto") -> int:
-    """Encode head_dim, ISA type, and KV cache type into a single int64_t."""
+def encode_params(head_dim: int, isa_type: str) -> int:
+    """Encode head_dim and ISA type into a single int64_t."""
     isa_val = ISA_TYPES[isa_type]
-    kv_val = KV_CACHE_IDX[kv_cache]
-    # Encoding: (head_dim << 16) | (kv_cache_idx << 8) | isa_type
-    # This allows head_dim up to 2^48 - 1, 256 KV cache types, and 256 ISA types
-    return (head_dim << 16) | (kv_val << 8) | isa_val
+    # Encoding: (head_dim << 8) | isa_type
+    # This allows head_dim up to 2^56 - 1 and 256 ISA types
+    return (head_dim << 8) | isa_val
 
 
-def _make_case(
-    head_dim: int, isa: str, kv_cache: str = "auto", isa_override: str | None = None
-) -> str:
-    """Generate a single switch case line."""
-    encoded = encode_params(head_dim, isa, kv_cache)
-    actual_isa = isa_override if isa_override else isa
-    cpp_type = KV_CACHE_CPP_TYPES[kv_cache]
-    attn_impl = (
-        f"cpu_attention::AttentionImpl<"
-        f"cpu_attention::ISA::{actual_isa}, \\\n"
-        f"                                                       "
-        f"scalar_t, head_dim, {cpp_type}>"
-    )
-    comment = (
-        f"head_dim={head_dim}, isa={isa}"
-        if kv_cache == "auto"
-        else f"head_dim={head_dim}, isa={isa}, kv_cache={kv_cache}"
-    )
-    return (
-        f"""      case {encoded}LL: {{ """
-        f"""/* {comment} */ \\"""
-        f"""
-        constexpr size_t head_dim = {head_dim}; \\"""
-        f"""
-        using attn_impl = {attn_impl}; \\"""
-        f"""
-        return __VA_ARGS__(); \\"""
-        f"""
-      }} \\"""
-    )
-
-
-def generate_cases_for_isa_group(isa_list: list[str], include_fp8: bool = False) -> str:
+def generate_cases_for_isa_group(isa_list: list[str]) -> str:
     """Generate switch cases for a specific ISA group."""
     cases = []
 
-    # Non-FP8 cases for head_dims divisible by 32
+    # Generate cases for head_dims divisible by 32
     for head_dim in HEAD_DIMS_32:
         for isa in isa_list:
             if isa not in ISA_FOR_32:
                 continue
-            cases.append(_make_case(head_dim, isa, "auto"))
+            encoded = encode_params(head_dim, isa)
+            case_str = (
+                f"""      case {encoded}LL: {{ """
+                f"""/* head_dim={head_dim}, isa={isa} */ \\"""
+                f"""
+        constexpr size_t head_dim = {head_dim}; \\"""
+                f"""
+        using attn_impl = cpu_attention::AttentionImpl<"""
+                f"""cpu_attention::ISA::{isa}, \\"""
+                f"""
+                                                       """
+                f"""scalar_t, head_dim>; \\"""
+                f"""
+        return __VA_ARGS__(); \\"""
+                f"""
+      }} \\"""
+            )
+            cases.append(case_str)
 
-    # Non-FP8 cases for head_dims divisible by 16 only
+    # Generate cases for head_dims divisible by 16 only
     for head_dim in HEAD_DIMS_16:
         for isa in isa_list:
-            cases.append(_make_case(head_dim, isa, "auto", isa_override="VEC16"))
-
-    # FP8 cases: only AMX and VEC, only head_dims divisible by 32
-    if include_fp8:
-        for fp8_type in ("fp8_e4m3", "fp8_e5m2"):
-            for head_dim in HEAD_DIMS_32:
-                for isa in isa_list:
-                    if isa not in ISA_FOR_FP8:
-                        continue
-                    cases.append(_make_case(head_dim, isa, fp8_type))
+            encoded = encode_params(head_dim, isa)
+            case_str = (
+                f"""      case {encoded}LL: {{ """
+                f"""/* head_dim={head_dim}, isa={isa} """
+                f"""(using VEC16) */ \\"""
+                f"""
+        constexpr size_t head_dim = {head_dim}; \\"""
+                f"""
+        using attn_impl = cpu_attention::AttentionImpl<"""
+                f"""cpu_attention::ISA::VEC16, \\"""
+                f"""
+                                                       """
+                f"""scalar_t, head_dim>; \\"""
+                f"""
+        return __VA_ARGS__(); \\"""
+                f"""
+      }} \\"""
+            )
+            cases.append(case_str)
 
     return "\n".join(cases)
 
@@ -119,9 +94,8 @@ def generate_cases_for_isa_group(isa_list: list[str], include_fp8: bool = False)
 def generate_helper_function() -> str:
     """Generate helper function to encode parameters."""
     return """
-inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa,
-                                      int64_t kv_cache_idx = 0) {
-  return (head_dim << 16) | (kv_cache_idx << 8) | static_cast<int64_t>(isa);
+inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa) {
+  return (head_dim << 8) | static_cast<int64_t>(isa);
 }
 """
 
@@ -149,94 +123,93 @@ def generate_header_file() -> str:
   #include "cpu_attn_vxe.hpp"
 #endif
 
-#ifdef __powerpc__
-  #include "cpu_attn_vsx.hpp"
-#endif
-
 """
 
     header += generate_helper_function()
 
     # Generate dispatch macro with conditional compilation for different ISA sets
     header += """
-// Dispatch macro using encoded parameters.
-// KV_CACHE_IDX: Fp8KVCacheDataType enum value (kAuto=0, kFp8E4M3=1, kFp8E5M2=2).
-// FP8 cases (kv_cache_idx != 0) are generated on x86 platforms with AVX2 or
-// AVX-512: BF16Vec32 FP8 constructors have both AVX-512 and AVX2 implementations
-// in cpu_types_x86.hpp. Non-x86 platforms (#else fallback) have fp8=False.
+// Dispatch macro using encoded parameters
 """
 
-    def _macro_block(guard: str, isa_list: list[str], fp8: bool) -> str:
-        """Return one CPU_ATTN_DISPATCH macro block for a given guard."""
-        enc = (
-            "    int64_t encoded_params = encode_cpu_attn_params("
-            "HEAD_DIM, ISA_TYPE, KV_CACHE_IDX); \\"
-        )
-        cases = generate_cases_for_isa_group(isa_list, include_fp8=fp8)
-        tail = (
-            "\n"
-            "      default: { \\\n"
-            "        TORCH_CHECK(false, "
-            '"Unsupported CPU attention configuration: head_dim=" + \\\n'
-            '                    std::to_string(HEAD_DIM) + " isa=" + \\\n'
-            "                    std::to_string(static_cast<int>(ISA_TYPE))"
-            " + \\\n"
-            '                    " kv_cache_idx=" + '
-            "std::to_string(KV_CACHE_IDX)); \\\n"
-            "      } \\\n"
-            "    } \\\n"
-            "  }()\n\n"
-        )
-        return (
-            f"{guard}\n"
-            "#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, KV_CACHE_IDX, ...) \\\n"
-            "  [&] { \\\n"
-            f"{enc}\n"
-            "    switch (encoded_params) { \\\n"
-            f"{cases}"
-            f"{tail}"
-        )
+    # x86_64 with AMX
+    header += """#if defined(CPU_CAPABILITY_AMXBF16)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["AMX", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
 
-    header += _macro_block(
-        "#if defined(CPU_CAPABILITY_AMXBF16)",
-        ["AMX", "VEC", "VEC16"],
-        fp8=True,
-    )
-    header += _macro_block(
-        "#elif defined(__aarch64__)",
-        ["NEON", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__s390x__)",
-        ["VXE", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__powerpc__)",
-        ["VSX", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__AVX512F__)",
-        ["VEC", "VEC16"],
-        fp8=True,
-    )
-    header += _macro_block(
-        "#elif defined(__AVX2__)",
-        ["VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#else",
-        ["VEC", "VEC16"],
-        fp8=False,
-    )
-    header += (
-        "#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / "
-        "__s390x__ / __powerpc__ */\n\n"
-        "#endif  // CPU_ATTN_DISPATCH_GENERATED_H\n"
-    )
+"""
+
+    # ARM64 with NEON
+    header += """#elif defined(__aarch64__)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["NEON", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+"""
+
+    # s390x with VXE
+    header += """#elif defined(__s390x__)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["VXE", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+"""
+
+    # Fallback: VEC and VEC16 only
+    header += """#else
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / __s390x__ */
+
+#endif  // CPU_ATTN_DISPATCH_GENERATED_H
+"""
 
     return header
 

csrc/cpu/pos_encoding.cpp

@@ -178,12 +178,7 @@ void rotary_embedding_gptj_impl(
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       std::optional<torch::Tensor> key, int64_t head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox,
-                      int64_t rope_dim_offset, bool inverse) {
-  TORCH_CHECK(rope_dim_offset == 0,
-              "rope_dim_offset != 0 is not supported on CPU");
-  TORCH_CHECK(!inverse, "inverse rotary embedding is not supported on CPU");
-
+                      torch::Tensor& cos_sin_cache, bool is_neox) {
   int num_tokens = positions.numel();
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;

csrc/cpu/torch_bindings.cpp

@@ -101,9 +101,7 @@ void cpu_attn_reshape_and_cache(const torch::Tensor& key,
                                 torch::Tensor& key_cache,
                                 torch::Tensor& value_cache,
                                 const torch::Tensor& slot_mapping,
-                                const std::string& isa, const double k_scale,
-                                const double v_scale,
-                                const std::string& kv_cache_dtype);
+                                const std::string& isa);
 
 void cpu_attention_with_kv_cache(
     const torch::Tensor& query, const torch::Tensor& key_cache,
@@ -114,8 +112,7 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table, const double softcap,
     const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux, const double k_scale,
-    const double v_scale, const std::string& kv_cache_dtype);
+    const std::optional<torch::Tensor>& s_aux);
 
 // Note: just for avoiding importing errors
 void placeholder_op() { TORCH_CHECK(false, "Unimplemented"); }
@@ -266,8 +263,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "rotary_embedding(Tensor positions, Tensor! query,"
       "                 Tensor!? key, int head_size,"
-      "                 Tensor cos_sin_cache, bool is_neox, int "
-      "rope_dim_offset=0, bool inverse=False) -> ()");
+      "                 Tensor cos_sin_cache, bool is_neox) -> ()");
   ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
 
   // Quantization
@@ -387,18 +383,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       &get_scheduler_metadata);
   ops.def(
       "cpu_attn_reshape_and_cache(Tensor key, Tensor value, Tensor(a2!) "
-      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str isa, "
-      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
-      "()",
+      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str "
+      "isa) -> ()",
       &cpu_attn_reshape_and_cache);
   ops.def(
       "cpu_attention_with_kv_cache(Tensor query, Tensor key_cache, Tensor "
       "value_cache, Tensor(a3!) output, Tensor query_start_loc, Tensor "
       "seq_lens, float scale, bool causal, Tensor? alibi_slopes, SymInt "
       "sliding_window_left, SymInt sliding_window_right, Tensor block_table, "
-      "float softcap, Tensor scheduler_metadata, Tensor? s_aux, "
-      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
-      "()",
+      "float softcap, Tensor scheduler_metadata, Tensor? s_aux) -> ()",
       &cpu_attention_with_kv_cache);
 
   // placeholders

csrc/cpu/utils.hpp

@@ -54,7 +54,7 @@ struct Counter {
 };
 
 inline int64_t get_available_l2_size() {
-#if defined(__s390x__) || defined(__powerpc__)
+#if defined(__s390x__)
   static int64_t size = []() {
     uint32_t l2_cache_size = 0;
     auto caps = at::cpu::get_cpu_capabilities();

csrc/cutlass_extensions/common.hpp

@@ -96,14 +96,44 @@ struct enable_sm90_or_later : Kernel {
 };
 
 template <typename Kernel>
-struct enable_sm100_to_sm120 : Kernel {
+struct enable_sm90_only : Kernel {
   template <typename... Args>
   CUTLASS_DEVICE void operator()(Args&&... args) {
 #if defined __CUDA_ARCH__
-  #if (__CUDA_ARCH__ >= 1000 && __CUDA_ARCH__ < 1200)
+  #if __CUDA_ARCH__ == 900
     Kernel::operator()(std::forward<Args>(args)...);
   #else
-    printf("This kernel only supports sm[100, 120).\n");
+    printf("This kernel only supports sm90.\n");
+    asm("trap;");
+  #endif
+#endif
+  }
+};
+
+template <typename Kernel>
+struct enable_sm100f_only : Kernel {
+  template <typename... Args>
+  CUTLASS_DEVICE void operator()(Args&&... args) {
+#if defined __CUDA_ARCH__
+  #if __CUDA_ARCH__ == 1000 || __CUDA_ARCH__ == 1030
+    Kernel::operator()(std::forward<Args>(args)...);
+  #else
+    printf("This kernel only supports sm100f.\n");
+    asm("trap;");
+  #endif
+#endif
+  }
+};
+
+template <typename Kernel>
+struct enable_sm100a_only : Kernel {
+  template <typename... Args>
+  CUTLASS_DEVICE void operator()(Args&&... args) {
+#if defined __CUDA_ARCH__
+  #if __CUDA_ARCH__ == 1000
+    Kernel::operator()(std::forward<Args>(args)...);
+  #else
+    printf("This kernel only supports sm100a.\n");
     asm("trap;");
   #endif
 #endif
@@ -118,7 +148,7 @@ struct enable_sm120_only : Kernel {
   #if __CUDA_ARCH__ == 1200
     Kernel::operator()(std::forward<Args>(args)...);
   #else
-    printf("This kernel only supports sm120a.\n");
+    printf("This kernel only supports sm120.\n");
     asm("trap;");
   #endif
 #endif
@@ -130,13 +160,8 @@ template <typename Kernel>
 struct enable_sm120_family : Kernel {
   template <typename... Args>
   CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__
-  #if (__CUDA_ARCH__ >= 1200 && __CUDA_ARCH__ < 1300)
+#if defined __CUDA_ARCH__ && (__CUDA_ARCH__ >= 1200 && __CUDA_ARCH__ < 1300)
     Kernel::operator()(std::forward<Args>(args)...);
-  #else
-    printf("This kernel only supports sm120f.\n");
-    asm("trap;");
-  #endif
 #endif
   }
 };

csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu

@@ -1,511 +0,0 @@
-/*
- * SPDX-License-Identifier: Apache-2.0
- * SPDX-FileCopyrightText: Copyright contributors to the vLLM project
- *
- * Horizontally-fused DeepseekV4-MLA kernel:
- *   - Q side:  per-head RMSNorm (no weight) + GPT-J RoPE on last ROPE_DIM
- *   - KV side: GPT-J RoPE on last ROPE_DIM + UE8M0 FP8 quant on NoPE + paged
- *              cache insert
- *
- * Structured after `applyMLARopeAndAssignQKVKernelGeneration` in
- * TensorRT-LLM's mlaKernels.cu: one kernel, one grid, with head-slot
- * dispatch choosing Q vs KV work per warp.  The per-warp RMSNorm/RoPE
- * skeleton is adapted from vllm-deepseek_v4's existing
- * `fusedQKNormRopeKernel` (csrc/fused_qknorm_rope_kernel.cu).
- *
- * Assumptions (hard-coded for DeepseekV4 attention):
- *   HEAD_DIM  = 512
- *   ROPE_DIM  = 64   (RoPE applied to dims [NOPE_DIM, HEAD_DIM))
- *   NOPE_DIM  = 448
- *   QUANT_BLOCK = 64 (UE8M0 FP8 quant block)
- *   FP8_MAX   = 448.0f
- *   is_neox=false (GPT-J interleaved pairs)
- *   cos_sin_cache layout [max_pos, rope_dim] = cos || sin (cos first, sin
- *     second along last dim; each half is rope_dim/2 = 32 values)
- *
- * Cache layout per paged-cache block (block_size tokens):
- *   [0,            bs*576):          token data, 448 fp8 + 128 bf16 each
- *   [bs*576,       bs*576 + bs*8):   UE8M0 scales, 7 real + 1 pad per token
- */
-
-#include <cmath>
-#ifndef USE_ROCM
-  #include <cuda_fp8.h>
-#else
-  #include <hip/hip_fp8.h>
-#endif
-#include <cuda_runtime.h>
-#include <type_traits>
-
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <torch/cuda.h>
-
-#include "cuda_compat.h"
-#include "dispatch_utils.h"
-#include "type_convert.cuh"
-
-#ifndef FINAL_MASK
-  #ifdef USE_ROCM
-    #define FINAL_MASK 0xffffffffffffffffULL
-  #else
-    #define FINAL_MASK 0xffffffffu
-  #endif
-#endif
-
-#ifdef USE_ROCM
-// ROCm-compatible FP8 conversion helpers
-__device__ __forceinline__ uint8_t rocm_cvt_float_to_fp8_e4m3(float val) {
-  #if defined(HIP_FP8_TYPE_OCP)
-  __hip_fp8_e4m3 fp8_val(val);
-  #else
-  __hip_fp8_e4m3_fnuz fp8_val(val);
-  #endif
-  return reinterpret_cast<uint8_t&>(fp8_val);
-}
-#endif
-
-namespace vllm {
-namespace deepseek_v4_fused_ops {
-
-namespace {
-inline int getSMVersion() {
-  auto* props = at::cuda::getCurrentDeviceProperties();
-  return props->major * 10 + props->minor;
-}
-}  // namespace
-
-// ────────────────────────────────────────────────────────────────────────────
-// Constants
-// ────────────────────────────────────────────────────────────────────────────
-constexpr int kHeadDim = 512;
-constexpr int kRopeDim = 64;
-constexpr int kNopeDim = kHeadDim - kRopeDim;  // 448
-constexpr int kQuantBlock = 64;
-constexpr int kNumQuantBlocks = kNopeDim / kQuantBlock;   // 7
-constexpr int kScaleBytesPerToken = kNumQuantBlocks + 1;  // 8 (7 real + 1 pad)
-constexpr int kTokenDataBytes = kNopeDim + kRopeDim * 2;  // 448 + 128 = 576
-constexpr float kFp8Max = 448.0f;
-
-// Per-warp layout:  32 lanes × 16 elems/lane = 512 elems = HEAD_DIM.
-constexpr int kNumLanes = 32;
-constexpr int kElemsPerLane = kHeadDim / kNumLanes;  // 16
-
-// ────────────────────────────────────────────────────────────────────────────
-// Small inline helpers
-// ────────────────────────────────────────────────────────────────────────────
-__device__ __forceinline__ float warp4MaxAbs(float val) {
-  // Reduce absolute max across 4 consecutive lanes (lane id & 3 group).
-  float peer = __shfl_xor_sync(FINAL_MASK, val, 1);
-  val = fmaxf(val, peer);
-  peer = __shfl_xor_sync(FINAL_MASK, val, 2);
-  val = fmaxf(val, peer);
-  return val;
-}
-
-template <typename T>
-__device__ __forceinline__ float warpSum(float val) {
-#pragma unroll
-  for (int mask = 16; mask > 0; mask >>= 1) {
-    val += __shfl_xor_sync(FINAL_MASK, val, mask, 32);
-  }
-  return val;
-}
-
-// ────────────────────────────────────────────────────────────────────────────
-// Kernel
-// ────────────────────────────────────────────────────────────────────────────
-//
-// Grid: 1D, gridDim.x = ceil(num_tokens_full * (num_heads_q + 1) /
-// warps_per_block) Block: blockDim.x = 256 threads (8 warps per block) Each
-// warp handles one (token, head_slot) pair. head_slot < num_heads_q          →
-// Q branch (RMSNorm + RoPE, in place) head_slot == num_heads_q         → KV
-// branch (RoPE + UE8M0 quant + insert)
-//
-// With DP padding, q/kv/position_ids can have more rows than slot_mapping.
-// The Q branch covers all `num_tokens_full` rows (downstream attention uses
-// them).  The KV branch only inserts the first `num_tokens_insert` tokens
-// (= slot_mapping length) into the paged cache.
-//
-template <typename scalar_t_in>
-__global__ void fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel(
-    scalar_t_in* __restrict__ q_inout,         // [N, H, 512] bf16, in place
-    scalar_t_in const* __restrict__ kv_in,     // [N, 512] bf16
-    uint8_t* __restrict__ k_cache,             // [num_blocks, block_stride]
-    int64_t const* __restrict__ slot_mapping,  // [num_tokens_insert] i64
-    int64_t const* __restrict__ position_ids,  // [N] i64
-    float const* __restrict__ cos_sin_cache,   // [max_pos, 64] fp32
-    float const eps,
-    int const num_tokens_full,    // = q.size(0) = kv.size(0)
-    int const num_tokens_insert,  // = slot_mapping.size(0), ≤ num_tokens_full
-    int const num_heads_q,        // H
-    int const cache_block_size,   // tokens per paged-cache block
-    int const kv_block_stride) {  // bytes per paged-cache block
-#if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
-  // BF16 _typeConvert specialization is unavailable on pre-Ampere.  The
-  // DeepseekV4 kernel only runs with bf16 inputs in practice, so compile a
-  // no-op stub for sm_70/sm_75 to keep multi-arch builds happy.
-  if constexpr (std::is_same_v<scalar_t_in, c10::BFloat16>) {
-    return;
-  } else {
-#endif
-    using Converter = vllm::_typeConvert<scalar_t_in>;
-
-    int const warpsPerBlock = blockDim.x / 32;
-    int const warpId = threadIdx.x / 32;
-    int const laneId = threadIdx.x % 32;
-    int const globalWarpIdx = blockIdx.x * warpsPerBlock + warpId;
-
-    int const total_slots_per_token = num_heads_q + 1;
-    int const tokenIdx = globalWarpIdx / total_slots_per_token;
-    int const slotIdx = globalWarpIdx % total_slots_per_token;
-    if (tokenIdx >= num_tokens_full) return;
-
-    bool const isKV = (slotIdx == num_heads_q);
-    // KV branch: skip DP-padded tokens (no slot reserved for them).
-    if (isKV && tokenIdx >= num_tokens_insert) return;
-
-    // PDL: wait for predecessor kernel (upstream q/kv producer) to signal
-    // before touching any global memory.  No-op when PDL is not enabled on
-    // the launch.  The CUDA runtime wrapper emits the griddepcontrol.wait
-    // PTX with the required memory clobber internally.
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
-    cudaGridDependencySynchronize();
-#endif
-
-    // Dim range this lane owns within the 512-wide head.
-    int const dim_base = laneId * kElemsPerLane;  // in [0, 512) step 16
-
-    // ── Load 16 bf16 → 16 fp32 registers (one 16-byte + one 16-byte LDG) ────
-    float elements[kElemsPerLane];
-    float sumOfSquares = 0.0f;
-
-    scalar_t_in const* src_ptr;
-    if (isKV) {
-      src_ptr = kv_in + static_cast<int64_t>(tokenIdx) * kHeadDim + dim_base;
-    } else {
-      int64_t const q_row_offset =
-          (static_cast<int64_t>(tokenIdx) * num_heads_q + slotIdx) * kHeadDim +
-          dim_base;
-      src_ptr = q_inout + q_row_offset;
-    }
-
-    // Two 16-byte loads per thread (8 bf16 each).  Use uint4 as the vector
-    // type and bitcast to scalar_t_in packed pairs for conversion.
-    uint4 v0 = *reinterpret_cast<uint4 const*>(src_ptr);
-    uint4 v1 = *reinterpret_cast<uint4 const*>(src_ptr + 8);
-
-    {
-      typename Converter::packed_hip_type const* p0 =
-          reinterpret_cast<typename Converter::packed_hip_type const*>(&v0);
-      typename Converter::packed_hip_type const* p1 =
-          reinterpret_cast<typename Converter::packed_hip_type const*>(&v1);
-// Each packed_hip_type holds 2 bf16 → 4 packed = 8 elems per uint4.
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        float2 f2 = Converter::convert(p0[i]);
-        elements[2 * i] = f2.x;
-        elements[2 * i + 1] = f2.y;
-      }
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        float2 f2 = Converter::convert(p1[i]);
-        elements[8 + 2 * i] = f2.x;
-        elements[8 + 2 * i + 1] = f2.y;
-      }
-    }
-
-    // ── Q branch: RMSNorm with no weight (has_weight=False) ─────────────────
-    // Variance + rsqrt + multiply all in fp32, no intermediate bf16 round.
-    // The downstream bf16 round only happens at the final store.
-    if (!isKV) {
-#pragma unroll
-      for (int i = 0; i < kElemsPerLane; i++) {
-        sumOfSquares += elements[i] * elements[i];
-      }
-      sumOfSquares = warpSum<float>(sumOfSquares);
-      float const rms_rcp =
-          rsqrtf(sumOfSquares / static_cast<float>(kHeadDim) + eps);
-#pragma unroll
-      for (int i = 0; i < kElemsPerLane; i++) {
-        elements[i] = elements[i] * rms_rcp;
-      }
-    }
-
-    // ── GPT-J RoPE on dims [NOPE_DIM, HEAD_DIM) ─────────────────────────────
-    // All math in fp32.  cos_sin_cache is loaded as fp32 (its native storage).
-    bool const is_rope_lane = dim_base >= kNopeDim;
-    if (is_rope_lane) {
-      int64_t const pos = position_ids[tokenIdx];
-      constexpr int kHalfRope = kRopeDim / 2;  // 32
-      float const* cos_ptr = cos_sin_cache + pos * kRopeDim;
-      float const* sin_ptr = cos_ptr + kHalfRope;
-
-      int const rope_local_base = dim_base - kNopeDim;  // in [0, 64) step 16
-#pragma unroll
-      for (int p = 0; p < kElemsPerLane / 2; p++) {
-        int const pair_dim = rope_local_base + 2 * p;
-        int const half_idx = pair_dim / 2;
-        float const cos_v = VLLM_LDG(cos_ptr + half_idx);
-        float const sin_v = VLLM_LDG(sin_ptr + half_idx);
-        float const x_even = elements[2 * p];
-        float const x_odd = elements[2 * p + 1];
-        elements[2 * p] = x_even * cos_v - x_odd * sin_v;
-        elements[2 * p + 1] = x_even * sin_v + x_odd * cos_v;
-      }
-    }
-
-    // ═══════════════════════════════════════════════════════════════════════
-    // Q branch: cast to bf16 and store back in place.
-    // ═══════════════════════════════════════════════════════════════════════
-    if (!isKV) {
-      uint4 out0, out1;
-      typename Converter::packed_hip_type* po0 =
-          reinterpret_cast<typename Converter::packed_hip_type*>(&out0);
-      typename Converter::packed_hip_type* po1 =
-          reinterpret_cast<typename Converter::packed_hip_type*>(&out1);
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        po0[i] = Converter::convert(
-            make_float2(elements[2 * i], elements[2 * i + 1]));
-      }
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        po1[i] = Converter::convert(
-            make_float2(elements[8 + 2 * i], elements[8 + 2 * i + 1]));
-      }
-      scalar_t_in* dst =
-          q_inout +
-          (static_cast<int64_t>(tokenIdx) * num_heads_q + slotIdx) * kHeadDim +
-          dim_base;
-      *reinterpret_cast<uint4*>(dst) = out0;
-      *reinterpret_cast<uint4*>(dst + 8) = out1;
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
-      cudaTriggerProgrammaticLaunchCompletion();
-#endif
-      return;
-    }
-
-    // ═══════════════════════════════════════════════════════════════════════
-    // KV branch.
-    // ═══════════════════════════════════════════════════════════════════════
-    int64_t const slot_id = slot_mapping[tokenIdx];
-    if (slot_id < 0) {
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
-      cudaTriggerProgrammaticLaunchCompletion();
-#endif
-      return;
-    }
-
-    int64_t const block_idx = slot_id / cache_block_size;
-    int64_t const pos_in_block = slot_id % cache_block_size;
-    uint8_t* block_base =
-        k_cache + block_idx * static_cast<int64_t>(kv_block_stride);
-    uint8_t* token_fp8_ptr = block_base + pos_in_block * kTokenDataBytes;
-    uint8_t* token_bf16_ptr = token_fp8_ptr + kNopeDim;
-    uint8_t* token_scale_ptr =
-        block_base + static_cast<int64_t>(cache_block_size) * kTokenDataBytes +
-        pos_in_block * kScaleBytesPerToken;
-
-    // Round K to bf16 first, matching the unfused reference path where K is
-    // materialized as bf16 before K quantization.  absmax, clamp, and FP8
-    // quant below all run on these bf16-rounded values.
-#pragma unroll
-    for (int i = 0; i < kElemsPerLane; i++) {
-      elements[i] = Converter::convert(Converter::convert(elements[i]));
-    }
-
-    // Per-quant-block absmax must be computed by ALL 32 lanes (warp-collective
-    // shuffle requires full participation).  RoPE lanes contribute garbage,
-    // but their values are gated out below via `!is_rope_lane`.
-    float local_absmax = 0.0f;
-#pragma unroll
-    for (int i = 0; i < kElemsPerLane; i++) {
-      local_absmax = fmaxf(local_absmax, fabsf(elements[i]));
-    }
-    float const absmax = fmaxf(warp4MaxAbs(local_absmax), 1e-4f);
-    float const exponent = ceilf(log2f(absmax / kFp8Max));
-    float const inv_scale = exp2f(-exponent);
-
-    if (!is_rope_lane) {
-      // ── NoPE lane: UE8M0 FP8 quant ───────────────────────────────────────
-      uint8_t out_bytes[kElemsPerLane];
-#pragma unroll
-      for (int i = 0; i < kElemsPerLane; i++) {
-        float scaled = elements[i] * inv_scale;
-        scaled = fminf(fmaxf(scaled, -kFp8Max), kFp8Max);
-#ifndef USE_ROCM
-        __nv_fp8_storage_t s =
-            __nv_cvt_float_to_fp8(scaled, __NV_SATFINITE, __NV_E4M3);
-        out_bytes[i] = static_cast<uint8_t>(s);
-#else
-      out_bytes[i] = rocm_cvt_float_to_fp8_e4m3(scaled);
-#endif
-      }
-      // One 16-byte STG per lane.
-      *reinterpret_cast<uint4*>(token_fp8_ptr + dim_base) =
-          *reinterpret_cast<uint4 const*>(out_bytes);
-
-      // Lane (4k) of each 4-lane group writes the scale byte for block k<7.
-      if ((laneId & 3) == 0) {
-        int const q_block_idx = laneId >> 2;  // 0..6 for NoPE lanes
-        float encoded = fmaxf(fminf(exponent + 127.0f, 255.0f), 0.0f);
-        token_scale_ptr[q_block_idx] = static_cast<uint8_t>(encoded);
-      }
-      // Lane 0 also writes the padding byte at index 7.
-      if (laneId == 0) {
-        token_scale_ptr[kNumQuantBlocks] = 0;  // pad
-      }
-    } else {
-      // ── RoPE lane: cast back to bf16 and store to cache bf16 tail ────────
-      uint4 out0, out1;
-      typename Converter::packed_hip_type* po0 =
-          reinterpret_cast<typename Converter::packed_hip_type*>(&out0);
-      typename Converter::packed_hip_type* po1 =
-          reinterpret_cast<typename Converter::packed_hip_type*>(&out1);
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        po0[i] = Converter::convert(
-            make_float2(elements[2 * i], elements[2 * i + 1]));
-      }
-#pragma unroll
-      for (int i = 0; i < 4; i++) {
-        po1[i] = Converter::convert(
-            make_float2(elements[8 + 2 * i], elements[8 + 2 * i + 1]));
-      }
-      int const rope_local_base = dim_base - kNopeDim;  // in [0, 64)
-      scalar_t_in* bf16_dst =
-          reinterpret_cast<scalar_t_in*>(token_bf16_ptr) + rope_local_base;
-      *reinterpret_cast<uint4*>(bf16_dst) = out0;
-      *reinterpret_cast<uint4*>(bf16_dst + 8) = out1;
-    }
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
-    cudaTriggerProgrammaticLaunchCompletion();
-#endif
-#if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
-  }
-#endif
-}
-
-// ────────────────────────────────────────────────────────────────────────────
-// Launch wrapper
-// ────────────────────────────────────────────────────────────────────────────
-template <typename scalar_t_in>
-void launchFusedDeepseekV4QNormRopeKVRopeQuantInsert(
-    scalar_t_in* q_inout, scalar_t_in const* kv_in, uint8_t* k_cache,
-    int64_t const* slot_mapping, int64_t const* position_ids,
-    float const* cos_sin_cache, float const eps, int const num_tokens_full,
-    int const num_tokens_insert, int const num_heads_q,
-    int const cache_block_size, int const kv_block_stride,
-    cudaStream_t stream) {
-  constexpr int kBlockSize = 256;
-  constexpr int kWarpsPerBlock = kBlockSize / 32;
-  int64_t const total_warps =
-      static_cast<int64_t>(num_tokens_full) * (num_heads_q + 1);
-  int const grid =
-      static_cast<int>((total_warps + kWarpsPerBlock - 1) / kWarpsPerBlock);
-
-  // PDL: enable programmatic stream serialization whenever the hardware
-  // supports it (SM90+).  On pre-Hopper GPUs the attribute is unavailable,
-  // so leave numAttrs = 0 and launch as a regular kernel.
-#ifndef USE_ROCM
-  static int const sm_version = getSMVersion();
-  // Host-side guard: the device kernel body is compiled as a no-op for
-  // bf16 on pre-Ampere (sm_70/sm_75) because _typeConvert<BFloat16> is
-  // unavailable there.  Refuse the launch loudly instead of silently
-  // skipping the work.
-  TORCH_CHECK(
-      sm_version >= 80,
-      "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert requires sm_80+ "
-      "(Ampere or newer); got sm_",
-      sm_version);
-  cudaLaunchConfig_t config;
-  config.gridDim = dim3(grid);
-  config.blockDim = dim3(kBlockSize);
-  config.dynamicSmemBytes = 0;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = 1;
-  config.attrs = attrs;
-  config.numAttrs = (sm_version >= 90) ? 1 : 0;
-
-  cudaLaunchKernelEx(
-      &config, fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel<scalar_t_in>,
-      q_inout, kv_in, k_cache, slot_mapping, position_ids, cos_sin_cache, eps,
-      num_tokens_full, num_tokens_insert, num_heads_q, cache_block_size,
-      kv_block_stride);
-#else
-  // ROCm: use standard kernel launch syntax (no PDL/stream serialization)
-  // clang-format off
-  fusedDeepseekV4QNormRopeKVRopeQuantInsertKernel<scalar_t_in>
-      <<<grid, kBlockSize, 0, stream>>>(
-          q_inout, kv_in, k_cache, slot_mapping, position_ids, cos_sin_cache,
-          eps, num_tokens_full, num_tokens_insert, num_heads_q,
-          cache_block_size, kv_block_stride);
-#endif
-}
-
-}  // namespace deepseek_v4_fused_ops
-}  // namespace vllm
-
-// ────────────────────────────────────────────────────────────────────────────
-// Torch op wrapper
-// ────────────────────────────────────────────────────────────────────────────
-void fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
-    torch::Tensor& q,                    // [N, H, 512] bf16, in place
-    torch::Tensor const& kv,             // [N, 512] bf16 (read-only)
-    torch::Tensor& k_cache,              // [num_blocks, block_bytes] uint8
-    torch::Tensor const& slot_mapping,   // [N] int64
-    torch::Tensor const& position_ids,   // [N] int64
-    torch::Tensor const& cos_sin_cache,  // [max_pos, rope_dim] bf16
-    double eps, int64_t cache_block_size) {
-  TORCH_CHECK(q.is_cuda() && q.is_contiguous(), "q must be contiguous CUDA");
-  TORCH_CHECK(kv.is_cuda() && kv.is_contiguous(), "kv must be contiguous CUDA");
-  TORCH_CHECK(k_cache.is_cuda(), "k_cache must be CUDA");
-  TORCH_CHECK(slot_mapping.is_cuda() && slot_mapping.dtype() == torch::kInt64,
-              "slot_mapping must be int64 CUDA");
-  TORCH_CHECK(position_ids.is_cuda() && position_ids.dtype() == torch::kInt64,
-              "position_ids must be int64 CUDA");
-  TORCH_CHECK(cos_sin_cache.is_cuda(), "cos_sin_cache must be CUDA");
-  TORCH_CHECK(q.dim() == 3 && q.size(2) == 512, "q shape [N, H, 512]");
-  TORCH_CHECK(kv.dim() == 2 && kv.size(1) == 512, "kv shape [N, 512]");
-  TORCH_CHECK(q.dtype() == kv.dtype(), "q and kv dtype must match");
-  TORCH_CHECK(k_cache.dtype() == torch::kUInt8, "k_cache must be uint8");
-  TORCH_CHECK(cos_sin_cache.dim() == 2 && cos_sin_cache.size(1) == 64,
-              "cos_sin_cache shape [max_pos, 64]");
-  TORCH_CHECK(cos_sin_cache.dtype() == torch::kFloat32,
-              "cos_sin_cache must be float32");
-
-  // With DP padding, slot_mapping can be shorter than q/kv/positions.
-  // Q-norm+RoPE runs on all q.size(0) rows (downstream attention uses them);
-  // KV quant+insert runs only on the first slot_mapping.size(0) rows.
-  int const num_tokens_full = static_cast<int>(q.size(0));
-  int const num_tokens_insert = static_cast<int>(slot_mapping.size(0));
-  TORCH_CHECK(static_cast<int>(kv.size(0)) == num_tokens_full &&
-                  static_cast<int>(position_ids.size(0)) == num_tokens_full,
-              "q/kv/position_ids row counts must match");
-  TORCH_CHECK(num_tokens_insert <= num_tokens_full,
-              "slot_mapping must not exceed q row count");
-  int const num_heads_q = static_cast<int>(q.size(1));
-  int const cache_block_size_i = static_cast<int>(cache_block_size);
-  int const kv_block_stride = static_cast<int>(k_cache.stride(0));
-
-  at::cuda::OptionalCUDAGuard device_guard(device_of(q));
-  auto stream = at::cuda::getCurrentCUDAStream();
-
-  VLLM_DISPATCH_HALF_TYPES(
-      q.scalar_type(), "fused_deepseek_v4_qnorm_rope_kv_insert", [&] {
-        using qkv_scalar_t = scalar_t;
-        vllm::deepseek_v4_fused_ops::
-            launchFusedDeepseekV4QNormRopeKVRopeQuantInsert<qkv_scalar_t>(
-                reinterpret_cast<qkv_scalar_t*>(q.data_ptr()),
-                reinterpret_cast<qkv_scalar_t const*>(kv.data_ptr()),
-                reinterpret_cast<uint8_t*>(k_cache.data_ptr()),
-                reinterpret_cast<int64_t const*>(slot_mapping.data_ptr()),
-                reinterpret_cast<int64_t const*>(position_ids.data_ptr()),
-                cos_sin_cache.data_ptr<float>(), static_cast<float>(eps),
-                num_tokens_full, num_tokens_insert, num_heads_q,
-                cache_block_size_i, kv_block_stride, stream);
-      });
-}

csrc/layernorm_kernels.cu

@@ -77,8 +77,7 @@ __global__ void rms_norm_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      float w = static_cast<float>(src2.val[j]);
-      dst.val[j] = static_cast<scalar_t>(x * s_variance * w);
+      dst.val[j] = ((scalar_t)(x * s_variance)) * src2.val[j];
     }
     v_out[i] = dst;
   }
@@ -135,17 +134,10 @@ fused_add_rms_norm_kernel(
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
     int64_t strided_id = blockIdx.x * vec_input_stride + idx;
-    _f16Vec<scalar_t, width> res = residual_v[id];
-    _f16Vec<scalar_t, width> w = weight_v[idx];
-    _f16Vec<scalar_t, width> out;
-    using Converter = _typeConvert<scalar_t>;
-#pragma unroll
-    for (int j = 0; j < width; ++j) {
-      float x = Converter::convert(res.data[j]);
-      float wf = Converter::convert(w.data[j]);
-      out.data[j] = Converter::convert(x * s_variance * wf);
-    }
-    input_v[strided_id] = out;
+    _f16Vec<scalar_t, width> temp = residual_v[id];
+    temp *= s_variance;
+    temp *= weight_v[idx];
+    input_v[strided_id] = temp;
   }
 }
 
@@ -182,8 +174,8 @@ fused_add_rms_norm_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    float w = (float)weight[idx];
-    input[blockIdx.x * input_stride + idx] = (scalar_t)(x * s_variance * w);
+    input[blockIdx.x * input_stride + idx] =
+        ((scalar_t)(x * s_variance)) * weight[idx];
   }
 }
 

csrc/layernorm_quant_kernels.cu

@@ -65,16 +65,9 @@ __global__ void rms_norm_static_fp8_quant_kernel(
 #pragma unroll
     for (int j = 0; j < VEC_SIZE; j++) {
       float x = static_cast<float>(src1.val[j]);
-      float w = static_cast<float>(src2.val[j]);
-      // Round normalized result through scalar_t to match the precision of the
-      // unfused composite (rms_norm writes scalar_t, then
-      // static_scaled_fp8_quant re-loads it as float before FP8 conversion).
-      // Without this round, the fused path is strictly more accurate and
-      // disagrees with the composite at exact E4M3 quantization tie boundaries.
-      scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
+      float const out_norm = ((scalar_t)(x * s_variance)) * src2.val[j];
       out[blockIdx.x * hidden_size + idx * VEC_SIZE + j] =
-          scaled_fp8_conversion<true, fp8_type>(static_cast<float>(out_norm),
-                                                scale_inv);
+          scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
     }
   }
 }
@@ -134,21 +127,13 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
-    _f16Vec<scalar_t, width> res = residual_v[id];
-    _f16Vec<scalar_t, width> w = weight_v[idx];
-    using Converter = _typeConvert<scalar_t>;
-    using HipT = typename Converter::hip_type;
+    _f16Vec<scalar_t, width> temp = residual_v[id];
+    temp *= s_variance;
+    temp *= weight_v[idx];
 #pragma unroll
     for (int i = 0; i < width; ++i) {
-      float x = Converter::convert(res.data[i]);
-      float wf = Converter::convert(w.data[i]);
-      // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
-      // to match the unfused composite path at FP8 boundaries. We use the
-      // backend's hip_type for the intermediate since c10::Half/BFloat16 has
-      // ambiguous conversions on CUDA and no implicit conversion on ROCm.
-      HipT out_norm_h = Converter::convert(x * s_variance * wf);
-      out[id * width + i] = scaled_fp8_conversion<true, fp8_type>(
-          Converter::convert(out_norm_h), scale_inv);
+      out[id * width + i] =
+          scaled_fp8_conversion<true, fp8_type>(float(temp.data[i]), scale_inv);
     }
   }
 }
@@ -191,12 +176,9 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    float w = (float)weight[idx];
-    // See note in rms_norm_static_fp8_quant_kernel: round through scalar_t
-    // to match the unfused composite path at FP8 boundaries.
-    scalar_t out_norm = static_cast<scalar_t>(x * s_variance * w);
-    out[blockIdx.x * hidden_size + idx] = scaled_fp8_conversion<true, fp8_type>(
-        static_cast<float>(out_norm), scale_inv);
+    float const out_norm = ((scalar_t)(x * s_variance)) * weight[idx];
+    out[blockIdx.x * hidden_size + idx] =
+        scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
   }
 }
 

csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm.cuh

@@ -141,7 +141,7 @@ struct cutlass_3x_gemm_sm100 {
               sizeof(typename CollectiveEpilogue::SharedStorage))>,
           KernelSchedule>::CollectiveOp;
 
-  using GemmKernel = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
+  using GemmKernel = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>>;
 };
 

csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh

@@ -125,7 +125,7 @@ struct cutlass_3x_gemm_fp8_blockwise {
           MainloopScheduler
       >::CollectiveOp>;
 
-  using KernelType = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
+  using KernelType = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue>>;
 
   struct GemmKernel : public KernelType {};

csrc/libtorch_stable/quantization/w8a8/cutlass/c3x/scaled_mm_sm100_fp8_dispatch.cuh

@@ -92,7 +92,7 @@ struct cutlass_3x_gemm_sm100_fp8 {
   // -----------------------------------------------------------
   // Kernel definition
   // -----------------------------------------------------------
-  using GemmKernel = enable_sm100_to_sm120<cutlass::gemm::kernel::GemmUniversal<
+  using GemmKernel = enable_sm100f_only<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>>;
 };
 

csrc/libtorch_stable/quantization/w8a8/fp8/per_token_group_quant.cu

@@ -236,41 +236,17 @@ void per_token_group_quant_8bit(const torch::stable::Tensor& input,
 #undef LAUNCH_KERNEL
 }
 
-// Register-resident fast path for group_size==128.
-//
-// Each thread holds 16 source elements (32 B = uint4 x 2) in registers across
-// the absmax reduce -> scale compute -> quantize pipeline. No shared memory.
-// UE8M0 scale extracted via bit math (bit-exact with exp2f(ceilf(log2f))).
-//
-// Loads two contiguous uint4s (16 B + 16 B = 32 B) per thread; on Blackwell
-// nvcc fuses these into a single 256-bit LDG.E.256.
-//
-// Constraints: GROUP_SIZE % (THREADS_PER_GROUP * VEC_SIZE) == 0; for
-// THREADS_PER_GROUP=8 and bf16/fp16 (VEC_SIZE=16), this means GROUP_SIZE=128.
-template <typename T, typename DST_DTYPE, int GROUP_SIZE>
-__global__ void per_token_group_quant_8bit_packed_register_kernel(
+template <typename T, typename DST_DTYPE>
+__global__ void per_token_group_quant_8bit_packed_kernel(
     const T* __restrict__ input, void* __restrict__ output_q,
-    unsigned int* __restrict__ output_s_packed, const int64_t num_groups_padded,
-    const int groups_per_block, const int padded_groups_per_row,
-    const int groups_per_row, const int mn, const int output_q_mn_extent,
-    const int tma_aligned_mn, const int64_t num_scale_elems, const float eps,
+    unsigned int* __restrict__ output_s_packed, const int group_size,
+    const int num_groups_padded, const int groups_per_block,
+    const int padded_groups_per_row, const int groups_per_row, const int mn,
+    const int tma_aligned_mn, const int num_scale_elems, const float eps,
     const float min_8bit, const float max_8bit) {
-  static_assert(GROUP_SIZE == 128, "fast path supports GROUP_SIZE==128");
-  constexpr int THREADS_PER_GROUP = 8;
-  constexpr int VEC_SIZE = 32 / sizeof(T);  // 16 for bf16/fp16
-  static_assert(GROUP_SIZE == THREADS_PER_GROUP * VEC_SIZE,
-                "GROUP_SIZE must equal THREADS_PER_GROUP * VEC_SIZE");
-  // Each group's 8 threads must live in a single warp octet so the
-  // 0xffu << (threadIdx.x & 24u) shuffle mask selects exactly the lanes
-  // that share a group. Requires 32 % THREADS_PER_GROUP == 0 and the host
-  // to launch num_threads as a multiple of THREADS_PER_GROUP (which it does
-  // via num_threads = groups_per_block * THREADS_PER_GROUP).
-  static_assert(32 % THREADS_PER_GROUP == 0,
-                "THREADS_PER_GROUP must divide warp size for the shuffle "
-                "mask to be valid");
-
-  const int local_group_id = threadIdx.x / THREADS_PER_GROUP;
-  const int lane_id = threadIdx.x % THREADS_PER_GROUP;
+  const int threads_per_group = 16;
+  const int64_t local_group_id = threadIdx.x / threads_per_group;
+  const int lane_id = threadIdx.x % threads_per_group;
 
   const int64_t block_group_id = blockIdx.x * groups_per_block;
   const int64_t global_group_id = block_group_id + local_group_id;
@@ -278,207 +254,141 @@ __global__ void per_token_group_quant_8bit_packed_register_kernel(
     return;
   }
 
+  // map flat group id to 2D indices (mn_idx, sf_k_idx)
   const int sf_k_idx =
       static_cast<int>(global_group_id % padded_groups_per_row);
   const int mn_idx = static_cast<int>(global_group_id / padded_groups_per_row);
+
+  // whether it is a valid group (not padding)
   const bool is_valid_group = (mn_idx < mn) && (sf_k_idx < groups_per_row);
 
-  // Load 16 input elements (32 B) into registers as two adjacent uint4
-  // loads. nvcc keeps these as 2x LDG.E.128 on sm_100; the per-thread cost
-  // is dominated by HBM bandwidth at large MN, so a fused 256-bit load via
-  // inline PTX gave no measurable speedup.
-  // alignas(16) is required so the uint4* reinterpret_cast below is
-  // well-defined for T == bf16/fp16 (default alignof is 2).
-  alignas(16) T regs[VEC_SIZE];
-  float local_absmax = eps;
+  // shared memory to cache each group's data to avoid double DRAM reads.
+  extern __shared__ __align__(16) char smem_raw[];
+  T* smem = reinterpret_cast<T*>(smem_raw);
+  T* smem_group = smem + local_group_id * group_size;
+
+  // compute scale for valid groups
+  float y_s = 0.f;
   if (is_valid_group) {
     const T* group_input =
-        input + static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
-        sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
-    uint4* dst = reinterpret_cast<uint4*>(&regs[0]);
-    const uint4* src = reinterpret_cast<const uint4*>(group_input);
-    dst[0] = src[0];
-    dst[1] = src[1];
-#pragma unroll
-    for (int i = 0; i < VEC_SIZE; ++i) {
-      float v = fabsf(static_cast<float>(regs[i]));
-      local_absmax = fmaxf(local_absmax, v);
-    }
+        input + static_cast<int64_t>(mn_idx) * groups_per_row * group_size +
+        sf_k_idx * group_size;
+    y_s = ComputeGroupScale<T, true>(group_input, smem_group, group_size,
+                                     lane_id, threads_per_group, eps, max_8bit);
   }
 
-  // 8-lane subgroup shuffle reduce (octet of the warp). The mask selects the
-  // 8 lanes within the warp that share a group.
-  unsigned mask = 0xffu << (threadIdx.x & 24u);
-  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 4));
-  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 2));
-  local_absmax = fmaxf(local_absmax, __shfl_xor_sync(mask, local_absmax, 1));
-
-  float y_s = local_absmax / max_8bit;
-  y_s = fmaxf(y_s, 1e-10f);
-  uint32_t bits = __float_as_uint(y_s);
-  uint32_t exp_bits = (bits >> 23) & 0xffu;
-  uint32_t mant_bits = bits & 0x7fffffu;
-  uint8_t exp_byte =
-      static_cast<uint8_t>(exp_bits + (mant_bits != 0u ? 1u : 0u));
-
-  // Lane 0 writes the packed scale byte.
+  // pack 4 scales into a uint32 exponent
   if (lane_id == 0) {
+    // each uint32 in output_s_packed stores 4 packed scales
     const int sf_k_pack_idx = sf_k_idx / 4;
     const int pos = sf_k_idx % 4;
     const int out_idx = sf_k_pack_idx * tma_aligned_mn + mn_idx;
+
     if (is_valid_group) {
-      reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = exp_byte;
+      // reinterpret the UE8M0 scale y_s as IEEE bits, extract the 8-bit
+      // exponent, and place it into the correct byte of the 32-bit word.
+      const unsigned int bits = __float_as_uint(y_s);
+      const uint8_t exponent = static_cast<uint8_t>((bits >> 23u) & 0xffu);
+      reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = exponent;
     } else if (out_idx < num_scale_elems) {
+      // write zero for padding groups if within bounds of output_s_packed
       reinterpret_cast<uint8_t*>(output_s_packed)[out_idx * 4 + pos] = 0;
     }
   }
 
-  // For padded mn rows that fall within output_q's allocated extent, write
-  // a uint4 of zeros to keep the buffer clean for downstream TMA loads.
-  // Skip writes for sf_k padding (those positions don't exist in output_q).
-  if (!is_valid_group) {
-    if (sf_k_idx < groups_per_row && mn_idx >= mn &&
-        mn_idx < output_q_mn_extent) {
-      DST_DTYPE* group_output =
-          static_cast<DST_DTYPE*>(output_q) +
-          static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
-          sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
-      *reinterpret_cast<uint4*>(group_output) = make_uint4(0, 0, 0, 0);
-    }
-    return;
+  __syncthreads();
+
+  if (is_valid_group) {
+    DST_DTYPE* group_output =
+        static_cast<DST_DTYPE*>(output_q) +
+        static_cast<int64_t>(mn_idx) * groups_per_row * group_size +
+        sf_k_idx * group_size;
+    QuantizeGroup<T, DST_DTYPE>(smem_group, group_output, group_size, lane_id,
+                                threads_per_group, y_s, min_8bit, max_8bit);
   }
-
-  // Reconstruct y_s as a power-of-2 float and use its reciprocal.
-  float y_s_q = __uint_as_float(static_cast<uint32_t>(exp_byte) << 23);
-  float inv_y = 1.0f / y_s_q;
-
-  // Quantize and pack into 16 fp8/int8 bytes (= uint4). VEC_SIZE==16 so we
-  // fill four 32-bit words, four bytes each.
-  uint32_t packed_lo = 0;
-  uint32_t packed_lo_hi = 0;
-  uint32_t packed_hi_lo = 0;
-  uint32_t packed_hi = 0;
-#pragma unroll
-  for (int i = 0; i < VEC_SIZE; ++i) {
-    float q =
-        fminf(fmaxf(static_cast<float>(regs[i]) * inv_y, min_8bit), max_8bit);
-    DST_DTYPE qb = DST_DTYPE(q);
-    uint8_t byte = *reinterpret_cast<uint8_t*>(&qb);
-    const int shift = (i & 3) * 8;
-    if (i < 4) {
-      packed_lo |= static_cast<uint32_t>(byte) << shift;
-    } else if (i < 8) {
-      packed_lo_hi |= static_cast<uint32_t>(byte) << shift;
-    } else if (i < 12) {
-      packed_hi_lo |= static_cast<uint32_t>(byte) << shift;
-    } else {
-      packed_hi |= static_cast<uint32_t>(byte) << shift;
-    }
-  }
-
-  uint4 packed_out =
-      make_uint4(packed_lo, packed_lo_hi, packed_hi_lo, packed_hi);
-  DST_DTYPE* group_output =
-      static_cast<DST_DTYPE*>(output_q) +
-      static_cast<int64_t>(mn_idx) * groups_per_row * GROUP_SIZE +
-      sf_k_idx * GROUP_SIZE + lane_id * VEC_SIZE;
-  *reinterpret_cast<uint4*>(group_output) = packed_out;
 }
 
-// Public entry point: register-resident packed quant kernel.
-// Constraints: group_size == 128 and bf16/fp16 input.
 void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
                                        torch::stable::Tensor& output_q,
                                        torch::stable::Tensor& output_s_packed,
                                        int64_t group_size, double eps,
                                        double min_8bit, double max_8bit) {
-  STD_TORCH_CHECK(group_size == 128,
-                  "per_token_group_quant_8bit_packed only supports "
-                  "group_size==128, got ",
-                  group_size, ".");
-  const auto in_dtype = input.scalar_type();
-  STD_TORCH_CHECK(
-      in_dtype == torch::headeronly::ScalarType::Half ||
-          in_dtype == torch::headeronly::ScalarType::BFloat16,
-      "per_token_group_quant_8bit_packed only supports bf16/fp16 input.");
-
   STD_TORCH_CHECK(input.is_contiguous());
   STD_TORCH_CHECK(output_q.is_contiguous());
 
   const int64_t k = input.size(-1);
-  STD_TORCH_CHECK(k % group_size == 0, "input last dim k=", k,
-                  " is not divisible by group_size=", group_size, ".");
+  STD_TORCH_CHECK(k % group_size == 0, "Last dimension (", k,
+                  ") must be divisible by group_size (", group_size, ").");
 
   const int64_t mn = input.numel() / k;
   const int64_t groups_per_row = k / group_size;
+
+  STD_TORCH_CHECK(output_s_packed.dim() == 2,
+                  "output_s_packed must be 2D, got dim=", output_s_packed.dim(),
+                  ".");
+
   const int64_t k_num_packed_sfk = (groups_per_row + 3) / 4;
   const int64_t tma_aligned_mn = ((mn + 3) / 4) * 4;
 
-  // output_q may be allocated with extra padded mn rows (e.g.,
-  // (tma_aligned_mn, k)) so the kernel can zero-fill them in-line and the
-  // caller can use torch.empty instead of torch.zeros. The grid only covers
-  // up to tma_aligned_mn, so we cap the extent there.
-  const int64_t output_q_mn_actual = output_q.numel() / k;
-  STD_TORCH_CHECK(output_q_mn_actual >= mn,
-                  "output_q must have at least mn rows; got ",
-                  output_q_mn_actual, " rows for mn=", mn, ".");
-  const int64_t output_q_mn_extent =
-      output_q_mn_actual < tma_aligned_mn ? output_q_mn_actual : tma_aligned_mn;
-
   STD_TORCH_CHECK(
       output_s_packed.scalar_type() == torch::headeronly::ScalarType::Int,
-      "output_s_packed must be int32 for UE8M0-packed scales.");
+      "output_s_packed must have dtype int32 for UE8M0-packed scales.");
+  // DeepGEMM expects SFA scales in MN-major form with shape
+  // [mn, ceil_div(K, 128 * 4)] and TMA-aligned stride on the last
+  // dimension.
   STD_TORCH_CHECK(output_s_packed.size(0) == mn &&
                       output_s_packed.size(1) == k_num_packed_sfk,
                   "output_s_packed shape must be [", mn, ", ", k_num_packed_sfk,
-                  "]; got [", output_s_packed.size(0), ", ",
+                  "], but got [", output_s_packed.size(0), ", ",
                   output_s_packed.size(1), "].");
+  // Verify column-major TMA-aligned layout
   STD_TORCH_CHECK(output_s_packed.stride(0) == 1 &&
                       output_s_packed.stride(1) == tma_aligned_mn,
-                  "output_s_packed strides must be [1, ", tma_aligned_mn,
-                  "]; got [", output_s_packed.stride(0), ", ",
+                  "output_s_packed must have strides [1, ", tma_aligned_mn,
+                  "], but got [", output_s_packed.stride(0), ", ",
                   output_s_packed.stride(1), "].");
 
   cudaStream_t stream = get_current_cuda_stream();
 
-  constexpr int THREADS_PER_GROUP = 8;
+  constexpr int THREADS_PER_GROUP = 16;
+
+  // Expand the grid to cover MN and K padding so every byte in
+  // output_s_packed is written (padding bytes get zeroed by the kernel).
   const int64_t padded_groups_per_row = k_num_packed_sfk * 4;
   const int64_t num_groups_padded = tma_aligned_mn * padded_groups_per_row;
+  // Number of elements in output_s_packed.
   const int64_t num_scale_elems = mn + (k_num_packed_sfk - 1) * tma_aligned_mn;
+
   const int groups_per_block = GetGroupsPerBlock(num_groups_padded);
 
   auto dst_type = output_q.scalar_type();
-  const int64_t num_blocks = num_groups_padded / groups_per_block;
+  const int num_blocks = num_groups_padded / groups_per_block;
   const int num_threads = groups_per_block * THREADS_PER_GROUP;
-  // CUDA caps grid.x at 2^31 - 1; this fits any realistic shape but guard
-  // against pathological inputs.
-  STD_TORCH_CHECK(num_blocks <= static_cast<int64_t>(INT32_MAX),
-                  "per_token_group_quant_8bit_packed grid too large: ",
-                  num_blocks, " blocks (max ", INT32_MAX, ").");
 
-#define LAUNCH_REG_KERNEL(T, DST_DTYPE)                                   \
-  do {                                                                    \
-    dim3 grid(static_cast<unsigned int>(num_blocks));                     \
-    dim3 block(num_threads);                                              \
-    per_token_group_quant_8bit_packed_register_kernel<T, DST_DTYPE, 128>  \
-        <<<grid, block, 0, stream>>>(                                     \
-            static_cast<const T*>(input.data_ptr()), output_q.data_ptr(), \
-            reinterpret_cast<unsigned int*>(output_s_packed.data_ptr()),  \
-            num_groups_padded, groups_per_block,                          \
-            static_cast<int>(padded_groups_per_row),                      \
-            static_cast<int>(groups_per_row), static_cast<int>(mn),       \
-            static_cast<int>(output_q_mn_extent),                         \
-            static_cast<int>(tma_aligned_mn), num_scale_elems,            \
-            static_cast<float>(eps), static_cast<float>(min_8bit),        \
-            static_cast<float>(max_8bit));                                \
+#define LAUNCH_PACKED_KERNEL(T, DST_DTYPE)                                     \
+  do {                                                                         \
+    dim3 grid(num_blocks);                                                     \
+    dim3 block(num_threads);                                                   \
+    size_t smem_bytes =                                                        \
+        static_cast<size_t>(groups_per_block) * group_size * sizeof(T);        \
+    per_token_group_quant_8bit_packed_kernel<T, DST_DTYPE>                     \
+        <<<grid, block, smem_bytes, stream>>>(                                 \
+            static_cast<const T*>(input.data_ptr()), output_q.data_ptr(),      \
+            reinterpret_cast<unsigned int*>(output_s_packed.data_ptr()),       \
+            static_cast<int>(group_size), static_cast<int>(num_groups_padded), \
+            groups_per_block, static_cast<int>(padded_groups_per_row),         \
+            static_cast<int>(groups_per_row), static_cast<int>(mn),            \
+            static_cast<int>(tma_aligned_mn),                                  \
+            static_cast<int>(num_scale_elems), static_cast<float>(eps),        \
+            static_cast<float>(min_8bit), static_cast<float>(max_8bit));       \
   } while (0)
 
-  VLLM_STABLE_DISPATCH_HALF_TYPES(
-      input.scalar_type(), "per_token_group_quant_8bit_packed_register", ([&] {
+  VLLM_STABLE_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "per_token_group_quant_8bit_packed", ([&] {
         if (dst_type == torch::headeronly::ScalarType::Float8_e4m3fn) {
-          LAUNCH_REG_KERNEL(scalar_t, __nv_fp8_e4m3);
+          LAUNCH_PACKED_KERNEL(scalar_t, __nv_fp8_e4m3);
         } else if (dst_type == torch::headeronly::ScalarType::Char) {
-          LAUNCH_REG_KERNEL(scalar_t, int8_t);
+          LAUNCH_PACKED_KERNEL(scalar_t, int8_t);
         } else {
           STD_TORCH_CHECK(
               false,
@@ -487,7 +397,7 @@ void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
         }
       }));
 
-#undef LAUNCH_REG_KERNEL
+#undef LAUNCH_PACKED_KERNEL
 }
 
 void per_token_group_quant_fp8(const torch::stable::Tensor& input,

csrc/libtorch_stable/quantization/w8a8/per_token_group_quant_8bit.h

@@ -8,13 +8,3 @@ void per_token_group_quant_8bit(const torch::stable::Tensor& input,
                                 torch::stable::Tensor& output_s,
                                 int64_t group_size, double eps, double min_8bit,
                                 double max_8bit, bool scale_ue8m0 = false);
-
-// Public op: register-resident packed quant for the DeepGEMM Blackwell path.
-// Restricted to group_size == 128 and bf16/fp16 input; other configurations
-// raise STD_TORCH_CHECK. The legacy shared-memory fallback was removed because
-// no production caller (deep_gemm_moe / input_quant_fp8) uses other shapes.
-void per_token_group_quant_8bit_packed(const torch::stable::Tensor& input,
-                                       torch::stable::Tensor& output_q,
-                                       torch::stable::Tensor& output_s_packed,
-                                       int64_t group_size, double eps,
-                                       double min_8bit, double max_8bit);

csrc/moe/moe_ops.h

@@ -12,15 +12,6 @@ void topk_sigmoid(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
                   torch::Tensor& gating_output, bool renormalize,
                   std::optional<torch::Tensor> bias);
 
-void topk_softplus_sqrt(torch::Tensor& topk_weights,
-                        torch::Tensor& topk_indices,
-                        torch::Tensor& token_expert_indices,
-                        torch::Tensor& gating_output, bool renormalize,
-                        double routed_scaling_factor,
-                        const c10::optional<torch::Tensor>& correction_bias,
-                        const c10::optional<torch::Tensor>& input_ids,
-                        const c10::optional<torch::Tensor>& tid2eid);
-
 void moe_sum(torch::Tensor& input, torch::Tensor& output);
 
 void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
@@ -67,6 +58,10 @@ void shuffle_rows(const torch::Tensor& input_tensor,
                   torch::Tensor& output_tensor);
 
 #ifndef USE_ROCM
+// cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
+torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
+                                    torch::Tensor const& weight);
+
 // DeepSeek V3 optimized router GEMM kernel for SM90+
 // Computes output = mat_a @ mat_b.T where:
 //   mat_a: [num_tokens, hidden_dim] in bf16

csrc/moe/router_gemm.cu

@@ -0,0 +1,52 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+// bf16 x bf16 -> fp32 router GEMM via cuBLAS.
+// Uses CUBLAS_COMPUTE_32F so bf16 operands accumulate into fp32,
+// matching TRT-LLM's cuBLAS fallback behaviour in dsv3RouterGemmOp.
+
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cublas_v2.h>
+
+// cuBLAS column-major math for row-major PyTorch tensors:
+//   weight[N,K]_row  lda=K  -> cuBLAS sees (K,N) col-major; CUBLAS_OP_T ->
+//   (N,K) input[M,K]_row   ldb=K  -> cuBLAS sees (K,M) col-major; CUBLAS_OP_N
+//   -> (K,M) out[M,N]_row     ldc=N  -> cuBLAS sees (N,M) col-major (written as
+//   output^T)
+// cuBLAS: C(N,M) = weight(N,K) @ input(K,M)  =>  C^T = output[M,N]
+// params: m=N, n=M, k=K, lda=K (weight), ldb=K (input), ldc=N (output)
+
+torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
+                                    torch::Tensor const& weight) {
+  TORCH_CHECK(input.dtype() == torch::kBFloat16,
+              "router_gemm_bf16_fp32: input must be bfloat16");
+  TORCH_CHECK(weight.dtype() == torch::kBFloat16,
+              "router_gemm_bf16_fp32: weight must be bfloat16");
+  TORCH_CHECK(input.dim() == 2 && weight.dim() == 2,
+              "router_gemm_bf16_fp32: input and weight must be 2-D");
+  TORCH_CHECK(input.size(1) == weight.size(1),
+              "router_gemm_bf16_fp32: inner dimensions must match");
+
+  int64_t const M = input.size(0);
+  int64_t const N = weight.size(0);
+  int64_t const K = input.size(1);
+
+  auto out = torch::empty({M, N}, input.options().dtype(torch::kFloat32));
+
+  cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
+  TORCH_CUDABLAS_CHECK(
+      cublasSetStream(handle, at::cuda::getCurrentCUDAStream()));
+
+  float const alpha = 1.0f;
+  float const beta = 0.0f;
+
+  TORCH_CUDABLAS_CHECK(cublasGemmEx(
+      handle, CUBLAS_OP_T, CUBLAS_OP_N, static_cast<int>(N),
+      static_cast<int>(M), static_cast<int>(K), &alpha, weight.data_ptr(),
+      CUDA_R_16BF, static_cast<int>(K), input.data_ptr(), CUDA_R_16BF,
+      static_cast<int>(K), &beta, out.data_ptr(), CUDA_R_32F,
+      static_cast<int>(N), CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
+
+  return out;
+}

csrc/moe/topk_softplus_sqrt_kernels.cu

@@ -1,726 +0,0 @@
-/*
- * Adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/v0.7.1/cpp/tensorrt_llm/kernels/mixtureOfExperts/moe_kernels.cu
- * Copyright (c) 2024, The vLLM team.
- * SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION &
- * AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include <type_traits>
-#include <torch/all.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "../cuda_compat.h"
-#include "../cub_helpers.h"
-#ifndef USE_ROCM
-  #include <cuda_bf16.h>
-  #include <cuda_fp16.h>
-#else
-  #include <hip/hip_bf16.h>
-  #include <hip/hip_fp16.h>
-typedef __hip_bfloat16 __nv_bfloat16;
-typedef __hip_bfloat162 __nv_bfloat162;
-#endif
-
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-
-namespace vllm {
-namespace moe {
-
-/// Aligned array type
-template <typename T,
-          /// Number of elements in the array
-          int N,
-          /// Alignment requirement in bytes
-          int Alignment = sizeof(T) * N>
-struct alignas(Alignment) AlignedArray {
-  T data[N];
-};
-
-template <typename T>
-__device__ __forceinline__ float toFloat(T value) {
-  if constexpr (std::is_same_v<T, float>) {
-    return value;
-  } else if constexpr (std::is_same_v<T, __nv_bfloat16>) {
-    return __bfloat162float(value);
-  } else if constexpr (std::is_same_v<T, __half>) {
-    return __half2float(value);
-  }
-}
-
-// ====================== TopK softplus_sqrt things
-// ===============================
-
-/*
-  A Top-K gating softplus_sqrt written to exploit when the number of experts in
-  the MoE layers are a small power of 2. This allows us to cleanly share the
-  rows among the threads in a single warp and eliminate communication between
-  warps (so no need to use shared mem).
-
-  It fuses the sigmoid, max and argmax into a single kernel.
-
-  Limitations:
-  1) This implementation is optimized for when the number of experts is a small
-  power of 2. Additionally it also supports when number of experts is multiple
-  of 64 which is still faster than the computing sigmoid and topK separately
-  (only tested on CUDA yet). 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, bool USE_HASH, typename IndType,
-          typename InputType = float>
-__launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
-    void topkGatingSoftplusSqrt(
-        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,
-        double routed_scaling_factor, const float* correction_bias,
-        const IndType* input_ids, const IndType* tid2eid) {
-  static_assert(std::is_same_v<InputType, float> ||
-                    std::is_same_v<InputType, __nv_bfloat16> ||
-                    std::is_same_v<InputType, __half>,
-                "InputType must be float, __nv_bfloat16, or __half");
-
-  // We begin by enforcing compile time assertions and setting up compile time
-  // constants.
-  static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG),
-                "BYTES_PER_LDG must be power of 2");
-  static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
-
-  // Number of bytes each thread pulls in per load
-  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
-  static constexpr int ELTS_PER_ROW = NUM_EXPERTS;
-  static constexpr int THREADS_PER_ROW = ELTS_PER_ROW / VPT;
-  static constexpr int LDG_PER_THREAD = VPT / ELTS_PER_LDG;
-
-  if constexpr (std::is_same_v<InputType, __nv_bfloat16> ||
-                std::is_same_v<InputType, __half>) {
-    static_assert(ELTS_PER_LDG == 1 || ELTS_PER_LDG % 2 == 0,
-                  "ELTS_PER_LDG must be 1 or even for 16-bit conversion");
-  }
-
-  // Restrictions based on previous section.
-  static_assert(
-      VPT % ELTS_PER_LDG == 0,
-      "The elements per thread must be a multiple of the elements per ldg");
-  static_assert(WARP_SIZE_PARAM % THREADS_PER_ROW == 0,
-                "The threads per row must cleanly divide the threads per warp");
-  static_assert(THREADS_PER_ROW == (THREADS_PER_ROW & -THREADS_PER_ROW),
-                "THREADS_PER_ROW must be power of 2");
-  static_assert(THREADS_PER_ROW <= WARP_SIZE_PARAM,
-                "THREADS_PER_ROW can be at most warp size");
-
-  // We have NUM_EXPERTS elements per row. We specialize for small #experts
-  static constexpr int ELTS_PER_WARP = WARP_SIZE_PARAM * VPT;
-  static constexpr int ROWS_PER_WARP = ELTS_PER_WARP / ELTS_PER_ROW;
-  static constexpr int ROWS_PER_CTA = WARPS_PER_CTA * ROWS_PER_WARP;
-
-  // Restrictions for previous section.
-  static_assert(ELTS_PER_WARP % ELTS_PER_ROW == 0,
-                "The elts per row must cleanly divide the total elt per warp");
-
-  // ===================== From this point, we finally start computing run-time
-  // variables. ========================
-
-  // Compute CTA and warp rows. We pack multiple rows into a single warp, and a
-  // block contains WARPS_PER_CTA warps. This, each block processes a chunk of
-  // rows. We start by computing the start row for each block.
-  const int cta_base_row = blockIdx.x * ROWS_PER_CTA;
-
-  // Now, using the base row per thread block, we compute the base row per warp.
-  const int warp_base_row = cta_base_row + threadIdx.y * ROWS_PER_WARP;
-
-  // The threads in a warp are split into sub-groups that will work on a row.
-  // We compute row offset for each thread sub-group
-  const int thread_row_in_warp = threadIdx.x / THREADS_PER_ROW;
-  const int thread_row = warp_base_row + thread_row_in_warp;
-
-  // Threads with indices out of bounds should early exit here.
-  if (thread_row >= num_rows) {
-    return;
-  }
-  const bool row_is_active = finished ? !finished[thread_row] : true;
-
-  // We finally start setting up the read pointers for each thread. First, each
-  // thread jumps to the start of the row it will read.
-  const InputType* thread_row_ptr = input + thread_row * ELTS_PER_ROW;
-
-  // Now, we compute the group each thread belong to in order to determine the
-  // first column to start loads.
-  const int thread_group_idx = threadIdx.x % THREADS_PER_ROW;
-  const int first_elt_read_by_thread = thread_group_idx * ELTS_PER_LDG;
-  const InputType* thread_read_ptr = thread_row_ptr + first_elt_read_by_thread;
-
-  // Finally, we pull in the data from global mem
-  float row_chunk[VPT];
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.wait;");
-#endif
-
-  // NOTE(zhuhaoran): dispatch different input types loading, BF16/FP16 convert
-  // to float
-  if constexpr (std::is_same_v<InputType, float>) {
-    using VecType = AlignedArray<float, ELTS_PER_LDG>;
-    VecType* row_chunk_vec_ptr = reinterpret_cast<VecType*>(&row_chunk);
-    const VecType* vec_thread_read_ptr =
-        reinterpret_cast<const VecType*>(thread_read_ptr);
-#pragma unroll
-    for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-      row_chunk_vec_ptr[ii] = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-    }
-  } else if constexpr (std::is_same_v<InputType, __nv_bfloat16>) {
-    if constexpr (ELTS_PER_LDG >= 2) {
-      using VecType = AlignedArray<__nv_bfloat16, ELTS_PER_LDG>;
-      float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
-      const VecType* vec_thread_read_ptr =
-          reinterpret_cast<const VecType*>(thread_read_ptr);
-#pragma unroll
-      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-        VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-        int base_idx_f2 = ii * ELTS_PER_LDG / 2;
-#pragma unroll
-        for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
-          row_chunk_f2[base_idx_f2 + jj] = __bfloat1622float2(
-              *reinterpret_cast<const __nv_bfloat162*>(vec.data + jj * 2));
-        }
-      }
-    } else {  // ELTS_PER_LDG == 1
-#pragma unroll
-      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-        const __nv_bfloat16* scalar_ptr =
-            thread_read_ptr + ii * THREADS_PER_ROW;
-        row_chunk[ii] = __bfloat162float(*scalar_ptr);
-      }
-    }
-  } else if constexpr (std::is_same_v<InputType, __half>) {
-    if constexpr (ELTS_PER_LDG >= 2) {
-      using VecType = AlignedArray<__half, ELTS_PER_LDG>;
-      float2* row_chunk_f2 = reinterpret_cast<float2*>(row_chunk);
-      const VecType* vec_thread_read_ptr =
-          reinterpret_cast<const VecType*>(thread_read_ptr);
-#pragma unroll
-      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-        VecType vec = vec_thread_read_ptr[ii * THREADS_PER_ROW];
-        int base_idx_f2 = ii * ELTS_PER_LDG / 2;
-#pragma unroll
-        for (int jj = 0; jj < ELTS_PER_LDG / 2; ++jj) {
-          row_chunk_f2[base_idx_f2 + jj] = __half22float2(
-              *reinterpret_cast<const __half2*>(vec.data + jj * 2));
-        }
-      }
-    } else {  // ELTS_PER_LDG == 1
-#pragma unroll
-      for (int ii = 0; ii < LDG_PER_THREAD; ++ii) {
-        const __half* scalar_ptr = thread_read_ptr + ii * THREADS_PER_ROW;
-        row_chunk[ii] = __half2float(*scalar_ptr);
-      }
-    }
-  }
-  constexpr float threshold = 20.0f;
-  constexpr float beta = 1.0f;
-
-  // Hash MoE path: indices are predetermined from lookup table
-  if constexpr (USE_HASH) {
-    const IndType token_id = input_ids[thread_row];
-    const IndType* expert_indices_for_token = tid2eid + token_id * k;
-#pragma unroll
-    for (int ii = 0; ii < VPT; ++ii) {
-      float val = row_chunk[ii];
-      float val_b = val * beta;
-      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-      row_chunk[ii] = sqrtf(val);
-    }
-    float selected_sum = 0.f;
-#pragma unroll
-    for (int k_idx = 0; k_idx < k; ++k_idx) {
-      const int expert = expert_indices_for_token[k_idx];
-      const int idx = k * thread_row + k_idx;
-      for (int ii = 0; ii < VPT; ++ii) {
-        const int group_id = ii / ELTS_PER_LDG;
-        const int local_id = ii % ELTS_PER_LDG;
-        const int expert_idx = first_elt_read_by_thread +
-                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
-                               local_id;
-        if (expert == expert_idx) {
-          indices[idx] = expert;
-          selected_sum += row_chunk[ii];
-          break;
-        }
-      }
-    }
-    // Compute per-thread scale (using warp reduction when renormalizing).
-    // THREADS_PER_ROW-parameterized butterfly works for both warp sizes (32
-    // on CUDA, 64 on ROCm CDNA) and any THREADS_PER_ROW the dispatch picks.
-    if (renormalize) {
-#pragma unroll
-      for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
-        selected_sum +=
-            VLLM_SHFL_XOR_SYNC_WIDTH(selected_sum, mask, THREADS_PER_ROW);
-      }
-    }
-    float scale = static_cast<float>(routed_scaling_factor);
-    if (renormalize) {
-      const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-      scale /= denom;
-    }
-
-#pragma unroll
-    for (int k_idx = 0; k_idx < k; ++k_idx) {
-      const int expert = expert_indices_for_token[k_idx];
-      const int idx = k * thread_row + k_idx;
-      for (int ii = 0; ii < VPT; ++ii) {
-        const int group_id = ii / ELTS_PER_LDG;
-        const int local_id = ii % ELTS_PER_LDG;
-        const int expert_idx = first_elt_read_by_thread +
-                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
-                               local_id;
-        if (expert == expert_idx) {
-          output[idx] = row_chunk[ii] * scale;
-          break;
-        }
-      }
-    }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-    asm volatile("griddepcontrol.launch_dependents;");
-#endif
-    return;
-  }
-
-#pragma unroll
-  for (int ii = 0; ii < VPT; ++ii) {
-    float val = row_chunk[ii];
-    float val_b = val * beta;
-    // Compute softplus: log(1 + exp(val)) with numerical stability
-    // When val > threshold, softplus(x) ≈ x to avoid exp overflow
-    val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-    val = sqrtf(val);
-    if (correction_bias) {
-      const int group_id = ii / ELTS_PER_LDG;
-      const int local_id = ii % ELTS_PER_LDG;
-      const int expert_idx = first_elt_read_by_thread +
-                             group_id * THREADS_PER_ROW * ELTS_PER_LDG +
-                             local_id;
-      val = val + correction_bias[expert_idx];
-    }
-    row_chunk[ii] = val;
-  }
-
-  // Original TopK path: find top-k experts by score
-  // Now, sigmoid_res contains the sigmoid 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 = row_chunk[0];
-    int expert = start_col;
-#pragma unroll
-    for (int ldg = 0, col = start_col; ldg < LDG_PER_THREAD;
-         ++ldg, col += COLS_PER_GROUP_LDG) {
-#pragma unroll
-      for (int ii = 0; ii < 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 > max_val) {
-          max_val = val;
-          expert = col + ii;
-        }
-      }
-    }
-
-// Now, we perform the argmax reduce. We use the butterfly pattern so threads
-// reach consensus about the max. This will be useful for K > 1 so that the
-// threads can agree on "who" had the max value. That thread can then blank out
-// their max with -inf and the warp can run more iterations...
-#pragma unroll
-    for (int mask = THREADS_PER_ROW / 2; mask > 0; mask /= 2) {
-      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 > max_val ||
-          (other_max == max_val && other_expert < expert)) {
-        max_val = other_max;
-        expert = other_expert;
-      }
-    }
-
-    // Write the max for this k iteration to global memory.
-    if (thread_group_idx == 0) {
-      // Add a guard to ignore experts not included by this node
-      const bool node_uses_expert =
-          expert >= start_expert && expert < end_expert;
-      const bool should_process_row = row_is_active && node_uses_expert;
-
-      // The lead thread from each sub-group will write out the final results to
-      // global memory. (This will be a single) thread per row of the
-      // input/output matrices.
-      const int idx = k * thread_row + k_idx;
-      if (correction_bias != nullptr) {
-        max_val -= correction_bias[expert];
-      }
-      output[idx] = max_val;
-      indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
-      source_rows[idx] = k_idx * num_rows + thread_row;
-      if (renormalize) {
-        selected_sum += max_val;
-      }
-    }
-
-    // Finally, we clear the value in the thread with the current max if there
-    // is another iteration to run.
-    if (k_idx + 1 < k) {
-      const int ldg_group_for_expert = expert / COLS_PER_GROUP_LDG;
-      const int thread_to_clear_in_group =
-          (expert / ELTS_PER_LDG) % THREADS_PER_ROW;
-
-      // Only the thread in the group which produced the max will reset the
-      // "winning" value to -inf.
-      if (thread_group_idx == thread_to_clear_in_group) {
-        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[ldg_group_for_expert * ELTS_PER_LDG + offset_for_expert] =
-            -10000.f;
-      }
-    }
-  }
-
-  // Apply renormalization and routed scaling factor to final weights.
-  if (thread_group_idx == 0) {
-    float scale = static_cast<float>(routed_scaling_factor);
-    if (renormalize) {
-      const float denom = selected_sum > 0.f ? selected_sum : 1.f;
-      scale /= denom;
-    }
-    for (int k_idx = 0; k_idx < k; ++k_idx) {
-      const int idx = k * thread_row + k_idx;
-      output[idx] = output[idx] * scale;
-    }
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-namespace detail {
-// Constructs some constants needed to partition the work across threads at
-// compile time.
-template <int EXPERTS, int BYTES_PER_LDG, int WARP_SIZE_PARAM,
-          typename InputType>
-struct TopkConstants {
-  static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(InputType);
-  static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0 ||
-                    EXPERTS % (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0,
-                "");
-  static constexpr int VECs_PER_THREAD =
-      MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM));
-  static constexpr int VPT = VECs_PER_THREAD * ELTS_PER_LDG;
-  static constexpr int THREADS_PER_ROW = EXPERTS / VPT;
-  static const int ROWS_PER_WARP = WARP_SIZE_PARAM / THREADS_PER_ROW;
-};
-}  // namespace detail
-
-#define DISPATCH_HASH(use_hash, USE_HASH, ...)                                 \
-  if (use_hash) {                                                              \
-    const bool USE_HASH = true;                                                \
-    static_assert(USE_HASH == true, "USE_HASH must be compile-time constant"); \
-    __VA_ARGS__                                                                \
-  } else {                                                                     \
-    const bool USE_HASH = false;                                               \
-    static_assert(USE_HASH == false,                                           \
-                  "USE_HASH must be compile-time constant");                   \
-    __VA_ARGS__                                                                \
-  }
-
-template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM,
-          int MAX_BYTES_PER_LDG, typename IndType, typename InputType>
-void topkGatingSoftplusSqrtLauncherHelper(
-    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,
-    double routed_scaling_factor, const float* correction_bias,
-    const bool use_hash, const IndType* input_ids, const IndType* tid2eid,
-    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>;
-  static constexpr int VPT = Constants::VPT;
-  static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
-  const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
-  const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
-  dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
-  DISPATCH_HASH(use_hash, USE_HASH, {
-    auto* kernel =
-        &topkGatingSoftplusSqrt<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG,
-                                WARP_SIZE_PARAM, USE_HASH, IndType, InputType>;
-#ifndef USE_ROCM
-    cudaLaunchConfig_t config = {};
-    config.gridDim = num_blocks;
-    config.blockDim = block_dim;
-    config.dynamicSmemBytes = 0;
-    config.stream = stream;
-    cudaLaunchAttribute attrs[1];
-    attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-    attrs[0].val.programmaticStreamSerializationAllowed = 1;
-    config.numAttrs = 1;
-    config.attrs = attrs;
-    cudaLaunchKernelEx(&config, kernel, input, finished, output, num_rows,
-                       indices, source_row, k, start_expert, end_expert,
-                       renormalize, routed_scaling_factor, correction_bias,
-                       input_ids, tid2eid);
-#else
-    kernel<<<num_blocks, block_dim, 0, stream>>>(
-        input, finished, output, num_rows, indices, source_row, k, start_expert,
-        end_expert, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid);
-#endif
-  })
-}
-
-#ifndef USE_ROCM
-  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
-    static_assert(WARP_SIZE == 32,                                             \
-                  "Unsupported warp size. Only 32 is supported for CUDA");     \
-    topkGatingSoftplusSqrtLauncherHelper<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,   \
-        routed_scaling_factor, correction_bias, use_hash, input_ids, tid2eid,  \
-        stream);
-#else
-  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
-    if (WARP_SIZE == 64) {                                                     \
-      topkGatingSoftplusSqrtLauncherHelper<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, \
-          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
-    } else if (WARP_SIZE == 32) {                                              \
-      topkGatingSoftplusSqrtLauncherHelper<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, \
-          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
-    } else {                                                                   \
-      assert(false &&                                                          \
-             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
-    }
-#endif
-
-template <typename IndType, typename InputType>
-void topkGatingSoftplusSqrtKernelLauncher(
-    const InputType* gating_output, float* topk_weights, IndType* topk_indices,
-    int* token_expert_indices, const int num_tokens, const int num_experts,
-    const int topk, const bool renormalize, double routed_scaling_factor,
-    const float* correction_bias, const bool use_hash, const IndType* input_ids,
-    const IndType* tid2eid, cudaStream_t stream) {
-  static constexpr int WARPS_PER_TB = 4;
-  static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
-  // for bfloat16 dtype, we need 4 bytes loading to make sure num_experts
-  // elements can be loaded by a warp
-  static constexpr int BYTES_PER_LDG_MULTIPLE_64 =
-      (std::is_same_v<InputType, __nv_bfloat16> ||
-       std::is_same_v<InputType, __half>)
-          ? 4
-          : 8;
-  // Narrower LDG (ELTS_PER_LDG=1) used by 192/320/448/576 on ROCm WARP_SIZE=64
-  // where ELTS_PER_LDG=2 fails the EXPERTS%(ELTS_PER_LDG*WARP_SIZE)==0 check.
-  // On CUDA WARP_SIZE=32 the wider LDG already aligns, so the alias collapses
-  // back to BYTES_PER_LDG_MULTIPLE_64 — no behavioral change for CUDA.
-#ifdef USE_ROCM
-  static constexpr int BYTES_PER_LDG_MULTIPLE_64_NARROW =
-      (std::is_same_v<InputType, __nv_bfloat16> ||
-       std::is_same_v<InputType, __half>)
-          ? 2
-          : 4;
-#else
-  static constexpr int BYTES_PER_LDG_MULTIPLE_64_NARROW =
-      BYTES_PER_LDG_MULTIPLE_64;
-#endif
-  switch (num_experts) {
-    case 1:
-      LAUNCH_SOFTPLUS_SQRT(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 2:
-      LAUNCH_SOFTPLUS_SQRT(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 4:
-      LAUNCH_SOFTPLUS_SQRT(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 8:
-      LAUNCH_SOFTPLUS_SQRT(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 16:
-      LAUNCH_SOFTPLUS_SQRT(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 32:
-      LAUNCH_SOFTPLUS_SQRT(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 64:
-      LAUNCH_SOFTPLUS_SQRT(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 128:
-      LAUNCH_SOFTPLUS_SQRT(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 256:
-      LAUNCH_SOFTPLUS_SQRT(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-    case 512:
-      LAUNCH_SOFTPLUS_SQRT(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
-      break;
-      // Multiples of 64 that are not powers of 2. The kernel requires
-      // EXPERTS % (ELTS_PER_LDG * WARP_SIZE) == 0. With ELTS_PER_LDG=2
-      // (BYTES_PER_LDG_MULTIPLE_64), this holds for all five values on CUDA
-      // WARP_SIZE=32 but only for 384 on ROCm WARP_SIZE=64. The other four
-      // use BYTES_PER_LDG_MULTIPLE_64_NARROW (ELTS_PER_LDG=1), which
-      // satisfies the assertion for any multiple of 64 on either backend;
-      // on CUDA the narrow alias collapses back to the wider load, so CUDA
-      // behavior is unchanged.
-    case 192:
-      LAUNCH_SOFTPLUS_SQRT(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
-      break;
-    case 320:
-      LAUNCH_SOFTPLUS_SQRT(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
-      break;
-    case 384:
-      LAUNCH_SOFTPLUS_SQRT(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
-      break;
-    case 448:
-      LAUNCH_SOFTPLUS_SQRT(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
-      break;
-    case 576:
-      LAUNCH_SOFTPLUS_SQRT(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64_NARROW);
-      break;
-    default: {
-      TORCH_CHECK(false, "Unsupported expert number: ", num_experts);
-    }
-  }
-}
-
-}  // namespace moe
-}  // namespace vllm
-
-template <typename ComputeType>
-void dispatch_topk_softplus_sqrt_launch(
-    const ComputeType* gating_output, torch::Tensor& topk_weights,
-    torch::Tensor& topk_indices, torch::Tensor& token_expert_indices,
-    int num_tokens, int num_experts, int topk, bool renormalize,
-    double routed_scaling_factor,
-    const c10::optional<torch::Tensor>& correction_bias,
-    const c10::optional<torch::Tensor>& input_ids,
-    const c10::optional<torch::Tensor>& tid2eid, cudaStream_t stream) {
-  const float* bias_ptr = nullptr;
-  if (correction_bias.has_value()) {
-    bias_ptr = correction_bias.value().data_ptr<float>();
-  }
-  bool use_hash = false;
-  if (tid2eid.has_value()) {
-    TORCH_CHECK(input_ids.has_value(), "input_ids is required for hash MoE");
-    use_hash = true;
-  }
-  if (topk_indices.scalar_type() == at::ScalarType::Int) {
-    const int* input_ids_ptr = nullptr;
-    const int* tid2eid_ptr = nullptr;
-    if (tid2eid.has_value()) {
-      input_ids_ptr = input_ids.value().data_ptr<int>();
-      tid2eid_ptr = tid2eid.value().data_ptr<int>();
-    }
-
-    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<int, ComputeType>(
-        gating_output, topk_weights.data_ptr<float>(),
-        topk_indices.data_ptr<int>(), token_expert_indices.data_ptr<int>(),
-        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
-        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
-  } else if (topk_indices.scalar_type() == at::ScalarType::UInt32) {
-    const uint32_t* input_ids_ptr = nullptr;
-    const uint32_t* tid2eid_ptr = nullptr;
-    if (tid2eid.has_value()) {
-      input_ids_ptr = input_ids.value().data_ptr<uint32_t>();
-      tid2eid_ptr = tid2eid.value().data_ptr<uint32_t>();
-    }
-    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<uint32_t, ComputeType>(
-        gating_output, topk_weights.data_ptr<float>(),
-        topk_indices.data_ptr<uint32_t>(), token_expert_indices.data_ptr<int>(),
-        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
-        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
-  } else {
-    TORCH_CHECK(topk_indices.scalar_type() == at::ScalarType::Long);
-
-    const int64_t* input_ids_ptr = nullptr;
-    const int64_t* tid2eid_ptr = nullptr;
-    if (tid2eid.has_value()) {
-      input_ids_ptr = input_ids.value().data_ptr<int64_t>();
-      tid2eid_ptr = tid2eid.value().data_ptr<int64_t>();
-    }
-
-    vllm::moe::topkGatingSoftplusSqrtKernelLauncher<int64_t, ComputeType>(
-        gating_output, topk_weights.data_ptr<float>(),
-        topk_indices.data_ptr<int64_t>(), token_expert_indices.data_ptr<int>(),
-        num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
-        bias_ptr, use_hash, input_ids_ptr, tid2eid_ptr, stream);
-  }
-}
-
-void topk_softplus_sqrt(
-    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, double routed_scaling_factor,
-    const c10::optional<torch::Tensor>& correction_bias,
-    const c10::optional<torch::Tensor>& input_ids,
-    const c10::optional<torch::Tensor>& tid2eid) {
-  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 at::cuda::OptionalCUDAGuard device_guard(device_of(gating_output));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  if (gating_output.scalar_type() == at::ScalarType::Float) {
-    dispatch_topk_softplus_sqrt_launch<float>(
-        gating_output.data_ptr<float>(), topk_weights, topk_indices,
-        token_expert_indices, num_tokens, num_experts, topk, renormalize,
-        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream);
-  } else if (gating_output.scalar_type() == at::ScalarType::Half) {
-    dispatch_topk_softplus_sqrt_launch<__half>(
-        reinterpret_cast<const __half*>(gating_output.data_ptr<at::Half>()),
-        topk_weights, topk_indices, token_expert_indices, num_tokens,
-        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
-  } else if (gating_output.scalar_type() == at::ScalarType::BFloat16) {
-    dispatch_topk_softplus_sqrt_launch<__nv_bfloat16>(
-        reinterpret_cast<const __nv_bfloat16*>(
-            gating_output.data_ptr<at::BFloat16>()),
-        topk_weights, topk_indices, token_expert_indices, num_tokens,
-        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
-  } else {
-    TORCH_CHECK(false, "Unsupported gating_output data type: ",
-                gating_output.scalar_type());
-  }
-}

csrc/moe/torch_bindings.cpp

@@ -16,13 +16,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "bias) -> ()");
   m.impl("topk_sigmoid", torch::kCUDA, &topk_sigmoid);
 
-  m.def(
-      "topk_softplus_sqrt(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
-      "token_expert_indices, Tensor gating_output, bool renormalize, float "
-      "routed_scaling_factor, Tensor? "
-      "bias, Tensor? input_ids, Tensor? tid2eid) -> ()");
-  m.impl("topk_softplus_sqrt", torch::kCUDA, &topk_softplus_sqrt);
-
   // Calculate the result of moe by summing up the partial results
   // from all selected experts.
   m.def("moe_sum(Tensor input, Tensor! output) -> ()");
@@ -132,6 +125,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "Tensor)");
   m.impl("grouped_topk", torch::kCUDA, &grouped_topk);
 
+  // cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
+  m.def("router_gemm_bf16_fp32(Tensor input, Tensor weight) -> Tensor");
+  m.impl("router_gemm_bf16_fp32", torch::kCUDA, &router_gemm_bf16_fp32);
+
   // DeepSeek V3 optimized router GEMM for SM90+
   m.def("dsv3_router_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
   // conditionally compiled so impl registration is in source file

csrc/ops.h

@@ -100,11 +100,6 @@ void fused_qk_norm_rope(torch::Tensor& qkv, int64_t num_heads_q,
                         bool is_neox, torch::Tensor& position_ids,
                         int64_t forced_token_heads_per_warp);
 
-void fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert(
-    torch::Tensor& q, torch::Tensor const& kv, torch::Tensor& k_cache,
-    torch::Tensor const& slot_mapping, torch::Tensor const& position_ids,
-    torch::Tensor const& cos_sin_cache, double eps, int64_t cache_block_size);
-
 void apply_repetition_penalties_(torch::Tensor& logits,
                                  const torch::Tensor& prompt_mask,
                                  const torch::Tensor& output_mask,
@@ -158,13 +153,10 @@ void silu_and_mul_per_block_quant(torch::Tensor& out,
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       std::optional<torch::Tensor> key, int64_t head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox,
-                      int64_t rope_dim_offset, bool inverse);
+                      torch::Tensor& cos_sin_cache, bool is_neox);
 
 void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
-void silu_and_mul_clamp(torch::Tensor& out, torch::Tensor& input, double limit);
-
 void silu_and_mul_quant(torch::Tensor& out, torch::Tensor& input,
                         torch::Tensor& scale);
 

csrc/persistent_topk.cuh

@@ -18,6 +18,7 @@ namespace persistent {
 // Constants
 // ============================================================================
 
+constexpr int TopK = 2048;
 constexpr int kThreadsPerBlock = 1024;
 constexpr int RADIX = 256;
 
@@ -127,12 +128,11 @@ struct RadixRowState {
 
 struct PersistentTopKParams {
   const float* __restrict__ input;  // [num_rows, stride]
-  int32_t* __restrict__ output;     // [num_rows, top_k]
+  int32_t* __restrict__ output;     // [num_rows, TopK]
   int32_t* __restrict__ lengths;    // [num_rows]
   RadixRowState* row_states;        // large path: per-group state
   uint32_t num_rows;
   uint32_t stride;
-  uint32_t top_k;           // actual k value for output stride
   uint32_t chunk_size;      // large path: elements per CTA
   uint32_t ctas_per_group;  // 1=medium, >1=large
   uint32_t max_seq_len;     // max seq_len across all rows (for early CTA exit)
@@ -154,7 +154,6 @@ __device__ __forceinline__ uint32_t decode_bin(float x) {
   return key >> 5;
 }
 
-template <int TopK>
 __device__ __noinline__ void histogram_2048_topk(
     const float* __restrict__ logits, int32_t* __restrict__ output_indices,
     int32_t seq_len) {
@@ -419,7 +418,6 @@ __device__ __noinline__ void histogram_2048_topk(
 // by: DarkSharpness
 // which at the same time is an optimized topk kernel copied from tilelang
 // kernel
-template <int TopK>
 __device__ __noinline__ void histogram_256_topk(
     const float* __restrict__ logits, int* __restrict__ output_indices,
     int logits_offset, int seq_len) {
@@ -651,7 +649,7 @@ __device__ __forceinline__ void wait_ge(int* ptr, int target_val,
 // Adapted from https://github.com/flashinfer-ai/flashinfer/pull/2215
 // ============================================================================
 
-template <int TopK, uint32_t VEC_SIZE>
+template <uint32_t VEC_SIZE>
 __device__ void radix_topk(const float* __restrict__ row_input,
                            int32_t* __restrict__ row_output, uint32_t seq_len,
                            uint32_t my_chunk_start, uint32_t chunk_size,
@@ -859,7 +857,7 @@ __device__ void radix_topk(const float* __restrict__ row_input,
 // see filtered_topk.cuh)
 // ============================================================================
 
-template <int TopK = 2048, uint32_t VEC_SIZE = 1>
+template <uint32_t VEC_SIZE = 1>
 __global__ void __launch_bounds__(kThreadsPerBlock, 2)
     persistent_topk_kernel(PersistentTopKParams params) {
   const uint32_t tx = threadIdx.x;
@@ -887,14 +885,27 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
   uint32_t* shared_ordered =
       reinterpret_cast<uint32_t*>(smem_raw + kFixedSmemLarge);
 
-  // RadixRowState for multi-CTA cooperative radix.
-  // Zero-initialization is done host-side via cudaMemsetAsync in topk.cu
-  // before launch — that gives a stream-ordered happens-before edge for all
-  // CTAs, which the previous in-kernel init (CTA-0 only + intra-CTA
-  // __syncthreads) did not provide and which manifested as a race against
-  // CTA-1+'s first red_release on arrival_counter.
+  // RadixRowState for multi-CTA cooperative radix
   RadixRowState* state = &params.row_states[group_id];
 
+  // -- Initialize RadixRowState (only needed if large rows exist) --
+  if (params.max_seq_len > RADIX_THRESHOLD) {
+    if (cta_in_group == 0) {
+      for (uint32_t buf = 0; buf < 3; buf++) {
+        for (uint32_t i = tx; i < RADIX; i += kThreadsPerBlock) {
+          state->histogram[buf][i] = 0;
+        }
+      }
+      if (tx == 0) {
+        state->remaining_k = 0;
+        state->prefix = 0;
+        state->arrival_counter = 0;
+        state->output_counter = 0;
+      }
+    }
+    __syncthreads();
+  }
+
   int barrier_phase = 0;
   const uint32_t total_iters = (params.num_rows + num_groups - 1) / num_groups;
 
@@ -904,7 +915,7 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
     if (row_idx >= params.num_rows) break;
 
     const uint32_t seq_len = params.lengths[row_idx];
-    int32_t* row_output = params.output + row_idx * params.top_k;
+    int32_t* row_output = params.output + row_idx * TopK;
     const float* row_input = params.input + row_idx * params.stride;
 
     if (seq_len <= RADIX_THRESHOLD) {
@@ -916,19 +927,19 @@ __global__ void __launch_bounds__(kThreadsPerBlock, 2)
             row_output[i] = (i < seq_len) ? static_cast<int32_t>(i) : -1;
           }
         } else if (seq_len <= static_cast<uint32_t>(HIST2048_THRESHOLD)) {
-          histogram_2048_topk<TopK>(row_input, row_output, seq_len);
+          histogram_2048_topk(row_input, row_output, seq_len);
         } else {
-          histogram_256_topk<TopK>(row_input, row_output, 0, seq_len);
+          histogram_256_topk(row_input, row_output, 0, seq_len);
         }
       }
       continue;
     }
 
     const uint32_t my_chunk_start = cta_in_group * chunk_size;
-    radix_topk<TopK, VEC_SIZE>(
-        row_input, row_output, seq_len, my_chunk_start, chunk_size,
-        local_histogram, suffix_sum, shared_scalars, shared_ordered, state,
-        cta_in_group, ctas_per_group, barrier_phase, iter, tx);
+    radix_topk<VEC_SIZE>(row_input, row_output, seq_len, my_chunk_start,
+                         chunk_size, local_histogram, suffix_sum,
+                         shared_scalars, shared_ordered, state, cta_in_group,
+                         ctas_per_group, barrier_phase, iter, tx);
   }
 }
 
@@ -1000,6 +1011,7 @@ struct FilteredTopKTraits<float> {
   }
 };
 
+constexpr uint32_t FILTERED_TOPK_MAX_K = 2048;
 constexpr uint32_t FILTERED_TOPK_BLOCK_THREADS = 1024;
 constexpr uint32_t FILTERED_TOPK_SMEM_INPUT_SIZE =
     16 * 1024;  // 16K indices per buffer
@@ -1013,7 +1025,7 @@ constexpr size_t FILTERED_TOPK_SMEM_DYNAMIC =
  * \tparam IdType Index type (int32_t)
  * \tparam VEC_SIZE Vector size for input loads (1, 2, 4, or 8)
  */
-template <typename DType, typename IdType, int VEC_SIZE, uint32_t MAX_K = 2048>
+template <typename DType, typename IdType, int VEC_SIZE>
 __global__ void __launch_bounds__(FILTERED_TOPK_BLOCK_THREADS)
     FilteredTopKUnifiedKernel(const DType* __restrict__ input,
                               IdType* __restrict__ output,
@@ -1047,7 +1059,7 @@ __global__ void __launch_bounds__(FILTERED_TOPK_BLOCK_THREADS)
   alignas(128) __shared__ int s_counter;
   alignas(128) __shared__ int s_threshold_bin_id;
   alignas(128) __shared__ int s_num_input[2];
-  alignas(128) __shared__ int s_indices[MAX_K];
+  alignas(128) __shared__ int s_indices[FILTERED_TOPK_MAX_K];
 
   auto& s_histogram = s_histogram_buf[0];
 
@@ -1268,7 +1280,7 @@ constexpr int ComputeFilteredTopKVecSize(uint32_t max_len) {
   return static_cast<int>(g);
 }
 
-template <typename DType, typename IdType, uint32_t MAX_K = 2048>
+template <typename DType, typename IdType>
 cudaError_t FilteredTopKRaggedTransform(DType* input, IdType* output_indices,
                                         IdType* lengths, uint32_t num_rows,
                                         uint32_t top_k_val, uint32_t max_len,
@@ -1285,7 +1297,7 @@ cudaError_t FilteredTopKRaggedTransform(DType* input, IdType* output_indices,
 
 #define DISPATCH_VEC_SIZE(VS)                                               \
   if (vec_size == VS) {                                                     \
-    auto kernel = FilteredTopKUnifiedKernel<DType, IdType, VS, MAX_K>;      \
+    auto kernel = FilteredTopKUnifiedKernel<DType, IdType, VS>;             \
     FLASHINFER_CUDA_CALL(cudaFuncSetAttribute(                              \
         kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));   \
     FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, grid, block, args, \

csrc/pos_encoding_kernels.cu

@@ -7,34 +7,33 @@
 
 namespace vllm {
 
-template <typename scalar_t, typename cache_t, bool IS_NEOX>
+template <typename scalar_t, bool IS_NEOX>
 inline __device__ void apply_token_rotary_embedding(
-    scalar_t* __restrict__ arr, const cache_t* __restrict__ cos_ptr,
-    const cache_t* __restrict__ sin_ptr, int rot_offset, int embed_dim,
-    const bool inverse) {
+    scalar_t* __restrict__ arr, const scalar_t* __restrict__ cos_ptr,
+    const scalar_t* __restrict__ sin_ptr, int rot_offset, int embed_dim) {
   int x_index, y_index;
-  float cos_f, sin_f;
+  scalar_t cos, sin;
   if (IS_NEOX) {
+    // GPT-NeoX style rotary embedding.
     x_index = rot_offset;
     y_index = embed_dim + rot_offset;
-    cos_f = static_cast<float>(VLLM_LDG(cos_ptr + x_index));
-    sin_f = static_cast<float>(VLLM_LDG(sin_ptr + x_index));
+    cos = VLLM_LDG(cos_ptr + x_index);
+    sin = VLLM_LDG(sin_ptr + x_index);
   } else {
+    // GPT-J style rotary embedding.
     x_index = 2 * rot_offset;
     y_index = 2 * rot_offset + 1;
-    cos_f = static_cast<float>(VLLM_LDG(cos_ptr + x_index / 2));
-    sin_f = static_cast<float>(VLLM_LDG(sin_ptr + x_index / 2));
+    cos = VLLM_LDG(cos_ptr + x_index / 2);
+    sin = VLLM_LDG(sin_ptr + x_index / 2);
   }
-  if (inverse) {
-    sin_f = -sin_f;
-  }
-  const float x_f = static_cast<float>(arr[x_index]);
-  const float y_f = static_cast<float>(arr[y_index]);
-  arr[x_index] = static_cast<scalar_t>(x_f * cos_f - y_f * sin_f);
-  arr[y_index] = static_cast<scalar_t>(y_f * cos_f + x_f * sin_f);
+
+  const scalar_t x = arr[x_index];
+  const scalar_t y = arr[y_index];
+  arr[x_index] = x * cos - y * sin;
+  arr[y_index] = y * cos + x * sin;
 }
 
-template <typename scalar_t, typename cache_t, bool IS_NEOX>
+template <typename scalar_t, bool IS_NEOX>
 inline __device__ void apply_rotary_embedding(
     scalar_t* __restrict__ query,  // [batch_size, seq_len, num_heads,
                                    // head_size] or [num_tokens, num_heads,
@@ -43,23 +42,22 @@ inline __device__ void apply_rotary_embedding(
                                    // [batch_size, seq_len, num_kv_heads,
                                    // head_size] or [num_tokens, num_kv_heads,
                                    // head_size]
-    const cache_t* cache_ptr, const int head_size, const int num_heads,
+    const scalar_t* cache_ptr, const int head_size, const int num_heads,
     const int num_kv_heads, const int rot_dim, const int token_idx,
     const int64_t query_stride, const int64_t key_stride,
-    const int64_t head_stride, const int64_t rope_dim_offset,
-    const bool inverse) {
+    const int64_t head_stride) {
   const int embed_dim = rot_dim / 2;
-  const cache_t* cos_ptr = cache_ptr;
-  const cache_t* sin_ptr = cache_ptr + embed_dim;
+  const scalar_t* cos_ptr = cache_ptr;
+  const scalar_t* sin_ptr = cache_ptr + embed_dim;
 
   const int nq = num_heads * embed_dim;
   for (int i = threadIdx.x; i < nq; i += blockDim.x) {
     const int head_idx = i / embed_dim;
     const int64_t token_head =
-        token_idx * query_stride + head_idx * head_stride + rope_dim_offset;
+        token_idx * query_stride + head_idx * head_stride;
     const int rot_offset = i % embed_dim;
-    apply_token_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
-        query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim, inverse);
+    apply_token_rotary_embedding<scalar_t, IS_NEOX>(
+        query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
   }
 
   if (key != nullptr) {
@@ -67,15 +65,15 @@ inline __device__ void apply_rotary_embedding(
     for (int i = threadIdx.x; i < nk; i += blockDim.x) {
       const int head_idx = i / embed_dim;
       const int64_t token_head =
-          token_idx * key_stride + head_idx * head_stride + rope_dim_offset;
+          token_idx * key_stride + head_idx * head_stride;
       const int rot_offset = i % embed_dim;
-      apply_token_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
-          key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim, inverse);
+      apply_token_rotary_embedding<scalar_t, IS_NEOX>(
+          key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
     }
   }
 }
 
-template <typename scalar_t, typename cache_t, bool IS_NEOX>
+template <typename scalar_t, bool IS_NEOX>
 __global__ void rotary_embedding_kernel(
     const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
                                             // [num_tokens]
@@ -86,18 +84,19 @@ __global__ void rotary_embedding_kernel(
                                  // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
-    const cache_t* __restrict__ cos_sin_cache,  // [max_position, rot_dim]
+    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
+                                                 // 2]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
     const int64_t head_stride, const int num_heads, const int num_kv_heads,
-    const int head_size, const int64_t rope_dim_offset, const bool inverse) {
+    const int head_size) {
+  // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
-  const cache_t* cache_ptr = cos_sin_cache + pos * rot_dim;
+  const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
 
-  apply_rotary_embedding<scalar_t, cache_t, IS_NEOX>(
+  apply_rotary_embedding<scalar_t, IS_NEOX>(
       query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride, head_stride, rope_dim_offset,
-      inverse);
+      token_idx, query_stride, key_stride, head_stride);
 }
 
 }  // namespace vllm
@@ -116,7 +115,7 @@ void rotary_embedding(
     // [num_tokens, num_heads, head_size]
     int64_t head_size,
     torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
-    bool is_neox, int64_t rope_dim_offset, bool inverse) {
+    bool is_neox) {
   // num_tokens = batch_size * seq_len
   int64_t num_tokens = positions.numel();
   int positions_ndim = positions.dim();
@@ -155,8 +154,6 @@ void rotary_embedding(
   int seq_dim_idx = positions_ndim - 1;
   int64_t query_stride = query.stride(seq_dim_idx);
   int64_t key_stride = key.has_value() ? key->stride(seq_dim_idx) : 0;
-
-  TORCH_CHECK((rot_dim + rope_dim_offset) <= head_size);
   // Determine head stride: for [*, heads, head_size] use stride of last dim;
   // for flat [*, heads*head_size], heads blocks are contiguous of size
   // head_size
@@ -169,27 +166,19 @@ void rotary_embedding(
   const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
-    using query_t = scalar_t;
-    VLLM_DISPATCH_FLOATING_TYPES(
-        cos_sin_cache.scalar_type(), "rotary_embedding_cache", [&] {
-          using cache_t = scalar_t;
-          if (is_neox) {
-            vllm::rotary_embedding_kernel<query_t, cache_t, true>
-                <<<grid, block, 0, stream>>>(
-                    positions.data_ptr<int64_t>(), query.data_ptr<query_t>(),
-                    key.has_value() ? key->data_ptr<query_t>() : nullptr,
-                    cos_sin_cache.data_ptr<cache_t>(), rot_dim, query_stride,
-                    key_stride, head_stride, num_heads, num_kv_heads, head_size,
-                    rope_dim_offset, inverse);
-          } else {
-            vllm::rotary_embedding_kernel<query_t, cache_t, false>
-                <<<grid, block, 0, stream>>>(
-                    positions.data_ptr<int64_t>(), query.data_ptr<query_t>(),
-                    key.has_value() ? key->data_ptr<query_t>() : nullptr,
-                    cos_sin_cache.data_ptr<cache_t>(), rot_dim, query_stride,
-                    key_stride, head_stride, num_heads, num_kv_heads, head_size,
-                    rope_dim_offset, inverse);
-          }
-        });
+    if (is_neox) {
+      vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
+          key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+          cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride, key_stride,
+          head_stride, num_heads, num_kv_heads, head_size);
+    } else {
+      vllm::rotary_embedding_kernel<scalar_t, false>
+          <<<grid, block, 0, stream>>>(
+              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
+              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
+              key_stride, head_stride, num_heads, num_kv_heads, head_size);
+    }
   });
 }

csrc/sampler.cu

@@ -258,13 +258,7 @@ __device__ bool processHistogramStep(
   auto processBins = [&](float logit, int idx) {
     if (isPartialMatch<patternShift>(logit, logitPattern)) {
       uint32_t binIdx = extractBinIdx<step>(logit);
-      // Only write elements with binIdx < thresholdBinIdx when:
-      // 1. This is step 0 and the threshold bin is small enough (no step 1)
-      // 2. This is step >= 1 (where pattern matching filters correctly)
-      // This prevents duplicates when step 0 and step 1 both run.
-      bool shouldWriteDirectly =
-          (step == 0 && smemFinalBinSize[0] <= kNumFinalItems) || (step >= 1);
-      if (binIdx < thresholdBinIdx && shouldWriteDirectly) {
+      if (binIdx < thresholdBinIdx) {
         // The element is part of the top-k selection
         int dstIdx = atomicAdd(&smemFoundTopKValues[0], 1);
 

csrc/topk.cu

@@ -10,17 +10,33 @@
   #include "persistent_topk.cuh"
 #endif
 
-namespace {
-
+void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
+                     torch::Tensor& output, torch::Tensor& workspace, int64_t k,
+                     int64_t max_seq_len) {
 #ifndef USE_ROCM
-template <int TopK>
-void launch_persistent_topk(const torch::Tensor& logits,
-                            const torch::Tensor& lengths, torch::Tensor& output,
-                            torch::Tensor& workspace, int64_t max_seq_len) {
-  namespace P = vllm::persistent;
+  TORCH_CHECK(logits.is_cuda(), "logits must be CUDA tensor");
+  TORCH_CHECK(lengths.is_cuda(), "lengths must be CUDA tensor");
+  TORCH_CHECK(output.is_cuda(), "output must be CUDA tensor");
+  TORCH_CHECK(logits.dtype() == torch::kFloat32, "Only float32 supported");
+  TORCH_CHECK(lengths.dtype() == torch::kInt32, "lengths must be int32");
+  TORCH_CHECK(output.dtype() == torch::kInt32, "output must be int32");
+  TORCH_CHECK(logits.dim() == 2, "logits must be 2D");
+  TORCH_CHECK(lengths.dim() == 1 || lengths.dim() == 2,
+              "lengths must be 1D or 2D");
+  TORCH_CHECK(lengths.is_contiguous(), "lengths must be contiguous");
+  TORCH_CHECK(output.dim() == 2, "output must be 2D");
 
   const int64_t num_rows = logits.size(0);
   const int64_t stride = logits.size(1);
+
+  TORCH_CHECK(lengths.numel() == num_rows, "lengths size mismatch");
+  TORCH_CHECK(output.size(0) == num_rows && output.size(1) == k,
+              "output size mismatch");
+  namespace P = vllm::persistent;
+
+  TORCH_CHECK(k == P::TopK, "k must be 2048");
+  TORCH_CHECK(k <= stride, "k out of range");
+
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   static int num_sms = 0;
@@ -34,17 +50,18 @@ void launch_persistent_topk(const torch::Tensor& logits,
   }
 
   if (num_rows > 32 && max_smem_per_block >= 128 * 1024) {
-    cudaError_t status =
-        vllm::FilteredTopKRaggedTransform<float, int32_t, TopK>(
-            logits.data_ptr<float>(), output.data_ptr<int32_t>(),
-            lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
-            static_cast<uint32_t>(TopK), static_cast<uint32_t>(stride), stream);
+    cudaError_t status = vllm::FilteredTopKRaggedTransform<float, int32_t>(
+        logits.data_ptr<float>(), output.data_ptr<int32_t>(),
+        lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
+        static_cast<uint32_t>(k), static_cast<uint32_t>(stride), stream);
     TORCH_CHECK(status == cudaSuccess,
                 "FilteredTopK failed: ", cudaGetErrorString(status));
   } else {
     TORCH_CHECK(workspace.is_cuda(), "workspace must be CUDA tensor");
     TORCH_CHECK(workspace.dtype() == torch::kUInt8, "workspace must be uint8");
 
+    // Smem cap: smaller smem → more CTAs/group → more per-row parallelism for
+    // large path. Empirically tuned.
     int effective_max_smem;
     if (num_rows <= 4) {
       effective_max_smem =
@@ -82,116 +99,37 @@ void launch_persistent_topk(const torch::Tensor& logits,
     size_t smem_size = P::kFixedSmemLarge + chunk_size * sizeof(uint32_t);
     if (smem_size < P::kSmemMedium) smem_size = P::kSmemMedium;
 
-    // Query occupancy for the instantiation that will actually launch;
-    // overestimating it deadlocks the cooperative barrier.
     int occupancy = 1;
-    cudaError_t occ_err = cudaSuccess;
-    if (vec_size == 4) {
-      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-          &occupancy, P::persistent_topk_kernel<TopK, 4>, P::kThreadsPerBlock,
-          smem_size);
-    } else if (vec_size == 2) {
-      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-          &occupancy, P::persistent_topk_kernel<TopK, 2>, P::kThreadsPerBlock,
-          smem_size);
-    } else {
-      occ_err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-          &occupancy, P::persistent_topk_kernel<TopK, 1>, P::kThreadsPerBlock,
-          smem_size);
-    }
-    TORCH_CHECK(occ_err == cudaSuccess,
-                "persistent_topk occupancy query failed: ",
-                cudaGetErrorString(occ_err));
+    cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+        &occupancy, P::persistent_topk_kernel<4>, P::kThreadsPerBlock,
+        smem_size);
     if (occupancy < 1) occupancy = 1;
 
-    // The cooperative spin-wait barrier only runs when at least one row hits
-    // the radix path (seq_len > RADIX_THRESHOLD). Below that, non-CTA-0 CTAs
-    // early-exit, so oversubscription can't deadlock and headroom is wasted.
-    const bool needs_cooperative =
-        static_cast<uint32_t>(max_seq_len) > P::RADIX_THRESHOLD;
-
-    const uint32_t hw_resident_cap =
-        static_cast<uint32_t>(num_sms) * static_cast<uint32_t>(occupancy);
-    uint32_t max_resident_ctas = hw_resident_cap;
-    if (needs_cooperative) {
-      // Reserve one CTA per SM when occupancy allows; fall back to a single
-      // CTA when occupancy == 1 (the most deadlock-prone case — any straggler
-      // kernel that takes the only slot on one SM hangs the barrier). Never
-      // drop below one full group's worth.
-      uint32_t headroom = (occupancy > 1) ? static_cast<uint32_t>(num_sms) : 1u;
-      if (max_resident_ctas >= headroom + ctas_per_group) {
-        max_resident_ctas -= headroom;
-      }
-    }
+    uint32_t max_resident_ctas = static_cast<uint32_t>(num_sms) * occupancy;
     uint32_t num_groups = std::min(max_resident_ctas / ctas_per_group,
                                    static_cast<uint32_t>(num_rows));
     if (num_groups == 0) num_groups = 1;
     uint32_t total_ctas = num_groups * ctas_per_group;
 
-    // If the cooperative launch wouldn't fit, fall back to FilteredTopK
-    // instead of deadlocking. Only relevant when needs_cooperative.
-    if (needs_cooperative && total_ctas > hw_resident_cap) {
-      TORCH_CHECK(max_smem_per_block >= 128 * 1024,
-                  "persistent_topk would oversubscribe and the FilteredTopK "
-                  "fallback requires >=128KB smem per block (have ",
-                  max_smem_per_block, "). total_ctas=", total_ctas,
-                  " > num_sms*occupancy=", hw_resident_cap, " (TopK=", TopK,
-                  ", vec_size=", vec_size, ", ctas_per_group=", ctas_per_group,
-                  ", smem=", smem_size, ").");
-      cudaError_t status =
-          vllm::FilteredTopKRaggedTransform<float, int32_t, TopK>(
-              logits.data_ptr<float>(), output.data_ptr<int32_t>(),
-              lengths.data_ptr<int32_t>(), static_cast<uint32_t>(num_rows),
-              static_cast<uint32_t>(TopK), static_cast<uint32_t>(stride),
-              stream);
-      TORCH_CHECK(status == cudaSuccess,
-                  "FilteredTopK fallback failed: ", cudaGetErrorString(status));
-      return;
-    }
-
     size_t state_bytes = num_groups * sizeof(P::RadixRowState);
     TORCH_CHECK(workspace.size(0) >= static_cast<int64_t>(state_bytes),
                 "workspace too small, need ", state_bytes, " bytes");
 
-    // Zero the per-group RadixRowState region before launch — only when the
-    // radix path will actually run (max_seq_len > RADIX_THRESHOLD). The
-    // RadixRowState fields (arrival_counter, histograms) are only touched by
-    // radix_topk; the decode/medium paths inside the persistent kernel
-    // operate purely in shared memory and never read these globals, so a
-    // stale workspace is harmless for them.
-    //
-    // Why we need the memset (when needs_cooperative is true):
-    //   1. arrival_counter accumulates within a launch and is never reset,
-    //      so a prior call leaves it at a large positive value. Without this
-    //      reset, the very first wait_ge in the next call sees counter >>
-    //      target and returns instantly, breaking the barrier.
-    //   2. The previous in-kernel init only ran in CTA-0 with intra-CTA
-    //      __syncthreads(), so it had no happens-before edge to CTA-1+'s
-    //      first red_release. cudaMemsetAsync is stream-ordered: the zero
-    //      is globally visible before any CTA runs.
-    if (needs_cooperative) {
-      cudaError_t mz_err = cudaMemsetAsync(workspace.data_ptr<uint8_t>(), 0,
-                                           state_bytes, stream);
-      TORCH_CHECK(mz_err == cudaSuccess,
-                  "row_states memset failed: ", cudaGetErrorString(mz_err));
-    }
-
     P::PersistentTopKParams params;
     params.input = logits.data_ptr<float>();
     params.output = output.data_ptr<int32_t>();
     params.lengths = lengths.data_ptr<int32_t>();
     params.num_rows = static_cast<uint32_t>(num_rows);
     params.stride = static_cast<uint32_t>(stride);
-    params.top_k = static_cast<uint32_t>(TopK);
     params.chunk_size = chunk_size;
     params.row_states =
         reinterpret_cast<P::RadixRowState*>(workspace.data_ptr<uint8_t>());
     params.ctas_per_group = ctas_per_group;
     params.max_seq_len = static_cast<uint32_t>(max_seq_len);
 
-  #define LAUNCH_PERSISTENT(TOPK_VAL, VS)                                     \
+  #define LAUNCH_PERSISTENT(VS)                                               \
     do {                                                                      \
-      auto kernel = &P::persistent_topk_kernel<TOPK_VAL, VS>;                 \
+      auto kernel = &P::persistent_topk_kernel<VS>;                           \
       cudaError_t err = cudaFuncSetAttribute(                                 \
           kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);    \
       TORCH_CHECK(err == cudaSuccess,                                         \
@@ -200,11 +138,11 @@ void launch_persistent_topk(const torch::Tensor& logits,
     } while (0)
 
     if (vec_size == 4) {
-      LAUNCH_PERSISTENT(TopK, 4);
+      LAUNCH_PERSISTENT(4);
     } else if (vec_size == 2) {
-      LAUNCH_PERSISTENT(TopK, 2);
+      LAUNCH_PERSISTENT(2);
     } else {
-      LAUNCH_PERSISTENT(TopK, 1);
+      LAUNCH_PERSISTENT(1);
     }
   #undef LAUNCH_PERSISTENT
   }
@@ -212,46 +150,6 @@ void launch_persistent_topk(const torch::Tensor& logits,
   cudaError_t err = cudaGetLastError();
   TORCH_CHECK(err == cudaSuccess,
               "persistent_topk failed: ", cudaGetErrorString(err));
-}
-#endif
-
-}  // anonymous namespace
-
-void persistent_topk(const torch::Tensor& logits, const torch::Tensor& lengths,
-                     torch::Tensor& output, torch::Tensor& workspace, int64_t k,
-                     int64_t max_seq_len) {
-#ifndef USE_ROCM
-  TORCH_CHECK(logits.is_cuda(), "logits must be CUDA tensor");
-  TORCH_CHECK(lengths.is_cuda(), "lengths must be CUDA tensor");
-  TORCH_CHECK(output.is_cuda(), "output must be CUDA tensor");
-  TORCH_CHECK(logits.dtype() == torch::kFloat32, "Only float32 supported");
-  TORCH_CHECK(lengths.dtype() == torch::kInt32, "lengths must be int32");
-  TORCH_CHECK(output.dtype() == torch::kInt32, "output must be int32");
-  TORCH_CHECK(logits.dim() == 2, "logits must be 2D");
-  TORCH_CHECK(lengths.dim() == 1 || lengths.dim() == 2,
-              "lengths must be 1D or 2D");
-  TORCH_CHECK(lengths.is_contiguous(), "lengths must be contiguous");
-  TORCH_CHECK(output.dim() == 2, "output must be 2D");
-
-  const int64_t num_rows = logits.size(0);
-  const int64_t stride = logits.size(1);
-
-  TORCH_CHECK(lengths.numel() == num_rows, "lengths size mismatch");
-  TORCH_CHECK(output.size(0) == num_rows && output.size(1) == k,
-              "output size mismatch");
-  TORCH_CHECK(k == 512 || k == 1024 || k == 2048,
-              "persistent_topk supports k=512, k=1024, or k=2048, got k=", k);
-
-  if (k == 512) {
-    launch_persistent_topk<512>(logits, lengths, output, workspace,
-                                max_seq_len);
-  } else if (k == 1024) {
-    launch_persistent_topk<1024>(logits, lengths, output, workspace,
-                                 max_seq_len);
-  } else {
-    launch_persistent_topk<2048>(logits, lengths, output, workspace,
-                                 max_seq_len);
-  }
 #else
   TORCH_CHECK(false, "persistent_topk is not supported on ROCm");
 #endif

csrc/torch_bindings.cpp

@@ -106,12 +106,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("silu_and_mul(Tensor! result, Tensor input) -> ()");
   ops.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
 
-  // SwiGLU activation with input clamping.
-  ops.def(
-      "silu_and_mul_with_clamp(Tensor! result, Tensor input, float limit) "
-      "-> ()");
-  ops.impl("silu_and_mul_with_clamp", torch::kCUDA, &silu_and_mul_clamp);
-
   ops.def(
       "silu_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> ()");
   ops.impl("silu_and_mul_quant", torch::kCUDA, &silu_and_mul_quant);
@@ -183,17 +177,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int forced_token_heads_per_warp=-1) -> ()");
   ops.impl("fused_qk_norm_rope", torch::kCUDA, &fused_qk_norm_rope);
 
-  // Horizontally-fused DeepseekV4-MLA: per-head RMSNorm + GPT-J RoPE for Q, and
-  // GPT-J RoPE + UE8M0 FP8 quant + paged cache insert for KV, all in one
-  // kernel launch.
-  ops.def(
-      "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert("
-      "Tensor! q, Tensor kv, Tensor! k_cache, "
-      "Tensor slot_mapping, Tensor position_ids, Tensor cos_sin_cache, "
-      "float eps, int cache_block_size) -> ()");
-  ops.impl("fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert", torch::kCUDA,
-           &fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert);
-
   // Apply repetition penalties to logits in-place
   ops.def(
       "apply_repetition_penalties_(Tensor! logits, Tensor prompt_mask, "
@@ -257,8 +240,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "rotary_embedding(Tensor positions, Tensor! query,"
       "                 Tensor!? key, int head_size,"
-      "                 Tensor cos_sin_cache, bool is_neox, int "
-      "rope_dim_offset=0, bool inverse=False) -> ()");
+      "                 Tensor cos_sin_cache, bool is_neox) -> ()");
   ops.impl("rotary_embedding", torch::kCUDA, &rotary_embedding);
 
   // Quantization ops

docker/Dockerfile

@@ -41,13 +41,6 @@ ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04
 # Using cuda base image with minimal dependencies necessary for JIT compilation (FlashInfer, DeepGEMM, EP kernels)
 ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-base-ubuntu${UBUNTU_VERSION}
 
-# OS family of BUILD_BASE_IMAGE. Controls package manager (apt vs dnf) and
-# Python bootstrap. Set to "manylinux" alongside a manylinux build base such
-# as pytorch/manylinux2_28-builder:cuda13.0 to produce wheels with a glibc
-# 2.28 floor (matches PyTorch's own published wheels). Default stays on
-# Ubuntu for backwards compatibility.
-ARG BUILD_OS=ubuntu
-
 # By parameterizing the Deadsnakes repository URL, we allow third-party to use
 # their own mirror. When doing so, we don't benefit from the transparent
 # installation of the GPG key of the PPA, as done by add-apt-repository, so we
@@ -101,64 +94,35 @@ FROM ${BUILD_BASE_IMAGE} AS base
 
 ARG CUDA_VERSION
 ARG PYTHON_VERSION
-ARG BUILD_OS
 
 ENV DEBIAN_FRONTEND=noninteractive
 
-# Install system dependencies including build tools.
-# The Ubuntu path uses apt + deadsnakes-via-uv for Python; the manylinux path
-# (AlmaLinux 8, e.g. pytorch/manylinux2_28-builder) uses dnf and the Python
-# interpreters pre-installed at /opt/python/cpXY-cpXY/.
-RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
-        # rdma-core-devel provides libibverbs headers; ccache lives in EPEL,
-        # which the pytorch manylinux image already enables. git/curl/sudo
-        # are typically pre-installed but listed defensively.
-        dnf install -y --setopt=install_weak_deps=False \
-            ccache \
-            git \
-            curl \
-            sudo \
-            rdma-core-devel \
-        && dnf clean all \
-        && rm -rf /var/cache/dnf; \
-    else \
-        apt-get update -y \
-        && apt-get install -y --no-install-recommends \
-            ccache \
-            software-properties-common \
-            git \
-            curl \
-            sudo \
-            python3-pip \
-            libibverbs-dev \
-            # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
-            # as it was causing spam when compiling the CUTLASS kernels
-            gcc-10 \
-            g++-10 \
-        && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
-        # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
-        # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
-        # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
-        && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
-        && rm -rf /var/lib/apt/lists/*; \
-    fi
-
-# Install uv and bootstrap /opt/venv. Both paths converge on /opt/venv so all
-# downstream stages stay distro-agnostic.
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
-    && if [ "${BUILD_OS}" = "manylinux" ]; then \
-           # manylinux images ship Python at /opt/python/cpXY-cpXY/; point uv
-           # at the matching interpreter rather than letting it fetch one.
-           PYV_NODOT=$(echo ${PYTHON_VERSION} | tr -d '.') \
-           && MANYLINUX_PY=/opt/python/cp${PYV_NODOT}-cp${PYV_NODOT}/bin/python${PYTHON_VERSION} \
-           && $HOME/.local/bin/uv venv /opt/venv --python "$MANYLINUX_PY"; \
-       else \
-           $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION}; \
-       fi \
+# Install system dependencies including build tools
+RUN apt-get update -y \
+    && apt-get install -y --no-install-recommends \
+        ccache \
+        software-properties-common \
+        git \
+        curl \
+        sudo \
+        python3-pip \
+        libibverbs-dev \
+        # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
+        # as it was causing spam when compiling the CUTLASS kernels
+        gcc-10 \
+        g++-10 \
+    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
+    # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
+    # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
+    # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
+    && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION} \
     && rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
-    && ln -sf /opt/venv/bin/python3 /usr/bin/python3 \
-    && ln -sf /opt/venv/bin/python3-config /usr/bin/python3-config \
-    && ln -sf /opt/venv/bin/pip /usr/bin/pip \
+    && ln -s /opt/venv/bin/python3 /usr/bin/python3 \
+    && ln -s /opt/venv/bin/python3-config /usr/bin/python3-config \
+    && ln -s /opt/venv/bin/pip /usr/bin/pip \
     && python3 --version && python3 -m pip --version
 
 # Activate virtual environment and add uv to PATH
@@ -469,7 +433,6 @@ FROM base AS dev
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ARG PYTORCH_CUDA_INDEX_BASE_URL
-ARG BUILD_OS
 
 # 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
@@ -479,11 +442,7 @@ ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE=copy
 
 # Install libnuma-dev, required by fastsafetensors (fixes #20384)
-RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
-        dnf install -y numactl-devel && dnf clean all && rm -rf /var/cache/dnf; \
-    else \
-        apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*; \
-    fi
+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
@@ -579,11 +538,9 @@ RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
         cuda-nvrtc-${CUDA_VERSION_DASH} \
         cuda-cuobjdump-${CUDA_VERSION_DASH} \
         libcurand-dev-${CUDA_VERSION_DASH} \
-        libcublas-dev-${CUDA_VERSION_DASH} \
+        libcublas-${CUDA_VERSION_DASH} \
         # Required by fastsafetensors (fixes #20384)
-        libnuma-dev \
-        # numactl CLI for NUMA binding at runtime
-        numactl && \
+        libnuma-dev && \
     # Fixes nccl_allocator requiring nccl.h at runtime
     # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
     # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,
@@ -626,7 +583,9 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 ARG FLASHINFER_VERSION=0.6.8.post1
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
-        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
+    && flashinfer show-config \
+    && flashinfer download-cubin
 
 # ============================================================
 # OPENAI API SERVER DEPENDENCIES
@@ -708,13 +667,6 @@ RUN --mount=type=bind,from=build,src=/tmp/ep_kernels_workspace/dist,target=/vllm
     uv pip install --system ep_kernels/dist/*.whl --verbose \
         --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
 
-# Download FlashInfer precompiled cubins AFTER all pip installs are done.
-# This must run after the vLLM wheel and EP kernels installs above, because
-# those can reinstall/touch flashinfer packages. Downloading cubins earlier
-# (in the flashinfer-jit-cache layer) causes ~2.5 GB of layer duplication
-# when a later pip install overwrites flashinfer package files.
-RUN flashinfer show-config && flashinfer download-cubin
-
 # CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will
 # return to /usr/local/nvidia in 13.0 to allow container providers to mount drivers
 # consistently from the host (see https://github.com/vllm-project/vllm/issues/18859).
@@ -804,10 +756,6 @@ FROM vllm-base AS vllm-openai-base
 ARG TARGETPLATFORM
 ARG INSTALL_KV_CONNECTORS=false
 ARG CUDA_VERSION
-ARG VLLM_BUILD_COMMIT
-ARG VLLM_BUILD_PIPELINE
-ARG VLLM_BUILD_URL
-ARG VLLM_IMAGE_TAG
 
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
@@ -844,18 +792,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image
-ENV VLLM_BUILD_COMMIT=${VLLM_BUILD_COMMIT:-unknown} \
-    VLLM_BUILD_PIPELINE=${VLLM_BUILD_PIPELINE:-local} \
-    VLLM_BUILD_URL=${VLLM_BUILD_URL:-} \
-    VLLM_IMAGE_TAG=${VLLM_IMAGE_TAG:-local/vllm-openai:dev}
-LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm" \
-      org.opencontainers.image.revision="${VLLM_BUILD_COMMIT}" \
-      org.opencontainers.image.version="${VLLM_IMAGE_TAG}" \
-      org.opencontainers.image.url="${VLLM_BUILD_URL}" \
-      ai.vllm.build.commit="${VLLM_BUILD_COMMIT}" \
-      ai.vllm.build.pipeline="${VLLM_BUILD_PIPELINE}" \
-      ai.vllm.build.url="${VLLM_BUILD_URL}" \
-      ai.vllm.image.tag="${VLLM_IMAGE_TAG}"
 
 # define sagemaker first, so it is not default from `docker build`
 FROM vllm-openai-base AS vllm-sagemaker

docker/Dockerfile.cpu

@@ -192,7 +192,6 @@ ADD ./tests/ ./tests/
 ADD ./examples/ ./examples/
 ADD ./benchmarks/ ./benchmarks/
 ADD ./vllm/collect_env.py .
-ADD ./docker/ ./docker/
 ADD ./.buildkite/ ./.buildkite/
 
 # install development dependencies (for testing)

docker/Dockerfile.rocm

@@ -124,9 +124,9 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1
 
 # RIXL/UCX build stages
 FROM base AS build_rixl
-ARG RIXL_BRANCH="39be1de8"
+ARG RIXL_BRANCH="bf4a7214"
 ARG RIXL_REPO="https://github.com/ROCm/RIXL.git"
-ARG UCX_BRANCH="bfb51733"
+ARG UCX_BRANCH="7009d7a1"
 ARG UCX_REPO="https://github.com/openucx/ucx.git"
 ENV ROCM_PATH=/opt/rocm
 ENV UCX_HOME=/usr/local/ucx
@@ -192,7 +192,6 @@ RUN cd /opt/rixl && \
     sed -i "s/--exclude 'libamdhip64\*'/--exclude 'libamdhip64*' --exclude 'libcore*' --exclude 'libpull*'/" \
         contrib/build-wheel.sh && \
     mkdir -p /app/install && \
-    _ucx_install_dir=${UCX_HOME} \
     ./contrib/build-wheel.sh \
         --output-dir /app/install \
         --rocm-dir ${ROCM_PATH} \
@@ -201,9 +200,9 @@ RUN cd /opt/rixl && \
 
 # DeepEP build stage
 FROM base AS build_deep
-ARG ROCSHMEM_BRANCH="f0acb0c6"
+ARG ROCSHMEM_BRANCH="ba0bf0f3"
 ARG ROCSHMEM_REPO="https://github.com/ROCm/rocm-systems.git"
-ARG DEEPEP_BRANCH="a9ea9774"
+ARG DEEPEP_BRANCH="5d90af8b"
 ARG DEEPEP_REPO="https://github.com/ROCm/DeepEP.git"
 ARG DEEPEP_NIC="cx7"
 ARG DEEPEP_ROCM_ARCH="gfx942;gfx950"
@@ -214,15 +213,18 @@ RUN git clone ${ROCSHMEM_REPO} \
  && git checkout ${ROCSHMEM_BRANCH} \
  && mkdir -p projects/rocshmem/build \
  && cd projects/rocshmem/build \
- && INSTALL_PREFIX=${ROCSHMEM_DIR} \
-    ../scripts/build_configs/all_backends -DUSE_EXTERNAL_MPI=OFF
+ && bash ../scripts/build_configs/all_backends \
+      -DCMAKE_INSTALL_PREFIX="${ROCSHMEM_DIR}" \
+      -DROCM_PATH=/opt/rocm \
+      -DGPU_TARGETS="${DEEPEP_ROCM_ARCH}" \
+      -DUSE_EXTERNAL_MPI=OFF
 
 # Build DeepEP wheel.
 # DeepEP looks for rocshmem at ROCSHMEM_DIR.
 RUN git clone ${DEEPEP_REPO} \
  && cd DeepEP \
  && git checkout ${DEEPEP_BRANCH} \
- && python3 setup.py --variant rocm --rocm-explicit-ctx --nic ${DEEPEP_NIC} bdist_wheel --dist-dir=/app/deep_install
+ && python3 setup.py --variant rocm --nic ${DEEPEP_NIC} bdist_wheel --dist-dir=/app/deep_install
 
 # MoRI runtime dependencies live in Dockerfile.rocm so NIC backend changes do
 # not force users to rebuild the long-lived Dockerfile.rocm_base image.
@@ -386,16 +388,6 @@ RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
 # above are not available once that RUN step completes.
 COPY --from=export_vllm /*.whl /opt/vllm-wheels/
 
-# Update rdma-core to support latest rocshmem
-ARG DEEPEP_NIC
-RUN if [ "${DEEPEP_NIC}" = "cx7" ] || [ "${DEEPEP_NIC}" = "io" ]; then \
-    git clone --branch v62.0 --depth 1 https://github.com/linux-rdma/rdma-core.git /tmp/rdma-core && \
-    cd /tmp/rdma-core && \
-    mkdir -p build && cd build && \
-    cmake -GNinja -DCMAKE_INSTALL_PREFIX=/usr -DNO_MAN_PAGES=1 .. && \
-    ninja &&     ninja install &&     ldconfig &&     rm -rf /tmp/rdma-core; \
-fi
-
 # Install RIXL wheel
 RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
     uv pip install --system /rixl_install/*.whl

docker/Dockerfile.rocm_base

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.2-complete
+ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:7.2.1-complete
 ARG TRITON_BRANCH="ba5c1517"
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
 ARG PYTORCH_BRANCH="8514f051" # release/2.10 as of 3/17
@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.12.post2"
+ARG AITER_BRANCH="v0.1.10.post3"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.1.0"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
@@ -104,28 +104,6 @@ ENV SCCACHE_REGION=${USE_SCCACHE:+${SCCACHE_REGION_NAME}}
 ENV SCCACHE_S3_NO_CREDENTIALS=${USE_SCCACHE:+${SCCACHE_S3_NO_CREDENTIALS}}
 ENV SCCACHE_IDLE_TIMEOUT=${USE_SCCACHE:+0}
 
-# torch profiler hotfix for 7.2.2: rebuild CLR with https://github.com/ROCm/rocm-systems/pull/5062
-# will be removed once we move to ROCm 7.2.3
-RUN apt-get update && apt-get install -y rocm-llvm-dev
-RUN pip install CppHeaderParser
-RUN git clone --no-checkout --filter=blob:none https://github.com/ROCm/rocm-systems /tmp/rocm-systems \
-    && cd /tmp/rocm-systems \
-    && git sparse-checkout init --cone \
-    && git sparse-checkout set projects/hip projects/clr \
-    && git checkout 35e8c7bf8911862e5389509800e65fdf125412b3 \
-    && export CLR_DIR=/tmp/rocm-systems/projects/clr \
-    && export HIP_DIR=/tmp/rocm-systems/projects/hip \
-    && mkdir -p $CLR_DIR/build && cd $CLR_DIR/build \
-    && cmake \
-        -DHIP_COMMON_DIR=$HIP_DIR \
-        -DCMAKE_PREFIX_PATH="/opt/rocm/" \
-        -DCLR_BUILD_HIP=ON \
-        -DCLR_BUILD_OCL=OFF \
-        -DHIP_PLATFORM=amd \
-        .. \
-    && make -j$(nproc) \
-    && make install \
-    && rm -rf /tmp/rocm-systems
 
 ###
 ### Triton Build
@@ -175,6 +153,8 @@ RUN git clone ${PYTORCH_REPO} pytorch
 RUN cd pytorch && git checkout ${PYTORCH_BRANCH}
 RUN cd pytorch \
     && pip install -r requirements.txt && git submodule update --init --recursive
+RUN cd pytorch/third_party/kineto \
+    && git remote add rocm https://github.com/ROCm/kineto && git fetch rocm && git checkout 2d73be3 
 RUN cd pytorch && python3 tools/amd_build/build_amd.py \
     && if [ "$USE_SCCACHE" = "1" ]; then \
            export HIP_CLANG_PATH=/opt/sccache-wrappers \

docker/Dockerfile.xpu

@@ -5,6 +5,9 @@ WORKDIR /workspace/
 ARG PYTHON_VERSION=3.12
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/xpu"
 
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
+
 RUN apt clean && apt-get update -y && \
     apt-get install -y --no-install-recommends --fix-missing \
     curl \
@@ -23,20 +26,8 @@ RUN apt clean && apt-get update -y && \
     python3.12-dev \
     python3-pip
 
-# Add oneAPI repo, pin oneAPI to 2025.3, then install pinned packages in one layer.
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
-    printf '%s\n' \
-    'Package: intel-oneapi-* intel-deep-learning-essentials* intel-pti*' \
-    'Pin: version 2025.3*' \
-    'Pin-Priority: 1001' \
-    > /etc/apt/preferences.d/oneapi-2025.3.pref && \
-    apt-get update -y && \
-    apt-get install -y --no-install-recommends \
-    intel-oneapi-compiler-dpcpp-cpp-2025.3 \
-    intel-oneapi-mkl-devel-2025.3 \
-    intel-oneapi-dnnl-devel-2025.3 && \
-    rm -rf /var/lib/apt/lists/*
+RUN apt update && apt upgrade -y && \
+    apt install -y intel-oneapi-compiler-dpcpp-cpp-2025.3
 
 # Install UMD
 RUN mkdir neo && \

docker/docker-bake.hcl

@@ -27,22 +27,6 @@ variable "COMMIT" {
   default = ""
 }
 
-variable "VLLM_BUILD_COMMIT" {
-  default = "unknown"
-}
-
-variable "VLLM_BUILD_PIPELINE" {
-  default = "local"
-}
-
-variable "VLLM_BUILD_URL" {
-  default = ""
-}
-
-variable "VLLM_IMAGE_TAG" {
-  default = "local/vllm-openai:dev"
-}
-
 # Groups
 
 group "default" {
@@ -62,10 +46,6 @@ target "_common" {
     max_jobs             = MAX_JOBS
     nvcc_threads         = NVCC_THREADS
     torch_cuda_arch_list = TORCH_CUDA_ARCH_LIST
-    VLLM_BUILD_COMMIT    = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
-    VLLM_BUILD_PIPELINE  = VLLM_BUILD_PIPELINE
-    VLLM_BUILD_URL       = VLLM_BUILD_URL
-    VLLM_IMAGE_TAG       = VLLM_IMAGE_TAG
   }
 }
 
@@ -76,16 +56,10 @@ target "_labels" {
     "org.opencontainers.image.title"       = "vLLM"
     "org.opencontainers.image.description" = "vLLM: A high-throughput and memory-efficient inference and serving engine for LLMs"
     "org.opencontainers.image.licenses"    = "Apache-2.0"
-    "org.opencontainers.image.revision"    = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
-    "org.opencontainers.image.version"     = VLLM_IMAGE_TAG
-    "org.opencontainers.image.url"         = VLLM_BUILD_URL
-    "ai.vllm.build.commit"                 = VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")
-    "ai.vllm.build.pipeline"               = VLLM_BUILD_PIPELINE
-    "ai.vllm.build.url"                    = VLLM_BUILD_URL
-    "ai.vllm.image.tag"                    = VLLM_IMAGE_TAG
+    "org.opencontainers.image.revision"    = COMMIT
   }
   annotations = [
-    "index,manifest:org.opencontainers.image.revision=${VLLM_BUILD_COMMIT != "unknown" ? VLLM_BUILD_COMMIT : (COMMIT != "" ? COMMIT : "unknown")}",
+      "index,manifest:org.opencontainers.image.revision=${COMMIT}",
   ]
 }