[torch.compile] Don't do the fast moe cold start optimization if there is speculative decoding (#33624)

Signed-off-by: Richard Zou <zou3519@gmail.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
(cherry picked from commit 5eac9a1b34)

.buildkite/release-pipeline.yaml

@@ -274,14 +274,14 @@ steps:
           - input-release-version
           - build-wheels
 
-      - label: "Upload release wheels to PyPI and GitHub"
+      - label: "Upload release wheels to PyPI"
         depends_on:
           - block-upload-release-wheels
         id: upload-release-wheels
         agents:
           queue: small_cpu_queue_postmerge
         commands:
-          - "bash .buildkite/scripts/upload-release-wheels.sh"
+          - "bash .buildkite/scripts/upload-release-wheels-pypi.sh"
 
   # =============================================================================
   # ROCm Release Pipeline (x86_64 only)
@@ -638,9 +638,93 @@ steps:
     depends_on:
       - step: upload-rocm-wheels
         allow_failure: true
+      - step: input-release-version
+        allow_failure: true
     agents:
       queue: cpu_queue_postmerge
     commands:
       - "bash .buildkite/scripts/annotate-rocm-release.sh"
     env:
       S3_BUCKET: "vllm-wheels"
+
+  # ROCm Job 5: Generate Root Index for ROCm Wheels (for release only)
+  # This is the job to create https://wheels.vllm.ai/rocm/ index allowing
+  # users to install with `uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/`
+  - block: "Generate Root Index for ROCm Wheels for Release"
+    key: block-generate-root-index-rocm-wheels
+    depends_on: upload-rocm-wheels
+
+  - label: ":package: Generate Root Index for ROCm Wheels for Release"
+    depends_on: block-generate-root-index-rocm-wheels
+    id: generate-root-index-rocm-wheels
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
+    env:
+      S3_BUCKET: "vllm-wheels"
+      VARIANT: "rocm700"
+
+  # ROCm Job 5: Build ROCm Release Docker Image
+  - label: ":rocm: :docker: Build ROCm Release Docker Image"
+    id: build-rocm-release-image
+    depends_on:
+      - step: build-rocm-base-wheels
+        allow_failure: false
+    agents:
+      queue: cpu_queue_postmerge
+    timeout_in_minutes: 60
+    commands:
+      - |
+        set -euo pipefail
+
+        # Login to ECR
+        aws ecr-public get-login-password --region us-east-1 | \
+          docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+
+        # Download Docker image from S3 (set by build-rocm-base-wheels)
+        DOCKER_IMAGE_S3_PATH="$$(buildkite-agent meta-data get rocm-docker-image-s3-path 2>/dev/null || echo '')"
+        if [ -z "$${DOCKER_IMAGE_S3_PATH}" ]; then
+          echo "ERROR: rocm-docker-image-s3-path metadata not found"
+          exit 1
+        fi
+
+        echo "Downloading base image from $${DOCKER_IMAGE_S3_PATH}"
+        mkdir -p artifacts/rocm-docker-image
+        aws s3 cp "$${DOCKER_IMAGE_S3_PATH}" artifacts/rocm-docker-image/rocm-base-image.tar.gz
+
+        # Load base Docker image
+        echo "Loading base Docker image..."
+        LOAD_OUTPUT=$$(gunzip -c artifacts/rocm-docker-image/rocm-base-image.tar.gz | docker load)
+        BASE_IMAGE_TAG=$$(echo "$${LOAD_OUTPUT}" | grep "Loaded image:" | sed 's/Loaded image: //')
+        echo "Loaded base image: $${BASE_IMAGE_TAG}"
+
+        # Tag and push the base image to ECR
+        docker tag "$${BASE_IMAGE_TAG}" public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm-base
+        docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm-base
+        echo "Pushed base image: public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm-base"
+
+        # Get GPU architectures from meta-data
+        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
+        PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
+
+        # Build vLLM ROCm release image using cached base
+        DOCKER_BUILDKIT=1 docker build \
+          --build-arg max_jobs=16 \
+          --build-arg BASE_IMAGE="$${BASE_IMAGE_TAG}" \
+          --build-arg ARG_PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
+          --build-arg USE_SCCACHE=1 \
+          --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
+          --build-arg SCCACHE_REGION_NAME=us-west-2 \
+          --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
+          --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm \
+          --target vllm-openai \
+          --progress plain \
+          -f docker/Dockerfile.rocm .
+
+        # Push to ECR
+        docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm
+        echo "Pushed: public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm"
+    env:
+      DOCKER_BUILDKIT: "1"
+      S3_BUCKET: "vllm-wheels"

.buildkite/scripts/annotate-release.sh

@@ -11,51 +11,80 @@ fi
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
 
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+(Optional) For CUDA 13.0:
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+
+(Optional) For CPU:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-To download the wheel (by version):
-\`\`\`
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
-
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu129/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu130/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux1_x86_64.whl .
-\`\`\`
 
 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:${BUILDKITE_COMMIT}-x86_64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
 
+Tag and push images:
+
 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:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+docker push vllm/vllm-openai:latest-x86_64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+
 docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${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:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai:rocm
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:latest-rocm
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-docker push vllm/vllm-openai:latest-rocm
+docker push vllm/vllm-openai:latest-aarch64-cu130
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-rocm
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}-rocm
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-rocm
+
+Create multi-arch manifest:
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base vllm/vllm-openai-rocm:${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
 
 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-cu130
+docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker manifest push vllm/vllm-openai:latest-cu130
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
 \`\`\`
 EOF 

.buildkite/scripts/annotate-rocm-release.sh

@@ -3,25 +3,32 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 #
 # Generate Buildkite annotation for ROCm wheel release
-
 set -ex
 
 # Get build configuration from meta-data
 # Extract ROCm version dynamically from Dockerfile.rocm_base
-# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.1-complete -> extracts "7.1"
+# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.0-complete -> extracts "7.0"
 ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
 PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
 PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
 
+# TODO: Enable the nightly build for ROCm
+# Get release version, default to 1.0.0.dev for nightly/per-commit builds
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null || echo "")
+if [ -z "${RELEASE_VERSION}" ]; then
+  RELEASE_VERSION="1.0.0.dev"
+fi
+
 # S3 URLs
 S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
 S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
-S3_URL="https://${S3_BUCKET}.s3.${S3_REGION}.amazonaws.com"
+S3_URL="http://${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com"
-ROCM_PATH="rocm/${BUILDKITE_COMMIT}"
 
+# Format ROCm version for path (e.g., "7.1" -> "rocm710")
+ROCM_VERSION_PATH="rocm$(echo ${ROCM_VERSION} | tr -d '.')"
+ROCM_PATH="rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}"
 buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
-## :rocm: ROCm Wheel Release
+## ROCm Wheel and Docker Image Releases
-
 ### Build Configuration
 | Setting | Value |
 |---------|-------|
@@ -34,41 +41,72 @@ buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' <<
 ### :package: Installation
 
 **Install from this build (by commit):**
-\`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/{rocm_variant}/
 
-# Example:
+\`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/rocm700/
+pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
+
+# Example for ROCm ${ROCM_VERSION}:
+pip install vllm --extra-index-url ${S3_URL}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
 \`\`\`
 
 **Install from nightly (if published):**
+
 \`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/
+pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
 \`\`\`
 
 ### :floppy_disk: Download Wheels Directly
 
 \`\`\`bash
 # List all ROCm wheels
-aws s3 ls s3://${S3_BUCKET}/${ROCM_PATH}/
+aws s3 ls s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/
-
 # Download specific wheels
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/vllm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/vllm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torch-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torch-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/triton_rocm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torchvision-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-kernels-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/amdsmi-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchvision-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchaudio-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amdsmi-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/aiter-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-attn-*.whl .
 \`\`\`
 
 ### :gear: Included Packages
 - **vllm**: vLLM with ROCm support
 - **torch**: PyTorch built for ROCm ${ROCM_VERSION}
-- **triton_rocm**: Triton built for ROCm
+- **triton**: Triton
+- **triton-kernels**: Triton kernels
 - **torchvision**: TorchVision for ROCm PyTorch
+- **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
+- **aiter**: Aiter for ROCm
+- **flash-attn**: Flash Attention for ROCm
 
 ### :warning: Notes
 - These wheels are built for **ROCm ${ROCM_VERSION}** and will NOT work with CUDA GPUs
 - Supported GPU architectures: ${PYTORCH_ROCM_ARCH}
 - Platform: Linux x86_64 only
+
+### :package: Docker Image Release
+
+To download and upload the image:
+
+\`\`\`
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-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${RELEASE_VERSION}-base
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${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}
+\`\`\`
+
 EOF

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

@@ -7,17 +7,19 @@ SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
 RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
-echo "Release version from Buildkite: $RELEASE_VERSION"
 GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
-if [ -z "$GIT_VERSION" ]; then
+
+echo "Release version from Buildkite: $RELEASE_VERSION"
+
+if [[ -z "$GIT_VERSION" ]]; then
     echo "[FATAL] Not on a git tag, cannot create release."
     exit 1
 else
     echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
 fi
 # sanity check for version mismatch
-if [ "$RELEASE_VERSION" != "$GIT_VERSION" ]; then
+if [[ "$RELEASE_VERSION" != "$GIT_VERSION" ]]; then
-  if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
+  if [[ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]]; then
     echo "[WARNING] Force release and ignore version mismatch"
   else
     echo "[FATAL] Release version from Buildkite does not match Git version."
@@ -27,7 +29,7 @@ fi
 PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
 
 # check pypi token
-if [ -z "$PYPI_TOKEN" ]; then
+if [[ -z "$PYPI_TOKEN" ]]; then
   echo "[FATAL] PYPI_TOKEN is not set."
   exit 1
 else
@@ -35,41 +37,8 @@ else
   export TWINE_PASSWORD="$PYPI_TOKEN"
 fi
 
-# check github token
-if [ -z "$GITHUB_TOKEN" ]; then
-  echo "[FATAL] GITHUB_TOKEN is not set."
-  exit 1
-else
-  export GH_TOKEN="$GITHUB_TOKEN"
-fi
-
 set -x # avoid printing secrets above
 
-# download gh CLI from github
-# Get latest gh CLI version from GitHub API
-GH_VERSION=$(curl -s https://api.github.com/repos/cli/cli/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/' | sed 's/^v//')
-if [ -z "$GH_VERSION" ]; then
-  echo "[FATAL] Failed to get latest gh CLI version from GitHub"
-  exit 1
-fi
-echo "Downloading gh CLI version: $GH_VERSION"
-GH_TARBALL="gh_${GH_VERSION}_linux_amd64.tar.gz"
-GH_URL="https://github.com/cli/cli/releases/download/v${GH_VERSION}/${GH_TARBALL}"
-GH_INSTALL_DIR="/tmp/gh-install"
-mkdir -p "$GH_INSTALL_DIR"
-pushd "$GH_INSTALL_DIR"
-curl -L -o "$GH_TARBALL" "$GH_URL"
-tar -xzf "$GH_TARBALL"
-GH_BIN=$(realpath $(find . -name "gh" -type f -executable | head -n 1))
-if [ -z "$GH_BIN" ]; then
-  echo "[FATAL] Failed to find gh CLI executable"
-  exit 1
-fi
-echo "gh CLI downloaded successfully, version: $($GH_BIN --version)"
-echo "Last 5 releases on GitHub:" # as a sanity check of gh and GH_TOKEN
-command "$GH_BIN" release list --limit 5
-popd
-
 # install twine from pypi
 python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
@@ -89,16 +58,13 @@ echo "Wheels copied to local directory"
 git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
 ls -la $DIST_DIR
 
-
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
 PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
-if [ -z "$PYPI_WHEEL_FILES" ]; then
+if [[ -z "$PYPI_WHEEL_FILES" ]]; then
   echo "No default variant wheels found, quitting..."
   exit 1
 fi
-python3 -m twine check $PYPI_WHEEL_FILES
-python3 -m twine --non-interactive --verbose upload $PYPI_WHEEL_FILES
-echo "Wheels uploaded to PyPI"
 
-# create release on GitHub with the release version and all wheels
+python3 -m twine check $PYPI_WHEEL_FILES
-command "$GH_BIN" release create $GIT_VERSION -d --latest --notes-from-tag --verify-tag $DIST_DIR/*.whl
+python3 -m twine upload --non-interactive --verbose $PYPI_WHEEL_FILES
+echo "Wheels uploaded to PyPI"

docker/Dockerfile.rocm

@@ -227,7 +227,7 @@ RUN if [ "$GIT_REPO_CHECK" != "0" ]; then \
 # This ensures setuptools_scm sees clean repo state for version detection
 RUN --mount=type=bind,source=.git,target=vllm/.git \
     cd vllm \
-    && pip install setuptools_scm \
+    && pip install setuptools_scm regex \
     && VLLM_VERSION=$(python3 -c "import setuptools_scm; print(setuptools_scm.get_version())") \
     && echo "Detected vLLM version: ${VLLM_VERSION}" \
     && echo "${VLLM_VERSION}" > /tmp/vllm_version.txt
@@ -342,6 +342,19 @@ RUN mkdir src && mv vllm src/vllm
 FROM base AS final
 
 RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/*
+
+# Clean up sccache from release image (not needed at runtime)
+# This removes the binary and wrappers that may have been installed during build
+RUN rm -f /usr/bin/sccache || true \
+    && rm -rf /opt/sccache-wrappers || true
+
+# Unset sccache environment variables for the release image
+# This prevents S3 bucket config from leaking into production images
+ENV SCCACHE_BUCKET=
+ENV SCCACHE_REGION=
+ENV SCCACHE_S3_NO_CREDENTIALS=
+ENV SCCACHE_IDLE_TIMEOUT=
+
 # Error related to odd state for numpy 1.20.3 where there is no METADATA etc, but an extra LICENSES_bundled.txt.
 # Manually remove it so that later steps of numpy upgrade can continue
 RUN case "$(which python3)" in \

docs/features/nixl_connector_usage.md

@@ -184,15 +184,6 @@ Support use case: Prefill with 'HND' and decode with 'NHD' with experimental con
 --kv-transfer-config '{..., "enable_permute_local_kv":"True"}'
 ```
 
-### Cross layers blocks
-
-By default, this feature is disabled. On attention backends that support this feature, each logical block is contiguous in physical memory. This reduces the number of buffers that need to be transferred.
-To enable this feature:
-
-```bash
---kv-transfer-config '{..., "kv_connector_extra_config": {"enable_cross_layers_blocks": "True"}}'
-```
-
 ## Example Scripts/Code
 
 Refer to these example scripts in the vLLM repository:

docs/models/supported_models.md

@@ -456,6 +456,7 @@ th {
 | `StableLmForCausalLM` | StableLM | `stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc. | | |
 | `Starcoder2ForCausalLM` | Starcoder2 | `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc. | | ✅︎ |
 | `Step1ForCausalLM` | Step-Audio | `stepfun-ai/Step-Audio-EditX`, etc. | ✅︎ | ✅︎ |
+| `Step3p5ForCausalLM` | Step-3.5-flash | `stepfun-ai/step-3.5-flash`, etc. |  | ✅︎ |
 | `TeleChat2ForCausalLM` | TeleChat2 | `Tele-AI/TeleChat2-3B`, `Tele-AI/TeleChat2-7B`, `Tele-AI/TeleChat2-35B`, etc. | ✅︎ | ✅︎ |
 | `TeleFLMForCausalLM` | TeleFLM | `CofeAI/FLM-2-52B-Instruct-2407`, `CofeAI/Tele-FLM`, etc. | ✅︎ | ✅︎ |
 | `XverseForCausalLM` | XVERSE | `xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc. | ✅︎ | ✅︎ |
@@ -686,6 +687,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-8B-Preview` | ✅︎ | ✅︎ |
 | `KeyeVL1_5ForConditionalGeneration` | Keye-VL-1_5-8B | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-1_5-8B` | ✅︎ | ✅︎ |
 | `KimiVLForConditionalGeneration` | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking | T + I<sup>+</sup> | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking` | | ✅︎ |
+| `KimiK25ForConditionalGeneration` | Kimi-K2.5 | T + I<sup>+</sup> | `moonshotai/Kimi-K2.5` | | ✅︎ |
 | `LightOnOCRForConditionalGeneration`  | LightOnOCR-1B  | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
 | `Lfm2VlForConditionalGeneration` | LFM2-VL | T + I<sup>+</sup> | `LiquidAI/LFM2-VL-450M`, `LiquidAI/LFM2-VL-3B`, `LiquidAI/LFM2-VL-8B-A1B`, etc. | ✅︎ | ✅︎ |
 | `Llama4ForConditionalGeneration` | Llama 4 | T + I<sup>+</sup> | `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc. | ✅︎ | ✅︎ |

examples/pooling/score/vision_rerank_api_online.py

@@ -18,48 +18,32 @@ e.g.
 """
 
 import argparse
-import base64
+import pprint
-import json
 
 import requests
 
-
+from vllm.multimodal.utils import encode_image_url, fetch_image
-def encode_base64_content_from_url(content_url: str) -> dict[str, str]:
-    """Encode a content retrieved from a remote url to base64 format."""
-
-    with requests.get(content_url, headers=headers) as response:
-        response.raise_for_status()
-        result = base64.b64encode(response.content).decode("utf-8")
-
-    return {"url": f"data:image/jpeg;base64,{result}"}
-
-
-headers = {"accept": "application/json", "Content-Type": "application/json"}
 
 query = "A woman playing with her dog on a beach at sunset."
-documents = {
+document = (
-    "content": [
+    "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, "
-        {
+    "as the dog offers its paw in a heartwarming display of companionship and trust."
-            "type": "text",
+)
-            "text": (
+image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
-                "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, "
+documents = [
-                "as the dog offers its paw in a heartwarming display of companionship and trust."
+    {
-            ),
+        "type": "text",
-        },
+        "text": document,
-        {
+    },
-            "type": "image_url",
+    {
-            "image_url": {
+        "type": "image_url",
-                "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
+        "image_url": {"url": image_url},
-            },
+    },
-        },
+    {
-        {
+        "type": "image_url",
-            "type": "image_url",
+        "image_url": {"url": encode_image_url(fetch_image(image_url))},
-            "image_url": encode_base64_content_from_url(
+    },
-                "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
+]
-            ),
-        },
-    ]
-}
 
 
 def parse_args():
@@ -74,23 +58,36 @@ def main(args):
     models_url = base_url + "/v1/models"
     rerank_url = base_url + "/rerank"
 
-    response = requests.get(models_url, headers=headers)
+    response = requests.get(models_url)
     model = response.json()["data"][0]["id"]
 
-    data = {
+    print("Query: string & Document: list of string")
+    prompt = {"model": model, "query": query, "documents": [document]}
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: text")
+    prompt = {"model": model, "query": query, "documents": {"content": [documents[0]]}}
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: image url")
+    prompt = {
         "model": model,
         "query": query,
-        "documents": documents,
+        "documents": {"content": [documents[1]]},
     }
-    response = requests.post(rerank_url, headers=headers, json=data)
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
 
-    # Check the response
+    print("Query: string & Document: image base64")
-    if response.status_code == 200:
+    prompt = {
-        print("Request successful!")
+        "model": model,
-        print(json.dumps(response.json(), indent=2))
+        "query": query,
-    else:
+        "documents": {"content": [documents[2]]},
-        print(f"Request failed with status code: {response.status_code}")
+    }
-        print(response.text)
+    response = requests.post(rerank_url, json=prompt)
+    pprint.pprint(response.json())
 
 
 if __name__ == "__main__":

examples/pooling/score/vision_score_api_online.py

@@ -17,48 +17,32 @@ e.g.
 """
 
 import argparse
-import base64
-import json
 import pprint
 
 import requests
 
+from vllm.multimodal.utils import encode_image_url, fetch_image
 
-def encode_base64_content_from_url(content_url: str) -> dict[str, str]:
+query = "A woman playing with her dog on a beach at sunset."
-    """Encode a content retrieved from a remote url to base64 format."""
+document = (
-
+    "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, "
-    with requests.get(content_url, headers=headers) as response:
+    "as the dog offers its paw in a heartwarming display of companionship and trust."
-        response.raise_for_status()
+)
-        result = base64.b64encode(response.content).decode("utf-8")
+image_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
-
+documents = [
-    return {"url": f"data:image/jpeg;base64,{result}"}
+    {
-
+        "type": "text",
-
+        "text": document,
-headers = {"accept": "application/json", "Content-Type": "application/json"}
+    },
-
+    {
-queries = "slm markdown"
+        "type": "image_url",
-documents = {
+        "image_url": {"url": image_url},
-    "content": [
+    },
-        {
+    {
-            "type": "image_url",
+        "type": "image_url",
-            "image_url": {
+        "image_url": {"url": encode_image_url(fetch_image(image_url))},
-                "url": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png"
+    },
-            },
+]
-        },
-        {
-            "type": "image_url",
-            "image_url": {
-                "url": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
-            },
-        },
-        {
-            "type": "image_url",
-            "image_url": encode_base64_content_from_url(
-                "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
-            ),
-        },
-    ]
-}
 
 
 def parse_args():
@@ -73,15 +57,40 @@ def main(args):
     models_url = base_url + "/v1/models"
     score_url = base_url + "/score"
 
-    response = requests.get(models_url, headers=headers)
+    response = requests.get(models_url)
     model = response.json()["data"][0]["id"]
 
-    prompt = {"model": model, "queries": queries, "documents": documents}
+    print("Query: string & Document: string")
-    response = requests.post(score_url, headers=headers, json=prompt)
+    prompt = {"model": model, "queries": query, "documents": document}
-    print("\nPrompt when queries is string and documents is a image list:")
+    response = requests.post(score_url, json=prompt)
-    pprint.pprint(prompt)
+    pprint.pprint(response.json())
-    print("\nScore Response:")
+
-    print(json.dumps(response.json(), indent=2))
+    print("Query: string & Document: text")
+    prompt = {
+        "model": model,
+        "queries": query,
+        "documents": {"content": [documents[0]]},
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: image url")
+    prompt = {
+        "model": model,
+        "queries": query,
+        "documents": {"content": [documents[1]]},
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
+
+    print("Query: string & Document: image base64")
+    prompt = {
+        "model": model,
+        "queries": query,
+        "documents": {"content": [documents[2]]},
+    }
+    response = requests.post(score_url, json=prompt)
+    pprint.pprint(response.json())
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -9,7 +9,7 @@ requires = [
     "torch == 2.9.1",
     "wheel",
     "jinja2",
-    "grpcio-tools>=1.76.0",
+    "grpcio-tools",
 ]
 build-backend = "setuptools.build_meta"
 

requirements/build.txt

@@ -9,5 +9,5 @@ wheel
 jinja2>=3.1.6
 regex
 build
-protobuf>=6.33.2
+protobuf >= 6.33.5
-grpcio-tools>=1.76.0
+grpcio-tools

requirements/common.txt

@@ -9,9 +9,9 @@ blake3
 py-cpuinfo
 transformers >= 4.56.0, < 5
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
-protobuf >= 6.30.0 # Required by LlamaTokenizer, gRPC.
+protobuf >= 6.33.5 # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
-aiohttp
+aiohttp >= 3.13.3
 openai >= 1.99.1  # For Responses API with reasoning content
 pydantic >= 2.12.0
 prometheus_client >= 0.18.0
@@ -51,5 +51,5 @@ openai-harmony >= 0.0.3  # Required for gpt-oss
 anthropic >= 0.71.0
 model-hosting-container-standards >= 0.1.13, < 1.0.0
 mcp
-grpcio>=1.76.0
+grpcio
-grpcio-reflection>=1.76.0
+grpcio-reflection

requirements/kv_connectors.txt

@@ -1,2 +1,2 @@
-lmcache
+lmcache >= 0.3.9
 nixl >= 0.7.1 # Required for disaggregated prefill

requirements/rocm-test.txt

@@ -14,7 +14,7 @@ pytest-shard==0.1.2
 # Async/HTTP dependencies
 anyio==4.6.2.post1
     # via httpx, starlette
-aiohttp==3.13.0
+aiohttp==3.13.3
     # via gpt-oss
 httpx==0.27.2
     # HTTP testing

requirements/test.txt

@@ -12,7 +12,7 @@ affine==2.4.0
     # via rasterio
 aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.13.0
+aiohttp==3.13.3
     # via
     #   aiohttp-cors
     #   datasets

tests/compile/test_cold_start.py

@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from torch._dynamo.utils import counters
+
+from vllm import LLM
+from vllm.config import CompilationConfig, CompilationMode, CUDAGraphMode
+
+
+def test_moe_compilation_cold_start(monkeypatch, use_fresh_inductor_cache):
+    # Run in same process so we can access PyTorch's internal counters
+    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+
+    # I'm not sure if this is going to affect the numbers
+    monkeypatch.setenv("VLLM_USE_AOT_COMPILE", "0")
+
+    # Force cold compilation
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    compilation_config = CompilationConfig(
+        mode=CompilationMode.VLLM_COMPILE,
+        cudagraph_mode=CUDAGraphMode.NONE,  # make the model loading faster
+    )
+
+    counters.clear()
+
+    _ = LLM(
+        model="microsoft/Phi-tiny-MoE-instruct",
+        max_model_len=256,
+        load_format="dummy",  # make the model loading faster
+        compilation_config=compilation_config,
+        num_gpu_blocks_override=8,  # make the model loading faster
+    )
+
+    # vLLM-compile cold start is special. By default, we do
+    # one full dynamo capture of the entire forward pass.
+    # The forward pass consists of 32 transformer layers.
+    # Then, we split on the attention operation. This results in
+    # 33 subgraphs (not including the attention operation).
+    # The 33 subgraphs then get standalone_compile'd.
+    #
+    # There are actually only 3 unique subgraphs for this model
+    # (all of its transformer layers are the same modulo weights);
+    # this is true for most vLLM models.
+    # So we test that during cold start, the aot_autograd cache
+    # misses for 3 subgraphs and hits for the rest.
+    assert counters["aot_autograd"]["autograd_cache_miss"] == 3
+    assert counters["aot_autograd"]["autograd_cache_hit"] == 30

tests/compile/test_graph_partition.py

@@ -8,6 +8,10 @@ import torch
 from torch.fx.experimental.proxy_tensor import make_fx
 
 from vllm.compilation.backends import split_graph
+from vllm.compilation.fx_utils import find_op_nodes
+
+# This import automatically registers `torch.ops.silly.attention`
+from . import silly_attention  # noqa: F401
 
 
 def test_getitem_moved_to_producer_subgraph():
@@ -122,3 +126,61 @@ def test_no_tuple_inputs_with_multiple_consumers():
     output_split = split_gm(new_x)
 
     assert torch.allclose(output_original, output_split), "Output mismatch after split"
+
+
+def test_consecutive_ops_in_split():
+    """
+    Test that consecutive splitting operations are grouped into the same subgraph
+    """
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        """
+        Define a simple model where consecutive operations create opportunities
+        for splitting subgraphs.
+        """
+        # Apply silly attention followed by consecutive operations
+        intermediate = torch.relu(x)
+        attn_inout = torch.sqrt(intermediate)
+        torch.ops.silly.attention(intermediate, intermediate, attn_inout, attn_inout)
+        final_result = torch.sigmoid(attn_inout)
+        return final_result
+
+    torch.set_default_device("cuda")
+
+    # Create the traced FX graph for the model
+    x = torch.randn(8, 4)
+
+    gm = make_fx(model_fn)(x)
+
+    # Assert presence of the expected operations in the setup
+    assert (
+        len(list(find_op_nodes(torch.ops.aten.relu, gm.graph))) == 1
+        and len(list(find_op_nodes(torch.ops.aten.sqrt, gm.graph))) == 1
+    ), "Test setup failed: Expected sqrt and relu operations in the graph."
+
+    # Configure split operations to test
+    splitting_ops = ["silly::attention", "aten::sqrt"]
+    split_gm, split_items = split_graph(gm, splitting_ops)
+
+    # Validate the number of partitions
+    assert len(split_items) == 3, (
+        "Consecutive splitting operations were not grouped correctly."
+    )
+
+    # Validate that correctness is preserved
+    new_x = torch.randn(8, 4)
+    output_original = gm(new_x)
+    output_split = split_gm(new_x)
+    assert torch.allclose(output_original, output_split), (
+        "Output mismatch after splitting."
+    )
+
+    # Check the splitting item has 2 nodes exactly (relu and attn)
+    splitting_items = list(s for s in split_items if s.is_splitting_graph)
+    assert len(splitting_items) == 1, "Expecting a single splitting graph"
+    print(splitting_items[0].graph.graph)
+    splitting_gm = splitting_items[0].graph
+    assert len(splitting_gm.graph.nodes) == 4, "Expecting 4 nodes in splitting graph"
+    assert [node.op for node in splitting_gm.graph.nodes] == ["placeholder"] + 2 * [
+        "call_function"
+    ] + ["output"]

tests/entrypoints/openai/responses/test_harmony.py

@@ -992,7 +992,7 @@ async def test_mcp_tool_multi_turn(client: OpenAI, model_name: str, server):
     # First turn - make a calculation
     response1 = await client.responses.create(
         model=model_name,
-        input="Calculate 123 * 456 using python and print the result.",
+        input="Calculate 1234 * 4567 using python tool and print the result.",
         tools=tools,
         temperature=0.0,
         instructions=(

tests/entrypoints/pooling/classify/test_online_vision.py

@@ -5,9 +5,9 @@ import json
 import pytest
 import requests
 
-from tests.entrypoints.test_utils import encode_base64_content_from_url
 from tests.utils import RemoteOpenAIServer
 from vllm.entrypoints.pooling.classify.protocol import ClassificationResponse
+from vllm.multimodal.utils import encode_image_url, fetch_image
 
 MODEL_NAME = "muziyongshixin/Qwen2.5-VL-7B-for-VideoCls"
 MAXIMUM_VIDEOS = 1
@@ -19,7 +19,7 @@ HF_OVERRIDES = {
 }
 input_text = "This product was excellent and exceeded my expectations"
 image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/cat_snow.jpg"
-image_base64 = encode_base64_content_from_url(image_url)
+image_base64 = {"url": encode_image_url(fetch_image(image_url))}
 video_url = "https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4"
 
 

tests/entrypoints/pooling/score/test_online_score_vision.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import requests
+
+from tests.utils import VLLM_PATH, RemoteOpenAIServer
+from vllm.entrypoints.pooling.score.protocol import ScoreResponse
+from vllm.multimodal.utils import encode_image_url, fetch_image
+
+MODEL_NAME = "Qwen/Qwen3-VL-Reranker-2B"
+HF_OVERRIDES = {
+    "architectures": ["Qwen3VLForSequenceClassification"],
+    "classifier_from_token": ["no", "yes"],
+    "is_original_qwen3_reranker": True,
+}
+
+query = "A cat standing in the snow."
+image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/cat_snow.jpg"
+documents = [
+    {
+        "type": "text",
+        "text": query,
+    },
+    {
+        "type": "image_url",
+        "image_url": {"url": image_url},
+    },
+    {
+        "type": "image_url",
+        "image_url": {"url": encode_image_url(fetch_image(image_url))},
+    },
+]
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--enforce-eager",
+        "--max-model-len",
+        "8192",
+        "--chat-template",
+        str(VLLM_PATH / "examples/pooling/score/template/qwen3_vl_reranker.jinja"),
+    ]
+
+    with RemoteOpenAIServer(
+        MODEL_NAME, args, override_hf_configs=HF_OVERRIDES
+    ) as remote_server:
+        yield remote_server
+
+
+def test_score_api_queries_str_documents_str(server: RemoteOpenAIServer):
+    queries = "What is the capital of France?"
+    documents = "The capital of France is Paris."
+
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": queries,
+            "documents": documents,
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 1
+
+
+def test_score_api_queries_str_documents_text_content(server: RemoteOpenAIServer):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": query,
+            "documents": {"content": [documents[0]]},
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 1
+
+
+def test_score_api_queries_str_documents_image_url_content(server: RemoteOpenAIServer):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": query,
+            "documents": {"content": [documents[1]]},
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 1
+
+
+def test_score_api_queries_str_documents_image_base64_content(
+    server: RemoteOpenAIServer,
+):
+    score_response = requests.post(
+        server.url_for("score"),
+        json={
+            "model": MODEL_NAME,
+            "queries": query,
+            "documents": {"content": [documents[2]]},
+        },
+    )
+    score_response.raise_for_status()
+    score = ScoreResponse.model_validate(score_response.json())
+
+    assert score.id is not None
+    assert score.data is not None
+    assert len(score.data) == 1

tests/entrypoints/test_utils.py

@@ -1,9 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import base64
-
-import requests
-
 from vllm.entrypoints.utils import sanitize_message
 
 
@@ -12,11 +8,3 @@ def test_sanitize_message():
         sanitize_message("<_io.BytesIO object at 0x7a95e299e750>")
         == "<_io.BytesIO object>"
     )
-
-
-def encode_base64_content_from_url(content_url: str) -> dict[str, str]:
-    with requests.get(content_url) as response:
-        response.raise_for_status()
-        result = base64.b64encode(response.content).decode("utf-8")
-
-    return {"url": f"data:image/jpeg;base64,{result}"}

tests/kernels/core/test_activation.py

@@ -17,6 +17,8 @@ from vllm.model_executor.layers.activation import (
     QuickGELU,
     SiluAndMul,
     SwigluOAIAndMul,
+    SwigluStepAndMul,
+    swiglustep_and_mul_triton,
 )
 from vllm.utils.torch_utils import set_random_seed
 
@@ -36,6 +38,7 @@ CUDA_DEVICES = [f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 e
         "gelu_tanh",
         "fatrelu",
         "swigluoai_and_mul",
+        "swiglustep_and_mul",
     ],
 )
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -75,9 +78,12 @@ def test_act_and_mul(
     elif activation == "swigluoai_and_mul":
         layer = SwigluOAIAndMul()
         fn = torch.ops._C.swigluoai_and_mul
+    elif activation == "swiglustep_and_mul":
+        layer = SwigluStepAndMul()
+        fn = swiglustep_and_mul_triton
     out = layer(x)
     ref_out = layer.forward_native(x)
-    if activation == "swigluoai_and_mul":
+    if activation in ["swigluoai_and_mul", "swiglustep_and_mul"]:
         rtol = {
             # For fp16, change the relative tolerance from 1e-3 to 2e-3
             torch.float16: 2e-3,
@@ -104,7 +110,7 @@ def test_act_and_mul(
         opcheck(fn, (out, x, threshold))
     elif activation == "swigluoai_and_mul":
         opcheck(fn, (out, x, layer.alpha, layer.limit))
-    else:
+    elif activation != "swiglustep_and_mul":
         opcheck(fn, (out, x))
 
 

tests/kernels/quantization/test_rocm_skinny_gemms.py

@@ -87,6 +87,13 @@ NKM_FACTORS_WVSPLITK_FP8 = [
 SEEDS = [0]
 
 
+def pad_weights_fp8(weight):
+    num_pad = 256 // weight.element_size()
+    import torch.nn.functional as F
+
+    return F.pad(weight, (0, num_pad), "constant", 0)[..., :-num_pad]
+
+
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITKRC)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
@@ -191,11 +198,12 @@ def test_rocm_wvsplitk_bias2D_kernel(n, k, m, dtype, seed):
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITK_FP8)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("padded", [False, True])
 @pytest.mark.skipif(
     not (current_platform.is_rocm() and current_platform.supports_fp8()),
     reason="only test for rocm fp8",
 )
-def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
+def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed, padded):
     torch.manual_seed(seed)
 
     A = torch.rand(n, k, device="cuda") - 0.5
@@ -203,6 +211,8 @@ def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
 
     A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
     B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+    if padded:
+        B = pad_weights_fp8(B)
 
     ref_out = torch._scaled_mm(
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b
@@ -222,11 +232,12 @@ def test_rocm_wvsplitk_fp8_kernel(n, k, m, dtype, seed):
 @pytest.mark.parametrize("n,k,m", NKM_FACTORS_WVSPLITK_FP8)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("padded", [False, True])
 @pytest.mark.skipif(
     not (current_platform.is_rocm() and current_platform.supports_fp8()),
     reason="only test for rocm fp8",
 )
-def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed):
+def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed, padded):
     torch.manual_seed(seed)
 
     xavier = math.sqrt(2 / k)  # normalize to avoid large output-bias deltas
@@ -236,6 +247,8 @@ def test_rocm_wvsplitk_fp8_bias1D_kernel(n, k, m, dtype, seed):
 
     A, scale_a = ref_dynamic_per_tensor_fp8_quant(A)
     B, scale_b = ref_dynamic_per_tensor_fp8_quant(B)
+    if padded:
+        B = pad_weights_fp8(B)
 
     ref_out = torch._scaled_mm(
         A, B.t(), out_dtype=dtype, scale_a=scale_a, scale_b=scale_b, bias=BIAS

tests/models/registry.py

@@ -480,6 +480,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "Step1ForCausalLM": _HfExamplesInfo(
         "stepfun-ai/Step-Audio-EditX", trust_remote_code=True
     ),
+    "Step3p5ForCausalLM": _HfExamplesInfo(
+        "stepfun-ai/step-3.5-flash", is_available_online=False
+    ),
     "SmolLM3ForCausalLM": _HfExamplesInfo("HuggingFaceTB/SmolLM3-3B"),
     "StableLMEpochForCausalLM": _HfExamplesInfo("stabilityai/stablelm-zephyr-3b"),
     "StableLmForCausalLM": _HfExamplesInfo("stabilityai/stablelm-3b-4e1t"),
@@ -771,6 +774,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
             )
         },
     ),
+    "KimiK25ForConditionalGeneration": _HfExamplesInfo(
+        "moonshotai/Kimi-K2.5",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
     "LightOnOCRForConditionalGeneration": _HfExamplesInfo(
         "lightonai/LightOnOCR-1B-1025"
     ),
@@ -1076,6 +1084,12 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
     "Qwen3NextMTP": _HfExamplesInfo(
         "Qwen/Qwen3-Next-80B-A3B-Instruct", min_transformers_version="4.56.3"
     ),
+    "Step3p5MTP": _HfExamplesInfo(
+        "stepfun-ai/Step-3.5-Flash",
+        trust_remote_code=True,
+        speculative_model="stepfun-ai/Step-3.5-Flash",
+        is_available_online=False,
+    ),
 }
 
 _TRANSFORMERS_BACKEND_MODELS = {

tests/v1/core/test_prefix_caching.py

@@ -107,7 +107,10 @@ def make_kv_cache_config(block_size: int, num_blocks: int) -> KVCacheConfig:
 
 
 def make_kv_cache_config_hybrid_model(
-    block_size: int, num_blocks: int, second_spec_type: str = "sliding_window"
+    block_size: int,
+    num_blocks: int,
+    sliding_window_blocks: int,
+    second_spec_type: str = "sliding_window",
 ) -> KVCacheConfig:
     if second_spec_type == "sliding_window":
         second_spec = SlidingWindowSpec(
@@ -115,7 +118,7 @@ def make_kv_cache_config_hybrid_model(
             num_kv_heads=1,
             head_size=1,
             dtype=torch.float32,
-            sliding_window=2 * block_size,
+            sliding_window=sliding_window_blocks * block_size,
         )
     elif second_spec_type == "mamba":
         second_spec = MambaSpec(
@@ -325,7 +328,7 @@ def test_prefill(hash_fn):
 def test_prefill_hybrid_model():
     block_size = 16
     manager = KVCacheManager(
-        make_kv_cache_config_hybrid_model(block_size, 21),
+        make_kv_cache_config_hybrid_model(block_size, 21, 2),
         max_model_len=8192,
         enable_caching=True,
         hash_block_size=block_size,
@@ -334,7 +337,8 @@ def test_prefill_hybrid_model():
     hash_fn = sha256
 
     # Complete 3 blocks (48 tokens)
-    common_token_ids = [i for i in range(3) for _ in range(block_size)]
+    num_full_blocks = 3
+    common_token_ids = [i for i in range(num_full_blocks) for _ in range(block_size)]
 
     # Fully cache miss
     # Incomplete 1 block (7 tokens)
@@ -375,6 +379,7 @@ def test_prefill_hybrid_model():
     # Cache hit in the common prefix
     # Incomplete 1 block (5 tokens)
     unique_token_ids = [3] * 5
+    all_token_ids = common_token_ids + unique_token_ids
     req1 = make_request("1", common_token_ids + unique_token_ids, block_size, hash_fn)
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
     assert len(req1.block_hashes) == 3
@@ -394,34 +399,13 @@ def test_prefill_hybrid_model():
     manager.free(req0)
     manager.free(req1)
 
-    cached_block_hash_to_block_bak = copy.copy(
-        manager.block_pool.cached_block_hash_to_block._cache
-    )
-
-    def test_partial_request_hit(
-        request_id: str,
-        hash_to_evict: list[BlockHashWithGroupId],
-        expect_hit_length: int,
-    ):
-        req = make_request(
-            request_id, common_token_ids + unique_token_ids, block_size, sha256
-        )
-        for hash_with_group_id in hash_to_evict:
-            manager.block_pool.cached_block_hash_to_block._cache.pop(hash_with_group_id)
-        computed_blocks, num_computed_tokens = manager.get_computed_blocks(req)
-        assert len(req.block_hashes) == 3
-        assert num_computed_tokens == expect_hit_length * block_size
-        for block_per_group in computed_blocks.blocks:
-            assert len(block_per_group) == num_computed_tokens // block_size
-        for hash_with_group_id in hash_to_evict:
-            manager.block_pool.cached_block_hash_to_block._cache[hash_with_group_id] = (
-                cached_block_hash_to_block_bak[hash_with_group_id]
-            )
-        manager.free(req)
-
     # Evict the blocks outside sliding window, does not affect the hit length.
-    test_partial_request_hit(
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
         "2",
+        all_token_ids,
         [
             make_block_hash_with_group_id(block_hashes[0], 1),
             make_block_hash_with_group_id(block_hashes[0], 2),
@@ -430,13 +414,23 @@ def test_prefill_hybrid_model():
     )
 
     # Evict the first block of full attention, makes total cache miss.
-    test_partial_request_hit(
+    _test_partial_request_hit(
-        "3", [make_block_hash_with_group_id(block_hashes[0], 0)], 0
+        manager,
+        block_size,
+        num_full_blocks,
+        "3",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[0], 0)],
+        0,
     )
 
     # Evict the last block of all layers, reduces the hit length to 2.
-    test_partial_request_hit(
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
         "4",
+        all_token_ids,
         [
             make_block_hash_with_group_id(block_hashes[2], 0),
             make_block_hash_with_group_id(block_hashes[2], 1),
@@ -446,18 +440,36 @@ def test_prefill_hybrid_model():
     )
 
     # Evict the last block of full attention, reduces the hit length to 2.
-    test_partial_request_hit(
+    _test_partial_request_hit(
-        "5", [make_block_hash_with_group_id(block_hashes[2], 0)], 2
+        manager,
+        block_size,
+        num_full_blocks,
+        "5",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[2], 0)],
+        2,
     )
 
     # Evict the last block of sliding window, reduces the hit length to 2.
-    test_partial_request_hit(
+    _test_partial_request_hit(
-        "6", [make_block_hash_with_group_id(block_hashes[2], 1)], 2
+        manager,
+        block_size,
+        num_full_blocks,
+        "6",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[2], 1)],
+        2,
     )
 
     # Evict the last block of sliding window, reduces the hit length to 2.
-    test_partial_request_hit(
+    _test_partial_request_hit(
-        "7", [make_block_hash_with_group_id(block_hashes[2], 2)], 2
+        manager,
+        block_size,
+        num_full_blocks,
+        "7",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[2], 2)],
+        2,
     )
 
     # Evict different set of blocks for full attention and sliding window makes
@@ -466,8 +478,12 @@ def test_prefill_hybrid_model():
     # The cache hit length of sliding window is 2 * block_size.
     # Then it is cache miss as the two type of layers
     # have different hit length.
-    test_partial_request_hit(
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
         "8",
+        all_token_ids,
         [
             make_block_hash_with_group_id(block_hashes[2], 0),
             make_block_hash_with_group_id(block_hashes[0], 1),
@@ -477,6 +493,214 @@ def test_prefill_hybrid_model():
     )
 
 
+def test_prefill_hybrid_model_eagle():
+    block_size = 16
+    kv_cache_config = make_kv_cache_config_hybrid_model(block_size, 31, 3)
+    manager = KVCacheManager(
+        kv_cache_config,
+        max_model_len=8192,
+        enable_caching=True,
+        hash_block_size=block_size,
+        use_eagle=True,
+    )
+
+    hash_fn = sha256
+
+    # Complete 6 blocks (96 tokens)
+    num_full_blocks = 6
+    common_token_ids = [i for i in range(num_full_blocks) for _ in range(block_size)]
+
+    # Fully cache miss
+    # Incomplete 1 block (7 tokens)
+    unique_token_ids = [6] * 7
+    all_token_ids = common_token_ids + unique_token_ids
+    req0 = make_request("0", all_token_ids, block_size, hash_fn)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req0)
+    assert len(req0.block_hashes) == len(all_token_ids) // block_size
+    assert not computed_blocks.blocks[0]
+    assert num_computed_tokens == 0
+    blocks = manager.allocate_slots(
+        req0, len(all_token_ids), num_computed_tokens, computed_blocks
+    )
+    block_ids = (
+        [1, 2, 3, 4, 5, 6, 7],
+        [8, 9, 10, 11, 12, 13, 14],
+        [15, 16, 17, 18, 19, 20, 21],
+    )
+    assert blocks is not None and blocks.get_block_ids() == block_ids
+
+    # Check full block metadata
+    parent_block_hash = None
+    for i, full_block_ids in enumerate(zip(*(row[:-1] for row in block_ids))):
+        block_tokens = tuple(all_token_ids[i * block_size : (i + 1) * block_size])
+        block_hash = hash_block_tokens(hash_fn, parent_block_hash, block_tokens)
+        for group_id, block_id in enumerate(full_block_ids):
+            blk_hash = manager.block_pool.blocks[block_id].block_hash
+            assert blk_hash is not None
+            assert get_block_hash(blk_hash) == block_hash
+            assert get_group_id(blk_hash) == group_id
+            assert manager.block_pool.blocks[block_id].ref_cnt == 1
+        parent_block_hash = block_hash
+
+    # Check partial block metadata
+    for partial_block_id in (row[-1] for row in block_ids):
+        assert manager.block_pool.blocks[partial_block_id].block_hash is None
+        assert manager.block_pool.blocks[partial_block_id].ref_cnt == 1
+
+    # Cache hit in the common prefix
+    # Incomplete 1 block (5 tokens)
+    unique_token_ids = [6] * 5
+    all_token_ids = common_token_ids + unique_token_ids
+    req1 = make_request("1", all_token_ids, block_size, hash_fn)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
+    assert len(req1.block_hashes) == num_full_blocks
+    assert computed_blocks.get_block_ids() == (
+        [1, 2, 3, 4],
+        [0, 9, 10, 11],
+        [0, 16, 17, 18],
+    )
+    assert num_computed_tokens == 4 * block_size
+    num_new_tokens = len(all_token_ids) - num_computed_tokens
+    blocks = manager.allocate_slots(
+        req1, num_new_tokens, num_computed_tokens, computed_blocks
+    )
+    assert blocks is not None and blocks.get_block_ids() == (
+        [22, 23, 24],
+        [25, 26, 27],
+        [28, 29, 30],
+    )
+    for block_per_group in computed_blocks.blocks:
+        for block in block_per_group:
+            if block != manager.block_pool.null_block:
+                assert block.ref_cnt == 2
+
+    block_hashes = req1.block_hashes
+    manager.free(req0)
+    manager.free(req1)
+
+    # Evict the blocks outside sliding window, does not affect the hit length.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "2",
+        all_token_ids,
+        [
+            make_block_hash_with_group_id(block_hashes[0], 1),
+            make_block_hash_with_group_id(block_hashes[0], 2),
+        ],
+        4,
+    )
+
+    # Evict the first block of full attention, makes total cache miss.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "3",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[0], 0)],
+        0,
+    )
+
+    # Evict the last block of all layers, reduces the hit length to 3.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "4",
+        all_token_ids,
+        [
+            make_block_hash_with_group_id(block_hashes[-1], 0),
+            make_block_hash_with_group_id(block_hashes[-1], 1),
+            make_block_hash_with_group_id(block_hashes[-1], 2),
+        ],
+        3,
+    )
+
+    # Evict the last block of full attention, reduces the hit length to 3.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "5",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[-1], 0)],
+        3,
+    )
+
+    # Since the last block of full attention is dropped for eagle, evict
+    # the second last block of sliding window, reduces the hit length to 3.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "6",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[-2], 1)],
+        3,
+    )
+
+    # Since the last block of full attention is dropped for eagle, evict
+    # the second last block of sliding window, reduces the hit length to 3.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "7",
+        all_token_ids,
+        [make_block_hash_with_group_id(block_hashes[-2], 2)],
+        3,
+    )
+
+    # Evict different set of blocks for full attention and sliding window makes
+    # total cache miss.
+    # The cache hit length of full attention is 4 * block_size.
+    # The cache hit length of sliding window is 3 * block_size.
+    # Then it is cache miss as the two type of layers
+    # have different hit length.
+    _test_partial_request_hit(
+        manager,
+        block_size,
+        num_full_blocks,
+        "8",
+        all_token_ids,
+        [
+            make_block_hash_with_group_id(block_hashes[-1], 0),
+            make_block_hash_with_group_id(block_hashes[0], 1),
+            make_block_hash_with_group_id(block_hashes[0], 2),
+        ],
+        0,
+    )
+
+
+def _test_partial_request_hit(
+    manager: KVCacheManager,
+    block_size: int,
+    num_full_blocks,
+    request_id: str,
+    prompt_token_ids: list[int],
+    hash_to_evict: list[BlockHashWithGroupId],
+    expect_hit_length: int,
+):
+    cached_block_hash_to_block_bak = copy.copy(
+        manager.block_pool.cached_block_hash_to_block._cache
+    )
+    req = make_request(request_id, prompt_token_ids, block_size, sha256)
+    for hash_with_group_id in hash_to_evict:
+        manager.block_pool.cached_block_hash_to_block._cache.pop(hash_with_group_id)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req)
+    assert len(req.block_hashes) == num_full_blocks
+    assert num_computed_tokens == expect_hit_length * block_size
+    for block_per_group in computed_blocks.blocks:
+        assert len(block_per_group) == num_computed_tokens // block_size
+    for hash_with_group_id in hash_to_evict:
+        manager.block_pool.cached_block_hash_to_block._cache[hash_with_group_id] = (
+            cached_block_hash_to_block_bak[hash_with_group_id]
+        )
+    manager.free(req)
+
+
 def _make_hybrid_kv_cache_config(
     block_size: int, num_blocks: int, spec_types: list[str]
 ) -> KVCacheConfig:
@@ -655,6 +879,85 @@ def test_prefill_hybrid_model_combinations(spec_types: list[str]):
     manager.free(req1)
 
 
+# Test cases with eagle enabled: Only test a single simple case for now.
+# - 2 groups: 1 full + 1 other
+_EAGLE_HYBRID_MODEL_TEST_CASES = [
+    # 2 groups: 1 full + 1 other
+    pytest.param(["full", "sliding_window"], 2, id="2g-full+sw"),
+]
+
+
+@pytest.mark.parametrize("spec_types,expect_hit_length", _EAGLE_HYBRID_MODEL_TEST_CASES)
+def test_prefill_hybrid_model_combinations_eagle(
+    spec_types: list[str], expect_hit_length: int
+):
+    """
+    Test prefix caching with hybrid models (1 full attn + 1 other) with EAGLE.
+    More complex hybrid models with EAGLE are not yet supported (see issue #32802).
+    """
+    block_size = 16
+    num_groups = len(spec_types)
+    # Allocate enough blocks for all groups
+    num_blocks = 10 * num_groups
+
+    kv_cache_config = _make_hybrid_kv_cache_config(block_size, num_blocks, spec_types)
+    manager = KVCacheManager(
+        kv_cache_config,
+        max_model_len=8192,
+        enable_caching=True,
+        hash_block_size=block_size,
+        use_eagle=True,
+    )
+
+    hash_fn = sha256
+
+    # Complete 3 blocks (48 tokens)
+    num_full_blocks = 4
+    common_token_ids = [i for i in range(num_full_blocks) for _ in range(block_size)]
+    unique_token_ids = [4] * 7
+    all_token_ids = common_token_ids + unique_token_ids
+
+    # First request: no cache hit initially
+    req0 = make_request("0", all_token_ids, block_size, hash_fn)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req0)
+
+    assert len(req0.block_hashes) == num_full_blocks
+    assert not computed_blocks.blocks[0]  # No cache hit initially
+    assert num_computed_tokens == 0
+
+    blocks = manager.allocate_slots(
+        req0, len(all_token_ids), num_computed_tokens, computed_blocks
+    )
+    assert blocks is not None
+    # Should have blocks for all groups
+    assert len(blocks.get_block_ids()) == num_groups
+
+    # Second request: should hit cached blocks for common prefix
+    all_token_ids = common_token_ids + [6] * 5
+    req1 = make_request("1", all_token_ids, block_size, hash_fn)
+    computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
+
+    # Should hit cached blocks for all groups
+    assert num_computed_tokens == expect_hit_length * block_size
+    assert len(computed_blocks.blocks) == num_groups
+    # Verify each group has the correct number of computed blocks
+    for block_per_group in computed_blocks.blocks:
+        assert len(block_per_group) == expect_hit_length
+
+    # Allocate and verify blocks for second request
+    blocks = manager.allocate_slots(
+        req1,
+        len(all_token_ids) - num_computed_tokens,
+        num_computed_tokens,
+        computed_blocks,
+    )
+    assert blocks is not None
+    assert len(blocks.get_block_ids()) == num_groups
+
+    manager.free(req0)
+    manager.free(req1)
+
+
 def test_prefill_plp():
     """Test prefill with APC and some prompt logprobs (plp) requests.
 

tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh

@@ -34,18 +34,11 @@ else
   KV_CONFIG_HETERO_LAYOUT=''
 fi
 
-CROSS_LAYERS_BLOCKS=${CROSS_LAYERS_BLOCKS:-"False"} # Default to non cross layers
-if [[ "$CROSS_LAYERS_BLOCKS" == "True" ]]; then
-  KV_EXTRA_CONFIG=',"kv_connector_extra_config":{"cross_layers_blocks": "True"}'
-else
-  KV_EXTRA_CONFIG=''
-fi
-
 # Build the kv-transfer-config once
 if [[ "$KV_BUFFER_DEVICE" == "cuda" ]]; then
-  KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"'${KV_CONFIG_HETERO_LAYOUT}${KV_EXTRA_CONFIG}'}'
+  KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"'${KV_CONFIG_HETERO_LAYOUT}'}'
 else
-  KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\""${KV_CONFIG_HETERO_LAYOUT}${KV_EXTRA_CONFIG}"}"
+  KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\""${KV_CONFIG_HETERO_LAYOUT}"}"
 fi
 
 # Models to run

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -18,12 +18,8 @@ import ray
 import torch
 
 from vllm import LLM
-from vllm.config import KVTransferConfig, set_current_vllm_config
+from vllm.config import KVTransferConfig
-from vllm.distributed.kv_transfer.kv_connector.utils import (
+from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
-    KVOutputAggregator,
-    TpKVTopology,
-    get_current_attn_backend,
-)
 from vllm.distributed.kv_transfer.kv_connector.v1 import nixl_connector
 from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
 from vllm.distributed.kv_transfer.kv_connector.v1.multi_connector import (
@@ -52,11 +48,8 @@ from vllm.sampling_params import SamplingParams
 from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.output_processor import OutputProcessor
-from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheConfig, KVCacheTensor
 from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput
 from vllm.v1.request import RequestStatus
-from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
-from vllm.v1.worker.utils import AttentionGroup
 
 from .utils import create_request, create_scheduler, create_vllm_config
 
@@ -373,7 +366,6 @@ def test_kv_transfer_handshake(dist_init):
 
         # Decode connector will be able to create handshake with the prefill connector.
         decode_connector = NixlConnector(vllm_config, KVConnectorRole.WORKER)
-        decode_connector.register_kv_caches(kv_caches)
 
         # Here we are testing the retrieval of NIXLAgentMetadata.
         # Knowing the implementation detail, we override the add_remote_agent
@@ -410,23 +402,6 @@ class FakeNixlConnectorWorker(NixlConnectorWorker):
         self.kv_cache_layout = kv_cache_layout
         # Mock register_kv_caches attribute needed for tests that do not call it.
         self.src_xfer_handles_by_block_size = {self.block_size: 1}
-        test_shape = self.attn_backend.get_kv_cache_shape(
-            num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
-        )
-        self.kv_topo = TpKVTopology(
-            tp_rank=self.tp_rank,
-            engine_id=self.engine_id,
-            remote_tp_size=self._tp_size,  # shared state
-            remote_block_size=self._block_size,  # shared state
-            is_mla=self.use_mla,
-            total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
-            attn_backend=self.attn_backend,
-            tensor_shape=test_shape,
-        )
-
-        self.compat_hash = compute_nixl_compatibility_hash(
-            self.vllm_config, self.backend_name, self.kv_topo.cross_layers_blocks
-        )
 
     def _nixl_handshake(
         self, host: str, port: int, remote_tp_size: int, expected_engine_id: str
@@ -1395,7 +1370,6 @@ def _run_abort_timeout_test(llm: LLM, timeout: int):
             ),
         ),
         "TRITON_ATTN",
-        "FLASHINFER",
     ],
 )
 def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
@@ -1412,11 +1386,6 @@ def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
 
     vllm_config = create_vllm_config(attention_backend=attn_backend)
 
-    # Enable cross layers blocks
-    vllm_config.kv_transfer_config.kv_connector_extra_config[
-        "enable_cross_layers_blocks"
-    ] = True
-
     # Import the appropriate backend based on the parameter
     if attn_backend == "FLASH_ATTN":
         from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
@@ -1426,11 +1395,49 @@ def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
         from vllm.v1.attention.backends.rocm_attn import RocmAttentionBackend
 
         backend_cls = RocmAttentionBackend
-    else:  # TRITON
+    else:  # TRITON_ATTN
         from vllm.v1.attention.backends.triton_attn import TritonAttentionBackend
 
         backend_cls = TritonAttentionBackend
 
+    # Create test kv cache tensors using proper backend shape
+    kv_cache_shape = backend_cls.get_kv_cache_shape(
+        num_blocks=2, block_size=16, num_kv_heads=4, head_size=64
+    )
+    shared_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
+    unique_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
+    kv_caches = {
+        "layer0": shared_tensor,
+        "layer1": unique_tensor,
+        "layer2": shared_tensor,
+    }
+
+    # Store tensor info for validation
+
+    test_shape = backend_cls.get_kv_cache_shape(
+        num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
+    )
+    is_blocks_first = len(test_shape) == 5 and test_shape[0] == 1
+
+    if is_blocks_first:
+        expected_tensor_size = shared_tensor.element_size() * shared_tensor.numel()
+        expected_base_addrs = [
+            shared_tensor.data_ptr(),
+            unique_tensor.data_ptr(),
+        ]
+        expected_num_entries = 2
+    else:
+        expected_tensor_size = (
+            shared_tensor[0].element_size() * shared_tensor[0].numel()
+        )
+        expected_base_addrs = [
+            shared_tensor[0].data_ptr(),
+            shared_tensor[1].data_ptr(),
+            unique_tensor[0].data_ptr(),
+            unique_tensor[1].data_ptr(),
+        ]
+        expected_num_entries = 4
+
     nixl_module = "vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector"
     with (
         patch(f"{nixl_module}.NixlWrapper") as mock_nixl_wrapper,
@@ -1459,107 +1466,6 @@ def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
         # Reassure the shutdown() check that the thread is terminated
         mock_thread.return_value.is_alive.return_value = False
 
-        expected_tensor_size: int
-        expected_base_addrs: list[int]
-        expected_num_entries: int
-        kv_caches: dict[str, torch.Tensor]
-        if connector.prefer_cross_layer_blocks:
-            num_layers = 32
-            block_size = 16
-            num_blocks = 8
-            kv_cache_spec = AttentionSpec(
-                block_size=block_size,
-                num_kv_heads=4,
-                head_size=64,
-                dtype=torch.bfloat16,
-            )
-            kv_cache_config = KVCacheConfig(
-                num_blocks=num_blocks,
-                kv_cache_tensors=[
-                    KVCacheTensor(
-                        size=kv_cache_spec.page_size_bytes * num_blocks,
-                        shared_by=["dummy-layer"],
-                    )
-                    for i in range(num_layers)
-                ],
-                # allocate_uniform_kv_caches does not use this
-                kv_cache_groups=[],
-            )
-
-            with set_current_vllm_config(vllm_config):
-                _, cross_layers_kv_cache, _ = (
-                    KVConnectorModelRunnerMixin.allocate_uniform_kv_caches(
-                        kv_cache_config=kv_cache_config,
-                        attn_groups=[
-                            [
-                                AttentionGroup(
-                                    backend=backend_cls,
-                                    layer_names=[],
-                                    kv_cache_spec=kv_cache_spec,
-                                    kv_cache_group_id=0,
-                                )
-                            ]
-                        ],
-                        cache_dtype=torch.bfloat16,
-                        device=torch.cuda.current_device(),
-                        kernel_block_sizes=[block_size],
-                    )
-                )
-            # Store tensor info for validation
-            expected_tensor_size = (
-                cross_layers_kv_cache.element_size() * cross_layers_kv_cache.numel()
-            )
-            expected_base_addrs = [
-                cross_layers_kv_cache.data_ptr(),
-            ]
-            expected_num_entries = 1
-
-            expected_blocks_count = 8
-
-            kv_caches = {"all-layers": cross_layers_kv_cache}
-
-        else:
-            # Create test kv cache tensors using proper backend shape
-            kv_cache_shape = backend_cls.get_kv_cache_shape(
-                num_blocks=2, block_size=16, num_kv_heads=4, head_size=64
-            )
-            shared_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
-            unique_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
-            kv_caches = {
-                "layer0": shared_tensor,
-                "layer1": unique_tensor,
-                "layer2": shared_tensor,
-            }
-
-            # Store tensor info for validation
-
-            test_shape = backend_cls.get_kv_cache_shape(
-                num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
-            )
-            is_blocks_first = len(test_shape) == 5 and test_shape[0] == 1
-
-            if is_blocks_first:
-                expected_tensor_size = (
-                    shared_tensor.element_size() * shared_tensor.numel()
-                )
-                expected_base_addrs = [
-                    shared_tensor.data_ptr(),
-                    unique_tensor.data_ptr(),
-                ]
-                expected_num_entries = 2
-            else:
-                expected_tensor_size = (
-                    shared_tensor[0].element_size() * shared_tensor[0].numel()
-                )
-                expected_base_addrs = [
-                    shared_tensor[0].data_ptr(),
-                    shared_tensor[1].data_ptr(),
-                    unique_tensor[0].data_ptr(),
-                    unique_tensor[1].data_ptr(),
-                ]
-                expected_num_entries = 4
-            expected_blocks_count = 8
-
         # Execute register_kv_caches
         connector.register_kv_caches(kv_caches)
 
@@ -1583,19 +1489,16 @@ def test_register_kv_caches(default_vllm_config, dist_init, attn_backend):
         blocks_data, _ = mock_wrapper_instance.get_xfer_descs.call_args[0]
 
         # Validate blocks_data structure and size
+        expected_blocks_count = 8
         assert len(blocks_data) == expected_blocks_count, (
             f"Expected {expected_blocks_count} blocks, got {len(blocks_data)}"
         )
 
-        if connector.prefer_cross_layer_blocks:
+        num_blocks = 2
-            num_blocks = 8
+        if is_blocks_first:
-            expected_block_len = expected_tensor_size // num_blocks
+            expected_block_len = expected_tensor_size // num_blocks // 2
         else:
-            num_blocks = 2
+            expected_block_len = expected_tensor_size // num_blocks
-            if is_blocks_first:
-                expected_block_len = expected_tensor_size // num_blocks // 2
-            else:
-                expected_block_len = expected_tensor_size // num_blocks
 
         for i, block_entry in enumerate(blocks_data):
             block_start_addr, block_len, tp_rank = block_entry
@@ -2146,17 +2049,6 @@ def test_compatibility_hash_validation(
     )
     decode_connector = NixlConnector(local_vllm_config, KVConnectorRole.WORKER)
     decode_worker = decode_connector.connector_worker
-    kv_cache_shape = decode_worker.attn_backend.get_kv_cache_shape(
-        num_blocks=2, block_size=16, num_kv_heads=4, head_size=64
-    )
-    shared_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
-    unique_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
-    kv_caches = {
-        "layer0": shared_tensor,
-        "layer1": unique_tensor,
-        "layer2": shared_tensor,
-    }
-    decode_connector.register_kv_caches(kv_caches)
 
     remote_config_params: dict[str, Any] = {
         "model": "facebook/opt-125m",
@@ -2179,9 +2071,7 @@ def test_compatibility_hash_validation(
                 )
             )
         remote_hash = compute_nixl_compatibility_hash(
-            remote_vllm_config,
+            remote_vllm_config, decode_worker.backend_name
-            decode_worker.backend_name,
-            decode_worker.kv_topo.cross_layers_blocks,
         )
 
     prefill_block_size = config_overrides.get("block_size", 16)
@@ -2260,27 +2150,6 @@ def test_handshake_decode_errors(default_vllm_config, dist_init, error_scenario)
     decode_connector = NixlConnector(local_vllm_config, KVConnectorRole.WORKER)
     decode_worker = decode_connector.connector_worker
 
-    backend = get_current_attn_backend(local_vllm_config)
-    test_shape = backend.get_kv_cache_shape(
-        num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
-    )
-    decode_worker.kv_topo = TpKVTopology(
-        tp_rank=decode_worker.tp_rank,
-        engine_id=decode_worker.engine_id,
-        remote_tp_size=decode_worker._tp_size,  # shared state
-        remote_block_size=decode_worker._block_size,  # shared state
-        is_mla=decode_worker.use_mla,
-        total_num_kv_heads=decode_worker.model_config.get_total_num_kv_heads(),
-        attn_backend=backend,
-        tensor_shape=test_shape,
-    )
-
-    decode_worker.compat_hash = compute_nixl_compatibility_hash(
-        decode_worker.vllm_config,
-        decode_worker.backend_name,
-        decode_worker.kv_topo.cross_layers_blocks,
-    )
-
     if error_scenario == "handshake_decode_error":
         msg_bytes = b"this is not valid msgpack data"
     elif error_scenario == "handshake_validation_error":

tests/weight_loading/models.txt

@@ -19,7 +19,6 @@ compressed-tensors, nm-testing/tinyllama-oneshot-w8a16-per-channel, main
 compressed-tensors, nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test, main
 compressed-tensors, nm-testing/Phi-3-mini-128k-instruct-FP8, main
 compressed-tensors, neuralmagic/Phi-3-medium-128k-instruct-quantized.w4a16, main
-compressed-tensors, nm-testing/TinyLlama-1.1B-Chat-v1.0-actorder-group, main
 #compressed-tensors, mgoin/DeepSeek-Coder-V2-Lite-Instruct-FP8, main
 compressed-tensors, nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-FP8-Dynamic-testing, main, 90
 compressed-tensors, nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-W8A8-testing, main, 90

tools/vllm-rocm/generate-rocm-wheels-root-index.sh

@@ -0,0 +1,233 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Generate S3 PyPI Root Index for Latest Version
+#
+# Creates a PEP 503 compatible index.html at rocm/ pointing to the latest
+# semantic version's packages. This enables users to install with:
+#   uv pip install vllm --extra-index-url s3://vllm-wheels/rocm
+#
+# Usage:
+#   generate-root-index.sh [options]
+#
+# Options:
+#   --dry-run      Preview changes without uploading
+#   --version VER  Use specific version instead of auto-detecting latest
+#
+# Environment variables:
+#   S3_BUCKET   - Bucket name (default: vllm-wheels)
+#   VARIANT     - ROCm variant (default: rocm700)
+#   DRY_RUN     - Set to 1 for preview mode (same as --dry-run)
+
+set -euo pipefail
+
+# ======== Configuration ========
+BUCKET="${S3_BUCKET:-vllm-wheels}"
+VARIANT="${VARIANT:-rocm700}"
+DRY_RUN="${DRY_RUN:-0}"
+FORCE_VERSION=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --dry-run)
+            DRY_RUN=1
+            shift
+            ;;
+        --version)
+            FORCE_VERSION="$2"
+            shift 2
+            ;;
+        *)
+            echo "Unknown option: $1"
+            exit 1
+            ;;
+    esac
+done
+
+# Working directory for generated files
+WORK_DIR=$(mktemp -d)
+trap 'rm -rf "$WORK_DIR"' EXIT
+
+echo "========================================"
+echo "Generate Root Index for Latest Version"
+echo "========================================"
+echo "S3 Bucket: $BUCKET"
+echo "ROCm Variant: $VARIANT"
+echo "Dry Run: $DRY_RUN"
+echo "========================================"
+echo ""
+
+# ======== Step 1: Find latest semantic version ========
+
+echo "Step 1: Finding latest semantic version..."
+
+# List all directories under rocm/
+aws s3api list-objects-v2 \
+    --bucket "$BUCKET" \
+    --prefix "rocm/" \
+    --delimiter "/" \
+    --query 'CommonPrefixes[].Prefix' \
+    --output text | tr '\t' '\n' > "$WORK_DIR/all_prefixes.txt"
+
+# Filter for semantic versions (x.y.z pattern)
+grep -oE 'rocm/[0-9]+\.[0-9]+\.[0-9]+/' "$WORK_DIR/all_prefixes.txt" | \
+    sed 's|rocm/||; s|/||' | \
+    sort -V > "$WORK_DIR/versions.txt" || true
+
+if [[ ! -s "$WORK_DIR/versions.txt" ]]; then
+    echo "ERROR: No semantic versions found under s3://$BUCKET/rocm/"
+    exit 1
+fi
+
+echo "Found versions:"
+cat "$WORK_DIR/versions.txt"
+echo ""
+
+if [[ -n "$FORCE_VERSION" ]]; then
+    LATEST_VERSION="$FORCE_VERSION"
+    echo "Using forced version: $LATEST_VERSION"
+else
+    LATEST_VERSION=$(tail -1 "$WORK_DIR/versions.txt")
+    echo "Latest version (auto-detected): $LATEST_VERSION"
+fi
+
+# Verify the version exists
+if ! grep -qx "$LATEST_VERSION" "$WORK_DIR/versions.txt"; then
+    echo "ERROR: Version $LATEST_VERSION not found in bucket"
+    exit 1
+fi
+
+# ======== Step 2: List packages from latest version ========
+
+echo ""
+echo "Step 2: Listing packages from rocm/$LATEST_VERSION/$VARIANT/..."
+
+VERSION_PREFIX="rocm/$LATEST_VERSION/$VARIANT/"
+
+# List package directories
+aws s3api list-objects-v2 \
+    --bucket "$BUCKET" \
+    --prefix "$VERSION_PREFIX" \
+    --delimiter "/" \
+    --query 'CommonPrefixes[].Prefix' \
+    --output text | tr '\t' '\n' > "$WORK_DIR/package_prefixes.txt" || true
+
+if [[ ! -s "$WORK_DIR/package_prefixes.txt" ]]; then
+    echo "ERROR: No packages found under s3://$BUCKET/$VERSION_PREFIX"
+    exit 1
+fi
+
+# Extract package names
+sed "s|${VERSION_PREFIX}||; s|/||g" "$WORK_DIR/package_prefixes.txt" | \
+    grep -v '^$' > "$WORK_DIR/packages.txt"
+
+echo "Found packages:"
+cat "$WORK_DIR/packages.txt"
+echo ""
+
+# ======== Step 3: Generate root index.html ========
+
+echo "Step 3: Generating root index.html..."
+
+mkdir -p "$WORK_DIR/output"
+
+{
+    cat <<'EOF'
+<!DOCTYPE html>
+<html>
+<head>
+    <meta name="pypi:repository-version" content="1.0">
+</head>
+<body>
+EOF
+
+    while read -r pkg; do
+        echo "    <a href=\"$pkg/\">$pkg</a><br>"
+    done < "$WORK_DIR/packages.txt"
+
+    cat <<'EOF'
+</body>
+</html>
+EOF
+} > "$WORK_DIR/output/index.html"
+
+echo "Generated root index.html:"
+cat "$WORK_DIR/output/index.html"
+echo ""
+
+# ======== Step 4: Copy and adjust package index files ========
+
+echo "Step 4: Copying and adjusting package index files..."
+
+while read -r pkg; do
+    echo "Processing package: $pkg"
+
+    # Download existing index.html from versioned path
+    SOURCE_INDEX="s3://$BUCKET/$VERSION_PREFIX$pkg/index.html"
+
+    mkdir -p "$WORK_DIR/output/$pkg"
+
+    if aws s3 cp "$SOURCE_INDEX" "$WORK_DIR/output/$pkg/index.html" 2>/dev/null; then
+        # Adjust relative paths:
+        # Original: href="../../../{commit}/wheel.whl" (from rocm/0.13.0/rocm710/vllm/)
+        # New:      href="../{commit}/wheel.whl"       (from rocm/vllm/)
+        sed -i 's|href="\.\./\.\./\.\./|href="../|g' "$WORK_DIR/output/$pkg/index.html"
+        echo "  - Downloaded and adjusted: $pkg/index.html"
+    else
+        echo "  - WARNING: Could not download index for $pkg"
+    fi
+done < "$WORK_DIR/packages.txt"
+
+echo ""
+
+# ======== Step 5: Upload to S3 ========
+
+echo "Step 5: Uploading to s3://$BUCKET/rocm/..."
+echo ""
+
+# List what would be uploaded
+echo "Files to upload:"
+find "$WORK_DIR/output" -name "*.html" -type f | while read -r file; do
+    rel_path="${file#$WORK_DIR/output/}"
+    echo "  rocm/$rel_path"
+done
+echo ""
+
+if [[ "$DRY_RUN" == "1" ]]; then
+    echo "DRY RUN - Skipping upload"
+    echo ""
+    echo "Preview of generated files:"
+    echo "----------------------------------------"
+    echo "rocm/index.html:"
+    cat "$WORK_DIR/output/index.html"
+    echo ""
+    echo "----------------------------------------"
+    echo "Sample package index (first package):"
+    FIRST_PKG=$(head -1 "$WORK_DIR/packages.txt")
+    if [[ -f "$WORK_DIR/output/$FIRST_PKG/index.html" ]]; then
+        echo "rocm/$FIRST_PKG/index.html:"
+        cat "$WORK_DIR/output/$FIRST_PKG/index.html"
+    fi
+else
+    # Upload all generated files
+    aws s3 cp --recursive "$WORK_DIR/output/" "s3://$BUCKET/rocm/" \
+        --content-type "text/html"
+
+    echo "Upload complete!"
+fi
+
+# ======== Summary ========
+
+echo ""
+echo "========================================"
+echo "Root Index Generation Complete!"
+echo "========================================"
+echo ""
+echo "Latest version: $LATEST_VERSION"
+echo "Packages indexed: $(wc -l < "$WORK_DIR/packages.txt")"
+echo ""
+echo "Install command:"
+echo "  uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/"
+echo "========================================"

vllm/_custom_ops.py

@@ -900,6 +900,8 @@ def cutlass_sparse_scaled_mm_supported(cuda_device_capability: int) -> bool:
 
 
 def cutlass_group_gemm_supported(cuda_device_capability: int) -> bool:
+    if cuda_device_capability < 90 or cuda_device_capability >= 110:
+        return False
     try:
         return torch.ops._C.cutlass_group_gemm_supported(cuda_device_capability)
     except AttributeError:
@@ -2032,35 +2034,20 @@ def selective_scan_fwd(
     )
 
 
-# NOTE: The wvSplitK kernel (and all of the kernels in skinny_gemms.cu)
-# are unable to properly handle non-contiguous
-# tensors.  It might be a good TODO(rasmith) to augment these kernels
-# to be able to handle non-contiguous kernels for better performance.
-def rocm_enforce_contiguous_skinny_gemm_inputs(
-    a: torch.Tensor, b: torch.Tensor
-) -> tuple[torch.Tensor, torch.Tensor]:
-    a = a.contiguous()  # no-op if already contiguous, else clone
-    b = b.contiguous()  # no-op if already contiguous, else clone
-    return a, b
-
-
 # ROCm skinny gemms
 def LLMM1(a: torch.Tensor, b: torch.Tensor, rows_per_block: int) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.LLMM1(a, b, rows_per_block)
 
 
 def wvSplitK(
     a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.wvSplitK(a, b, bias, cu_count)
 
 
 def wvSplitKrc(
     a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     return torch.ops._rocm_C.wvSplitKrc(a, b, bias, cu_count)
 
 
@@ -2073,7 +2060,6 @@ def wvSplitKQ(
     cu_count: int,
     bias: torch.Tensor = None,
 ) -> torch.Tensor:
-    a, b = rocm_enforce_contiguous_skinny_gemm_inputs(a, b)
     out = torch.empty((b.shape[0], a.shape[0]), dtype=out_dtype, device=b.device)
     torch.ops._rocm_C.wvSplitKQ(a, b, bias, out, scale_a, scale_b, cu_count)
     return out

vllm/compilation/backends.py

@@ -361,7 +361,14 @@ def split_graph(
             subgraph_id += 1
             node_to_subgraph_id[node] = subgraph_id
             split_op_graphs.append(subgraph_id)
-            subgraph_id += 1
+
+            # keep consecutive splitting ops together
+            # (we know node.next exists because node isn't the last (output) node)
+            if should_split(node.next, splitting_ops):
+                # this will get incremented by the next node
+                subgraph_id -= 1
+            else:
+                subgraph_id += 1
         else:
             node_to_subgraph_id[node] = subgraph_id
 

vllm/config/compilation.py

@@ -581,6 +581,24 @@ class CompilationConfig:
     local_cache_dir: str = field(default=None, init=False)  # type: ignore
     """local cache dir for each rank"""
 
+    fast_moe_cold_start = True
+    """Optimization for fast MOE cold start.
+
+    This is a bit of a hack that assumes that:
+    1. the only decoder forward pass being run is the current model
+    2. the decoder forward pass runs all of the MOEs in the order in which they
+       are initialized
+
+    When the above two conditions hold, this option greatly decreases cold start
+    time for MOE models.
+
+    If the above two conditions don't hold, then this option will lead to silent
+    incorrectness. The only condition in which this doesn't hold is speculative
+    decoding, where there is a draft model that may have MOEs in them.
+
+    NB: We're working on a longer-term solution that doesn't need these assumptions.
+    """
+
     # keep track of enabled and disabled custom ops
     enabled_custom_ops: Counter[str] = field(default_factory=Counter, init=False)
     """custom ops that are enabled"""
@@ -925,6 +943,15 @@ class CompilationConfig:
                 # for details. Make a copy to avoid mutating the class-level
                 # list via reference.
                 self.splitting_ops = list(self._attention_ops)
+
+                # unified_kv_cache_update has a string param that prevents Inductor
+                # from reusing piecewise graphs. Remove it from the compiled graph.
+                # This has the side-effect of excluding cache from cudagraphs but
+                # that doesn't seem to affect performance.
+                # https://github.com/vllm-project/vllm/issues/33267
+                if not self.use_inductor_graph_partition:
+                    self.splitting_ops.append("vllm::unified_kv_cache_update")
+
             elif len(self.splitting_ops) == 0:
                 if (
                     self.cudagraph_mode == CUDAGraphMode.PIECEWISE

vllm/config/speculative.py

@@ -40,6 +40,7 @@ MTPModelTypes = Literal[
     "longcat_flash_mtp",
     "mtp",
     "pangu_ultra_moe_mtp",
+    "step3p5_mtp",
 ]
 EagleModelTypes = Literal["eagle", "eagle3", MTPModelTypes]
 SpeculativeMethod = Literal[
@@ -252,6 +253,11 @@ class SpeculativeConfig:
                 {"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]}
             )
 
+        if hf_config.model_type == "step3p5":
+            hf_config.model_type = "step3p5_mtp"
+            n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
+            hf_config.update({"n_predict": n_predict, "architectures": ["Step3p5MTP"]})
+
         if initial_architecture == "MistralLarge3ForCausalLM":
             hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})
 

vllm/distributed/device_communicators/pynccl_wrapper.py

@@ -72,7 +72,8 @@ class ncclDataTypeEnum:
     ncclFloat64 = 8
     ncclDouble = 8
     ncclBfloat16 = 9
-    ncclNumTypes = 10
+    ncclFloat8e4m3 = 10
+    ncclNumTypes = 11
 
     @classmethod
     def from_torch(cls, dtype: torch.dtype) -> int:
@@ -92,9 +93,12 @@ class ncclDataTypeEnum:
             return cls.ncclFloat64
         if dtype == torch.bfloat16:
             return cls.ncclBfloat16
+        if dtype == torch.float8_e4m3fn:
+            return cls.ncclFloat8e4m3
         raise ValueError(
             f"Unsupported dtype {dtype}: should be one of "
-            f"int8, uint8, int32, int64, float16, float32, float64, bfloat16."
+            f"int8, uint8, int32, int64, float16, float32, float64, bfloat16,"
+            " float8e4m3."
         )
 
 

vllm/distributed/kv_transfer/kv_connector/utils.py

@@ -316,13 +316,12 @@ class TpKVTopology:
     attn_backend: type[AttentionBackend]
     engine_id: EngineId
     remote_block_size: dict[EngineId, int]
-    tensor_shape: torch.Size | None = None
 
     def __post_init__(self):
         # Figure out whether the first dimension of the cache is K/V
         # or num_blocks. This is used to register the memory regions correctly.
         kv_cache_shape = self.attn_backend.get_kv_cache_shape(
-            num_blocks=1, block_size=16, num_kv_heads=4, head_size=1
+            num_blocks=1, block_size=16, num_kv_heads=1, head_size=1
         )
         # Non-MLA backends caches have 5 dims [2, num_blocks, H,N,D],
         # we just mock num_blocks to 1 for the dimension check below.
@@ -330,32 +329,6 @@ class TpKVTopology:
             len(kv_cache_shape) == 5 and kv_cache_shape[0] == 1
         )
 
-        self._kv_heads_position: int | None = None
-        self._cross_layers_blocks = False
-        if self.tensor_shape is not None:
-            self._cross_layers_blocks = (
-                len(self.tensor_shape) == len(kv_cache_shape) + 1
-            )
-
-            if self._cross_layers_blocks:
-                # prepend layers dimension
-                kv_cache_shape = (80,) + kv_cache_shape
-            try:
-                kv_cache_stride_order = self.attn_backend.get_kv_cache_stride_order(
-                    include_num_layers_dimension=self._cross_layers_blocks
-                )
-            except (AttributeError, NotImplementedError):
-                kv_cache_stride_order = tuple(range(len(self.tensor_shape)))
-
-            # permute kv_cache_shape according to stride_order
-            kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
-
-            physical_block_size_position = kv_cache_shape.index(16)
-            assert physical_block_size_position is not None
-            self._physical_block_size_position = -(
-                len(kv_cache_shape) - physical_block_size_position
-            )
-
     @property
     def is_kv_layout_blocks_first(self) -> bool:
         return self._is_kv_layout_blocks_first
@@ -363,9 +336,7 @@ class TpKVTopology:
     @property
     def split_k_and_v(self) -> bool:
         # Whether to register regions for K and V separately (when present).
-        return not (
+        return not (self.is_mla or self.is_kv_layout_blocks_first)
-            self._cross_layers_blocks or self.is_mla or self.is_kv_layout_blocks_first
-        )
 
     @property
     def tp_size(self) -> int:
@@ -375,14 +346,6 @@ class TpKVTopology:
     def block_size(self) -> int:
         return self.remote_block_size[self.engine_id]
 
-    @property
-    def cross_layers_blocks(self) -> bool:
-        return self._cross_layers_blocks
-
-    @property
-    def block_size_position(self) -> int:
-        return self._physical_block_size_position
-
     def tp_ratio(
         self,
         remote_tp_size: int,

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -54,7 +54,7 @@ from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_path, make_zmq_socket
-from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.worker.block_table import BlockTable
@@ -173,7 +173,7 @@ class NixlHandshakePayload(KVConnectorHandshakeMetadata):
 
 
 def compute_nixl_compatibility_hash(
-    vllm_config: VllmConfig, attn_backend_name: str, cross_layers_blocks: bool
+    vllm_config: VllmConfig, attn_backend_name: str
 ) -> str:
     """
     Compute compatibility hash for NIXL KV transfer.
@@ -216,7 +216,6 @@ def compute_nixl_compatibility_hash(
         # Attention backend and KV cache dtype affect memory layout
         "attn_backend_name": attn_backend_name,
         "cache_dtype": str(cache_config.cache_dtype),
-        "cross_layers_blocks": cross_layers_blocks,
     }
 
     compat_hash = hash_factors(factors)
@@ -299,20 +298,6 @@ class NixlConnectorMetadata(KVConnectorMetadata):
 
 
 class NixlConnector(KVConnectorBase_V1):
-    @property
-    def prefer_cross_layer_blocks(self) -> bool:
-        backend = get_current_attn_backend(self._vllm_config)
-        if backend().get_name() not in (
-            "FLASH_ATTN",
-            "FLASHINFER",
-        ):
-            # For now there is no benefit to run cross layers when backend
-            # does not support on HND
-            return False
-
-        extra_config = self.kv_transfer_config.kv_connector_extra_config
-        return bool(str(extra_config.get("enable_cross_layers_blocks", "False")))
-
     def __init__(
         self,
         vllm_config: VllmConfig,
@@ -324,7 +309,6 @@ class NixlConnector(KVConnectorBase_V1):
         assert vllm_config.kv_transfer_config is not None
         assert vllm_config.kv_transfer_config.engine_id is not None
         self.engine_id: EngineId = vllm_config.kv_transfer_config.engine_id
-        self.kv_transfer_config = vllm_config.kv_transfer_config
 
         if role == KVConnectorRole.SCHEDULER:
             self.connector_scheduler: NixlConnectorScheduler | None = (
@@ -411,16 +395,6 @@ class NixlConnector(KVConnectorBase_V1):
         assert self.connector_worker is not None
         self.connector_worker.register_kv_caches(kv_caches)
 
-    def register_cross_layers_kv_cache(
-        self, kv_cache: torch.Tensor, attn_backend: type[AttentionBackend]
-    ):
-        assert self.connector_worker is not None
-
-        cross_layer_name = "ALL_LAYERS"
-        kv_caches = {cross_layer_name: kv_cache}
-
-        self.connector_worker.register_kv_caches(kv_caches)
-
     def set_host_xfer_buffer_ops(self, copy_operation: CopyBlocksOp):
         assert self.connector_worker is not None
         self.connector_worker.set_host_xfer_buffer_ops(copy_operation)
@@ -1002,17 +976,20 @@ class NixlConnectorWorker:
 
         # Get the attention backend from the first layer
         # NOTE (NickLucche) models with multiple backends are not supported yet
-        self.attn_backend = get_current_attn_backend(vllm_config)
+        backend = get_current_attn_backend(vllm_config)
 
-        self.backend_name = self.attn_backend.get_name()
+        self.backend_name = backend.get_name()
         self.kv_cache_layout = get_kv_cache_layout()
         self.host_buffer_kv_cache_layout = self.kv_cache_layout
         logger.debug("Detected attention backend %s", self.backend_name)
         logger.debug("Detected kv cache layout %s", self.kv_cache_layout)
 
-        # lazy initialized in register_kv_caches
+        self.compat_hash = compute_nixl_compatibility_hash(
-        self.compat_hash: str | None = None
+            self.vllm_config, self.backend_name
-        self.kv_topo: TpKVTopology | None = None
+        )
+        self.enforce_compat_hash = self.kv_transfer_config.get_from_extra_config(
+            "enforce_handshake_compat", True
+        )
 
         self._tp_size: dict[EngineId, int] = {self.engine_id: self.world_size}
         self._block_size: dict[EngineId, int] = {self.engine_id: self.block_size}
@@ -1021,11 +998,16 @@ class NixlConnectorWorker:
         self.consumer_notification_counts_by_req = defaultdict[ReqId, int](int)
         self.xfer_stats = NixlKVConnectorStats()
 
-        self._physical_blocks_per_logical_kv_block = 1
+        self.kv_topo = TpKVTopology(
-
+            tp_rank=self.tp_rank,
-        self.enforce_compat_hash = self.kv_transfer_config.get_from_extra_config(
+            engine_id=self.engine_id,
-            "enforce_handshake_compat", True
+            remote_tp_size=self._tp_size,  # shared state
+            remote_block_size=self._block_size,  # shared state
+            is_mla=self.use_mla,
+            total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
+            attn_backend=backend,
         )
+        self._physical_blocks_per_logical_kv_block = 1
 
     def _nixl_handshake(
         self,
@@ -1040,7 +1022,6 @@ class NixlConnectorWorker:
         # Regardless, only handshake with the remote TP rank(s) that current
         # local rank will read from. Note that With homogeneous TP,
         # this happens to be the same single rank_i.
-        assert self.kv_topo is not None
         p_remote_ranks = self.kv_topo.get_target_remote_ranks(remote_tp_size)
         remote_rank_to_agent_name = {}
         path = make_zmq_path("tcp", host, port)
@@ -1078,7 +1059,6 @@ class NixlConnectorWorker:
                 )
 
                 # Check compatibility hash BEFORE decoding agent metadata
-                assert self.compat_hash is not None
                 if (
                     self.enforce_compat_hash
                     and handshake_payload.compatibility_hash != self.compat_hash
@@ -1287,20 +1267,6 @@ class NixlConnectorWorker:
     def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         """Register the KV Cache data in nixl."""
 
-        self.kv_topo = TpKVTopology(
-            tp_rank=self.tp_rank,
-            engine_id=self.engine_id,
-            remote_tp_size=self._tp_size,  # shared state
-            remote_block_size=self._block_size,  # shared state
-            is_mla=self.use_mla,
-            total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
-            attn_backend=self.attn_backend,
-            tensor_shape=next(iter(kv_caches.values())).shape,
-        )
-        self.compat_hash = compute_nixl_compatibility_hash(
-            self.vllm_config, self.backend_name, self.kv_topo.cross_layers_blocks
-        )
-
         if self.use_host_buffer:
             self.initialize_host_xfer_buffer(kv_caches=kv_caches)
             assert len(self.host_xfer_buffers) == len(kv_caches), (
@@ -1335,21 +1301,29 @@ class NixlConnectorWorker:
         # (roughly 8KB vs 5KB).
         # Conversely for FlashInfer, K and V are registered in the same region
         # to better exploit the memory layout (ie num_blocks is the first dim).
+        split_k_and_v = self.kv_topo.split_k_and_v
         tensor_size_bytes = None
 
+        # TODO (NickLucche): Get kernel_block_size in a cleaner way
+        # NHD default "view" for non-MLA cache
+        if self.device_type == "cpu":
+            block_size_position = -2
+        else:
+            block_size_position = -2 if self.use_mla else -3
+
         # Enable different block lengths for different layers when MLA is used.
         self.block_len_per_layer = list[int]()
         self.slot_size_per_layer = list[int]()  # HD bytes in kv terms
         for layer_name, cache_or_caches in xfer_buffers.items():
-            cache_list = (
+            cache_list = cache_or_caches if split_k_and_v else [cache_or_caches]
-                cache_or_caches if self.kv_topo.split_k_and_v else [cache_or_caches]
+
-            )
             for cache in cache_list:
                 base_addr = cache.data_ptr()
                 if base_addr in seen_base_addresses:
                     continue
 
-                kernel_block_size = cache.shape[self.kv_topo.block_size_position]
+                kernel_block_size = cache.shape[block_size_position]
+
                 if self.block_size != kernel_block_size:
                     logger.info_once(
                         "User-specified logical block size (%s) does not match"
@@ -1411,7 +1385,6 @@ class NixlConnectorWorker:
 
         self.device_kv_caches = kv_caches
         self.dst_num_blocks[self.engine_id] = self.num_blocks
-
         if self.kv_topo.is_kv_layout_blocks_first:
             for i in range(len(self.slot_size_per_layer)):
                 assert self.slot_size_per_layer[i] % 2 == 0
@@ -1467,7 +1440,6 @@ class NixlConnectorWorker:
             block_size=self.block_size,
         )
         # Wrap metadata in payload with hash for defensive decoding
-        assert self.compat_hash is not None
         encoder = msgspec.msgpack.Encoder()
         self.xfer_handshake_metadata = NixlHandshakePayload(
             compatibility_hash=self.compat_hash,
@@ -1489,8 +1461,6 @@ class NixlConnectorWorker:
         register another local_xfer_handler using remote block len to ensure
         data copy correctness.
         """
-        assert self.kv_topo is not None
-
         block_size_ratio = self.block_size // block_size
         blocks_data = []
         for i, base_addr in enumerate(self.seen_base_addresses):
@@ -1603,7 +1573,6 @@ class NixlConnectorWorker:
         # remote:               | 0| 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|
         # local origin:|          0|          1|          8|         12|
         # local mapped:| 0| 1| 2| 3| 4| 5| 6| 7| 8| 9|10|11|12|13|14|15|
-        assert self.kv_topo is not None
         block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(engine_id)
 
         if engine_id not in self.dst_num_blocks:
@@ -1731,10 +1700,7 @@ class NixlConnectorWorker:
         """
         remote_engine_id = nixl_agent_meta.engine_id
 
-        assert (
+        assert self._tp_size[remote_engine_id] == remote_tp_size
-            self._tp_size[remote_engine_id] == remote_tp_size
-            and self.kv_topo is not None
-        )
 
         tp_ratio = self.kv_topo.tp_ratio_from_engine_id(remote_engine_id)
         block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(
@@ -1871,7 +1837,6 @@ class NixlConnectorWorker:
         if len(self.device_kv_caches) == 0:
             return
         assert block_size_ratio >= 1, "Only nP < nD supported currently."
-        assert self.kv_topo is not None
         if self.enable_permute_local_kv and block_size_ratio > 1:
             logger.debug(
                 "Post-processing device kv cache on receive by converting "
@@ -1891,7 +1856,7 @@ class NixlConnectorWorker:
                 block_size_ratio,
             )
 
-        split_k_and_v = self.kv_topo.split_k_and_v
+        split_k_and_v = not (self.use_mla or self.kv_topo.is_kv_layout_blocks_first)
 
         for block_ids in block_ids_list:
             indices = torch.tensor(block_ids, device=self.device_type, dtype=torch.long)
@@ -1916,7 +1881,6 @@ class NixlConnectorWorker:
         The scheduler process (via the MultiprocExecutor) will use this output
         to track which workers are done.
         """
-        assert self.kv_topo is not None
         done_sending = self._get_new_notifs()
         done_recving = self._pop_done_transfers(self._recving_transfers)
 
@@ -1986,7 +1950,6 @@ class NixlConnectorWorker:
         are reading from the same producer (heterogeneous TP scenario), wait
         for all consumers to be done pulling.
         """
-        assert self.kv_topo is not None
         notified_req_ids: set[str] = set()
         for notifs in self.nixl_wrapper.get_new_notifs().values():
             for notif in notifs:
@@ -2146,7 +2109,7 @@ class NixlConnectorWorker:
                 self._reqs_to_send[req_id] = expiration_time
 
     def _read_blocks_for_req(self, req_id: str, meta: ReqMeta):
-        assert meta.remote is not None and self.kv_topo is not None
+        assert meta.remote is not None
         remote_ranks = self.kv_topo.get_target_remote_ranks_from_engine_id(
             meta.remote.engine_id
         )
@@ -2215,7 +2178,10 @@ class NixlConnectorWorker:
         local_xfer_side_handle: int,
         remote_xfer_side_handle: int,
     ):
-        assert self.kv_topo is not None
+        """
+        Post a READ point-to-point xfer request from a single local worker to
+        a single remote worker.
+        """
         block_size_ratio = self.kv_topo.block_size_ratio_from_engine_id(dst_engine_id)
         if block_size_ratio > 1:
             local_block_ids = self.get_mapped_blocks(
@@ -2448,7 +2414,6 @@ class NixlConnectorWorker:
         For FlashInfer, this is half the length of the whole block, as K and V
         share the same region.
         """
-        assert self.kv_topo is not None
         if self.kv_topo.is_kv_layout_blocks_first:
             # For indexing only half (either just the K or V part).
             block_len = self.block_len_per_layer[layer_idx] // 2

vllm/entrypoints/chat_utils.py

@@ -46,6 +46,9 @@ from vllm.multimodal.inputs import (
     MultiModalBatchedField,
     MultiModalFlatField,
     MultiModalSharedField,
+    VisionChunk,
+    VisionChunkImage,
+    VisionChunkVideo,
 )
 from vllm.multimodal.processing import BaseMultiModalProcessor
 from vllm.multimodal.utils import MEDIA_CONNECTOR_REGISTRY, MediaConnector
@@ -336,7 +339,9 @@ ChatTemplateContentFormatOption = Literal["auto", "string", "openai"]
 ChatTemplateContentFormat = Literal["string", "openai"]
 
 
-ModalityStr = Literal["image", "audio", "video", "image_embeds", "audio_embeds"]
+ModalityStr = Literal[
+    "image", "audio", "video", "image_embeds", "audio_embeds", "vision_chunk"
+]
 _T = TypeVar("_T")
 
 
@@ -449,6 +454,78 @@ def _get_embeds_data(
     raise NotImplementedError(type(data_items))
 
 
+def rebuild_mm_uuids_from_mm_data(
+    mm_uuids: MultiModalUUIDDict,
+    mm_data: MultiModalDataDict,
+) -> MultiModalUUIDDict:
+    """Rebuild mm_uuids after vision_chunk processing.
+
+    When videos are split into chunks, the original UUIDs need to be updated
+    to reflect the new UUIDs generated for each chunk.
+
+    Args:
+        mm_uuids: Original UUIDs dictionary
+        mm_data: Processed multimodal data with vision_chunk items
+
+    Returns:
+        Updated UUIDs dictionary with chunk UUIDs
+    """
+    vision_chunks = mm_data.get("vision_chunk")
+    if vision_chunks is None:
+        return mm_uuids
+
+    new_uuids = dict(mm_uuids)
+    vision_chunk_uuids = []
+
+    for item in vision_chunks:
+        # vision_chunk items are always dicts (VisionChunkImage/VisionChunkVideo)
+        assert isinstance(item, dict)
+        uuid_val = item.get("uuid")
+        if uuid_val is not None:
+            vision_chunk_uuids.append(uuid_val)
+
+    if vision_chunk_uuids:
+        new_uuids["vision_chunk"] = vision_chunk_uuids
+
+    return new_uuids
+
+
+def build_video_prompts_from_mm_data(
+    mm_data: MultiModalDataDict,
+) -> list[str]:
+    """Build video prompts from vision_chunk data.
+
+    Collects prompts from video chunks and groups them by video_idx.
+
+    Args:
+        mm_data: Processed multimodal data with vision_chunk items
+
+    Returns:
+        List of video prompts, one per video.
+    """
+    vision_chunks = mm_data.get("vision_chunk")
+    if vision_chunks is None:
+        return []
+
+    # Group chunks by video_idx
+    video_prompts_dict: dict[int, list[str]] = defaultdict(list)
+
+    for item in vision_chunks:
+        # vision_chunk items are always dicts (VisionChunkImage/VisionChunkVideo)
+        assert isinstance(item, dict)
+        if item.get("type") == "video_chunk":
+            video_idx = item.get("video_idx", 0)
+            prompt = item.get("prompt", "")
+            video_prompts_dict[video_idx].append(prompt)
+
+    # Build prompts in video order
+    video_prompts = []
+    for video_idx in sorted(video_prompts_dict.keys()):
+        video_prompts.append("".join(video_prompts_dict[video_idx]))
+
+    return video_prompts
+
+
 class BaseMultiModalItemTracker(ABC, Generic[_T]):
     """
     Tracks multi-modal items in a given request and ensures that the number
@@ -462,6 +539,13 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         self._model_config = model_config
 
         self._items_by_modality = defaultdict[str, list[_T]](list)
+        # Track original modality for each vision_chunk item (image or video)
+        self._modality_order = defaultdict[str, list[str]](list)
+
+    @cached_property
+    def use_unified_vision_chunk_modality(self) -> bool:
+        """Check if model uses unified vision_chunk modality for images/videos."""
+        return getattr(self._model_config.hf_config, "use_unified_vision_chunk", False)
 
     @property
     def model_config(self) -> ModelConfig:
@@ -499,11 +583,31 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         media.
         """
         input_modality = modality.replace("_embeds", "")
-        num_items = len(self._items_by_modality[modality]) + 1
+        original_modality = modality
+        use_vision_chunk = (
+            self.use_unified_vision_chunk_modality
+            and original_modality in ["video", "image"]
+        )
+
+        # If use_unified_vision_chunk_modality is enabled,
+        # map image/video to vision_chunk
+        if use_vision_chunk:
+            # To avoid validation fail
+            # because models with use_unified_vision_chunk_modality=True
+            # will only accept vision_chunk modality.
+            input_modality = "vision_chunk"
+            num_items = len(self._items_by_modality[input_modality]) + 1
+        else:
+            num_items = len(self._items_by_modality[original_modality]) + 1
 
         self.mm_processor.validate_num_items(input_modality, num_items)
 
-        self._items_by_modality[modality].append(item)
+        # Track original modality for vision_chunk items
+        if use_vision_chunk:
+            self._items_by_modality[input_modality].append(item)  # type: ignore
+            self._modality_order["vision_chunk"].append(original_modality)
+        else:
+            self._items_by_modality[original_modality].append(item)
 
         return self.model_cls.get_placeholder_str(modality, num_items)
 
@@ -515,6 +619,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
 def _resolve_items(
     items_by_modality: dict[str, list[tuple[object, str | None]]],
     mm_processor: BaseMultiModalProcessor,
+    vision_chunk_modality_order: dict[str, list[str]],
 ) -> tuple[MultiModalDataDict, MultiModalUUIDDict]:
     if "image" in items_by_modality and "image_embeds" in items_by_modality:
         raise ValueError("Mixing raw image and embedding inputs is not allowed")
@@ -546,6 +651,74 @@ def _resolve_items(
     if "video" in items_by_modality:
         mm_data["video"] = [data for data, uuid in items_by_modality["video"]]
         mm_uuids["video"] = [uuid for data, uuid in items_by_modality["video"]]
+    if "vision_chunk" in items_by_modality:
+        # Process vision_chunk items - extract from (data, modality) tuples
+        # and convert to VisionChunk types with proper UUID handling
+        vision_chunk_items = items_by_modality["vision_chunk"]
+        modality_order = vision_chunk_modality_order.get("vision_chunk", [])
+        mm_uuids["vision_chunk"] = [
+            uuid for data, uuid in items_by_modality["vision_chunk"]
+        ]
+
+        # Filter out None items (from asyncio.sleep(0) placeholders)
+        filtered_items = [
+            (idx, item)
+            for idx, item in enumerate(vision_chunk_items)
+            if item is not None
+        ]
+
+        assert len(filtered_items) == len(modality_order), (
+            f"vision_chunk items ({len(filtered_items)}) and "
+            f"modality_order ({len(modality_order)}) must have same length"
+        )
+
+        processed_chunks: list[VisionChunk] = []
+        video_idx = 0
+        for i, (idx, item) in enumerate(filtered_items):
+            inner_modality = modality_order[i]
+            data, uuid = item
+            uuid_val = uuid if idx < len(mm_uuids["vision_chunk"]) else None
+            if inner_modality == "image":
+                # Cast data to proper type for image
+                # Use .media (PIL.Image) directly to avoid redundant
+                # bytes→PIL conversion in media_processor
+                if hasattr(data, "media"):
+                    image_data = data.media  # type: ignore[union-attr]
+                    processed_chunks.append(
+                        VisionChunkImage(type="image", image=image_data, uuid=uuid_val)
+                    )
+                else:
+                    processed_chunks.append(data)  # type: ignore[arg-type]
+            elif inner_modality == "video":
+                # For video, we may need to split into chunks
+                # if processor supports it
+                # For now, just wrap as a video chunk placeholder
+                if hasattr(mm_processor, "split_video_chunks") and data is not None:
+                    try:
+                        video_uuid = uuid_val or random_uuid()
+                        # video await result is (video_data, video_meta) tuple
+                        if isinstance(data, tuple) and len(data) >= 1:
+                            video_data = data[0]
+                        else:
+                            video_data = data
+                        video_chunks = mm_processor.split_video_chunks(video_data)
+                        for i, vc in enumerate(video_chunks):
+                            processed_chunks.append(
+                                VisionChunkVideo(
+                                    type="video_chunk",
+                                    video_chunk=vc["video_chunk"],
+                                    uuid=f"{video_uuid}-{i}",
+                                    video_idx=video_idx,
+                                    prompt=vc["prompt"],
+                                )
+                            )
+                        video_idx += 1
+                    except Exception as e:
+                        logger.warning("Failed to split video chunks: %s", e)
+                        processed_chunks.append(data)  # type: ignore[arg-type]
+                else:
+                    processed_chunks.append(data)  # type: ignore[arg-type]
+        mm_data["vision_chunk"] = processed_chunks
 
     return mm_data, mm_uuids
 
@@ -557,7 +730,9 @@ class MultiModalItemTracker(BaseMultiModalItemTracker[tuple[object, str | None]]
         if not self._items_by_modality:
             return None, None
 
-        return _resolve_items(dict(self._items_by_modality), self.mm_processor)
+        return _resolve_items(
+            dict(self._items_by_modality), self.mm_processor, self._modality_order
+        )
 
     def create_parser(self) -> "BaseMultiModalContentParser":
         return MultiModalContentParser(self)
@@ -577,7 +752,9 @@ class AsyncMultiModalItemTracker(
             for modality, coros in self._items_by_modality.items()
         }
 
-        return _resolve_items(resolved_items_by_modality, self.mm_processor)
+        return _resolve_items(
+            resolved_items_by_modality, self.mm_processor, self._modality_order
+        )
 
     def create_parser(self) -> "BaseMultiModalContentParser":
         return AsyncMultiModalContentParser(self)

vllm/envs.py

@@ -782,6 +782,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     ),
     # Backend for Video IO
     # - "opencv": Default backend that uses OpenCV stream buffered backend.
+    # - "identity": Returns raw video bytes for model processor to handle.
     #
     # Custom backend implementations can be registered
     # via `@VIDEO_LOADER_REGISTRY.register("my_custom_video_loader")` and

vllm/forward_context.py

@@ -271,17 +271,22 @@ def create_forward_context(
     additional_kwargs: dict[str, Any] | None = None,
     skip_compiled: bool = False,
 ):
-    no_compile_layers = vllm_config.compilation_config.static_forward_context
+    if vllm_config.compilation_config.fast_moe_cold_start:
-    from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+        if vllm_config.speculative_config is None:
-
+            all_moe_layers = vllm_config.compilation_config.static_all_moe_layers
-    remaining_moe_layers = [
+        else:
-        name for name, layer in no_compile_layers.items() if isinstance(layer, FusedMoE)
+            logger.warning_once(
-    ]
+                "vllm_config.compilation_config.fast_moe_cold_start is not "
-    remaining_moe_layers.reverse()
+                "compatible with speculative decoding so we are ignoring "
+                "fast_moe_cold_start."
+            )
+            all_moe_layers = None
+    else:
+        all_moe_layers = None
 
     return ForwardContext(
-        no_compile_layers=no_compile_layers,
+        no_compile_layers=vllm_config.compilation_config.static_forward_context,
-        remaining_moe_layers=remaining_moe_layers,
+        all_moe_layers=all_moe_layers,
         virtual_engine=virtual_engine,
         attn_metadata=attn_metadata,
         slot_mapping=slot_mapping or {},

vllm/model_executor/layers/activation.py

@@ -17,11 +17,63 @@ from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 from vllm.utils.collection_utils import LazyDict
 
 logger = init_logger(__name__)
 
 
+@triton.jit
+def _swiglustep_and_mul_kernel(
+    o_ptr,
+    o_stride,
+    x_ptr,
+    x_stride,
+    limit: tl.constexpr,
+    d: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+) -> None:
+    i = tl.program_id(axis=0).to(tl.int64)
+    j = tl.program_id(axis=1)
+    o_row_ptr = o_ptr + o_stride * i
+    x_row_ptr = x_ptr + x_stride * i
+    offsets = j * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < d
+
+    gate = tl.load(x_row_ptr + offsets, mask=mask).to(tl.float32)
+    up = tl.load(x_row_ptr + offsets + d, mask=mask).to(tl.float32)
+
+    gate_silu = tl.sigmoid(gate) * gate
+    gate_clamped = tl.minimum(gate_silu, limit)
+    up_clamped = tl.minimum(tl.maximum(up, -limit), limit)
+
+    result = gate_clamped * up_clamped
+    result = result.to(x_ptr.dtype.element_ty)
+    tl.store(o_row_ptr + offsets, result, mask=mask)
+
+
+def swiglustep_and_mul_triton(
+    output: torch.Tensor, input: torch.Tensor, limit: float = 7.0
+):
+    b, n = input.shape
+    assert input.ndim == 2
+    assert n % 2 == 0
+    d = n // 2
+
+    def grid(meta):
+        return (b, triton.cdiv(d, meta["BLOCK_SIZE"]))
+
+    _swiglustep_and_mul_kernel[grid](
+        output,
+        output.stride(0),
+        input,
+        input.stride(0),
+        limit=limit,
+        d=d,
+        BLOCK_SIZE=1024,
+    )
+
+
 # --8<-- [start:fatrelu_and_mul]
 @CustomOp.register("fatrelu_and_mul")
 class FatreluAndMul(CustomOp):
@@ -304,6 +356,44 @@ class SwigluOAIAndMul(CustomOp):
         return f"alpha={repr(self.alpha)}, limit={repr(self.limit)}"
 
 
+# --8<-- [start:swiglustep_and_mul]
+@CustomOp.register("swiglustep_and_mul")
+class SwigluStepAndMul(CustomOp):
+    """An activation function for SwiGLU with clamping.
+
+    Computes x -> silu(x[:d]).clamp(max=limit) * x[d:].clamp(-limit, limit)
+    where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, limit: float = 7.0):
+        super().__init__()
+        if limit is None:
+            raise ValueError("SwigluStepAndMul requires limit to be set.")
+        self.limit = limit
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        gate, up = x.chunk(2, dim=-1)
+        gate = F.silu(gate)
+        gate = gate.clamp(max=self.limit)
+        up = up.clamp(min=-self.limit, max=self.limit)
+        return gate * up
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        swiglustep_and_mul_triton(out, x, self.limit)
+        return out
+
+    def extra_repr(self) -> str:
+        return f"limit={repr(self.limit)}"
+
+
 # --8<-- [start:gelu_new]
 @CustomOp.register("gelu_new")
 class NewGELU(CustomOp):

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -649,7 +649,12 @@ class CutlassExpertsFp4(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
-        return current_platform.has_device_capability((10, 0))
+        p = current_platform
+        return p.is_cuda() and (
+            p.is_device_capability_family(100)
+            or p.is_device_capability_family(110)
+            or p.is_device_capability_family(120)
+        )
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -144,7 +144,7 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_activation(activation: str) -> bool:
-        return activation in ["silu"]
+        return activation in ["silu", "swiglustep"]
 
     @staticmethod
     def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:

vllm/model_executor/layers/fused_moe/flashinfer_cutedsl_moe.py

@@ -54,7 +54,8 @@ class FlashInferCuteDSLExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
-        return current_platform.is_device_capability_family(100)
+        p = current_platform
+        return p.is_cuda() and p.is_device_capability_family(100)
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:

vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py

@@ -91,11 +91,14 @@ class FlashInferExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
     @staticmethod
     def _supports_current_device() -> bool:
+        p = current_platform
         return (
-            current_platform.is_cuda()
+            p.is_cuda()
             and (
-                current_platform.is_device_capability((9, 0))
+                p.is_device_capability(90)
-                or current_platform.is_device_capability_family(100)
+                or p.is_device_capability_family(100)
+                or p.is_device_capability_family(110)
+                or p.is_device_capability_family(120)
             )
             and has_flashinfer_cutlass_fused_moe()
         )
@@ -109,29 +112,27 @@ class FlashInferExperts(mk.FusedMoEPermuteExpertsUnpermute):
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        # The following are supported by FlashInferExperts:
-        #   * unquantized
-        #   * fp8 static per-tensor on 9.0+
-        #   * fp8 block on 9.0
-        #   * nvfp4 on 10.0+
-
         p = current_platform
         scheme = (weight_key, activation_key)
+        # The following are supported by FlashInferExperts:
         return (
+            # unquantized and fp8 static per-tensor on 9.0+
             (
                 scheme
                 in [
                     (None, None),
                     (kFp8StaticTensorSym, kFp8StaticTensorSym),
                 ]
+                and p.has_device_capability(90)
             )
+            # fp8 block-scale on 9.0
             or (
-                (scheme == (kFp8Static128BlockSym, kFp8Dynamic128Sym))
+                scheme == (kFp8Static128BlockSym, kFp8Dynamic128Sym)
-                and (p.is_device_capability((9, 0)))
+                and p.is_device_capability(90)
             )
+            # nvfp4 on 10.0+
             or (
-                (scheme == (kNvfp4Static, kNvfp4Dynamic))
+                scheme == (kNvfp4Static, kNvfp4Dynamic) and p.has_device_capability(100)
-                and (p.is_device_capability_family(100))
             )
         )
 

vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py

@@ -224,6 +224,8 @@ class FlashInferAllGatherMoEPrepareAndFinalize(FlashInferCutlassMoEPrepareAndFin
                 a1q_scale = None
 
         if is_nvfp4 and a1q_scale is not None:
+            if a1q_scale.element_size() == 1:
+                a1q_scale = a1q_scale.view(torch.uint8)
             a1q_scale = nvfp4_block_scale_interleave(a1q_scale)
 
         return a1q, a1q_scale, None, topk_ids, topk_weights

vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py

@@ -30,7 +30,6 @@ from vllm.utils.torch_utils import direct_register_custom_op
 def _supports_current_device() -> bool:
     """Supports only Blackwell-family GPUs."""
     p = current_platform
-    # Add check flashinfer trtllm is available
     return p.is_cuda() and p.is_device_capability_family(100)
 
 

vllm/model_executor/layers/fused_moe/fused_batched_moe.py

@@ -927,6 +927,7 @@ class BatchedTritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
         SUPPORTED_W_A_FP8 = [
             (kFp8Static128BlockSym, kFp8Dynamic128Sym),
             (kFp8StaticChannelSym, kFp8DynamicTokenSym),
+            (kFp8StaticTensorSym, kFp8DynamicTokenSym),
             (kFp8StaticTensorSym, kFp8StaticTensorSym),
             (kFp8StaticTensorSym, kFp8DynamicTensorSym),
         ]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -45,6 +45,7 @@ from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_Sc
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8Dynamic128Sym,
+    kFp8DynamicTensorSym,
     kFp8DynamicTokenSym,
     kFp8Static128BlockSym,
     kFp8StaticChannelSym,
@@ -1942,12 +1943,13 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
             (kFp8StaticChannelSym, kFp8DynamicTokenSym),
             (kFp8StaticTensorSym, kFp8DynamicTokenSym),
             (kFp8StaticTensorSym, kFp8StaticTensorSym),
+            (kFp8StaticTensorSym, kFp8DynamicTensorSym),
         ]
         return (weight_key, activation_key) in SUPPORTED_W_A
 
     @staticmethod
     def _supports_activation(activation: str) -> bool:
-        return activation in ["silu", "gelu", "swigluoai"]
+        return activation in ["silu", "gelu", "swigluoai", "swiglustep"]
 
     @staticmethod
     def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:

vllm/model_executor/layers/fused_moe/utils.py

@@ -358,6 +358,11 @@ def apply_moe_activation(
         torch.ops._C.gelu_and_mul(output, input)
     elif activation == "swigluoai":
         torch.ops._C.swigluoai_and_mul(output, input)
+    elif activation == "swiglustep":
+        from vllm.model_executor.layers.activation import swiglustep_and_mul_triton
+
+        swiglustep_and_mul_triton(output, input)
+
     # Activations without gated multiplication
     elif activation == SILU_NO_MUL:
         output.copy_(F.silu(input))

vllm/model_executor/layers/quantization/kernels/scaled_mm/rocm.py

@@ -28,6 +28,7 @@ def rocm_per_tensor_float_w8a8_scaled_mm_impl(
         A.shape[0] == 1
         and B.shape[1] % 16 == 0
         and ((bias is None) or (bias.dtype == out_dtype))
+        and A.is_contiguous()
     ):
         output = ops.wvSplitKQ(
             B.t(),

vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py

@@ -6,7 +6,6 @@ from typing import TYPE_CHECKING
 
 import torch
 
-import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -25,10 +24,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     swizzle_blockscale,
 )
 from vllm.platforms import current_platform
-from vllm.utils.flashinfer import (
-    has_flashinfer_cutedsl_grouped_gemm_nt_masked,
-    has_flashinfer_cutlass_fused_moe,
-)
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.fused_moe.oracle.nvfp4 import (
@@ -39,8 +34,6 @@ logger = init_logger(__name__)
 
 
 __all__ = [
-    "is_flashinfer_fp4_cutlass_moe_available",
-    "is_flashinfer_fp4_cutedsl_moe_available",
     "reorder_w1w3_to_w3w1",
     "build_flashinfer_fp4_cutlass_moe_prepare_finalize",
 ]
@@ -126,26 +119,6 @@ def is_supported_config_trtllm(
     return True, None
 
 
-def is_flashinfer_fp4_cutlass_moe_available() -> bool:
-    """Return `True` when FlashInfer CUTLASS NV-FP4 kernels can be used."""
-    return (
-        envs.VLLM_USE_FLASHINFER_MOE_FP4
-        and has_flashinfer_cutlass_fused_moe()
-        and current_platform.is_cuda()
-        and current_platform.has_device_capability(100)
-    )
-
-
-def is_flashinfer_fp4_cutedsl_moe_available() -> bool:
-    """Return ``True`` when FlashInfer CUTEDSL NV-FP4 kernels can be used."""
-    return (
-        envs.VLLM_USE_FLASHINFER_MOE_FP4
-        and has_flashinfer_cutedsl_grouped_gemm_nt_masked()
-        and current_platform.is_cuda()
-        and current_platform.is_device_capability_family(100)
-    )
-
-
 def reorder_w1w3_to_w3w1(
     weight: torch.Tensor, scale: torch.Tensor, dim: int = -2
 ) -> tuple[torch.Tensor, torch.Tensor]:

vllm/model_executor/layers/quantization/utils/nvfp4_moe_support.py

@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from dataclasses import dataclass
-
-import vllm.envs as envs
-from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
-    is_flashinfer_fp4_cutedsl_moe_available,
-    is_flashinfer_fp4_cutlass_moe_available,
-)
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
-    is_fp4_marlin_supported,
-)
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    cutlass_fp4_supported,
-)
-
-__all__ = ["detect_nvfp4_moe_support", "NvFp4Support"]
-
-_logger = init_logger(__name__)
-
-
-@dataclass(frozen=True)
-class NvFp4Support:
-    """Result container for NV-FP4 capability probing."""
-
-    cutlass_supported: bool
-    allow_flashinfer: bool
-    use_marlin: bool
-
-
-def detect_nvfp4_moe_support(class_name: str = "") -> NvFp4Support:
-    """Detect platform support for NV-FP4 fused-MoE path"""
-    cutlass_supported = cutlass_fp4_supported()
-
-    allow_flashinfer = cutlass_supported and (
-        is_flashinfer_fp4_cutlass_moe_available()
-        or is_flashinfer_fp4_cutedsl_moe_available()
-    )
-
-    if allow_flashinfer:
-        _logger.info_once(
-            "Using FlashInfer kernels for %s.", class_name or "NVFP4 path"
-        )
-    else:
-        if envs.VLLM_USE_FLASHINFER_MOE_FP4:
-            _logger.warning_once(
-                "FlashInfer kernels unavailable for %s on current platform.",
-                class_name or "NVFP4 path",
-            )
-
-    use_marlin = False
-    if not cutlass_supported:
-        if is_fp4_marlin_supported():
-            use_marlin = True
-            _logger.info_once("Falling back to Marlin FP4 MoE kernel.")
-        else:
-            raise ValueError(
-                "Current platform does not support NVFP4 quantization. "
-                "Please use Blackwell GPUs or enable FlashInfer."
-            )
-
-    return NvFp4Support(
-        cutlass_supported=cutlass_supported,
-        allow_flashinfer=allow_flashinfer,
-        use_marlin=use_marlin,
-    )

vllm/model_executor/layers/utils.py

@@ -146,6 +146,7 @@ def rocm_unquantized_gemm_impl(
             and n <= 128
             and k > 512
             and math.ceil(k / 512) * math.ceil(m / 16) < get_cu_count()
+            and x.is_contiguous()
         )
         # k == 2880 and (m == 640 or m == 128))
     )
@@ -165,6 +166,7 @@ def rocm_unquantized_gemm_impl(
         and on_gfx9()
         and x.dtype in [torch.float16, torch.bfloat16]
         and k % 8 == 0
+        and x.is_contiguous()
     )
 
     if use_skinny is not True:

vllm/model_executor/models/adapters.py

@@ -466,6 +466,7 @@ def load_weights_using_from_2_way_softmax(
 
     language_model = _get_language_model_for_seq_cls(model)
     is_vlm = language_model is not model
+    using_vlm_head = is_vlm and hasattr(language_model, "score")
 
     language_model.lm_head = ParallelLMHead(
         text_config.vocab_size, text_config.hidden_size, quant_config=quant_config
@@ -506,14 +507,16 @@ def load_weights_using_from_2_way_softmax(
         torch.float32
     ) - lm_head_weight.data[[false_id]].to(torch.float32)
 
-    score_layer = language_model.score if is_vlm else model.score
+    score_layer = language_model.score if using_vlm_head else model.score
     param = score_layer.weight
     weight_loader = getattr(param, "weight_loader", default_weight_loader)
     weight_loader(param, score_weight)
 
     del language_model.lm_head
 
-    score_weight_name = "language_model.score.weight" if is_vlm else "score.weight"
+    score_weight_name = (
+        "language_model.score.weight" if using_vlm_head else "score.weight"
+    )
     loaded_weights.add(score_weight_name)
 
     lm_head_name = "lm_head.weight"
@@ -537,6 +540,7 @@ def load_weights_no_post_processing(model, weights: Iterable[tuple[str, torch.Te
 
     language_model = _get_language_model_for_seq_cls(model)
     is_vlm = language_model is not model
+    using_vlm_head = is_vlm and hasattr(language_model, "score")
 
     language_model.lm_head = ParallelLMHead(
         text_config.vocab_size, text_config.hidden_size, quant_config=quant_config
@@ -572,14 +576,16 @@ def load_weights_no_post_processing(model, weights: Iterable[tuple[str, torch.Te
     token_ids = [tokenizer.convert_tokens_to_ids(t) for t in tokens]
     score_weight = language_model.lm_head.weight.data[token_ids]
 
-    score_layer = language_model.score if is_vlm else model.score
+    score_layer = language_model.score if using_vlm_head else model.score
     param = score_layer.weight
     weight_loader = getattr(param, "weight_loader", default_weight_loader)
     weight_loader(param, score_weight)
 
     del language_model.lm_head
 
-    score_weight_name = "language_model.score.weight" if is_vlm else "score.weight"
+    score_weight_name = (
+        "language_model.score.weight" if using_vlm_head else "score.weight"
+    )
     loaded_weights.add(score_weight_name)
 
     lm_head_name = "lm_head.weight"

vllm/model_executor/models/kimi_k25.py

@@ -0,0 +1,581 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+"""
+Kimi-K2.5 Model Implementation for vLLM.
+
+Kimi-K2.5 extends Kimi-K2 with vision support
+
+This module defines:
+- KimiK25ProcessingInfo/KimiK25MultiModalProcessor: Processing logic
+- KimiK25ForConditionalGeneration: Main model class
+"""
+
+import copy
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Annotated, Any, Literal
+
+import torch
+from torch import nn
+from transformers import BatchFeature
+from transformers.processing_utils import ProcessorMixin
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.model_executor.models.deepseek_v2 import DeepseekV2Model
+from vllm.model_executor.models.interfaces import SupportsMultiModal, SupportsPP
+from vllm.model_executor.models.kimi_k25_vit import (
+    KimiK25MultiModalProjector,
+    MoonViT3dPretrainedModel,
+    vision_tower_forward,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+    NestedTensors,
+    VisionChunk,
+    VisionChunkImage,
+    VisionChunkVideo,
+)
+from vllm.multimodal.parse import MultiModalDataItems, VisionChunkProcessorItems
+from vllm.multimodal.processing import (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    InputProcessingContext,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs import KimiK25Config
+from vllm.transformers_utils.processor import cached_get_image_processor
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .utils import PPMissingLayer, is_pp_missing_parameter, maybe_prefix
+
+logger = init_logger(__name__)
+
+
+# Dummy input dimensions for profiling.
+@dataclass
+class MaxImageTokenMeta:
+    width: int = 3000
+    height: int = 3000
+
+
+class KimiK25MediaPixelInputs(TensorSchema):
+    """
+    Media input schema for K2-VL model.
+
+    Dimensions:
+        - np: Number of patches (flattened from all media items)
+        - ps: Patch size
+        - nm: Number of media items
+    """
+
+    type: Literal["pixel_values"] = "pixel_values"
+
+    pixel_values: Annotated[
+        torch.Tensor | list[torch.Tensor],
+        TensorShape("np", 3, "ps", "ps"),
+    ]
+
+    grid_thws: Annotated[torch.Tensor, TensorShape("nm", 3)]
+
+
+class MoonshotKimiVAutoProcessor(ProcessorMixin):
+    attributes = ["tokenizer"]
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, media_processor=None, tokenizer=None):
+        super().__init__(tokenizer)
+        self.media_processor = media_processor
+
+    # We do not support str input for text here
+    def __call__(
+        self,
+        vision_chunks: list[VisionChunk] | None = None,
+        *,
+        text: list[int],
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Args:
+            vision_chunks: List of VisionChunk items to be processed.
+                For image: VisionChunkImage with type='image', image=PIL.Image
+                For video_chunk: VisionChunkVideo with type='video_chunk', video_chunk=list[PIL.Image]
+            text: The token ids to be fed to a model (required).
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- list of token ids to be fed to a model.
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `vision_chunks` is not `None`.
+            - **grid_thws** -- list of image 3D grid in LLM. Returned when `vision_chunks` is not `None`.
+        """
+        mm_inputs = {}
+        if vision_chunks is not None:
+            assert isinstance(vision_chunks, list)
+            mm_inputs = self.media_processor.preprocess(vision_chunks)
+        # XXX: _apply_hf_processor_text_mm will call tolist() on input_ids
+        return BatchFeature(
+            data={
+                "input_ids": torch.tensor([text]),
+                **mm_inputs,
+            }
+        )
+
+
+class KimiK25ProcessingInfo(BaseProcessingInfo):
+    """Processing information for Kimi-K2.5 model.
+
+    Provides configuration and utilities for processing both
+    images and video-chunks.
+    """
+
+    def __init__(self, ctx: InputProcessingContext) -> None:
+        super().__init__(ctx)
+        self.hf_config = self.get_hf_config()
+        self.media_token_id = self.hf_config.media_placeholder_token_id
+        media_processor = cached_get_image_processor(
+            self.ctx.model_config.model, trust_remote_code=True
+        )
+        self.media_processor = media_processor
+        self.hf_processor = MoonshotKimiVAutoProcessor(
+            media_processor=self.media_processor,
+            tokenizer=self.get_tokenizer(),
+        )
+        self.media_tokens_calculator = self.media_processor.media_tokens_calculator
+
+    def get_hf_processor(self):
+        return self.hf_processor
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(KimiK25Config)
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        # None means unlimited
+        return {"vision_chunk": None}
+
+
+class KimiK25DummyInputsBuilder(BaseDummyInputsBuilder[KimiK25ProcessingInfo]):
+    """Builds dummy inputs for Kimi-K2.5 model profiling."""
+
+    def __init__(self, info: KimiK25ProcessingInfo) -> None:
+        super().__init__(info)
+        self.media_token_id = self.info.media_token_id
+        self.frame_per_chunk = self.info.media_processor.num_frames_per_chunk
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> list[int]:
+        num_media = mm_counts.get("vision_chunk", 0)
+        return [self.media_token_id] * num_media
+
+    def get_dummy_mm_items(self):
+        dummy_videos = self._get_dummy_images(
+            height=MaxImageTokenMeta.height,
+            width=MaxImageTokenMeta.width,
+            num_images=self.frame_per_chunk,
+        )
+
+        video_chunk_dummy_item = VisionChunkVideo(
+            type="video_chunk", video_chunk=dummy_videos
+        )
+        video_chunk_num_tokens = self.info.media_tokens_calculator(
+            video_chunk_dummy_item
+        )
+
+        image_dummy_item = VisionChunkImage(
+            type="image",
+            image=self._get_dummy_images(
+                height=MaxImageTokenMeta.height,
+                width=MaxImageTokenMeta.width,
+                num_images=1,
+            )[0],
+        )
+        image_num_tokens = self.info.media_tokens_calculator(image_dummy_item)
+        # return the larger one
+        if video_chunk_num_tokens >= image_num_tokens:
+            return [video_chunk_dummy_item]
+        else:
+            return [image_dummy_item]
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
+    ) -> MultiModalDataDict:
+        # TODO: Support mm_options for vision_chunk to allow user configuration
+        dummy_items = self.get_dummy_mm_items()
+        return {"vision_chunk": dummy_items}
+
+
+class KimiK25MultiModalProcessor(BaseMultiModalProcessor[KimiK25ProcessingInfo]):
+    """Multi-modal processor for Kimi-K2.5.
+
+    Handles both image and video-chunk modalities.
+    """
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        """Indicates how to slice media input into multiple items.
+
+        pixel_values: [N, 3, patch_size, patch_size], all patches collected from B medias
+        grid_thws: [B,3], each item: [N_t, N_h ,N_w], indicates the grid size in time/height/width direction
+                    for current item.
+
+        by multiplying [N_t, N_h ,N_w], we get the number of patches for each media item, thus we can slice
+        pixel_values by pixel_values[start:start + N_t*N_h*N_w] to get patches of one item.
+
+        """
+        grid_thws = hf_inputs.get("grid_thws", torch.empty((0, 3)))
+        grid_sizes = grid_thws.prod(-1)
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "vision_chunk", grid_sizes
+            ),
+            grid_thws=MultiModalFieldConfig.batched("vision_chunk"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_config = self.info.get_hf_config()
+        media_token_id = hf_config.media_placeholder_token_id
+
+        def get_replacement(item_idx: int):
+            media = mm_items.get_items("vision_chunk", (VisionChunkProcessorItems,))
+            num_media_token = self.info.media_tokens_calculator(media[item_idx])
+            return [media_token_id] * num_media_token
+
+        return [
+            PromptReplacement(
+                modality="vision_chunk",
+                target=[media_token_id],
+                replacement=get_replacement,
+            ),
+        ]
+
+    def split_video_chunks(self, video):
+        return self.info.media_processor.split_video_chunks(video)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    KimiK25MultiModalProcessor,
+    info=KimiK25ProcessingInfo,
+    dummy_inputs=KimiK25DummyInputsBuilder,
+)
+class KimiK25ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+    """Kimi-K2.5 model for conditional generation.
+
+    Supports both image and video-chunk modalities.
+    Video-chunks are temporal segments (typically 4 frames) that are
+    processed with temporal pooling.
+    """
+
+    supports_encoder_tp_data = True
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        # Kimi-K2.5 uses video_chunk for all media types
+        if modality == "image":
+            return "<|media_begin|>image<|media_content|><|media_pad|><|media_end|>"
+        elif modality == "video":
+            # return a placeholder, to be replaced in the future.
+            return "<|kimi_k25_video_placeholder|>"
+
+        raise ValueError(f"Unsupported modality: {modality}")
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        model_config = vllm_config.model_config
+        config: KimiK25Config = model_config.hf_config
+        self.config = config
+        quant_config = vllm_config.quant_config
+
+        # Check for MoonViT config compatibility
+        self.use_data_parallel = (
+            model_config.multimodal_config.mm_encoder_tp_mode == "data"
+        )
+        self.hidden_size = config.text_config.hidden_size
+        self.device = torch.cuda.current_device()
+        # Build vision tower directly with KimiK25VisionConfig
+        self.vision_tower = MoonViT3dPretrainedModel(
+            config.vision_config,
+            prefix=maybe_prefix(prefix, "vision_tower"),
+        )
+        self.vision_tower = self.vision_tower.to(
+            device=self.device, dtype=model_config.dtype
+        )
+
+        self.mm_projector = KimiK25MultiModalProjector(
+            config=config.vision_config,
+            use_data_parallel=self.use_data_parallel,
+            prefix=maybe_prefix(prefix, "mm_projector"),
+        )
+        self.mm_projector = self.mm_projector.to(
+            device=self.device, dtype=model_config.dtype
+        )
+
+        self.quant_config = quant_config
+        sub_vllm_config = copy.deepcopy(vllm_config)
+        sub_vllm_config.model_config.hf_config = (
+            sub_vllm_config.model_config.hf_config.text_config
+        )
+        self.language_model = DeepseekV2Model(
+            vllm_config=sub_vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.text_config.hidden_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(config.vocab_size, scale=logit_scale)
+        self.media_placeholder: int = self.config.media_placeholder_token_id
+
+    def _parse_and_validate_media_input(
+        self, **kwargs: object
+    ) -> KimiK25MediaPixelInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        grid_thws = kwargs.pop("grid_thws", None)
+        if pixel_values is None:
+            return None
+
+        if isinstance(pixel_values, list):
+            pixel_values = torch.cat(pixel_values, dim=0)
+
+        if len(pixel_values.shape) == 5 or len(pixel_values.shape) == 3:
+            pixel_values = pixel_values.reshape(
+                pixel_values.shape[0] * pixel_values.shape[1], *pixel_values.shape[2:]
+            )
+
+        # The batch dimension of pixel_values has been flattened into shape[0]
+        target_dtype = next(self.vision_tower.parameters()).dtype
+        pixel_values = pixel_values.to(target_dtype)
+        assert isinstance(grid_thws, torch.Tensor), (
+            f"expect grid_thws to be a tensor, get {type(grid_thws)}"
+        )
+        # In some cases (e.g. with merger), grid_thws has an extra middle dimension
+        grid_thws = grid_thws.reshape(-1, grid_thws.shape[-1])
+        assert grid_thws.ndim == 2 and grid_thws.size(1) == 3, (
+            f"unexpected shape for grid_thws: {grid_thws.shape}"
+        )
+
+        return KimiK25MediaPixelInputs(
+            type="pixel_values",
+            pixel_values=pixel_values,
+            grid_thws=grid_thws,
+        )
+
+    def _process_media_input(
+        self, media_input: KimiK25MediaPixelInputs
+    ) -> list[torch.Tensor]:
+        # NOTE(moyan): This forward will automatically batch the forward pass internally
+        media_features = vision_tower_forward(
+            self.vision_tower,
+            media_input["pixel_values"],
+            media_input["grid_thws"],
+            mm_projector=self.mm_projector,
+            use_data_parallel=self.use_data_parallel,
+        )
+        return media_features
+
+    def embed_multimodal(self, **kwargs: object) -> NestedTensors | None:
+        # Validate the multimodal input keyword arguments
+        media_input = self._parse_and_validate_media_input(**kwargs)
+        if media_input is None:
+            return None
+
+        # Run multimodal inputs through encoder and projector
+        vision_embeddings = self._process_media_input(media_input)
+        return vision_embeddings
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+        hidden_states = self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states, **kwargs)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        config = self.config.text_config
+        if not getattr(config, "n_routed_experts", None):
+            return []
+        return SharedFusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=config.n_routed_experts,
+            num_redundant_experts=0,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        config = self.config.text_config
+        _KEYS_TO_MODIFY_MAPPING = {
+            "language_model.lm_head": "lm_head",
+            "language_model.model": "language_model",
+            # mm_projector -> mm_projector mapping
+            # "mm_projector": "mm_projector",
+            "mm_projector.proj.0": "mm_projector.linear_1",
+            "mm_projector.proj.2": "mm_projector.linear_2",
+        }
+        stacked_params_mapping = [
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        if getattr(config, "kv_lora_rank", None) and getattr(
+            config, "q_lora_rank", None
+        ):
+            stacked_params_mapping += [
+                (".fused_qkv_a_proj", ".q_a_proj", 0),
+                (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
+            ]
+        expert_params_mapping = self.get_expert_mapping()
+
+        params_dict = dict(self.named_parameters())
+
+        for args in weights:
+            name, loaded_weight = args[:2]
+            kwargs = args[2] if len(args) > 2 else {}
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            spec_layer = get_spec_layer_idx_from_weight_name(config, name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+
+            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
+                if key_to_modify in name:
+                    name = name.replace(key_to_modify, new_key)
+
+            use_default_weight_loading = False
+            if "vision" in name:
+                if self.vision_tower is not None:
+                    use_default_weight_loading = True
+            else:
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    if ("mlp.experts." in name) and name not in params_dict:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id, **kwargs)
+                    break
+                else:
+                    for _, (
+                        param_name,
+                        weight_name,
+                        expert_id,
+                        shard_id,
+                    ) in enumerate(expert_params_mapping):
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+
+                        if is_pp_missing_parameter(name, self):
+                            continue
+
+                        param = params_dict[name]
+                        weight_loader = param.weight_loader
+                        weight_loader(
+                            param,
+                            loaded_weight,
+                            name,
+                            expert_id=expert_id,
+                            shard_id=shard_id,
+                            **kwargs,
+                        )
+                        break
+                    else:
+                        use_default_weight_loading = True
+
+            if use_default_weight_loading:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, **kwargs)
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: KimiK25Config, weight_name: str
+) -> int | None:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            # might start with language_model.model.layers
+            if f"model.layers.{layer_idx + i}." in weight_name:
+                return layer_idx + i
+    return None

vllm/model_executor/models/kimi_k25_vit.py

@@ -0,0 +1,678 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Vision tower implementation for Kimi-K2.5 model.
+
+This module provides the vision encoder components for Kimi-K2.5,
+including 3D patch embedding, RoPE position embedding, and
+temporal pooling for video chunks.
+"""
+
+from collections.abc import Sequence
+from copy import deepcopy
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.activations import GELUActivation
+
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.model_executor.models.vision import (
+    is_vit_use_data_parallel,
+    run_dp_sharded_mrope_vision_model,
+)
+from vllm.transformers_utils.configs.kimi_k25 import KimiK25VisionConfig
+
+logger = init_logger(__name__)
+
+
+def _apply_rope_input_validation(x, freqs_cis):
+    assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape)
+    assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape)
+    assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape)
+    assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype
+
+
+def get_rope_shape_decorate(func):
+    _get_rope_shape_first_call_flag = set()
+
+    def wrapper(org, interpolation_mode, shape):
+        key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
+        if key not in _get_rope_shape_first_call_flag:
+            _get_rope_shape_first_call_flag.add(key)
+            _ = func(org, interpolation_mode, shape=(64, 64))
+        return func(org, interpolation_mode, shape)
+
+    return wrapper
+
+
+@get_rope_shape_decorate
+@torch.compile(dynamic=True)
+def get_rope_shape(org, interpolation_mode, shape):
+    return (
+        F.interpolate(
+            org.permute((2, 0, 1)).unsqueeze(0),
+            size=shape,
+            mode=interpolation_mode,
+        )
+        .squeeze(0)
+        .permute((1, 2, 0))
+        .flatten(end_dim=1)
+    )
+
+
+def apply_rope(
+    xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args: (The leading dimensions of all inputs should be the same)
+        xq: query, tensor of shape (..., num_heads, head_dim)
+        xk: key, tensor of shape (..., num_heads, head_dim)
+        freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64.
+    Returns:
+        xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
+    """
+    _apply_rope_input_validation(xq, freqs_cis)
+    _apply_rope_input_validation(xk, freqs_cis)
+
+    freqs_cis = freqs_cis.unsqueeze(-2)  # ..., 1, head_dim/2
+    # ..., num_heads, head_dim/2
+    xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """Generate 1D sincos positional embedding from grid positions."""
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
+    """Generate 1D sincos positional embedding."""
+    grid_t = np.arange(t_size, dtype=np.float32)
+    pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
+    """2D learnable position embedding with temporal extension."""
+
+    def __init__(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        dim: int,
+        interpolation_mode: str = "bicubic",
+    ) -> None:
+        super().__init__()
+        self.height = height
+        self.width = width
+        self.num_frames = num_frames
+        self.dim = dim
+        self.interpolation_mode = interpolation_mode
+        self.weight = nn.Parameter(torch.empty(height, width, dim))
+        self.register_buffer(
+            "time_weight",
+            torch.from_numpy(get_1d_sincos_pos_embed(self.dim, self.num_frames))
+            .float()
+            .unsqueeze(1),
+            persistent=False,
+        )
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight)
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thws.tolist():
+            assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
+            if (h, w) == self.weight.shape[:-1]:
+                pos_emb_2d = self.weight.flatten(end_dim=1)
+            else:
+                pos_emb_2d = get_rope_shape(
+                    self.weight,
+                    interpolation_mode=self.interpolation_mode,
+                    shape=(h, w),
+                )
+
+            if t == 1:
+                pos_emb_3d = pos_emb_2d
+            else:
+                pos_emb_3d = (
+                    pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
+                )
+
+            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
+
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+class MoonVision3dPatchEmbed(nn.Module):
+    """3D patch embedding for vision tower."""
+
+    def __init__(
+        self,
+        out_dim: int,
+        in_dim: int = 3,
+        patch_size: int | tuple[int, int] = (14, 14),
+        pos_emb_height: int = 14,
+        pos_emb_width: int = 14,
+        pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+    ):
+        super().__init__()
+        assert isinstance(patch_size, int | Sequence), (
+            f"Invalid patch_size type: {type(patch_size)}"
+        )
+        if isinstance(patch_size, int):
+            patch_size = (patch_size, patch_size)
+        assert len(patch_size) == 2, (
+            f"Expected patch_size to be a tuple of 2, got {patch_size}"
+        )
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(
+            in_dim, out_dim, kernel_size=patch_size, stride=patch_size
+        )
+
+        if pos_emb_type == "divided_fixed":
+            self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
+                height=pos_emb_height,
+                width=pos_emb_width,
+                num_frames=pos_emb_time,
+                dim=out_dim,
+            )
+        else:
+            raise NotImplementedError(f"Not support pos_emb_type: {pos_emb_type}")
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x).view(x.size(0), -1)
+        # apply positional embedding
+        x = self.pos_emb(x, grid_thws)
+        return x
+
+
+class Rope2DPosEmbRepeated(nn.Module):
+    """2D rotary position embedding with multi-resolution support."""
+
+    def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 4 == 0, "dim must be divisible by 4"
+        self.max_height = max_height
+        self.max_width = max_width
+        self.theta_base = theta_base
+
+    def extra_repr(self):
+        return (
+            f"dim={self.dim}, max_height={self.max_height}, "
+            f"max_width={self.max_width}, theta_base={self.theta_base}"
+        )
+
+    def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
+        """Calculate the cis(freqs) for each position in the 2D grid."""
+        N = self.max_height * self.max_width
+        flat_pos = torch.arange(0, N).float().to(device)
+        x_pos = flat_pos % self.max_width
+        y_pos = flat_pos // self.max_width
+        dim_range = (
+            torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device)
+        )  # C/4
+        freqs = 1.0 / (self.theta_base ** (dim_range / self.dim))
+        x_freqs = torch.outer(x_pos, freqs).float()  # N, C/4
+        y_freqs = torch.outer(y_pos, freqs).float()  # N, C/4
+        x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)  # N, C/4
+        y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)  # N, C/4
+        # N, C/4, 2
+        freqs_cis = torch.cat(
+            [x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1
+        )
+        # max_height, max_width, C/2
+        freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
+        return freqs_cis
+
+    def get_freqs_cis(
+        self, grid_thws: torch.Tensor, device: torch.device
+    ) -> torch.Tensor:
+        """
+        Args:
+            grid_thws (torch.Tensor): grid time, height and width
+
+        Returns:
+            freqs_cis: tensor of shape (sum(t * height * width), dim//2)
+        """
+        if not hasattr(self, "freqs_cis"):
+            self.register_buffer(
+                "freqs_cis", self._precompute_freqs_cis(device), persistent=False
+            )
+
+        shapes = grid_thws.tolist()
+        assert all(
+            1 <= h <= self.max_height and 1 <= w <= self.max_width for t, h, w in shapes
+        ), (
+            shapes,
+            self.max_height,
+            self.max_width,
+        )
+        freqs_cis = torch.cat(
+            [
+                self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
+                for t, h, w in shapes
+            ],
+            dim=0,
+        )
+        return freqs_cis
+
+
+class MLP2(nn.Module):
+    """Two-layer MLP with tensor parallel support."""
+
+    def __init__(
+        self,
+        dims: list[int],
+        activation,
+        bias: bool = True,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        assert len(dims) == 3
+        self.use_data_parallel = use_data_parallel
+        self.fc0 = ColumnParallelLinear(
+            dims[0],
+            dims[1],
+            bias=bias,
+            prefix=maybe_prefix(prefix, "fc0"),
+            disable_tp=self.use_data_parallel,
+        )
+        self.fc1 = RowParallelLinear(
+            dims[1],
+            dims[2],
+            bias=bias,
+            prefix=maybe_prefix(prefix, "fc1"),
+            disable_tp=self.use_data_parallel,
+        )
+        self.activation = activation
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc0(x)
+        x = self.activation(x)
+        x, _ = self.fc1(x)
+        return x
+
+
+class MoonViTEncoderLayer(nn.Module):
+    """Single encoder layer for MoonViT with TP/DP support."""
+
+    def __init__(
+        self,
+        num_heads: int,
+        hidden_dim: int,
+        mlp_dim: int,
+        prefix: str = "",
+        *,
+        activation=F.gelu,
+        attn_bias: bool = False,
+    ):
+        super().__init__()
+        self.use_data_parallel = is_vit_use_data_parallel()
+
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
+        self.tp_size = (
+            1 if self.use_data_parallel else get_tensor_model_parallel_world_size()
+        )
+        self.num_attention_heads_per_partition = divide(num_heads, self.tp_size)
+
+        self.norm0 = nn.LayerNorm(hidden_dim)
+        self.norm1 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP2(
+            [hidden_dim, mlp_dim, hidden_dim],
+            activation,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=self.use_data_parallel,
+        )
+        self.wqkv = QKVParallelLinear(
+            hidden_size=hidden_dim,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_heads,
+            bias=attn_bias,
+            prefix=f"{prefix}.wqkv",
+            disable_tp=self.use_data_parallel,
+        )
+        self.wo = RowParallelLinear(
+            hidden_dim,
+            hidden_dim,
+            bias=attn_bias,
+            prefix=f"{prefix}.wo",
+            disable_tp=self.use_data_parallel,
+        )
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_attention_heads_per_partition,
+            head_size=self.hidden_size_per_attention_head,
+            scale=self.hidden_size_per_attention_head**-0.5,
+            prefix=f"{prefix}.attn",
+        )
+
+    def attention_qkvpacked(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        """Compute self-attention with packed QKV.
+
+        Args:
+            x (torch.Tensor): (seqlen, hidden_dim)
+            cu_seqlens (torch.Tensor): cumulative sequence lengths
+        """
+        seq_length = x.size(0)
+        xqkv, _ = self.wqkv(x)
+
+        qkv_shape = xqkv.size()[:-1] + (
+            3,
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+        # xqkv: (seqlen, 3, nheads, headdim)
+        xqkv = xqkv.view(*qkv_shape)
+        xq, xk, xv = torch.unbind(xqkv, dim=-3)
+
+        xq, xk = apply_rope(xq, xk, rope_freqs_cis)
+
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        attn_out = self.attn(
+            xq.unsqueeze(0),
+            xk.unsqueeze(0),
+            xv.unsqueeze(0),
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        attn_out = attn_out.reshape(
+            seq_length,
+            self.num_attention_heads_per_partition
+            * self.hidden_size_per_attention_head,
+        )
+        attn_out, _ = self.wo(attn_out)
+        return attn_out
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        residual = hidden_states
+        hidden_states = self.norm0(hidden_states)
+
+        hidden_states = self.attention_qkvpacked(
+            hidden_states, cu_seqlens, rope_freqs_cis
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class MoonViT3dEncoder(nn.Module):
+    """Full encoder stack for MoonViT 3D."""
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_layers: int,
+        block_cfg: dict,
+        video_attn_type: str = "spatial_temporal",
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert video_attn_type == "spatial_temporal", (
+            f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
+        )
+        self.video_attn_type = video_attn_type
+        self.rope_2d = Rope2DPosEmbRepeated(
+            block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512
+        )
+        self.blocks = nn.ModuleList(
+            [
+                MoonViTEncoderLayer(
+                    **block_cfg,
+                    prefix=f"{prefix}.blocks.{layer_idx}",
+                )
+                for layer_idx in range(num_layers)
+            ]
+        )
+        self.final_layernorm = nn.LayerNorm(hidden_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thws: torch.Tensor,
+    ) -> torch.Tensor:
+        rope_freqs_cis = self.rope_2d.get_freqs_cis(
+            grid_thws=grid_thws, device=hidden_states.device
+        )
+
+        lengths = torch.cat(
+            (
+                torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
+                grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
+            )
+        )
+
+        cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
+
+        for block in self.blocks:
+            hidden_states = block(
+                hidden_states, cu_seqlens, rope_freqs_cis=rope_freqs_cis
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+def tpool_patch_merger(
+    x: torch.Tensor,
+    grid_thws: torch.Tensor,
+    merge_kernel_size: tuple[int, int] = (2, 2),
+) -> list[torch.Tensor]:
+    """Temporal pooling patch merger."""
+    kh, kw = merge_kernel_size
+    lengths = (grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2]).tolist()
+    seqs = x.split(lengths, dim=0)
+
+    outputs = []
+    for seq, (t, h, w) in zip(seqs, grid_thws.tolist()):
+        nh, nw = h // kh, w // kw
+        # Reshape: (t*h*w, d) -> (t, nh, kh, nw, kw, d)
+        v = seq.view(t, nh, kh, nw, kw, -1)
+        # Temporal pooling first (reduces tensor size before permute)
+        v = v.mean(dim=0)  # (nh, kh, nw, kw, d)
+        # Spatial rearrangement: (nh, kh, nw, kw, d) -> (nh, nw, kh, kw, d)
+        out = v.permute(0, 2, 1, 3, 4).reshape(nh * nw, kh * kw, -1)
+        outputs.append(out)
+
+    return outputs
+
+
+class MoonViT3dPretrainedModel(nn.Module):
+    """Main vision tower model.
+
+    Uses KimiK25VisionConfig directly from transformers_utils/configs/kimi_k25.py.
+    """
+
+    def __init__(
+        self,
+        config: KimiK25VisionConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = deepcopy(config)
+        self.config = config  # Required for run_dp_sharded_mrope_vision_model
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_size = config.patch_size
+        self.merge_type = config.merge_type
+
+        self.patch_embed = MoonVision3dPatchEmbed(
+            out_dim=config.hidden_size,
+            patch_size=config.patch_size,
+            pos_emb_height=config.init_pos_emb_height,
+            pos_emb_width=config.init_pos_emb_width,
+            pos_emb_time=config.init_pos_emb_time,
+            pos_emb_type=config.pos_emb_type,
+        )
+
+        self.encoder = MoonViT3dEncoder(
+            hidden_dim=config.hidden_size,
+            num_layers=config.num_hidden_layers,
+            block_cfg={
+                "num_heads": config.num_attention_heads,
+                "hidden_dim": config.hidden_size,
+                "mlp_dim": config.intermediate_size,
+                "activation": get_act_fn("gelu_pytorch_tanh"),
+                "attn_bias": True,
+            },
+            video_attn_type=config.video_attn_type,
+            prefix=maybe_prefix(prefix, "encoder"),
+        )
+
+    def forward(
+        self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (torch.Tensor): The input pixel values.
+            grid_thws (torch.Tensor): Temporal, height and width.
+
+        Returns:
+            torch.Tensor: The output tokens.
+        """
+        hidden_states = self.patch_embed(pixel_values, grid_thws)
+        hidden_states = self.encoder(hidden_states, grid_thws)
+        if (
+            self.merge_type == "sd2_tpool"
+        ):  # spatial downsampling 2x with temporal pooling all
+            hidden_states = tpool_patch_merger(
+                hidden_states, grid_thws, merge_kernel_size=self.merge_kernel_size
+            )
+        else:
+            raise NotImplementedError(f"Not support {self.merge_type}")
+
+        return hidden_states
+
+
+@torch.inference_mode()
+def mm_projector_forward(mm_projector: torch.nn.Module, vt_output: list[torch.Tensor]):
+    """Apply MM projector to vision tower outputs."""
+    num_embedding_list = [x.shape[0] for x in vt_output]
+    batched = torch.cat(vt_output, dim=0)
+    proj_out = mm_projector(batched)
+    proj_out = proj_out.reshape(-1, proj_out.shape[-1])
+    proj_out = torch.split(proj_out, num_embedding_list)
+    return proj_out
+
+
+@torch.inference_mode()
+def vision_tower_forward(
+    vision_tower: Any,
+    pixel_values: torch.Tensor,
+    grid_thw: torch.Tensor,
+    mm_projector: Any,
+    use_data_parallel: bool,
+) -> list[torch.Tensor]:
+    """DP-sharded vision tower forward with mrope.
+
+    Uses vLLM's standard data parallelism utility to shard the batch
+    across available GPUs, enabling parallel processing of vision features.
+    """
+    if use_data_parallel:
+        grid_thw_list = grid_thw.tolist()
+        vt_outputs = run_dp_sharded_mrope_vision_model(
+            vision_model=vision_tower,
+            pixel_values=pixel_values,
+            grid_thw_list=grid_thw_list,
+            rope_type="rope_2d",
+        )
+    else:
+        vt_outputs = vision_tower(pixel_values, grid_thw)
+    tensors = mm_projector_forward(mm_projector, list(vt_outputs))
+    return list(tensors)
+
+
+class KimiK25MultiModalProjector(nn.Module):
+    """Multi-modal projector with patch merging for Kimi-K2.5."""
+
+    def __init__(
+        self,
+        config: KimiK25VisionConfig,
+        use_data_parallel: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.use_data_parallel = use_data_parallel
+
+        # Hidden size after patch merging
+        merge_h, merge_w = config.merge_kernel_size
+        self.hidden_size = config.hidden_size * merge_h * merge_w
+
+        self.pre_norm = torch.nn.LayerNorm(config.hidden_size, eps=1e-5)
+        self.linear_1 = ReplicatedLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            prefix=maybe_prefix(prefix, "linear_1"),
+        )
+        self.linear_2 = ReplicatedLinear(
+            self.hidden_size,
+            config.mm_hidden_size,
+            bias=True,
+            prefix=maybe_prefix(prefix, "linear_2"),
+        )
+        self.act = GELUActivation()
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
+        hidden_states, _ = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states

vllm/model_executor/models/nemotron_parse.py

@@ -11,7 +11,6 @@ import math
 from collections.abc import Iterable, Mapping, Sequence
 from typing import Annotated, Literal
 
-import cv2
 import numpy as np
 import torch
 import torch.nn as nn
@@ -416,6 +415,8 @@ class NemotronParseImageProcessor:
         else:
             self.target_height = self.target_width = int(self.final_size)
 
+        import cv2
+
         self.transform = A.Compose(
             [
                 A.PadIfNeeded(
@@ -457,6 +458,8 @@ class NemotronParseImageProcessor:
             new_height = int(new_width / aspect_ratio)
 
         # Use cv2.INTER_LINEAR like the original
+        import cv2
+
         return cv2.resize(
             image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
         )

vllm/model_executor/models/registry.py

@@ -188,6 +188,7 @@ _TEXT_GENERATION_MODELS = {
     "SeedOssForCausalLM": ("seed_oss", "SeedOssForCausalLM"),
     "Step1ForCausalLM": ("step1", "Step1ForCausalLM"),
     "Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
+    "Step3p5ForCausalLM": ("step3p5", "Step3p5ForCausalLM"),
     "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
     "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
@@ -359,6 +360,7 @@ _MULTIMODAL_MODELS = {
     ),
     "RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
     "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
+    "KimiK25ForConditionalGeneration": ("kimi_k25", "KimiK25ForConditionalGeneration"),  # noqa: E501
     "LightOnOCRForConditionalGeneration": (
         "lightonocr",
         "LightOnOCRForConditionalGeneration",
@@ -475,6 +477,7 @@ _SPECULATIVE_DECODING_MODELS = {
     "MedusaModel": ("medusa", "Medusa"),
     "OpenPanguMTPModel": ("openpangu_mtp", "OpenPanguMTP"),
     "Qwen3NextMTP": ("qwen3_next_mtp", "Qwen3NextMTP"),
+    "Step3p5MTP": ("step3p5_mtp", "Step3p5MTP"),
     # Temporarily disabled.
     # # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
     # "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),

vllm/model_executor/models/step3p5.py

@@ -0,0 +1,894 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Jurassic model."""
+
+from collections.abc import Iterable
+from typing import Any
+
+import torch
+from torch import nn
+from torch.nn.parameter import Parameter
+
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import (
+    get_dp_group,
+    get_ep_group,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    get_tp_group,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE,
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
+
+from .interfaces import MixtureOfExperts, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    WeightsMapper,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class FP32ReplicatedLinear(ReplicatedLinear):
+    """
+    Use FP32 for higher precision.
+    """
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor | tuple[torch.Tensor, Parameter | None]:
+        assert self.params_dtype == torch.float32
+        return super().forward(x.to(torch.float32))
+
+
+class Step3p5MLP(nn.Module):
+    def __init__(
+        self,
+        config: ModelConfig,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: QuantizationConfig | None = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+        self.prefix = prefix
+        self.hidden_size = hidden_size
+        self.limit = None
+        layer_idx = extract_layer_index(prefix)
+        if (
+            config.swiglu_limits_shared
+            and config.swiglu_limits_shared[layer_idx] is not None
+            and config.swiglu_limits_shared[layer_idx] != 0
+        ):
+            self.limit = config.swiglu_limits_shared[layer_idx]
+            self.act_fn = SwigluStepAndMul(limit=self.limit)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        intermediate_act = self.act_fn(gate_up)
+        output, _ = self.down_proj(intermediate_act)
+        return output
+
+
+class Step3p5Attention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        head_dim: int | None = None,
+        rms_norm_eps: float = 1e-06,
+        qkv_bias: bool = False,
+        rope_theta: float | list[float] | None = 10000,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        rope_scaling: dict[str, Any] | None = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+        # Step3p5 specific args
+        sliding_window: int | None = None,
+        use_head_wise_attn_gate: bool = False,
+        layer_types: list = None,
+        use_rope_layers: list = None,
+        yarn_only_types: list = None,
+        swa_num_attention_heads: int | None = None,
+        partial_rotary_factor: float = 1.0,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.total_num_heads = num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        self.layer_idx = extract_layer_index(prefix)
+        if layer_types:
+            enable_sliding_window = layer_types[self.layer_idx] == "sliding_attention"
+        else:
+            enable_sliding_window = self.layer_idx % 2 == 0
+        if yarn_only_types and layer_types[self.layer_idx] not in yarn_only_types:
+            rope_scaling = None
+
+        if sliding_window is not None and enable_sliding_window:
+            sliding_window = sliding_window
+            if swa_num_attention_heads is not None:
+                num_heads = swa_num_attention_heads
+                self.total_num_heads = swa_num_attention_heads
+        else:
+            sliding_window = None
+
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[self.layer_idx]
+
+        self.rank = get_tensor_model_parallel_rank()
+        self.partial_rotary_factor = partial_rotary_factor
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=qkv_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        if rope_scaling is not None and not isinstance(rope_scaling, dict):
+            raise ValueError("rope_scaling must be a dict for Step3p5Attention.")
+
+        rope_parameters: dict[str, Any] = (
+            dict(rope_scaling) if rope_scaling is not None else {}
+        )
+        rope_parameters.setdefault("rope_type", "default")
+        rope_parameters["rope_theta"] = self.rope_theta
+        rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            max_position=max_position,
+            rope_parameters=rope_parameters,
+        )
+
+        self.q_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
+        self.k_norm = GemmaRMSNorm(self.head_dim, rms_norm_eps)
+        self.use_head_wise_attn_gate = use_head_wise_attn_gate
+        if use_head_wise_attn_gate:
+            self.g_proj = ColumnParallelLinear(
+                hidden_size,
+                self.total_num_heads,
+                bias=False,
+                prefix=f"{prefix}.g_proj",
+            )
+
+        self.use_rope = True
+        if use_rope_layers:
+            self.use_rope = use_rope_layers[self.layer_idx]
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            per_layer_sliding_window=sliding_window,
+            attn_type=attn_type,
+        )
+
+        self.max_position_embeddings = max_position
+        assert self.partial_rotary_factor == 1 or self.partial_rotary_factor == 0.5
+        self.rotary_dim = (
+            self.head_dim if self.partial_rotary_factor == 1 else self.head_dim // 2
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        # Add qk-norm inline similar to Qwen3 MOE attention
+        q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
+        q_by_head = self.q_norm(q_by_head.contiguous())
+        q = q_by_head.view(q.shape)
+
+        k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
+        k_by_head = self.k_norm(k_by_head.contiguous())
+        k = k_by_head.view(k.shape)
+        if self.use_rope:
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        if self.use_head_wise_attn_gate:
+            extra_dims, _ = self.g_proj(hidden_states)
+            output = (
+                attn_output.view(*attn_output.shape[:-1], self.num_heads, self.head_dim)
+                * extra_dims.unsqueeze(-1).sigmoid()
+            )
+            attn_output = output.view(*attn_output.shape)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class FusedMoEBlock(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.layer_idx = extract_layer_index(prefix)
+
+        self.ep_size = get_ep_group().device_group.size()
+        self.ep_rank = get_ep_group().device_group.rank()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        parallel_config = vllm_config.parallel_config
+
+        self.hidden_size = config.hidden_size
+        self.enable_eplb = parallel_config.enable_eplb
+        self.n_routed_experts = config.moe_num_experts
+        self.n_logical_experts = self.n_routed_experts
+        self.n_redundant_experts = parallel_config.eplb_config.num_redundant_experts
+        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+
+        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
+        self.physical_expert_end = (
+            self.physical_expert_start + self.n_local_physical_experts
+        )
+
+        if self.tp_size > config.moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.moe_num_experts}."
+            )
+
+        self.gate = FP32ReplicatedLinear(
+            config.hidden_size,
+            config.moe_num_experts,
+            bias=False,
+            quant_config=None,
+            params_dtype=torch.float32,  # Use FP32 for higher precision.
+            prefix=f"{prefix}.gate",
+        )
+        self.use_moe_router_bias = config.use_moe_router_bias
+        assert self.use_moe_router_bias, "Only support use_moe_router_bias is true."
+        self.routed_scaling_factor = config.moe_router_scaling_factor
+        self.router_bias = nn.Parameter(
+            torch.zeros(config.moe_num_experts, dtype=torch.float32),
+            requires_grad=False,
+        )
+        self.need_fp32_gate = config.need_fp32_gate
+        assert self.need_fp32_gate, (
+            "Router logits must use FP32 precision for numerical stability."
+        )
+
+        activation = "silu"
+        swiglu_limits = config.swiglu_limits or []
+        swiglu_limit = (
+            swiglu_limits[self.layer_idx]
+            if self.layer_idx < len(swiglu_limits)
+            else None
+        )
+        if swiglu_limit not in (None, 0):
+            swiglu_limit = float(swiglu_limit)
+            assert swiglu_limit == 7.0, (
+                "Swiglu limit in fused moe block only suport 7.0 now."
+            )
+            activation = "swiglustep"
+            logger.debug(
+                "step3p5 layer_idx: %s, activation: %s, limit: %s",
+                self.layer_idx,
+                activation,
+                swiglu_limit,
+            )
+
+        self.share_expert = Step3p5MLP(
+            config=config,
+            hidden_size=self.hidden_size,
+            intermediate_size=config.share_expert_dim,
+            hidden_act="silu",
+            reduce_results=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.share_expert",
+        )
+        self.experts = SharedFusedMoE(
+            shared_experts=self.share_expert,
+            gate=self.gate,
+            num_experts=config.moe_num_experts,
+            top_k=config.moe_top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_expert_weight,
+            quant_config=quant_config,
+            activation=activation,
+            prefix=f"{prefix}.experts",
+            scoring_func=getattr(config, "moe_router_activation", "sigmoid"),
+            e_score_correction_bias=self.router_bias,
+            routed_scaling_factor=config.moe_router_scaling_factor,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.experts.is_internal_router:
+            # In this case, the gate/router runs inside the FusedMoE class
+            fused_moe_out = self.experts(
+                hidden_states=hidden_states, router_logits=hidden_states
+            )
+        else:
+            # router_logits: (num_tokens, n_experts)
+            router_logits, _ = self.gate(hidden_states)
+            fused_moe_out = self.experts(
+                hidden_states=hidden_states, router_logits=router_logits
+            )
+
+        shared_output, final_hidden_states = fused_moe_out
+        if self.share_expert is None:
+            assert shared_output is None
+
+        if self.share_expert is not None:
+            assert shared_output is not None
+            final_hidden_states += shared_output
+
+        if self.tp_size > 1:
+            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
+                final_hidden_states
+            )
+
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+
+class Step3p5DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.hidden_size = config.hidden_size
+        layer_idx = extract_layer_index(prefix)
+        self.layer_idx = layer_idx
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        if cache_config is not None:
+            cache_config.sliding_window = None
+        if config.att_impl_type == "GQA":
+            num_attention_heads = None
+            num_attention_groups = None
+            head_dim = None
+            if (
+                getattr(config, "attention_other_setting", None)
+                and getattr(config, "layer_types", [])
+                and config.layer_types[layer_idx]
+                == config.attention_other_setting["attention_type"]
+            ):
+                num_attention_heads = config.attention_other_setting[
+                    "num_attention_heads"
+                ]
+                num_attention_groups = config.attention_other_setting[
+                    "num_attention_groups"
+                ]
+                head_dim = config.attention_other_setting["head_dim"]
+            partial_rotary_factors = getattr(config, "partial_rotary_factors", [])
+            self.self_attn = Step3p5Attention(
+                hidden_size=self.hidden_size,
+                num_heads=num_attention_heads
+                if num_attention_heads
+                else config.num_attention_heads,
+                max_position=config.max_position_embeddings,
+                num_kv_heads=num_attention_groups
+                if num_attention_groups
+                else config.num_attention_groups,
+                rope_theta=config.rope_theta,
+                rms_norm_eps=config.rms_norm_eps,
+                qkv_bias=getattr(config, "attention_bias", False),
+                head_dim=head_dim if head_dim else getattr(config, "head_dim", None),
+                cache_config=cache_config,
+                quant_config=quant_config,
+                rope_scaling=getattr(config, "rope_scaling", None),
+                sliding_window=getattr(config, "sliding_window", None),
+                use_head_wise_attn_gate=getattr(
+                    config, "use_head_wise_attn_gate", False
+                ),
+                layer_types=getattr(config, "layer_types", []),
+                use_rope_layers=getattr(config, "use_rope_layers", []),
+                yarn_only_types=getattr(config, "yarn_only_types", []),
+                partial_rotary_factor=partial_rotary_factors[layer_idx]
+                if partial_rotary_factors
+                else 1.0,
+                prefix=f"{prefix}.self_attn",
+            )
+        else:
+            raise ValueError(
+                f"Unsupported attention implementation: {config.att_impl_type}"
+            )
+        self.use_moe = False
+        self.tp_group = get_tp_group()
+        self.use_fused_all_reduce = (
+            get_tensor_model_parallel_world_size() > 1
+            and get_dp_group().world_size == 1
+        )
+        if self.use_fused_all_reduce:
+            logger.warning_once("Enable custom fused all reduce...")
+        else:
+            logger.warning_once("Disable custom fused all reduce...")
+
+        moe_layers_enum = getattr(config, "moe_layers_enum", None)
+        if moe_layers_enum is not None:
+            moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
+        else:
+            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+        if layer_idx in moe_layers_idx:
+            self.moe = FusedMoEBlock(
+                vllm_config,
+                prefix=f"{prefix}.moe",
+            )
+            self.use_moe = True
+        else:
+            self.mlp = Step3p5MLP(
+                config=config,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act="silu",
+                quant_config=quant_config,
+                reduce_results=True,
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(
+            config.hidden_size, config.rms_norm_eps
+        )
+        self.prefix = prefix
+
+    def add_and_maybe_inplace_all_reduce(
+        self, in1: torch.Tensor, in2: torch.Tensor
+    ) -> torch.Tensor:
+        if not self.use_fused_all_reduce:
+            return in1 + in2
+        return self.tp_group.all_reduce(in1 + in2)
+
+    def forward(
+        self, positions: torch.Tensor, hidden_states: torch.Tensor
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states += residual
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        if self.use_moe:
+            ffn_output = self.moe(hidden_states)
+        else:
+            ffn_output = self.mlp(hidden_states)
+        hidden_states = ffn_output + residual
+        return hidden_states
+
+
+@support_torch_compile
+class Step3p5Model(nn.Module):
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        self.vllm_config = vllm_config
+        config = vllm_config.model_config.hf_config
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.moe_num_experts = config.moe_num_experts
+
+        if get_pp_group().is_first_rank or (
+            config.tie_word_embeddings and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Step3p5DecoderLayer(
+                vllm_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {
+                    "hidden_states": hidden_states,
+                }
+            )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        config = self.config
+        assert config.num_attention_groups > 1, "Only support GQA"
+        qkv_params_mapping = []
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+        ]
+
+        disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
+
+        for name, loaded_weight in weights:
+            if name.startswith("model."):
+                local_name = name[len("model.") :]
+                full_name = name
+            else:
+                local_name = name
+                full_name = f"model.{name}" if name else "model"
+
+            spec_layer = get_spec_layer_idx_from_weight_name(config, full_name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            # Skip any layers beyond the main model's depth (e.g., MTP layers)
+            if full_name.startswith("model.layers."):
+                parts = full_name.split(".")
+                if len(parts) > 2 and parts[2].isdigit():
+                    layer_idx = int(parts[2])
+                    if layer_idx >= config.num_hidden_layers:
+                        continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in local_name:
+                    continue
+                if any(
+                    disable_moe_stacked_param in local_name
+                    for disable_moe_stacked_param in disable_moe_stacked_params
+                ):
+                    continue
+                replaced_name = local_name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(replaced_name, self):
+                    continue
+                if replaced_name not in params_dict:
+                    continue
+                param = params_dict[replaced_name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(replaced_name)
+                break
+            else:
+                for param_name, weight_name, shard_id in expert_params_mapping:
+                    if weight_name not in local_name:
+                        continue
+                    replaced_name = local_name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(replaced_name, self):
+                        continue
+                    if (
+                        replaced_name.endswith(".bias")
+                        or replaced_name.endswith("_bias")
+                    ) and replaced_name not in params_dict:
+                        continue
+                    if replaced_name not in params_dict:
+                        continue
+                    param = params_dict[replaced_name]
+                    weight_loader = param.weight_loader
+                    moe_expert_num = self.moe_num_experts
+                    assert loaded_weight.shape[0] == moe_expert_num
+                    for expert_id in range(moe_expert_num):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(
+                            param,
+                            loaded_weight_expert,
+                            replaced_name,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    loaded_params.add(replaced_name)
+                    break
+                else:
+                    for (
+                        param_name,
+                        weight_name,
+                        start_idx,
+                        end_idx,
+                    ) in qkv_params_mapping:
+                        if weight_name not in local_name:
+                            continue
+                        replaced_name = local_name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(replaced_name, self):
+                            continue
+                        if replaced_name not in params_dict:
+                            continue
+                        param = params_dict[replaced_name]
+                        dim = param.shape[param.output_dim]
+                        begin_idx = int(start_idx * dim)
+                        end_idx = int(end_idx * dim)
+                        param_slice = param.narrow(
+                            param.output_dim, begin_idx, end_idx - begin_idx
+                        )
+                        param_slice.copy_(loaded_weight)
+                        loaded_params.add(replaced_name)
+                        break
+                    else:
+                        if is_pp_missing_parameter(local_name, self):
+                            continue
+                        if "expert_bias" in local_name:
+                            logger.warning_once("ignore expert_bias")
+                            continue
+                        if local_name not in params_dict:
+                            continue
+                        param = params_dict[local_name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+                        loaded_params.add(local_name)
+        return loaded_params
+
+
+class Step3p5ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={".share_expert.": ".moe.share_expert."}
+    )
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.vllm_config = vllm_config
+
+        self.model = Step3p5Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        self.moe_layers: list[FusedMoEBlock] = []
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+            assert isinstance(layer, Step3p5DecoderLayer)
+            if hasattr(layer, "moe") and isinstance(layer.moe, FusedMoEBlock):
+                self.moe_layers.append(layer.moe)
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                if not lora_config
+                else lora_config.lora_vocab_padding_size,
+            )
+            self.logits_processor = LogitsProcessor(
+                self.unpadded_vocab_size, config.vocab_size
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+        # Set MoE hyperparameters
+        self.expert_weights = []
+        assert len(self.moe_layers) > 0, "No MoE layers found in the model."
+        example_layer = self.moe_layers[0]
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+        self.num_logical_experts = example_layer.n_logical_experts
+        self.num_physical_experts = example_layer.n_physical_experts
+        self.num_local_physical_experts = example_layer.n_local_physical_experts
+        self.num_routed_experts = example_layer.n_routed_experts
+        self.num_redundant_experts = example_layer.n_redundant_experts
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.model.norm(hidden_states)
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_tokens(input_ids)
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            experts = layer.experts
+            assert isinstance(experts, FusedMoE)
+            # Register the expert weights.
+            self.expert_weights.append(experts.get_expert_weights())
+            experts.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for layer in self.moe_layers:
+            assert isinstance(layer, FusedMoEBlock)
+            layer.n_local_physical_experts = num_local_physical_experts
+            layer.n_physical_experts = num_physical_experts
+            layer.n_redundant_experts = self.num_redundant_experts
+            layer.experts.update_expert_map()
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: ModelConfig, weight_name: str
+) -> int | None:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if weight_name.startswith(
+                f"layers.{layer_idx + i}."  # Step3p5Model
+            ) or weight_name.startswith(f"model.layers.{layer_idx + i}."):  # Step3p5MTP
+                return layer_idx + i
+    return None

vllm/model_executor/models/step3p5_mtp.py

@@ -0,0 +1,315 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.sequence import IntermediateTensors
+
+from .step3p5 import Step3p5DecoderLayer, get_spec_layer_idx_from_weight_name
+from .utils import maybe_prefix
+
+logger = init_logger(__name__)
+
+
+class SharedHead(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.head = ParallelLMHead(
+            config.vocab_size, config.hidden_size, quant_config=quant_config
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(hidden_states)
+
+
+class Step3p5AMultiTokenPredictorLayer(nn.Module):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.enorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.hnorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
+        self.shared_head = SharedHead(config=config, quant_config=quant_config)
+        self.mtp_block = Step3p5DecoderLayer(
+            vllm_config,
+            prefix=f"{prefix}.mtp_block",
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        inputs_embeds = self.enorm(inputs_embeds)
+        previous_hidden_states = self.hnorm(previous_hidden_states)
+
+        hidden_states = self.eh_proj(
+            torch.cat([inputs_embeds, previous_hidden_states], dim=-1)
+        )
+
+        hidden_states = self.mtp_block(positions=positions, hidden_states=hidden_states)
+        return hidden_states
+
+
+class Step3p5AMultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+        # to map the exact layer index from weights
+        self.layers = torch.nn.ModuleDict(
+            {
+                str(idx): Step3p5AMultiTokenPredictorLayer(
+                    vllm_config,
+                    f"{prefix}.layers.{idx}",
+                )
+                for idx in range(
+                    self.mtp_start_layer_idx,
+                    self.mtp_start_layer_idx + self.num_mtp_layers,
+                )
+            }
+        )
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
+            input_ids,
+            positions,
+            previous_hidden_states,
+            inputs_embeds,
+            current_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
+        logits = self.logits_processor(
+            mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
+        )
+        return logits
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+
+class Step3p5MTP(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.vllm_config = vllm_config
+        self.model = Step3p5AMultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.model.compute_logits(hidden_states, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if "embed_tokens" not in name and spec_layer is None:
+                continue
+            name = self._rewrite_spec_layer_name(spec_layer, name)
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    continue
+                if "experts" in name or "moe" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias") or name.endswith("_bias")
+                    ) and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    for expert_id in range(loaded_weight.shape[0]):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(
+                            param,
+                            loaded_weight_expert,
+                            name,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                        )
+                    loaded_params.add(name)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if (
+                        name.endswith(".bias")
+                        and name not in params_dict
+                        or "tok_embeddings" in name
+                    ):
+                        continue
+
+                    if spec_layer is not None and ".transformer." in name:
+                        name = name.replace(".transformer.", ".")
+                    if "shared_head" in name:
+                        name = name.replace("shared_head.output", "shared_head.head")
+                    if "embed_tokens" in name:
+                        assert (
+                            hasattr(self.config, "num_nextn_predict_layers")
+                            and self.config.num_nextn_predict_layers > 0
+                        )
+                        name = "model.embed_tokens.weight"
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        params_need_to_load = set(params_dict.keys())
+        # Some KV cache scales are optional: checkpoints may omit them and vLLM
+        # will fall back to default scales during initialization.
+        optional_params = {
+            name
+            for name, param in params_dict.items()
+            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale"))
+            and getattr(param, "numel", lambda: 0)() == 1
+            and getattr(param, "requires_grad", False) is False
+        }
+        params_need_to_load -= optional_params
+        if params_need_to_load != loaded_params:
+            missing_params = list(params_need_to_load - loaded_params)
+            param_name_example = missing_params[0]
+            raise RuntimeError(
+                "Some parameters like "
+                f"{param_name_example} are not in the checkpoint and will falsely "
+                "use random initialization"
+            )
+        return loaded_params
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
+        """
+        Rewrite the weight name to match the format of the original model.
+        Add .mtp_block for modules in transformer layer block for spec layer
+        """
+        spec_layer_weight_names = [
+            "embed_tokens",
+            "enorm",
+            "hnorm",
+            "eh_proj",
+            "shared_head",
+        ]
+        spec_layer_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                break
+        if not spec_layer_weight:
+            # treat rest weights as weights for transformer layer block
+            name = name.replace(
+                f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
+            )
+        return name

vllm/multimodal/inputs.py

@@ -20,6 +20,7 @@ from typing import (
 )
 
 import numpy as np
+from PIL.Image import Image
 from typing_extensions import NotRequired, TypeVar
 
 from vllm.utils.collection_utils import full_groupby, is_list_of
@@ -29,7 +30,6 @@ from vllm.utils.jsontree import json_map_leaves
 if TYPE_CHECKING:
     import torch
     import torch.types
-    from PIL.Image import Image
     from transformers.feature_extraction_utils import BatchFeature
 
     from .media import MediaWithBytes
@@ -105,6 +105,28 @@ The number of data items allowed per modality is restricted by
 """
 
 
+class VisionChunkImage(TypedDict):
+    """Represents an image wrapped as a vision chunk."""
+
+    type: Literal["image"]
+    image: Image
+    uuid: str | None
+
+
+class VisionChunkVideo(TypedDict):
+    """Represents a video chunk with metadata."""
+
+    type: Literal["video_chunk"]
+    video_chunk: list[Image]
+    uuid: str | None
+    prompt: str
+    video_idx: int
+
+
+VisionChunk = VisionChunkImage | VisionChunkVideo
+"""A vision chunk is either an image or a video chunk."""
+
+
 @final
 class MultiModalDataBuiltins(TypedDict, total=False):
     """Type annotations for modality types predefined by vLLM."""
@@ -118,6 +140,9 @@ class MultiModalDataBuiltins(TypedDict, total=False):
     audio: ModalityData[AudioItem]
     """The input audio(s)."""
 
+    vision_chunk: ModalityData[VisionChunk]
+    """The input visual atom(s) - unified modality for images and video chunks."""
+
 
 MultiModalDataDict: TypeAlias = Mapping[str, ModalityData[Any]]
 """

vllm/multimodal/parse.py

@@ -384,6 +384,13 @@ class VideoEmbeddingItems(EmbeddingItems):
         super().__init__(data, "video", expected_hidden_size)
 
 
+class VisionChunkProcessorItems(ProcessorBatchItems[Any]):
+    """Processor items for vision chunks (unified image and video chunks)."""
+
+    def __init__(self, data: Sequence[Any]) -> None:
+        super().__init__(data, "vision_chunk")
+
+
 _D = TypeVar("_D", bound=ModalityDataItems[Any, Any])
 
 
@@ -652,11 +659,23 @@ class MultiModalDataParser:
 
         return VideoProcessorItems(new_videos, metadata=metadata_lst)
 
+    def _parse_vision_chunk_data(
+        self,
+        data: ModalityData[Any],
+    ) -> ModalityDataItems[Any, Any] | None:
+        """Parse vision chunk data (unified image and video chunks)."""
+        if data is None or self._is_empty(data):
+            return None
+        if self.is_embeddings(data):
+            raise ValueError("Do not support embedding data for vision_chunk right now")
+        return VisionChunkProcessorItems(data)
+
     def _get_subparsers(self) -> Mapping[str, ModalityDataParser]:
         return {
             "audio": self._parse_audio_data,
             "image": self._parse_image_data,
             "video": self._parse_video_data,
+            "vision_chunk": self._parse_vision_chunk_data,
         }
 
     def parse_mm_data(self, mm_data: MultiModalDataDict) -> MultiModalDataItems:

vllm/multimodal/video.py

@@ -235,6 +235,27 @@ class VideoLoader:
 VIDEO_LOADER_REGISTRY = ExtensionManager()
 
 
+@VIDEO_LOADER_REGISTRY.register("identity")
+class IdentityVideoLoader(VideoLoader):
+    """IdentityVideoLoader returns raw video bytes without decoding.
+
+    This allows the model processor to handle video decoding and
+    is required for models like Kimi-K2.5 that need custom video chunk splitting.
+
+    NOTE: This is temporary for Kimi-K2.5 testing. Remember to change back
+    to opencv before release if needed.
+    """
+
+    @classmethod
+    def load_bytes(
+        cls,
+        data: bytes,
+        num_frames: int = -1,
+        **kwargs: Any,
+    ) -> tuple[Any, Any]:
+        return data, None
+
+
 @VIDEO_LOADER_REGISTRY.register("opencv")
 class OpenCVVideoBackend(VideoLoader):
     def get_cv2_video_api(self):

vllm/reasoning/__init__.py

@@ -53,8 +53,8 @@ _REASONING_PARSERS_TO_REGISTER = {
         "HunyuanA13BReasoningParser",
     ),
     "kimi_k2": (
-        "deepseek_r1_reasoning_parser",
+        "kimi_k2_reasoning_parser",
-        "DeepSeekR1ReasoningParser",
+        "KimiK2ReasoningParser",
     ),
     "minimax_m2": (
         "minimax_m2_reasoning_parser",
@@ -84,6 +84,10 @@ _REASONING_PARSERS_TO_REGISTER = {
         "step3_reasoning_parser",
         "Step3ReasoningParser",
     ),
+    "step3p5": (
+        "step3p5_reasoning_parser",
+        "Step3p5ReasoningParser",
+    ),
 }
 
 

vllm/reasoning/kimi_k2_reasoning_parser.py

@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+
+from .identity_reasoning_parser import IdentityReasoningParser
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.openai.chat_completion.protocol import (
+        ChatCompletionRequest,
+    )
+else:
+    ChatCompletionRequest = Any
+
+
+logger = init_logger(__name__)
+
+
+class KimiK2ReasoningParser(ReasoningParser):
+    """
+    Kimi K2 parser that delegates to either DeepSeekR1ReasoningParser or
+    IdentityReasoningParser based on `thinking` and `separate_reasoning`.
+
+    Unlike DeepSeekV3ReasoningParser which defaults to NOT thinking,
+    KimiK2ReasoningParser defaults to thinking mode (uses DeepSeekR1ReasoningParser).
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        chat_kwargs = kwargs.pop("chat_template_kwargs", {}) or {}
+        # Key difference: default to True instead of False
+        thinking = bool(chat_kwargs.pop("thinking", True))
+
+        if thinking:
+            self._parser = DeepSeekR1ReasoningParser(tokenizer, *args, **kwargs)
+        else:
+            self._parser = IdentityReasoningParser(tokenizer, *args, **kwargs)
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        return self._parser.is_reasoning_end(input_ids)
+
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        return self._parser.is_reasoning_end_streaming(input_ids, delta_ids)
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        return self._parser.extract_content_ids(input_ids)
+
+    def extract_reasoning(
+        self, model_output: str, request: "ChatCompletionRequest"
+    ) -> tuple[str | None, str | None]:
+        return self._parser.extract_reasoning(model_output, request)
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        return self._parser.extract_reasoning_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+        )

vllm/reasoning/step3p5_reasoning_parser.py

@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+)
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+from vllm.tokenizers import TokenizerLike
+
+
+class Step3p5ReasoningParser(BaseThinkingReasoningParser):
+    """
+    Reasoning parser for Step3p5 model.
+
+    Step3p5 uses the <think>...</think> format, but it tends to emit an extra
+    newline immediately before and/or after the </think> token. This parser trims:
+      - the newline right before </think>
+      - the newline right after </think>
+    """
+
+    @property
+    def start_token(self) -> str:
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        return "</think>"
+
+    def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        # Used to hold a trailing "\n" from reasoning content so we can decide
+        # whether it is immediately before </think>.
+        self._pending_reasoning_newline = False
+
+        # Used to delay the reasoning end detection.
+        # This is necessary to remove the newline appears immediately after </think>,
+        # which may cause the end detection to be delayed by one round.
+        self.end_offset = 1
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        if self.end_token_id in input_ids and self.end_offset > 0:
+            self.end_offset -= 1
+            return False
+        return self.end_offset < 1
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Sequence[int]
+    ) -> bool:
+        if self.end_token_id in input_ids and self.end_offset > 0:
+            self.end_offset -= 1
+            return False
+        return self.end_offset < 1
+
+    def extract_reasoning(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest | ResponsesRequest,
+    ) -> tuple[str | None, str | None]:
+        reasoning, content = super().extract_reasoning(model_output, request)
+        if reasoning is not None:
+            reasoning = reasoning.removesuffix("\n")
+        if content is not None:
+            content = content.removeprefix("\n")
+        return reasoning or None, content or None
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        # Drop the immediate newline that models often emit after </think>.
+        if previous_text.endswith(self.end_token) and delta_text:
+            if delta_text == "\n":
+                return None
+            elif delta_text.startswith("\n"):
+                remaining = delta_text.removeprefix("\n")
+                return DeltaMessage(content=remaining) if remaining else None
+
+        ret = super().extract_reasoning_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+        )
+
+        if ret is None:
+            return None
+
+        # Compatibility path for models that don't generate the start token:
+        # treat everything before </think> as reasoning and everything after
+        # as content.
+        if (
+            self.start_token_id not in previous_token_ids
+            and self.start_token_id not in delta_token_ids
+        ):
+            if self.end_token_id in delta_token_ids:
+                end_index = delta_text.find(self.end_token)
+                reasoning = delta_text[:end_index]
+                content = delta_text[end_index + len(self.end_token) :]
+                ret = DeltaMessage(reasoning=reasoning, content=content or None)
+            elif self.end_token_id in previous_token_ids:
+                ret = DeltaMessage(content=delta_text)
+            else:
+                ret = DeltaMessage(reasoning=delta_text)
+
+        reasoning_to_output = ret.reasoning
+        content_to_output = ret.content
+
+        # Reasoning: handle the newline immediately before </think>.
+        if reasoning_to_output is not None:
+            if self._pending_reasoning_newline:
+                reasoning_to_output = "\n" + reasoning_to_output
+                self._pending_reasoning_newline = False
+
+            if reasoning_to_output.endswith("\n"):
+                reasoning_to_output = reasoning_to_output.removesuffix("\n")
+                if self.end_token in delta_text:
+                    # Trailing "\n" is right before </think>, drop it.
+                    self._pending_reasoning_newline = False
+                else:
+                    # Hold the trailing "\n" until we know whether </think> follows.
+                    self._pending_reasoning_newline = True
+
+        # Content: handle the newline immediately after </think>.
+        if content_to_output is not None:
+            # No need to get into parser again to remove newline after </think>.
+            self.end_offset -= 1
+
+            # If we have content, reasoning must have ended.
+            self._pending_reasoning_newline = False
+
+            if self.end_token in delta_text and content_to_output.startswith("\n"):
+                content_to_output = content_to_output.removeprefix("\n")
+
+        reasoning_to_output = reasoning_to_output or None
+        content_to_output = content_to_output or None
+        if reasoning_to_output is None and content_to_output is None:
+            return None
+
+        return DeltaMessage(reasoning=reasoning_to_output, content=content_to_output)

vllm/renderers/hf.py

@@ -20,9 +20,11 @@ from vllm.entrypoints.chat_utils import (
     ChatTemplateContentFormatOption,
     ChatTemplateResolutionError,
     ConversationMessage,
+    build_video_prompts_from_mm_data,
     load_chat_template,
     parse_chat_messages,
     parse_chat_messages_async,
+    rebuild_mm_uuids_from_mm_data,
 )
 from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.logger import init_logger
@@ -547,6 +549,40 @@ class HfRenderer(RendererLike):
             **kwargs,
         )
 
+        # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
+        # model which uses unified vision chunks for both images and videos.
+        if (
+            getattr(model_config.hf_config, "use_unified_vision_chunk", False)
+            and mm_uuids is not None
+            and mm_data is not None
+        ):
+            mm_uuids = rebuild_mm_uuids_from_mm_data(mm_uuids, mm_data)
+
+            # get video placehoder, replace it with runtime video-chunk prompts
+            video_placeholder = getattr(
+                model_config.hf_config, "video_placeholder", None
+            )
+            if video_placeholder and isinstance(prompt_raw, str):
+                video_prompts = build_video_prompts_from_mm_data(mm_data)
+
+                # replace in order
+                prompt_raw_parts = prompt_raw.split(video_placeholder)
+                if len(prompt_raw_parts) == len(video_prompts) + 1:
+                    prompt_raw = "".join(
+                        [
+                            prompt_raw_parts[i] + video_prompts[i]
+                            for i in range(len(video_prompts))
+                        ]
+                    )
+                    prompt_raw += prompt_raw_parts[-1]
+                else:
+                    logger.warning(
+                        "Number of video placeholders (%d) does not match "
+                        "number of videos (%d) in the request.",
+                        len(prompt_raw_parts) - 1,
+                        len(video_prompts),
+                    )
+
         prompt = (
             TextPrompt(prompt=prompt_raw)
             if isinstance(prompt_raw, str)
@@ -587,6 +623,40 @@ class HfRenderer(RendererLike):
             **kwargs,
         )
 
+        # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
+        # model which uses unified vision chunks for both images and videos.
+        if (
+            getattr(model_config.hf_config, "use_unified_vision_chunk", False)
+            and mm_uuids is not None
+            and mm_data is not None
+        ):
+            mm_uuids = rebuild_mm_uuids_from_mm_data(mm_uuids, mm_data)
+
+            # get video placehoder, replace it with runtime video-chunk prompts
+            video_placeholder = getattr(
+                model_config.hf_config, "video_placeholder", None
+            )
+            if video_placeholder and isinstance(prompt_raw, str):
+                video_prompts = build_video_prompts_from_mm_data(mm_data)
+
+                # replace in order
+                prompt_raw_parts = prompt_raw.split(video_placeholder)
+                if len(prompt_raw_parts) == len(video_prompts) + 1:
+                    prompt_raw = "".join(
+                        [
+                            prompt_raw_parts[i] + video_prompts[i]
+                            for i in range(len(video_prompts))
+                        ]
+                    )
+                    prompt_raw += prompt_raw_parts[-1]
+                else:
+                    logger.warning(
+                        "Number of video placeholders (%d) does not match "
+                        "number of videos (%d) in the request.",
+                        len(prompt_raw_parts) - 1,
+                        len(video_prompts),
+                    )
+
         prompt = (
             TextPrompt(prompt=prompt_raw)
             if isinstance(prompt_raw, str)

vllm/tool_parsers/__init__.py

@@ -134,6 +134,10 @@ _TOOL_PARSERS_TO_REGISTER = {
         "step3_tool_parser",
         "Step3ToolParser",
     ),
+    "step3p5": (
+        "step3p5_tool_parser",
+        "Step3p5ToolParser",
+    ),
     "xlam": (
         "xlam_tool_parser",
         "xLAMToolParser",

vllm/transformers_utils/config.py

@@ -81,6 +81,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     isaac="IsaacConfig",
     kimi_linear="KimiLinearConfig",
     kimi_vl="KimiVLConfig",
+    kimi_k25="KimiK25Config",
     RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)
     RefinedWebModel="RWConfig",  # For tiiuae/falcon-7b(-instruct)
     jais="JAISConfig",
@@ -95,6 +96,8 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     ultravox="UltravoxConfig",
     step3_vl="Step3VLConfig",
     step3_text="Step3TextConfig",
+    step3p5="Step3p5Config",
+    qwen3_asr="Qwen3ASRConfig",
     qwen3_next="Qwen3NextConfig",
     lfm2_moe="Lfm2MoeConfig",
     tarsier2="Tarsier2Config",

vllm/transformers_utils/configs/__init__.py

@@ -38,6 +38,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "MoonViTConfig": "vllm.transformers_utils.configs.moonvit",
     "KimiLinearConfig": "vllm.transformers_utils.configs.kimi_linear",
     "KimiVLConfig": "vllm.transformers_utils.configs.kimi_vl",
+    "KimiK25Config": "vllm.transformers_utils.configs.kimi_k25",
     "NemotronConfig": "vllm.transformers_utils.configs.nemotron",
     "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
     "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
@@ -49,6 +50,8 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "Step3VLConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3VisionEncoderConfig": "vllm.transformers_utils.configs.step3_vl",
     "Step3TextConfig": "vllm.transformers_utils.configs.step3_vl",
+    "Step3p5Config": "vllm.transformers_utils.configs.step3p5",
+    "Qwen3ASRConfig": "vllm.transformers_utils.configs.qwen3_asr",
     "Qwen3NextConfig": "vllm.transformers_utils.configs.qwen3_next",
     "Tarsier2Config": "vllm.transformers_utils.configs.tarsier2",
     # Special case: DeepseekV3Config is from HuggingFace Transformers
@@ -77,6 +80,7 @@ __all__ = [
     "MoonViTConfig",
     "KimiLinearConfig",
     "KimiVLConfig",
+    "KimiK25Config",
     "NemotronConfig",
     "NemotronHConfig",
     "Olmo3Config",
@@ -88,6 +92,8 @@ __all__ = [
     "Step3VLConfig",
     "Step3VisionEncoderConfig",
     "Step3TextConfig",
+    "Step3p5Config",
+    "Qwen3ASRConfig",
     "Qwen3NextConfig",
     "Tarsier2Config",
 ]

vllm/transformers_utils/configs/kimi_k25.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Kimi-K2.5 Model Configuration.
+
+This configuration supports video-chunk as an internal modality type.
+A video-chunk is the smallest independently processable unit of video.
+"""
+
+from transformers import DeepseekV3Config
+from transformers.configuration_utils import PretrainedConfig
+
+
+class KimiK25VisionConfig(PretrainedConfig):
+    model_type = "kimi_k25_vision"
+
+    def __init__(
+        self,
+        # Vision Tower
+        patch_size: int = 14,
+        init_pos_emb_height: int = 64,
+        init_pos_emb_width: int = 64,
+        init_pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+        num_attention_heads: int = 16,
+        num_hidden_layers: int = 27,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+        video_attn_type: str = "spatial_temporal",
+        merge_type: str = "sd2_tpool",
+        # MM Projector
+        mm_projector_type: str = "patchmerger",
+        mm_hidden_size: int | None = None,
+        projector_hidden_act: str = "gelu",
+        projector_ln_eps: float = 1e-5,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # Vision Tower
+        self.patch_size = patch_size
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        self.init_pos_emb_time = init_pos_emb_time
+        self.pos_emb_type = pos_emb_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.merge_kernel_size = merge_kernel_size
+        self.video_attn_type = video_attn_type
+        self.merge_type = merge_type
+        # MM Projector
+        self.mm_projector_type = mm_projector_type
+        if mm_hidden_size is not None:
+            self.mm_hidden_size = mm_hidden_size
+        else:
+            self.mm_hidden_size = hidden_size
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_ln_eps = projector_ln_eps
+
+
+class KimiK25Config(PretrainedConfig):
+    """Kimi-K2.5 model configuration.
+
+    Kimi-K2.5 extends Kimi-K2 with vision support using video-chunks.
+    A video-chunk consists of multiple consecutive frames
+    that are processed together with temporal pooling.
+
+    Args:
+        vision_config: Configuration for the vision tower and projector.
+        text_config: Configuration for the text model (DeepseekV3).
+        ignore_index: The ignore index for the loss function.
+        media_placeholder_token_id: The token ID for media placeholders.
+        pad_token_id: The token ID for padding.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        vision_config: dict | KimiK25VisionConfig | None = None,
+        text_config: dict | DeepseekV3Config | None = None,
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        use_unified_vision_chunk: bool = False,
+        video_placeholder: str = "<|kimi_k25_video_placeholder|>",
+        **kwargs,
+    ):
+        # Vision config
+        if vision_config is None:
+            vision_config = KimiK25VisionConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = KimiK25VisionConfig(**vision_config)
+        self.vision_config: KimiK25VisionConfig = vision_config
+
+        # Text config
+        if text_config is None:
+            text_config = DeepseekV3Config()
+        elif isinstance(text_config, dict):
+            text_config = DeepseekV3Config(**text_config)
+        self.text_config: DeepseekV3Config = text_config
+
+        # Set mm_hidden_size to text hidden size if not explicitly set
+        if self.vision_config.mm_hidden_size == self.vision_config.hidden_size:
+            self.vision_config.mm_hidden_size = self.text_config.hidden_size
+
+        # Other config
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+        self.use_unified_vision_chunk = use_unified_vision_chunk
+        self.video_placeholder = video_placeholder
+
+        # Propagate quantization config from text model
+        if getattr(self.text_config, "quantization_config", None) is not None:
+            self.quantization_config = self.text_config.quantization_config
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+    @property
+    def hidden_size(self) -> int:
+        """Get hidden size from text config for compatibility."""
+        return self.text_config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        """Get vocab size from text config for compatibility."""
+        return self.text_config.vocab_size

vllm/transformers_utils/configs/step3p5.py

@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Step3p5Config(PretrainedConfig):
+    model_type = "step3p5"
+
+    def __init__(
+        self,
+        hidden_size: int = 5120,
+        intermediate_size: int = 13312,
+        num_attention_heads: int = 40,
+        num_attention_groups: int = 8,
+        num_hidden_layers: int = 48,
+        max_seq_len: int = 4096,
+        vocab_size: int = 65536,
+        rms_norm_eps: float = 1e-5,
+        moe_every_n_layer: int = 2,
+        use_moe: bool = False,
+        moe_intermediate_size: int = 10240,
+        moe_num_experts: int = 16,
+        moe_top_k: int = 4,
+        moe_layer_offset: int = 0,
+        rope_theta: float | list[float] | None = 500000,
+        rope_scaling: dict[str, Any] | None = None,
+        head_dim: int | None = None,
+        share_expert_dim: int | None = None,
+        norm_expert_weight: bool = True,
+        bos_token_id: list[int] | int | None = None,
+        eos_token_id: list[int] | int | None = None,
+        moe_router_activation: str = "softmax",
+        moe_router_scaling_factor: float = 1.0,
+        att_impl_type: str = "GQA",
+        use_head_wise_attn_gate: bool = False,
+        use_moe_router_bias: bool = True,
+        need_fp32_gate: bool = True,
+        layer_types: list[str] | None = None,
+        use_rope_layers: list[bool] | None = None,
+        yarn_only_types: list[str] | None = None,
+        attention_other_setting: dict[str, Any] | None = None,
+        num_nextn_predict_layers: int = 0,
+        swiglu_limits: list[float] | None = None,
+        swiglu_limits_shared: list[float] | None = None,
+        max_position_embeddings: int | None = None,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_attention_groups = num_attention_groups
+        self.num_hidden_layers = num_hidden_layers
+        self.max_seq_len = max_seq_len
+        self.vocab_size = vocab_size
+        self.rms_norm_eps = rms_norm_eps
+        self.use_moe = use_moe
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_every_n_layer = moe_every_n_layer
+        self.moe_num_experts = moe_num_experts
+        self.num_experts_per_tok = moe_top_k
+        self.moe_top_k = moe_top_k
+        self.moe_layer_offset = moe_layer_offset
+
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.head_dim = head_dim
+        if share_expert_dim is None:
+            self.share_expert_dim = self.moe_intermediate_size * self.moe_top_k
+        else:
+            self.share_expert_dim = share_expert_dim
+        self.norm_expert_weight = norm_expert_weight
+
+        self.max_position_embeddings = max_position_embeddings
+        self.moe_router_activation = moe_router_activation
+        self.moe_router_scaling_factor = moe_router_scaling_factor
+        self.use_moe_router_bias = use_moe_router_bias
+        self.need_fp32_gate = need_fp32_gate
+
+        self.att_impl_type = att_impl_type
+        self.use_head_wise_attn_gate = use_head_wise_attn_gate
+        self.layer_types = layer_types
+        self.use_rope_layers = use_rope_layers
+        self.yarn_only_types = yarn_only_types
+        self.attention_other_setting = attention_other_setting
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.swiglu_limits = swiglu_limits
+        self.swiglu_limits_shared = swiglu_limits_shared
+
+        resolved_bos_token_id = 1 if bos_token_id is None else bos_token_id
+        resolved_eos_token_id = [2, 3] if eos_token_id is None else eos_token_id
+        self.bos_token_id = resolved_bos_token_id
+        self.eos_token_id = resolved_eos_token_id
+
+        super().__init__(
+            bos_token_id=resolved_bos_token_id,
+            eos_token_id=resolved_eos_token_id,
+            **kwargs,
+        )

vllm/v1/attention/backends/flash_attn.py

@@ -257,6 +257,14 @@ class FlashAttentionMetadataBuilder(AttentionMetadataBuilder[FlashAttentionMetad
     )
     supports_update_block_table: bool = True
 
+    @classmethod
+    def get_cudagraph_support(
+        cls,
+        vllm_config: "VllmConfig",
+        kv_cache_spec: "AttentionSpec",
+    ) -> AttentionCGSupport:
+        return cls._cudagraph_support
+
     def __init__(
         self,
         kv_cache_spec: AttentionSpec,

vllm/v1/core/kv_cache_coordinator.py

@@ -479,6 +479,16 @@ class HybridKVCacheCoordinator(KVCacheCoordinator):
         hit_length = max_cache_hit_length
         hit_blocks_by_group: list[list[KVCacheBlock] | None] = [None] * num_groups
 
+        # Simple hybrid (1 full attn + 1 other): one iteration suffices.
+        # Full attn is always first if it exists. This avoids EAGLE drops
+        # being applied multiple times to non-full-attn groups.
+        # FIXME (yifan): However, for complex hybrid models with multiple attn
+        # groups, we still have the EAGLE spiral block dropping problem. See
+        # discussion in issue https://github.com/vllm-project/vllm/issues/32802.
+        is_simple_hybrid = len(self.attention_groups) == 2 and isinstance(
+            self.attention_groups[0][0], FullAttentionSpec
+        )
+
         while True:
             curr_hit_length = hit_length
 
@@ -495,10 +505,6 @@ class HybridKVCacheCoordinator(KVCacheCoordinator):
                     # the last iteration.
                     num_blocks = curr_hit_length // spec.block_size
                     curr_hit_length = num_blocks * spec.block_size
-                    for group_id in group_ids:
-                        blocks = hit_blocks_by_group[group_id]
-                        assert blocks is not None
-                        del blocks[num_blocks:]
                 else:
                     hit_blocks = manager_cls.find_longest_cache_hit(
                         block_hashes=_get_block_hashes(spec),
@@ -513,10 +519,20 @@ class HybridKVCacheCoordinator(KVCacheCoordinator):
                     for group_id, blocks in zip(group_ids, hit_blocks):
                         hit_blocks_by_group[group_id] = blocks
 
-            if curr_hit_length < hit_length:
+            if curr_hit_length >= hit_length:
-                hit_length = curr_hit_length
-            else:
                 break
+            hit_length = curr_hit_length
+            # Simple hybrid: exit after one iteration
+            if is_simple_hybrid:
+                break
+
+        # Truncate full attention blocks to final hit_length (if present)
+        spec, group_ids, _ = self.attention_groups[0]
+        if isinstance(spec, FullAttentionSpec):
+            num_blocks = hit_length // spec.block_size
+            for group_id in group_ids:
+                if (blks := hit_blocks_by_group[group_id]) is not None:
+                    del blks[num_blocks:]
 
         return tuple(
             blocks if blocks is not None else [] for blocks in hit_blocks_by_group

vllm/v1/engine/core.py

@@ -911,6 +911,17 @@ class EngineCoreProc(EngineCore):
                 set_process_title("EngineCore")
             decorate_logs()
 
+            if data_parallel and vllm_config.kv_transfer_config is not None:
+                # modify the engine_id and append the local_dp_rank to it to ensure
+                # that the kv_transfer_config is unique for each DP rank.
+                vllm_config.kv_transfer_config.engine_id = (
+                    f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}"
+                )
+                logger.debug(
+                    "Setting kv_transfer_config.engine_id to %s",
+                    vllm_config.kv_transfer_config.engine_id,
+                )
+
             parallel_config.data_parallel_index = dp_rank
             if data_parallel and vllm_config.model_config.is_moe:
                 # Set data parallel rank for this engine process.
@@ -1285,17 +1296,6 @@ class DPEngineCoreProc(EngineCoreProc):
         assert local_dp_rank is not None
         assert 0 <= local_dp_rank <= dp_rank < dp_size
 
-        if vllm_config.kv_transfer_config is not None:
-            # modify the engine_id and append the local_dp_rank to it to ensure
-            # that the kv_transfer_config is unique for each DP rank.
-            vllm_config.kv_transfer_config.engine_id = (
-                f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}"
-            )
-            logger.debug(
-                "Setting kv_transfer_config.engine_id to %s",
-                vllm_config.kv_transfer_config.engine_id,
-            )
-
         self.dp_rank = dp_rank
         self.dp_group = vllm_config.parallel_config.stateless_init_dp_group()
 

vllm/v1/engine/utils.py

@@ -313,6 +313,13 @@ class CoreEngineActorManager:
             dp_vllm_config.parallel_config.placement_group = pg
             local_client = index < local_engine_count
 
+            if dp_size > 1 and dp_vllm_config.kv_transfer_config is not None:
+                # modify the engine_id and append the local_dp_rank to it to ensure
+                # that the kv_transfer_config is unique for each DP rank.
+                dp_vllm_config.kv_transfer_config.engine_id = (
+                    f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{local_index}"
+                )
+
             # Ray XPU known issue: dpctl initializes the GPU runtime early, so
             # setting device env vars in Ray actor's initialization method
             # will not affect device selection. See:

vllm/v1/worker/gpu_model_runner.py

@@ -1382,12 +1382,14 @@ class GPUModelRunner(
         num_scheduled_tokens: dict[str, int],
         kv_cache_spec: KVCacheSpec,
         num_reqs: int,
+        for_cudagraph_capture: bool = False,
     ) -> tuple[torch.Tensor | None, np.ndarray | None]:
         if not isinstance(kv_cache_spec, CrossAttentionSpec):
             return None, None
 
         # Zero out buffer for padding requests that are not actually scheduled (CGs)
         self.encoder_seq_lens.np[:num_reqs] = 0
+
         # Build encoder_seq_lens array mapping request indices to
         # encoder lengths for inputs scheduled in this batch
         for req_id in num_scheduled_tokens:
@@ -1404,6 +1406,15 @@ class GPUModelRunner(
                 feature.mm_position.length for feature in req_state.mm_features
             )
             self.encoder_seq_lens.np[req_index] = encoder_input_tokens
+        if for_cudagraph_capture:
+            # During CUDA graph capture, we need to use realistic encoder lengths
+            # so that max_seqlen_k is captured with the correct value.
+            max_encoder_len = getattr(
+                self.model_config.hf_config,
+                "max_source_positions",
+                self.max_encoder_len,
+            )
+            self.encoder_seq_lens.np[:num_reqs] = max_encoder_len
 
         self.encoder_seq_lens.copy_to_gpu(num_reqs)
         encoder_seq_lens = self.encoder_seq_lens.gpu[:num_reqs]
@@ -1821,6 +1832,7 @@ class GPUModelRunner(
                 num_scheduled_tokens or {},
                 kv_cache_group.kv_cache_spec,
                 num_reqs_padded,
+                for_cudagraph_capture=for_cudagraph_capture,
             )
             if kv_cache_gid > 0:
                 cm.block_table_tensor = _get_block_table(kv_cache_gid)