Add GPU memory utilization warning for values >= 0.95

Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>

.github/workflows/_accuracy_test.yml

@@ -44,7 +44,7 @@ jobs:
             FULL_REPO="${{ github.repository }}"
             REPO_NAME="${FULL_REPO##*/}"
             BASE_BRANCH="${{ github.base_ref }}"
-
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \
@@ -80,12 +80,14 @@ jobs:
           FD_API_PORT=$((42088 + DEVICE_PORT * 100))
           FD_ENGINE_QUEUE_PORT=$((42058 + DEVICE_PORT * 100))
           FD_METRICS_PORT=$((42078 + DEVICE_PORT * 100))
+          FD_CACHE_QUEUE_PORT=$((42098 + DEVICE_PORT * 100))
           echo "Test ENV Parameter:"
           echo "========================================================="
           echo "FLASK_PORT=${FLASK_PORT}"
           echo "FD_API_PORT=${FD_API_PORT}"
           echo "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}"
           echo "FD_METRICS_PORT=${FD_METRICS_PORT}"
+          echo "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}"
           echo "DEVICES=${DEVICES}"
           echo "========================================================="
 
@@ -99,7 +101,7 @@ jobs:
             exit 1
           fi
 
-          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT)
+          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT $FD_CACHE_QUEUE_PORT)
           LOG_FILE="./port_cleanup_$(date +%Y%m%d_%H%M%S).log"
           echo "==== LOG_FILE is ${LOG_FILE} ===="
 
@@ -133,6 +135,7 @@ jobs:
           -e "FD_API_PORT=${FD_API_PORT}" \
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
+          -e "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}" \
           -e "FLASK_PORT=${FLASK_PORT}" \
           -v "${MODEL_CACHE_DIR}:/MODELDATA" \
           -v "${CACHE_DIR}/gitconfig:/etc/gitconfig:ro" \

.github/workflows/_base_test.yml

@@ -44,7 +44,7 @@ jobs:
             FULL_REPO="${{ github.repository }}"
             REPO_NAME="${FULL_REPO##*/}"
             BASE_BRANCH="${{ github.base_ref }}"
-
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \
@@ -80,12 +80,14 @@ jobs:
           FD_API_PORT=$((42088 + DEVICE_PORT * 100))
           FD_ENGINE_QUEUE_PORT=$((42058 + DEVICE_PORT * 100))
           FD_METRICS_PORT=$((42078 + DEVICE_PORT * 100))
+          FD_CACHE_QUEUE_PORT=$((42098 + DEVICE_PORT * 100))
           echo "Test ENV Parameter:"
           echo "========================================================="
           echo "FLASK_PORT=${FLASK_PORT}"
           echo "FD_API_PORT=${FD_API_PORT}"
           echo "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}"
           echo "FD_METRICS_PORT=${FD_METRICS_PORT}"
+          echo "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}"
           echo "DEVICES=${DEVICES}"
           echo "========================================================="
 
@@ -99,7 +101,7 @@ jobs:
             exit 1
           fi
 
-          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT)
+          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT $FD_CACHE_QUEUE_PORT)
           LOG_FILE="./port_cleanup_$(date +%Y%m%d_%H%M%S).log"
           echo "==== LOG_FILE is ${LOG_FILE} ===="
 
@@ -134,7 +136,7 @@ jobs:
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
           -e "FLASK_PORT=${FLASK_PORT}" \
-          -e "FD_FORCE_CHUNKED_PREFILL=1" \
+          -e "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}" \
           -v "${MODEL_CACHE_DIR}:/MODELDATA" \
           -v "${CACHE_DIR}/gitconfig:/etc/gitconfig:ro" \
           -v "${CACHE_DIR}/.cache:/root/.cache" \

.github/workflows/_ci_image_build.yml

@@ -0,0 +1,73 @@
+name: Docker Build
+description: "FastDeploy CI Image Build"
+
+on:
+  workflow_call:
+    inputs:
+      CI_DOCKER_IMAGE_NAME:
+        description: "Build Images"
+        required: true
+        type: string
+        default: "ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:cuda126-py310"
+      FASTDEPLOY_ARCHIVE_URL:
+        description: "URL of the compressed FastDeploy code archive."
+        required: true
+        type: string
+      DOCKER_IMAGE_NAME:
+        description: "Build Images"
+        required: false
+        type: string
+        default: "ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:fastdeploy-ciuse-cuda126-dailyupdate"
+    outputs:
+      docker_name_precheck:
+        description: "Output path of the generated wheel"
+        value: ${{ jobs.docker_build.outputs.docker_name_precheck }}
+
+jobs:
+  docker_build:
+    runs-on: [self-hosted, Docker-Build]
+    outputs:
+      docker_name_precheck: ${{ steps.docker_build.outputs.docker_name_precheck }}
+    steps:
+      - name: Code Prepare
+        id: docker_build
+        shell: bash
+        env:
+          docker_image_name: ${{ inputs.CI_DOCKER_IMAGE_NAME }}
+          docker_image: ${{ inputs.DOCKER_IMAGE_NAME }}
+          fd_archive_url: ${{ inputs.FASTDEPLOY_ARCHIVE_URL }}
+        run: |
+            set -x
+            REPO="https://github.com/${{ github.repository }}.git"
+            FULL_REPO="${{ github.repository }}"
+            REPO_NAME="${FULL_REPO##*/}"
+            BASE_BRANCH="${{ github.base_ref }}"
+
+            # Clean the repository directory before starting
+            docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
+            -e "REPO_NAME=${REPO_NAME}" \
+            ${docker_image} /bin/bash -c '
+              if [ -d ${REPO_NAME} ]; then
+                echo "Directory ${REPO_NAME} exists, removing it..."
+                rm -rf ${REPO_NAME}*
+              fi
+            '
+
+            wget -q ${fd_archive_url}
+            tar -xf FastDeploy.tar.gz
+            rm -rf FastDeploy.tar.gz
+            cd FastDeploy
+            git config --global user.name "FastDeployCI"
+            git config --global user.email "fastdeploy_ci@example.com"
+            git log -n 3 --oneline
+
+            # Docker Build
+            cd tools/dockerfile/
+            set -e
+            cp ../../requirements.txt ./
+            cp ../../scripts/unittest_requirement.txt ./
+            docker build -t ${docker_image_name} -f Dockerfile.ci . \
+                --network host \
+                --no-cache
+            docker push ${docker_image_name}
+            echo "docker_name_precheck=${docker_image_name}" >> $GITHUB_OUTPUT

.github/workflows/_logprob_test_linux.yml

@@ -39,6 +39,7 @@ jobs:
           docker_image: ${{ inputs.DOCKER_IMAGE }}
           paddletest_archive_url: ${{ inputs.PADDLETEST_ARCHIVE_URL }}
         run: |
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \
@@ -70,12 +71,14 @@ jobs:
           FD_API_PORT=$((42088 + DEVICE_PORT * 100))
           FD_ENGINE_QUEUE_PORT=$((42058 + DEVICE_PORT * 100))
           FD_METRICS_PORT=$((42078 + DEVICE_PORT * 100))
+          FD_CACHE_QUEUE_PORT=$((42098 + DEVICE_PORT * 100))
           echo "Test ENV Parameter:"
           echo "========================================================="
           echo "FLASK_PORT=${FLASK_PORT}"
           echo "FD_API_PORT=${FD_API_PORT}"
           echo "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}"
           echo "FD_METRICS_PORT=${FD_METRICS_PORT}"
+          echo "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}"
           echo "DEVICES=${DEVICES}"
           echo "========================================================="
 
@@ -89,7 +92,7 @@ jobs:
             exit 1
           fi
 
-          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT)
+          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT $FD_CACHE_QUEUE_PORT)
           LOG_FILE="./port_cleanup_$(date +%Y%m%d_%H%M%S).log"
           echo "==== LOG_FILE is ${LOG_FILE} ===="
 
@@ -114,7 +117,6 @@ jobs:
             echo "Removing stale container: ${runner_name}"
             docker rm -f ${runner_name} || true
           fi
-
           docker run --rm --ipc=host --pid=host --net=host \
           --name ${runner_name} \
           -v $(pwd):/workspace \
@@ -123,6 +125,7 @@ jobs:
           -e "FD_API_PORT=${FD_API_PORT}" \
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
+          -e "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}" \
           -e "FLASK_PORT=${FLASK_PORT}" \
           -v "${MODEL_CACHE_DIR}:/MODELDATA" \
           -v "${CACHE_DIR}/gitconfig:/etc/gitconfig:ro" \

.github/workflows/_pre_ce_test.yml

@@ -46,7 +46,7 @@ jobs:
             FULL_REPO="${{ github.repository }}"
             REPO_NAME="${FULL_REPO##*/}"
             BASE_BRANCH="${{ github.base_ref }}"
-
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \
@@ -81,12 +81,14 @@ jobs:
           FD_API_PORT=$((42088 + DEVICE_PORT * 100))
           FD_ENGINE_QUEUE_PORT=$((42058 + DEVICE_PORT * 100))
           FD_METRICS_PORT=$((42078 + DEVICE_PORT * 100))
+          FD_CACHE_QUEUE_PORT=$((42098 + DEVICE_PORT * 100))
           echo "Test ENV Parameter:"
           echo "========================================================="
           echo "FLASK_PORT=${FLASK_PORT}"
           echo "FD_API_PORT=${FD_API_PORT}"
           echo "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}"
           echo "FD_METRICS_PORT=${FD_METRICS_PORT}"
+          echo "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}"
           echo "DEVICES=${DEVICES}"
           echo "========================================================="
 
@@ -96,7 +98,7 @@ jobs:
             touch "${CACHE_DIR}/gitconfig"
           fi
 
-          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT)
+          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT $FD_CACHE_QUEUE_PORT)
           LOG_FILE="./port_cleanup_$(date +%Y%m%d_%H%M%S).log"
           echo "==== LOG_FILE is ${LOG_FILE} ===="
 
@@ -134,6 +136,7 @@ jobs:
           -e "FD_API_PORT=${FD_API_PORT}" \
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
+          -e "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}" \
           -e "FLASK_PORT=${FLASK_PORT}" \
           -e "fd_wheel_url=${fd_wheel_url}" \
           --gpus "\"device=${DEVICES}\"" ${docker_image} /bin/bash -c '

.github/workflows/_stable_test.yml

@@ -44,7 +44,7 @@ jobs:
             FULL_REPO="${{ github.repository }}"
             REPO_NAME="${FULL_REPO##*/}"
             BASE_BRANCH="${{ github.base_ref }}"
-
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \

.github/workflows/_unit_test_coverage.yml

@@ -60,7 +60,7 @@ jobs:
             FULL_REPO="${{ github.repository }}"
             REPO_NAME="${FULL_REPO##*/}"
             BASE_BRANCH="${{ github.base_ref }}"
-
+            docker pull ${docker_image}
             # Clean the repository directory before starting
             docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
             -e "REPO_NAME=${REPO_NAME}" \
@@ -102,12 +102,14 @@ jobs:
           FD_API_PORT=$((42088 + DEVICE_PORT * 100))
           FD_ENGINE_QUEUE_PORT=$((42058 + DEVICE_PORT * 100))
           FD_METRICS_PORT=$((42078 + DEVICE_PORT * 100))
+          FD_CACHE_QUEUE_PORT=$((42098 + DEVICE_PORT * 100))
           echo "Test ENV Parameter:"
           echo "========================================================="
           echo "FLASK_PORT=${FLASK_PORT}"
           echo "FD_API_PORT=${FD_API_PORT}"
           echo "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}"
           echo "FD_METRICS_PORT=${FD_METRICS_PORT}"
+          echo "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}"
           echo "DEVICES=${DEVICES}"
           echo "========================================================="
 
@@ -117,7 +119,7 @@ jobs:
             touch "${CACHE_DIR}/gitconfig"
           fi
 
-          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT)
+          PORTS=($FLASK_PORT $FD_API_PORT $FD_ENGINE_QUEUE_PORT $FD_METRICS_PORT $FD_CACHE_QUEUE_PORT)
           LOG_FILE="./port_cleanup_$(date +%Y%m%d_%H%M%S).log"
           echo "==== LOG_FILE is ${LOG_FILE} ===="
 
@@ -156,6 +158,7 @@ jobs:
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
           -e "FLASK_PORT=${FLASK_PORT}" \
+          -e "FD_CACHE_QUEUE_PORT=${FD_CACHE_QUEUE_PORT}" \
           -e TZ="Asia/Shanghai" \
           -e "fd_wheel_url=${fd_wheel_url}" \
           -e "BASE_REF=${BASE_REF}" \
@@ -168,10 +171,7 @@ jobs:
           python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/
           pip config set global.extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
 
-          python -m pip install coverage
-          python -m pip install diff-cover
-          python -m pip install pytest-cov
-          python -m pip install jsonschema aistudio_sdk==0.3.5
+          python -m pip install -r scripts/unittest_requirement.txt
           python -m pip install ${fd_wheel_url}
           rm -rf fastdeploy
           # coverage subprocess use

.github/workflows/ce_job.yml

@@ -154,6 +154,7 @@ jobs:
       COMPILE_ARCH: "80,90"
       WITH_NIGHTLY_BUILD: OFF
       FD_VERSION: 0.0.0
+      PADDLE_WHL_URL: ${{ needs.ce_job_pre_check.outputs.compile_use_paddle_whl_url }}
 
   build_sm8689:
     name: BUILD_SM8689
@@ -166,6 +167,7 @@ jobs:
       COMPILE_ARCH: "86,89"
       WITH_NIGHTLY_BUILD: OFF
       FD_VERSION: 0.0.0
+      PADDLE_WHL_URL: ${{ needs.ce_job_pre_check.outputs.compile_use_paddle_whl_url }}
 
   ce_upload_sm8090:
     environment: CodeSync
@@ -175,14 +177,13 @@ jobs:
     env:
       AK: ${{ secrets.BOS_AK }}
       SK: ${{ secrets.BOS_SK }}
-      FASTDEPLOY_WHEEL_URL: ${{ needs.build.outputs.wheel_path }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
       COMPILE_ARCH: "80,90"
     steps:
       - uses: actions/setup-python@v5
         with:
           python-version: '3.10'
       - name: Wheel Info Show and Upload
-        if: github.ref_name == 'develop' || github.ref_type == 'tag'
         run: |
           echo "The wheel is located at: ${{ needs.build_sm8090.outputs.wheel_path }}"
           wget -q --no-check-certificate ${{ needs.build_sm8090.outputs.wheel_path }}
@@ -190,7 +191,7 @@ jobs:
 
           commit_id=${{ github.sha }}
           branch_name=${{ github.ref_name }}
-          target_path=paddle-qa/paddle-pipeline/FastDeploy_ActionCE${COMPILE_ARCH//,/_}/${branch_name}/${commit_id}
+          target_path=paddle-qa/paddle-pipeline/FastDeploy_ActionCE/SM${COMPILE_ARCH//,/_}/${branch_name}/${commit_id}
 
           wget  -q --no-proxy --no-check-certificate https://paddle-qa.bj.bcebos.com/CodeSync/develop/PaddlePaddle/PaddleTest/tools/bos_tools.py
           push_file=$(realpath bos_tools.py)
@@ -198,12 +199,14 @@ jobs:
           ls
           python ${push_file} ${filename} ${target_path}
           target_path_stripped="${target_path#paddle-qa/}"
-          WHEEL_PATH=https://paddle-qa.bj.bcebos.com/${target_path_stripped}/${fd_wheel_name}
+          WHEEL_PATH=https://paddle-qa.bj.bcebos.com/${target_path_stripped}/${filename}
 
-          target_path_latest=paddle-qa/paddle-pipeline/FastDeploy_ActionCE${COMPILE_ARCH//,/_}/${branch_name}/latest
+          target_path_latest=paddle-qa/paddle-pipeline/FastDeploy_ActionCE/SM${COMPILE_ARCH//,/_}/${branch_name}/latest
           python ${push_file} ${filename} ${target_path_latest}
           target_path_stripped_latest="${target_path_latest#paddle-qa/}"
-          WHEEL_PATH_LATEST=https://paddle-qa.bj.bcebos.com/${target_path_stripped_latest}/${fd_wheel_name}
+          WHEEL_PATH_LATEST=https://paddle-qa.bj.bcebos.com/${target_path_stripped_latest}/${filename}
+          echo "commit wheel url is ${WHEEL_PATH}"
+          echo "latest wheel url is ${WHEEL_PATH_LATEST}"
 
   ce_upload_sm8689:
     environment: CodeSync
@@ -213,22 +216,21 @@ jobs:
     env:
       AK: ${{ secrets.BOS_AK }}
       SK: ${{ secrets.BOS_SK }}
-      FASTDEPLOY_WHEEL_URL: ${{ needs.build.outputs.wheel_path }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8689.outputs.wheel_path }}
       COMPILE_ARCH: "86,89"
     steps:
       - uses: actions/setup-python@v5
         with:
           python-version: '3.10'
       - name: Wheel Info Show and Upload
-        if: github.ref_name == 'develop' || github.ref_type == 'tag'
         run: |
-          echo "The wheel is located at: ${{ needs.build_sm8090.outputs.wheel_path }}"
-          wget -q --no-check-certificate ${{ needs.build_sm8090.outputs.wheel_path }}
-          filename=$(basename ${{ needs.build_sm8090.outputs.wheel_path }})
+          echo "The wheel is located at: ${{ needs.build_sm8689.outputs.wheel_path }}"
+          wget -q --no-check-certificate ${{ needs.build_sm8689.outputs.wheel_path }}
+          filename=$(basename ${{ needs.build_sm8689.outputs.wheel_path }})
 
           commit_id=${{ github.sha }}
           branch_name=${{ github.ref_name }}
-          target_path=paddle-qa/paddle-pipeline/FastDeploy_ActionCE${COMPILE_ARCH//,/_}/${branch_name}/${commit_id}
+          target_path=paddle-qa/paddle-pipeline/FastDeploy_ActionCE/SM${COMPILE_ARCH//,/_}/${branch_name}/${commit_id}
 
           wget  -q --no-proxy --no-check-certificate https://paddle-qa.bj.bcebos.com/CodeSync/develop/PaddlePaddle/PaddleTest/tools/bos_tools.py
           push_file=$(realpath bos_tools.py)
@@ -236,9 +238,11 @@ jobs:
           ls
           python ${push_file} ${filename} ${target_path}
           target_path_stripped="${target_path#paddle-qa/}"
-          WHEEL_PATH=https://paddle-qa.bj.bcebos.com/${target_path_stripped}/${fd_wheel_name}
+          WHEEL_PATH=https://paddle-qa.bj.bcebos.com/${target_path_stripped}/${filename}
 
-          target_path_latest=paddle-qa/paddle-pipeline/FastDeploy_ActionCE${COMPILE_ARCH//,/_}/${branch_name}/latest
+          target_path_latest=paddle-qa/paddle-pipeline/FastDeploy_ActionCE/SM${COMPILE_ARCH//,/_}/${branch_name}/latest
           python ${push_file} ${filename} ${target_path_latest}
           target_path_stripped_latest="${target_path_latest#paddle-qa/}"
-          WHEEL_PATH_LATEST=https://paddle-qa.bj.bcebos.com/${target_path_stripped_latest}/${fd_wheel_name}
+          WHEEL_PATH_LATEST=https://paddle-qa.bj.bcebos.com/${target_path_stripped_latest}/${filename}
+          echo "commit wheel url is ${WHEEL_PATH}"
+          echo "latest wheel url is ${WHEEL_PATH_LATEST}"

.github/workflows/ci_image_update.yml

@@ -0,0 +1,174 @@
+name: CI Images Build
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 18 * * *'   # 2:00 AM China Standard Time (UTC+8)
+
+permissions: read-all
+
+concurrency:
+  group: ${{ github.ref }}-${{ github.sha }}
+  cancel-in-progress: true
+
+
+jobs:
+  clone:
+    environment: CodeSync
+    name: FD-Clone-Linux
+    runs-on: ubuntu-latest
+    outputs:
+      repo_archive_url: ${{ steps.set_output.outputs.repo_archive_url }}
+    steps:
+      - name: Clone FastDeploy
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.ref_name }}
+          submodules: 'recursive'
+          fetch-depth: 1000
+
+      - name: Python Setup
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+      - name: Code Info Show and Upload
+        id: set_output
+        env:
+          AK: ${{ secrets.BOS_AK }}
+          SK: ${{ secrets.BOS_SK }}
+        run: |
+          git config --unset http.https://github.com/.extraheader
+          git submodule foreach --recursive sh -c "git config --local --unset-all 'http.https://github.com/.extraheader'"
+          git submodule foreach --recursive sh -c "git config remote.origin.fetch '+refs/heads/*:refs/remotes/origin/*'"
+          echo "Current HEAD Log:"
+          git log --oneline -n 5
+          ls
+          cd ..
+          tar -zcf FastDeploy.tar.gz FastDeploy
+          if [[ "${{ github.ref_type }}" == "tag" ]]; then
+            commit_id=${{ github.sha }}
+            tag_name=${{ github.ref_name }}
+            target_path=paddle-qa/TAG/FastDeploy/${tag_name}/${commit_id}
+          else
+            commit_id=${{ github.sha }}
+            branch_name=${{ github.ref_name }}
+            target_path=paddle-qa/BRANCH/FastDeploy/${branch_name}/${commit_id}
+          fi
+          wget  -q --no-proxy --no-check-certificate https://paddle-qa.bj.bcebos.com/CodeSync/develop/PaddlePaddle/PaddleTest/tools/bos_tools.py
+          push_file=$(realpath bos_tools.py)
+          python -m pip install bce-python-sdk==0.9.29
+          ls
+          python ${push_file} FastDeploy.tar.gz ${target_path}
+          target_path_stripped="${target_path#paddle-qa/}"
+          REPO_ARCHIVE_URL=https://paddle-qa.bj.bcebos.com/${target_path_stripped}/FastDeploy.tar.gz
+          echo "repo_archive_url=${REPO_ARCHIVE_URL}" >> $GITHUB_OUTPUT
+
+  resultshow:
+    name: Show Code Archive Output
+    needs: clone
+    runs-on: ubuntu-latest
+    steps:
+      - name: Print wheel path
+        run: |
+          echo "The code archive is located at: ${{ needs.clone.outputs.repo_archive_url }}"
+
+  ci_image_build:
+    name: CI Images Build
+    needs: clone
+    uses: ./.github/workflows/_ci_image_build.yml
+    with:
+      CI_DOCKER_IMAGE_NAME: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:fastdeploy-ciuse-cuda126-dailyupdate-precheck
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+
+
+  build_sm8090:
+    name: BUILD_SM8090
+    needs: [clone, ci_image_build]
+    uses: ./.github/workflows/_build_linux.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      COMPILE_ARCH: "90"
+      WITH_NIGHTLY_BUILD: ${{ needs.publish_pre_check.outputs.with_nightly_build }}
+      FD_VERSION: ${{ needs.publish_pre_check.outputs.fd_version }}
+      PADDLEVERSION: ${{ needs.publish_pre_check.outputs.compile_use_paddle_version }}
+      PADDLE_WHL_URL: ${{ needs.publish_pre_check.outputs.compile_use_paddle_whl_url }}
+
+
+  unittest_coverage:
+    name: Run FastDeploy Unit Tests and Coverage
+    needs: [clone,build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_unit_test_coverage.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+
+  logprob_test:
+    name: Run FastDeploy LogProb Tests
+    needs: [build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_logprob_test_linux.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      PADDLETEST_ARCHIVE_URL: "https://xly-devops.bj.bcebos.com/PaddleTest/PaddleTest.tar.gz"
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+  pre_ce_test:
+    name: Extracted partial CE model tasks to run in CI.
+    needs: [clone,build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_pre_ce_test.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+  base_test:
+    name: Run Base Tests
+    needs: [clone,build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_base_test.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+  accuracy_test:
+    name: Run Accuracy Tests
+    needs: [clone,build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_accuracy_test.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+  stable_test:
+    name: Run Stable Tests
+    needs: [clone,build_sm8090,ci_image_build]
+    uses: ./.github/workflows/_stable_test.yml
+    with:
+      DOCKER_IMAGE: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+
+  publish_pre_check:
+    name: Publish Docker Images Pre Check
+    needs: [ci_image_build, unittest_coverage,logprob_test,pre_ce_test,base_test,accuracy_test,stable_test]
+    runs-on: [self-hosted, Docker-Build]
+    steps:
+      - name: Images Uploading
+        env:
+          images_name: ${{ needs.ci_image_build.outputs.docker_name_precheck }}
+          ci_image_name: "ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:fastdeploy-ciuse-cuda126-dailyupdate"
+        run: |
+          echo "images_name=${images_name}"
+          docker images ${ci_image_name}
+          docker tag ${images_name} ${ci_image_name}
+          docker push ${ci_image_name}

.github/workflows/ci_xpu.yml

@@ -77,7 +77,7 @@ jobs:
           -e "MODEL_PATH=/ssd3/model" \
           -e "http_proxy=$(git config --global --get http.proxy)" \
           -e "https_proxy=$(git config --global --get https.proxy)" \
-          -e "no_proxy=bcebos.com" \
+          -e "no_proxy=bcebos.com,mirrors.tuna.tsinghua.edu.cn,127.0.0.1,localhost" \
           -e "FD_API_PORT=${FD_API_PORT}" \
           -e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
           -e "FD_METRICS_PORT=${FD_METRICS_PORT}" \

.github/workflows/pr_build_and_test.yml

@@ -21,7 +21,7 @@ jobs:
     with:
       DOCKER_IMAGE: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:fastdeploy-ciuse-cuda126-dailyupdate
       FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
-      COMPILE_ARCH: "89,90"
+      COMPILE_ARCH: "90"
       WITH_NIGHTLY_BUILD: "OFF"
       FD_VERSION: "0.0.0"
 

.github/workflows/publish_job.yml

@@ -319,3 +319,13 @@ jobs:
       FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
       FASTDEPLOY_WHEEL_URL: ${{ needs.build_sm8090.outputs.wheel_path }}
       MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"
+
+  stable_test:
+    name: Run Stable Tests
+    needs: [clone,build_sm8090]
+    uses: ./.github/workflows/_stable_test.yml
+    with:
+      DOCKER_IMAGE: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleqa:fastdeploy-ciuse-cuda126-dailyupdate
+      FASTDEPLOY_ARCHIVE_URL: ${{ needs.clone.outputs.repo_archive_url }}
+      FASTDEPLOY_WHEEL_URL: ${{ needs.build.outputs.wheel_path }}
+      MODEL_CACHE_DIR: "/ssd2/actions-runner/ModelData"

README.md

@@ -26,6 +26,8 @@ English | [简体中文](README_CN.md)
 # FastDeploy : Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
 
 ## News
+**[2025-09] 🔥 FastDeploy v2.2 is newly released!** It now offers compatibility with models in the HuggingFace ecosystem, has further optimized performance, and newly adds support for [baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)!
+
 **[2025-08] 🔥 Released FastDeploy v2.1:** A brand-new KV Cache scheduling strategy has been introduced, and expanded support for PD separation and CUDA Graph across more models. Enhanced hardware support has been added for platforms like Kunlun and Hygon, along with comprehensive optimizations to improve the performance of both the service and inference engine.
 
 **[2025-07] The FastDeploy 2.0 Inference Deployment Challenge is now live!** Complete the inference deployment task for the ERNIE 4.5 series open-source models to win official FastDeploy 2.0 merch and generous prizes! 🎁 You're welcome to try it out and share your feedback! 📌[Sign up here](https://www.wjx.top/vm/meSsp3L.aspx#) 📌[Event details](https://github.com/PaddlePaddle/FastDeploy/discussions/2728)
@@ -57,8 +59,9 @@ FastDeploy supports inference deployment on **NVIDIA GPUs**, **Kunlunxin XPUs**,
 - [Iluvatar GPU](./docs/get_started/installation/iluvatar_gpu.md)
 - [Enflame GCU](./docs/get_started/installation/Enflame_gcu.md)
 - [Hygon DCU](./docs/get_started/installation/hygon_dcu.md)
+- [MetaX GPU](./docs/get_started/installation/metax_gpu.md)
 
-**Note:** We are actively working on expanding hardware support. Additional hardware platforms including Ascend NPU and MetaX GPU are currently under development and testing. Stay tuned for updates!
+**Note:** We are actively working on expanding hardware support. Additional hardware platforms including Ascend NPU are currently under development and testing. Stay tuned for updates!
 
 ## Get Started
 
@@ -68,20 +71,12 @@ Learn how to use FastDeploy through our documentation:
 - [ERNIE-4.5-VL Multimodal Model Deployment](./docs/get_started/ernie-4.5-vl.md)
 - [Offline Inference Development](./docs/offline_inference.md)
 - [Online Service Deployment](./docs/online_serving/README.md)
-- [Full Supported Models List](./docs/supported_models.md)
 - [Best Practices](./docs/best_practices/README.md)
 
 ## Supported Models
 
-| Model | Data Type | PD Disaggregation | Chunked Prefill | Prefix Caching |  MTP | CUDA Graph | Maximum Context Length |
-|:--- | :------- | :---------- | :-------- | :-------- | :----- | :----- | :----- |
-|ERNIE-4.5-300B-A47B | BF16/WINT4/WINT8/W4A8C8/WINT2/FP8 | ✅| ✅ | ✅|✅| ✅ |128K |
-|ERNIE-4.5-300B-A47B-Base| BF16/WINT4/WINT8 | ✅| ✅ | ✅|❌| ✅ | 128K |
-|ERNIE-4.5-VL-424B-A47B | BF16/WINT4/WINT8 | WIP | ✅ | WIP | ❌ | WIP |128K |
-|ERNIE-4.5-VL-28B-A3B | BF16/WINT4/WINT8 | ❌ | ✅ | WIP | ❌ | WIP |128K |
-|ERNIE-4.5-21B-A3B | BF16/WINT4/WINT8/FP8  |  ❌ |  ✅ |  ✅ | ✅ | ✅|128K |
-|ERNIE-4.5-21B-A3B-Base | BF16/WINT4/WINT8/FP8  |  ✅ |  ✅ |  ✅ | ❌  | ✅|128K |
-|ERNIE-4.5-0.3B | BF16/WINT8/FP8  |  ✅ |  ✅ |  ✅ | ❌ | ✅| 128K |
+Learn how to download models, enable using the torch format, and more:
+- [Full Supported Models List](./docs/supported_models.md)
 
 ## Advanced Usage
 

README_CN.md

@@ -26,7 +26,9 @@
 # FastDeploy ：基于飞桨的大语言模型与视觉语言模型推理部署工具包
 
 ## 最新活动
-**[2025-08] 🔥 FastDeploy v2.1 全新发布:** 全新的KV Cache调度策略，更多模型支持PD分离和CUDA Graph，昆仑、海光等更多硬件支持增强，全方面优化服务和推理引擎的性能。
+**[2025-09] 🔥 FastDeploy v2.2 全新发布**: HuggingFace生态模型兼容，性能进一步优化，更新增对[baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)支持!
+
+**[2025-08] FastDeploy v2.1 发布**:全新的KV Cache调度策略，更多模型支持PD分离和CUDA Graph，昆仑、海光等更多硬件支持增强，全方面优化服务和推理引擎的性能。
 
 **[2025-07] 《FastDeploy2.0推理部署实测》专题活动已上线!** 完成文心4.5系列开源模型的推理部署等任务，即可获得骨瓷马克杯等FastDeploy2.0官方周边及丰富奖金！🎁 欢迎大家体验反馈～ 📌[报名地址](https://www.wjx.top/vm/meSsp3L.aspx#)   📌[活动详情](https://github.com/PaddlePaddle/FastDeploy/discussions/2728)
 
@@ -55,8 +57,9 @@ FastDeploy 支持在**英伟达（NVIDIA）GPU**、**昆仑芯（Kunlunxin）XPU
 - [天数 CoreX](./docs/zh/get_started/installation/iluvatar_gpu.md)
 - [燧原 S60](./docs/zh/get_started/installation/Enflame_gcu.md)
 - [海光 DCU](./docs/zh/get_started/installation/hygon_dcu.md)
+- [沐曦 GPU](./docs/zh/get_started/installation/metax_gpu.md)
 
-**注意:** 我们正在积极拓展硬件支持范围。目前，包括昇腾（Ascend）NPU 和 沐曦（MetaX）GPU 在内的其他硬件平台正在开发测试中。敬请关注更新！
+**注意:** 我们正在积极拓展硬件支持范围。目前，包括昇腾（Ascend）NPU 等其他硬件平台正在开发测试中。敬请关注更新！
 
 ## 入门指南
 
@@ -66,20 +69,12 @@ FastDeploy 支持在**英伟达（NVIDIA）GPU**、**昆仑芯（Kunlunxin）XPU
 - [ERNIE-4.5-VL 部署](./docs/zh/get_started/ernie-4.5-vl.md)
 - [离线推理](./docs/zh/offline_inference.md)
 - [在线服务](./docs/zh/online_serving/README.md)
-- [模型支持列表](./docs/zh/supported_models.md)
 - [最佳实践](./docs/zh/best_practices/README.md)
 
 ## 支持模型列表
 
-| Model | Data Type | PD Disaggregation | Chunked Prefill | Prefix Caching |  MTP | CUDA Graph | Maximum Context Length |
-|:--- | :------- | :---------- | :-------- | :-------- | :----- | :----- | :----- |
-|ERNIE-4.5-300B-A47B | BF16/WINT4/WINT8/W4A8C8/WINT2/FP8 | ✅| ✅ | ✅|✅| ✅ |128K |
-|ERNIE-4.5-300B-A47B-Base| BF16/WINT4/WINT8 | ✅| ✅ | ✅|❌| ✅ | 128K |
-|ERNIE-4.5-VL-424B-A47B | BF16/WINT4/WINT8 | WIP | ✅ | WIP | ❌ | WIP |128K |
-|ERNIE-4.5-VL-28B-A3B | BF16/WINT4/WINT8 | ❌ | ✅ | WIP | ❌ | WIP |128K |
-|ERNIE-4.5-21B-A3B | BF16/WINT4/WINT8/FP8  |  ❌ |  ✅ |  ✅ | ✅ | ✅|128K |
-|ERNIE-4.5-21B-A3B-Base | BF16/WINT4/WINT8/FP8  |  ✅ |  ✅ |  ✅ | ❌  | ✅|128K |
-|ERNIE-4.5-0.3B | BF16/WINT8/FP8  |  ✅ |  ✅ |  ✅ | ❌ | ✅| 128K |
+通过我们的文档了解如何下载模型，如何支持torch格式等：
+- [模型支持列表](./docs/zh/supported_models.md)
 
 ## 进阶用法
 

custom_ops/cpu_ops/set_value_by_flags.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/extension.h"
 
-void set_value_by_flag_and_id(const bool *stop_flags,
+void set_value_by_flags_and_idx(const bool *stop_flags,
                               int64_t *pre_ids_all,
                               const int64_t *input_ids,
                               const int *seq_lens_encoder,
@@ -50,7 +50,7 @@ void SetValueByFlagsAndIdx(const paddle::Tensor &pre_ids_all,
     int length = pre_ids_all_shape[1];
     int length_input_ids = input_ids.shape()[1];
 
-    set_value_by_flag_and_id(stop_flags.data<bool>(),
+    set_value_by_flags_and_idx(stop_flags.data<bool>(),
                              const_cast<int64_t *>(pre_ids_all.data<int64_t>()),
                              input_ids.data<int64_t>(),
                              seq_lens_encoder.data<int>(),

custom_ops/cpu_ops/update_inputs.cc

@@ -46,7 +46,7 @@ void update_inputs_kernel(bool *not_need_stop,
     not_need_stop[0] = stop_sum < stop_nums[0];
 }
 
-void UpdateInputes(const paddle::Tensor &stop_flags,
+void UpdateInputs(const paddle::Tensor &stop_flags,
                    const paddle::Tensor &not_need_stop,
                    const paddle::Tensor &seq_lens_this_time,
                    const paddle::Tensor &seq_lens_encoder,
@@ -90,4 +90,4 @@ PD_BUILD_STATIC_OP(update_inputs_cpu)
                     {"seq_lens_encoder", "seq_lens_encoder_out"},
                     {"seq_lens_decoder", "seq_lens_decoder_out"},
                     {"input_ids", "input_ids_out"}})
-    .SetKernelFn(PD_KERNEL(UpdateInputes));
+    .SetKernelFn(PD_KERNEL(UpdateInputs));

custom_ops/gpu_ops/append_attention.cu

@@ -140,8 +140,8 @@ void AppendAttentionKernel(
           key_cache,
           value_cache,
           attn_mask,
-          cache_k_dequant_scales,
-          cache_v_dequant_scales,
+          cache_quant_type_str == "block_wise_fp8" ? cache_k_quant_scales : cache_k_dequant_scales,
+          cache_quant_type_str == "block_wise_fp8" ? cache_v_quant_scales : cache_v_dequant_scales,
           cache_k_zp,
           cache_v_zp,
           out_linear_shifts,
@@ -273,11 +273,15 @@ void AppendAttentionKernel(
             cache_v_zp,
             cache_quant_type_str,
             use_neox_rotary_style,
+            rope_3d,
             max_input_length,
             exec_stream,
             &qkv_out,
             const_cast<paddle::Tensor*>(&key_cache),
-            const_cast<paddle::Tensor*>(&value_cache));
+            const_cast<paddle::Tensor*>(&value_cache),
+            q_norm_weight,
+            k_norm_weight,
+            rms_norm_eps);
       } else {
         SpeculateWriteCacheWithRoPEKernel<data_t, data_t>(
             meta_data,
@@ -296,11 +300,15 @@ void AppendAttentionKernel(
             cache_v_zp,
             cache_quant_type_str,
             use_neox_rotary_style,
+            rope_3d,
             max_input_length,
             exec_stream,
             &qkv_out,
             const_cast<paddle::Tensor*>(&key_cache),
-            const_cast<paddle::Tensor*>(&value_cache));
+            const_cast<paddle::Tensor*>(&value_cache),
+            q_norm_weight,
+            k_norm_weight,
+            rms_norm_eps);
       }
     } else {
       if (qkv_out_scales) {
@@ -309,7 +317,6 @@ void AppendAttentionKernel(
             qkv,  // [token_num, num_heads, head_dim]
             seq_lens_decoder,
             seq_lens_encoder,
-            batch_id_per_token,
             cu_seqlens_q,
             block_tables,
             rotary_embs,
@@ -336,7 +343,6 @@ void AppendAttentionKernel(
             qkv_out,  // [token_num, num_heads, head_dim]
             seq_lens_decoder,
             seq_lens_encoder,
-            batch_id_per_token,
             cu_seqlens_q,
             block_tables,
             rotary_embs,

custom_ops/gpu_ops/append_attn/append_attention_c16_impl.cuh

@@ -52,6 +52,7 @@ __global__ void multi_query_append_attention_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -74,6 +75,11 @@ __global__ void multi_query_append_attention_kernel(
 
   block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
@@ -142,7 +148,7 @@ __global__ void multi_query_append_attention_kernel(
   } else {
     o_base_ptr_int8 = out + o_offset;
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -422,6 +428,7 @@ __global__ void multi_query_append_attention_warp1_4_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -445,6 +452,11 @@ __global__ void multi_query_append_attention_warp1_4_kernel(
   const uint32_t num_rows_per_block = num_frags_x * 16;
   const int *block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
@@ -511,7 +523,7 @@ __global__ void multi_query_append_attention_warp1_4_kernel(
           tid % 8 * num_elems_per_128b<T>();
     }
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -902,6 +914,7 @@ void MultiQueryAppendAttention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -960,6 +973,7 @@ void MultiQueryAppendAttention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),
@@ -1134,6 +1148,7 @@ void MultiQueryAppendAttention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -1206,6 +1221,7 @@ void MultiQueryAppendAttention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),

custom_ops/gpu_ops/append_attn/append_attention_c4_impl.cuh

@@ -57,6 +57,7 @@ __global__ void multi_query_append_attention_c4_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -85,6 +86,11 @@ __global__ void multi_query_append_attention_c4_kernel(
 
   block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
@@ -173,7 +179,7 @@ __global__ void multi_query_append_attention_c4_kernel(
   } else {
     o_base_ptr_int8 = out + o_offset;
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -520,6 +526,7 @@ __global__ void multi_query_append_attention_c4_warp1_4_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -549,6 +556,11 @@ __global__ void multi_query_append_attention_c4_warp1_4_kernel(
   const uint32_t num_rows_per_block = num_frags_x * 16;
   const int *block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
@@ -635,7 +647,7 @@ __global__ void multi_query_append_attention_c4_warp1_4_kernel(
           tid % 8 * num_elems_per_128b<T>();
     }
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -1107,6 +1119,7 @@ void MultiQueryAppendC4Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -1171,6 +1184,7 @@ void MultiQueryAppendC4Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),
@@ -1365,6 +1379,7 @@ void MultiQueryAppendC4Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -1445,6 +1460,7 @@ void MultiQueryAppendC4Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),

custom_ops/gpu_ops/append_attn/append_attention_c8_impl.cuh

@@ -32,14 +32,15 @@ template <typename T,
           typename OutT = T,
           bool ENABLE_PREFILL = true,
           bool is_scale_channel_wise = false,
-          bool IsFP8=false>
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 __global__ void multi_query_append_attention_c8_kernel(
     T *__restrict__ q,             // [token_num, (num_heads + 2* kv_num_head) * head_dim]
     CacheT *__restrict__ cache_k,  // [max_block_num, num_heads, block_size,
                                    // head_dim]
     CacheT *__restrict__ cache_v,
-    const T *__restrict__ cache_k_scale,  // [num_kv_heads]
-    const T *__restrict__ cache_v_scale,  // [num_kv_heads]
+    const T *__restrict__ cache_k_scale,  // [num_kv_heads] or [max_block_num, num_heads, block_size]
+    const T *__restrict__ cache_v_scale,  // [num_kv_heads] or [max_block_num, num_heads, block_size]
     const T *__restrict__ shift_bias,     // [q_num_heads * HEAD_DIM]
     const T *__restrict__ smooth_weight,  // [q_num_heads * HEAD_DIM]
     const int *__restrict__ seq_lens,
@@ -57,6 +58,7 @@ __global__ void multi_query_append_attention_c8_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -86,33 +88,40 @@ __global__ void multi_query_append_attention_c8_kernel(
 
   block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
   }
 
-  T cache_k_scale_reg[num_frags_y * 4];
-  T cache_v_scale_reg[num_frags_y * 2];
-  if (is_scale_channel_wise) {
-    int scale_col_base = threadIdx.x % 4 * 2 + kv_head_idx * HEAD_DIM;
-    const T *cache_k_scale_cur_head = cache_k_scale + scale_col_base;
-    for (int i = 0; i < num_frags_y; ++i) {
-      const int scale_idx = i * 16;
-      cache_k_scale_reg[i * 4] = cache_k_scale_cur_head[scale_idx];
-      cache_k_scale_reg[i * 4 + 1] = cache_k_scale_cur_head[scale_idx + 1];
-      cache_k_scale_reg[i * 4 + 2] = cache_k_scale_cur_head[scale_idx + 8];
-      cache_k_scale_reg[i * 4 + 3] = cache_k_scale_cur_head[scale_idx + 9];
+  T cache_k_scale_reg[IsDynamicC8 ? num_frags_z * 2 : num_frags_y * 4];
+  T cache_v_scale_reg[IsDynamicC8 ? num_frags_z * 4 : num_frags_y * 2];
+  if constexpr (!IsDynamicC8) {
+    if constexpr (is_scale_channel_wise) {
+      int scale_col_base = threadIdx.x % 4 * 2 + kv_head_idx * HEAD_DIM;
+      const T *cache_k_scale_cur_head = cache_k_scale + scale_col_base;
+      for (int i = 0; i < num_frags_y; ++i) {
+        const int scale_idx = i * 16;
+        cache_k_scale_reg[i * 4] = cache_k_scale_cur_head[scale_idx];
+        cache_k_scale_reg[i * 4 + 1] = cache_k_scale_cur_head[scale_idx + 1];
+        cache_k_scale_reg[i * 4 + 2] = cache_k_scale_cur_head[scale_idx + 8];
+        cache_k_scale_reg[i * 4 + 3] = cache_k_scale_cur_head[scale_idx + 9];
+      }
+      scale_col_base = threadIdx.x / 4 + kv_head_idx * HEAD_DIM;
+      const T *cache_v_scale_cur_head = cache_v_scale + scale_col_base;
+      for (int i = 0; i < num_frags_y; ++i) {
+        const int scale_idx = i * 16;
+        cache_v_scale_reg[i * 2] = cache_v_scale_cur_head[scale_idx];
+        cache_v_scale_reg[i * 2 + 1] = cache_v_scale_cur_head[scale_idx + 8];
+      }
+    } else {
+      cache_k_scale_reg[0] = cache_k_scale[kv_head_idx];
+      cache_v_scale_reg[0] = cache_v_scale[kv_head_idx];
     }
-    scale_col_base = threadIdx.x / 4 + kv_head_idx * HEAD_DIM;
-    const T *cache_v_scale_cur_head = cache_v_scale + scale_col_base;
-    for (int i = 0; i < num_frags_y; ++i) {
-      const int scale_idx = i * 16;
-      cache_v_scale_reg[i * 2] = cache_v_scale_cur_head[scale_idx];
-      cache_v_scale_reg[i * 2 + 1] = cache_v_scale_cur_head[scale_idx + 8];
-    }
-  } else {
-    cache_k_scale_reg[0] = cache_k_scale[kv_head_idx];
-    cache_v_scale_reg[0] = cache_v_scale[kv_head_idx];
   }
 
   const uint32_t q_end =
@@ -180,7 +189,7 @@ __global__ void multi_query_append_attention_c8_kernel(
   } else {
     o_base_ptr_int8 = out + o_offset;
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -201,6 +210,13 @@ __global__ void multi_query_append_attention_c8_kernel(
   smem_t k_smem(smem + NUM_WARPS * num_frags_x * 16 * HEAD_DIM * sizeof(T)),
       v_smem(smem + NUM_WARPS * num_frags_x * 16 * HEAD_DIM * sizeof(T) +
              num_frags_z * 16 * HEAD_DIM * sizeof(CacheT));
+  T* k_smem_scale = nullptr;
+  T* v_smem_scale = nullptr;
+  if constexpr (IsDynamicC8) {
+    k_smem_scale = reinterpret_cast<T*>(smem + NUM_WARPS * num_frags_x * 16 * HEAD_DIM * sizeof(T) +
+                                         num_frags_z * 16 * HEAD_DIM * sizeof(CacheT) * 2);
+    v_smem_scale = k_smem_scale + num_frags_z * 16;
+  }
 
 
   const uint32_t num_iterations = div_up(
@@ -282,10 +298,22 @@ __global__ void multi_query_append_attention_c8_kernel(
 
 #pragma unroll 1
   for (uint32_t iter = 0; iter < num_iterations; ++iter) {
+    if constexpr (IsDynamicC8) {
+      produce_k_dynamic_scale<BLOCK_SIZE, num_frags_z, NUM_WARP_Q, T>(
+        k_smem_scale,
+        cache_k_scale_reg,
+        block_table_now,
+        cache_k_scale,
+        kv_idx_base,
+        kv_num_heads,
+        kv_head_idx,
+        chunk_end
+      );
+    }
     wait_group<1>();
     __syncthreads();
     // s = qk
-    compute_qk_c8<num_frags_x, num_frags_y, num_frags_z, T, CacheT, is_scale_channel_wise, IsFP8>(
+    compute_qk_c8<num_frags_x, num_frags_y, num_frags_z, T, CacheT, is_scale_channel_wise, IsFP8, IsDynamicC8>(
         &qo_smem,
         &q_smem_offset_r,
         &k_smem,
@@ -318,6 +346,7 @@ __global__ void multi_query_append_attention_c8_kernel(
         s_frag, o_frag, m_frag, d_frag);
     __syncthreads();
 
+    const int ori_kv_idx_base = kv_idx_base;
     kv_idx_base += num_frags_z * 16;
     produce_k_blockwise_c8<SharedMemFillMode::kNoFill,
                            NUM_WARPS,
@@ -336,6 +365,18 @@ __global__ void multi_query_append_attention_c8_kernel(
                                        chunk_end,
                                        const_k_offset);
     commit_group();
+    if constexpr (IsDynamicC8) {
+      produce_v_dynamic_scale<BLOCK_SIZE, num_frags_z, NUM_WARP_Q, T>(
+        v_smem_scale,
+        cache_v_scale_reg,
+        block_table_now,
+        cache_v_scale,
+        ori_kv_idx_base,
+        kv_num_heads,
+        kv_head_idx,
+        chunk_end
+      );
+    }
     wait_group<1>();
     __syncthreads();
 
@@ -346,7 +387,9 @@ __global__ void multi_query_append_attention_c8_kernel(
                      BLOCK_SIZE,
                      T,
                      CacheT,
-                     is_scale_channel_wise, IsFP8>(
+                     is_scale_channel_wise,
+                     IsFP8,
+                     IsDynamicC8>(
         &v_smem, &v_smem_offset_r, s_frag, o_frag, d_frag, cache_v_scale_reg);
     __syncthreads();
 
@@ -463,14 +506,15 @@ template <typename T,
           typename OutT = T,
           bool ENABLE_PREFILL = true,
           bool is_scale_channel_wise=false,
-          bool IsFP8=false>
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 __global__ void multi_query_append_attention_c8_warp1_4_kernel(
     T *__restrict__ q,             // [token_num, (num_heads + 2* kv_num_head) * head_dim]
     CacheT *__restrict__ cache_k,  // [max_block_num, num_heads, block_size,
                                    // head_dim]
     CacheT *__restrict__ cache_v,
-    const T *__restrict__ cache_k_scale,  // [num_kv_heads, head_dim]
-    const T *__restrict__ cache_v_scale,  // [num_kv_heads, head_dim]
+    const T *__restrict__ cache_k_scale,  // [num_kv_heads] or [max_block_num, num_heads, block_size]
+    const T *__restrict__ cache_v_scale,  // [num_kv_heads] or [max_block_num, num_heads, block_size]
     const T *__restrict__ shift_bias,     // [q_num_heads * HEAD_DIM]
     const T *__restrict__ smooth_weight,  // [q_num_heads * HEAD_DIM]
     const int *__restrict__ seq_lens,
@@ -489,6 +533,7 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
     const float quant_min_bound,
     const float in_scale,
     const uint32_t chunk_size,
+    const int num_blocks_x_cpu,
     T *__restrict__ tmp_workspace,  // split kv [token_num, num_chunks,
                                     // num_heads, head_dim]
     float *__restrict__ tmp_m,      // [token_num, num_chunks, num_heads]
@@ -518,32 +563,39 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
   const uint32_t num_rows_per_block = num_frags_x * 16;
   const int *block_table_now = block_table + batch_id * max_block_num_per_seq;
 
+  //When cudagraph capture prefill, may launch more gridDim.x
+  if(btid >= static_cast<uint32_t>(num_blocks_x_cpu)){
+    return;
+  }
+
   const uint32_t q_len = seq_lens[batch_id];
   if (q_len <= 0) {
     return;
   }
-  T cache_k_scale_reg[num_frags_y * 4];
-  T cache_v_scale_reg[num_frags_y * 2];
-  if (is_scale_channel_wise) {
-    int scale_col_base = threadIdx.x % 4 * 2 + kv_head_idx * HEAD_DIM;
-    const T *cache_k_scale_cur_head = cache_k_scale + scale_col_base;
-    for (int i = 0; i < num_frags_y; ++i) {
-      const int scale_idx = i * 16;
-      cache_k_scale_reg[i * 4] = cache_k_scale_cur_head[scale_idx];
-      cache_k_scale_reg[i * 4 + 1] = cache_k_scale_cur_head[scale_idx + 1];
-      cache_k_scale_reg[i * 4 + 2] = cache_k_scale_cur_head[scale_idx + 8];
-      cache_k_scale_reg[i * 4 + 3] = cache_k_scale_cur_head[scale_idx + 9];
+  T cache_k_scale_reg[IsDynamicC8 ? num_frags_z * 2 : num_frags_y * 4];
+  T cache_v_scale_reg[IsDynamicC8 ? num_frags_z * 4 : num_frags_y * 2];
+  if constexpr (!IsDynamicC8) {
+    if constexpr (is_scale_channel_wise) {
+      int scale_col_base = threadIdx.x % 4 * 2 + kv_head_idx * HEAD_DIM;
+      const T *cache_k_scale_cur_head = cache_k_scale + scale_col_base;
+      for (int i = 0; i < num_frags_y; ++i) {
+        const int scale_idx = i * 16;
+        cache_k_scale_reg[i * 4] = cache_k_scale_cur_head[scale_idx];
+        cache_k_scale_reg[i * 4 + 1] = cache_k_scale_cur_head[scale_idx + 1];
+        cache_k_scale_reg[i * 4 + 2] = cache_k_scale_cur_head[scale_idx + 8];
+        cache_k_scale_reg[i * 4 + 3] = cache_k_scale_cur_head[scale_idx + 9];
+      }
+      scale_col_base = threadIdx.x / 4 + kv_head_idx * HEAD_DIM;
+      const T *cache_v_scale_cur_head = cache_v_scale + scale_col_base;
+      for (int i = 0; i < num_frags_y; ++i) {
+        const int scale_idx = i * 16;
+        cache_v_scale_reg[i * 2] = cache_v_scale_cur_head[scale_idx];
+        cache_v_scale_reg[i * 2 + 1] = cache_v_scale_cur_head[scale_idx + 8];
+      }
+    } else {
+      cache_k_scale_reg[0] = cache_k_scale[kv_head_idx];
+      cache_v_scale_reg[0] = cache_v_scale[kv_head_idx];
     }
-    scale_col_base = threadIdx.x / 4 + kv_head_idx * HEAD_DIM;
-    const T *cache_v_scale_cur_head = cache_v_scale + scale_col_base;
-    for (int i = 0; i < num_frags_y; ++i) {
-      const int scale_idx = i * 16;
-      cache_v_scale_reg[i * 2] = cache_v_scale_cur_head[scale_idx];
-      cache_v_scale_reg[i * 2 + 1] = cache_v_scale_cur_head[scale_idx + 8];
-    }
-  } else {
-    cache_k_scale_reg[0] = cache_k_scale[kv_head_idx];
-    cache_v_scale_reg[0] = cache_v_scale[kv_head_idx];
   }
   const uint32_t q_end =
       min(q_len, div_up((tile_id + 1) * num_rows_per_block, GROUP_SIZE));
@@ -609,7 +661,7 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
           tid % 8 * num_elems_per_128b<T>();
     }
   }
-  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id : nullptr;
+  const int *mask_offset_this_seq = mask_offset ? mask_offset + q_start_seq_id * 2 : nullptr;
   smem_t qo_smem(smem);
 
   uint32_t q_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
@@ -634,6 +686,13 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
   smem_t k_smem(smem + num_frags_x * 16 * HEAD_DIM * sizeof(T)),
       v_smem(smem + num_frags_x * 16 * HEAD_DIM * sizeof(T) +
              NUM_WARP_KV * num_frags_z * 16 * HEAD_DIM * sizeof(CacheT));
+  T* k_smem_scale = nullptr;
+  T* v_smem_scale = nullptr;
+  if constexpr (IsDynamicC8) {
+    k_smem_scale = reinterpret_cast<T*>(smem + num_frags_x * 16 * HEAD_DIM * sizeof(T) +
+                                        NUM_WARP_KV * num_frags_z * 16 * HEAD_DIM * sizeof(CacheT) * 2);
+    v_smem_scale = k_smem_scale + NUM_WARP_KV * num_frags_z * 16;
+  }
 
   const uint32_t num_iterations = div_up(
       CAUSAL
@@ -716,11 +775,23 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
   commit_group();
 #pragma unroll 1
   for (uint32_t iter = 0; iter < num_iterations; ++iter) {
+    if constexpr (IsDynamicC8) {
+      produce_k_dynamic_scale<BLOCK_SIZE, num_frags_z, NUM_WARP_Q, T>(
+        k_smem_scale,
+        cache_k_scale_reg,
+        block_table_now,
+        cache_k_scale,
+        kv_idx_base,
+        kv_num_heads,
+        kv_head_idx,
+        chunk_end
+      );
+    }
     wait_group<1>();
     __syncthreads();
 
     // s = qk
-    compute_qk_c8<num_frags_x, num_frags_y, num_frags_z, T, CacheT, is_scale_channel_wise, IsFP8>(
+    compute_qk_c8<num_frags_x, num_frags_y, num_frags_z, T, CacheT, is_scale_channel_wise, IsFP8, IsDynamicC8>(
         &qo_smem,
         &q_smem_offset_r,
         &k_smem,
@@ -753,6 +824,7 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
         s_frag, o_frag, m_frag, d_frag);
     __syncthreads();
 
+    const uint32_t ori_kv_idx_base = kv_idx_base;
     kv_idx_base += NUM_WARP_KV * num_frags_z * 16;
     produce_k_blockwise_c8<SharedMemFillMode::kNoFill,
                            NUM_WARPS,
@@ -771,6 +843,18 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
                                        chunk_end,
                                        const_k_offset);
     commit_group();
+    if constexpr (IsDynamicC8) {
+      produce_v_dynamic_scale<BLOCK_SIZE, num_frags_z, NUM_WARP_Q, T>(
+        v_smem_scale,
+        cache_v_scale_reg,
+        block_table_now,
+        cache_v_scale,
+        ori_kv_idx_base,
+        kv_num_heads,
+        kv_head_idx,
+        chunk_end
+      );
+    }
     wait_group<1>();
     __syncthreads();
 
@@ -781,7 +865,9 @@ __global__ void multi_query_append_attention_c8_warp1_4_kernel(
                                   BLOCK_SIZE,
                                   T,
                                   CacheT,
-                                  is_scale_channel_wise, IsFP8>(
+                                  is_scale_channel_wise,
+                                  IsFP8,
+                                  IsDynamicC8>(
         &v_smem, &v_smem_offset_r, s_frag, o_frag, d_frag, cache_v_scale_reg);
     __syncthreads();
 
@@ -895,7 +981,8 @@ template <typename T,
           uint32_t NUM_WARP_Q,
           typename OutT = T,
           bool ENABLE_PREFILL = true,
-          bool IsFP8=false>
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 void MultiQueryAppendC8Attention(
     const AppendAttnMetaData &meta_data,
     const paddle::Tensor &qkv,
@@ -953,7 +1040,8 @@ void MultiQueryAppendC8Attention(
     constexpr uint32_t num_frags_z = BLOCK_SIZE / 16;
     constexpr uint32_t smem_size =
         num_warps * num_frags_x * 16 * HEAD_DIM * sizeof(T) +
-        num_frags_z * 16 * HEAD_DIM * sizeof(uint8_t) * 2;
+        num_frags_z * 16 * HEAD_DIM * sizeof(uint8_t) * 2 +
+        num_frags_z * 16 * sizeof(T) * 2;
     auto split_kv_kernel =
         multi_query_append_attention_c8_kernel<NV_TYPE,
                                                uint8_t,
@@ -970,7 +1058,9 @@ void MultiQueryAppendC8Attention(
                                                num_frags_y,
                                                OUT_NV_TYPE,
                                                ENABLE_PREFILL,
-                                               false, IsFP8>;
+                                               false,
+                                               IsFP8,
+                                               IsDynamicC8>;
     if (is_scale_channel_wise) {
       split_kv_kernel =
         multi_query_append_attention_c8_kernel<NV_TYPE,
@@ -988,7 +1078,9 @@ void MultiQueryAppendC8Attention(
                                                num_frags_y,
                                                OUT_NV_TYPE,
                                                ENABLE_PREFILL,
-                                               true, IsFP8>;
+                                               true,
+                                               IsFP8,
+                                               IsDynamicC8>;
     }
     if (smem_size >= 48 * 1024) {
       cudaFuncSetAttribute(split_kv_kernel,
@@ -1022,7 +1114,9 @@ void MultiQueryAppendC8Attention(
                                                  num_frags_y,
                                                  OUT_NV_TYPE,
                                                  ENABLE_PREFILL,
-                                                 false, IsFP8>;
+                                                 false,
+                                                 IsFP8,
+                                                 IsDynamicC8>;
       if (is_scale_channel_wise) {
         nosplit_kv_kernel =
           multi_query_append_attention_c8_kernel<NV_TYPE,
@@ -1040,7 +1134,9 @@ void MultiQueryAppendC8Attention(
                                                  num_frags_y,
                                                  OUT_NV_TYPE,
                                                  ENABLE_PREFILL,
-                                                 true, IsFP8>;
+                                                 true,
+                                                 IsFP8,
+                                                 IsDynamicC8>;
       }
       if (smem_size >= 48 * 1024) {
         cudaFuncSetAttribute(nosplit_kv_kernel,
@@ -1075,6 +1171,7 @@ void MultiQueryAppendC8Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -1133,6 +1230,7 @@ void MultiQueryAppendC8Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),
@@ -1218,7 +1316,8 @@ void MultiQueryAppendC8Attention(
     constexpr uint32_t num_frags_z = BLOCK_SIZE / 16 / NUM_WARP_KV * 2;
     constexpr uint32_t smem_size =
         num_frags_x * 16 * HEAD_DIM * sizeof(T) +
-        NUM_WARP_KV * num_frags_z * 16 * HEAD_DIM * sizeof(uint8_t) * 2;
+        NUM_WARP_KV * num_frags_z * 16 * HEAD_DIM * sizeof(uint8_t) * 2 +
+        NUM_WARP_KV * num_frags_z * 16 * sizeof(T) * 2;
     auto split_kv_kernel =
         multi_query_append_attention_c8_warp1_4_kernel<NV_TYPE,
                                                        uint8_t,
@@ -1235,7 +1334,9 @@ void MultiQueryAppendC8Attention(
                                                        num_frags_y,
                                                        OUT_NV_TYPE,
                                                        ENABLE_PREFILL,
-                                                       false, IsFP8>;
+                                                       false,
+                                                       IsFP8,
+                                                       IsDynamicC8>;
     if (is_scale_channel_wise) {
       split_kv_kernel =
         multi_query_append_attention_c8_warp1_4_kernel<NV_TYPE,
@@ -1253,7 +1354,9 @@ void MultiQueryAppendC8Attention(
                                                        num_frags_y,
                                                        OUT_NV_TYPE,
                                                        ENABLE_PREFILL,
-                                                       true, IsFP8>;
+                                                       true,
+                                                       IsFP8,
+                                                       IsDynamicC8>;
     }
     if (smem_size >= 48 * 1024) {
       cudaFuncSetAttribute(split_kv_kernel,
@@ -1295,7 +1398,9 @@ void MultiQueryAppendC8Attention(
                                                          num_frags_y,
                                                          OUT_NV_TYPE,
                                                          ENABLE_PREFILL,
-                                                         false, IsFP8>;
+                                                         false,
+                                                         IsFP8,
+                                                         IsDynamicC8>;
       if (is_scale_channel_wise) {
         nosplit_kv_kernel =
           multi_query_append_attention_c8_warp1_4_kernel<NV_TYPE,
@@ -1313,7 +1418,9 @@ void MultiQueryAppendC8Attention(
                                                          num_frags_y,
                                                          OUT_NV_TYPE,
                                                          ENABLE_PREFILL,
-                                                         true, IsFP8>;
+                                                         true,
+                                                         IsFP8,
+                                                         IsDynamicC8>;
       }
       if (smem_size >= 48 * 1024) {
         cudaFuncSetAttribute(nosplit_kv_kernel,
@@ -1350,6 +1457,7 @@ void MultiQueryAppendC8Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           nullptr,
           nullptr,
           nullptr,
@@ -1424,6 +1532,7 @@ void MultiQueryAppendC8Attention(
           quant_min_bound,
           in_scale,
           chunk_size,
+          num_blocks_x_cpu,
           reinterpret_cast<NV_TYPE *>(tmp_workspace->ptr()),
           static_cast<float *>(tmp_m->ptr()),
           static_cast<float *>(tmp_d->ptr()),
@@ -1546,6 +1655,7 @@ void CascadeAppendAttentionC8Kernel(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out) {
   const auto token_num = meta_data.token_nums;
@@ -1554,6 +1664,7 @@ void CascadeAppendAttentionC8Kernel(
   const auto num_heads = meta_data.q_num_heads;
   const auto group_size = meta_data.q_num_heads / meta_data.kv_num_heads;
   const auto head_dim = meta_data.head_dims;
+  bool is_dynamic_cfp8 = cache_quant_type_str == "block_wise_fp8";
 
   DISPATCH_CAUSAL(
       causal,
@@ -1572,43 +1683,46 @@ void CascadeAppendAttentionC8Kernel(
                       BLOCK_SIZE,
                       {DISPATCH_BLOCKSHAPE_Q(
                           block_shape_q, BLOCK_SHAPE_Q, NUM_WARP_Q, {
-                            MultiQueryAppendC8Attention<T,
-                                                        GROUP_SIZE,
-                                                        HEAD_DIM,
-                                                        BLOCK_SIZE,
-                                                        CAUSAL,
-                                                        BLOCK_SHAPE_Q,
-                                                        NUM_WARP_Q,
-                                                        OutT,
-                                                        ENABLE_PREFILL, IsFP8>(
-                                meta_data,
-                                qkv,
-                                cache_k,
-                                cache_v,
-                                attn_mask,
-                                cache_k_scale.get(),
-                                cache_v_scale.get(),
-                                shift_bias,
-                                smooth_weight,
-                                seq_lens_q,
-                                seq_lens_kv,
-                                seq_lens_encoder,
-                                batch_id_per_token,
-                                cu_seqlens_q,
-                                block_table,
-                                batch_ids,
-                                tile_ids_per_batch,
-                                num_blocks,
-                                max_seq_len,
-                                max_dec_len,
-                                quant_max_bound,
-                                quant_min_bound,
-                                in_scale,
-                                max_partition_size,
-                                encoder_max_partition_size,
-                                speculate_max_draft_token_num,
-                                is_decoder,
-                                stream,
-                                out);
-                          })})})})})})
+                            DISPATCH_DyCfp8(is_dynamic_cfp8, IsDynamicC8, {
+                              MultiQueryAppendC8Attention<T,
+                                                          GROUP_SIZE,
+                                                          HEAD_DIM,
+                                                          BLOCK_SIZE,
+                                                          CAUSAL,
+                                                          BLOCK_SHAPE_Q,
+                                                          NUM_WARP_Q,
+                                                          OutT,
+                                                          ENABLE_PREFILL,
+                                                          IsFP8,
+                                                          IsDynamicC8>(
+                                  meta_data,
+                                  qkv,
+                                  cache_k,
+                                  cache_v,
+                                  attn_mask,
+                                  cache_k_scale.get(),
+                                  cache_v_scale.get(),
+                                  shift_bias,
+                                  smooth_weight,
+                                  seq_lens_q,
+                                  seq_lens_kv,
+                                  seq_lens_encoder,
+                                  batch_id_per_token,
+                                  cu_seqlens_q,
+                                  block_table,
+                                  batch_ids,
+                                  tile_ids_per_batch,
+                                  num_blocks,
+                                  max_seq_len,
+                                  max_dec_len,
+                                  quant_max_bound,
+                                  quant_min_bound,
+                                  in_scale,
+                                  max_partition_size,
+                                  encoder_max_partition_size,
+                                  speculate_max_draft_token_num,
+                                  is_decoder,
+                                  stream,
+                                  out);
+      })})})})})})})
 }

custom_ops/gpu_ops/append_attn/append_attention_func.cuh

@@ -384,6 +384,113 @@ __device__ __forceinline__ void produce_v_blockwise_c8(
   }
 }
 
+template<uint32_t block_size,
+         uint32_t num_frags_z,
+         uint32_t NUM_WARP_Q,
+         typename T>
+__device__ __forceinline__ void produce_k_dynamic_scale(
+  T* k_smem_scale,
+  T* cache_k_reg,
+  const int* block_table_now,
+  const T* cache_k_scale,
+  const uint32_t kv_idx,
+  const uint32_t kv_num_heads,
+  const uint32_t kv_head_idx,
+  const uint32_t chunk_end
+) {
+  const uint32_t tx = threadIdx.x, ty = threadIdx.y;
+  if constexpr (NUM_WARP_Q == 4) {
+    // 4 warps shared block_size
+    const uint32_t tid = ty * 32 + tx;
+    int block_id = __ldg(&block_table_now[kv_idx / block_size]);
+    if (block_id < 0) block_id = 0;
+    const T* cache_k_scale_now = cache_k_scale + block_id * kv_num_heads * block_size + kv_head_idx * block_size;
+    if (tid < block_size) {
+      k_smem_scale[tid] = cache_k_scale_now[tid];
+    }
+    __syncthreads();
+    const uint32_t row_id = tx / 4;
+    for (uint32_t fz = 0; fz < num_frags_z; fz++) {
+      cache_k_reg[fz * 2] = k_smem_scale[fz * 16 + row_id];
+      cache_k_reg[fz * 2 + 1] = k_smem_scale[fz * 16 + row_id + 8];
+    }
+  } else {
+    // 1 warp 32 tokens
+    const uint32_t kv_idx_now = kv_idx + block_size * ty / 2;
+    int block_id = __ldg(&block_table_now[kv_idx_now / block_size]);
+    if (block_id < 0) block_id = 0;
+    const T* cache_k_scale_now = cache_k_scale + block_id * kv_num_heads * block_size + kv_head_idx * block_size;
+    const int kv_idx_this_thread = kv_idx + ty * 32 + tx;
+    if (kv_idx_this_thread < chunk_end) {
+      k_smem_scale[ty * 32 + tx] = cache_k_scale_now[(ty  % 2) * 32 + tx];
+    } else {
+      k_smem_scale[ty * 32 + tx] = 0;
+    }
+    __syncwarp();
+    const uint32_t row_id = tx / 4;
+    for (uint32_t fz = 0; fz < num_frags_z; fz++) {
+      cache_k_reg[fz * 2] = k_smem_scale[ty * 32 + fz * 16 + row_id];
+      cache_k_reg[fz * 2 + 1] = k_smem_scale[ty * 32 + fz * 16 + row_id + 8];
+    }
+  }
+}
+
+template<uint32_t block_size,
+         uint32_t num_frags_z,
+         uint32_t NUM_WARP_Q,
+         typename T>
+__device__ __forceinline__ void produce_v_dynamic_scale(
+  T* v_smem_scale,
+  T* cache_v_reg,
+  const int* block_table_now,
+  const T* cache_v_scale,
+  const uint32_t kv_idx,
+  const uint32_t kv_num_heads,
+  const uint32_t kv_head_idx,
+  const uint32_t chunk_end
+) {
+  const uint32_t tx = threadIdx.x, ty = threadIdx.y;
+
+  if constexpr (NUM_WARP_Q == 4) {
+    // 4 warps shared block_size
+    const uint32_t tid = ty * 32 + tx;
+    int block_id = __ldg(&block_table_now[kv_idx / block_size]);
+    if (block_id < 0) block_id = 0;
+    const T* cache_v_scale_now = cache_v_scale + block_id * kv_num_heads * block_size + kv_head_idx * block_size;
+    if (tid < block_size) {
+      v_smem_scale[tid] = cache_v_scale_now[tid];
+    }
+    __syncthreads();
+    const uint32_t row_id = tx % 4 * 2;
+    for (uint32_t fz = 0; fz < num_frags_z; fz++) {
+      cache_v_reg[fz * 4] = v_smem_scale[fz * 16 + row_id];
+      cache_v_reg[fz * 4 + 1] = v_smem_scale[fz * 16 + row_id + 1];
+      cache_v_reg[fz * 4 + 2] = v_smem_scale[fz * 16 + row_id + 8];
+      cache_v_reg[fz * 4 + 3] = v_smem_scale[fz * 16 + row_id + 9];
+    }
+  } else {
+    // 1 warp 32 tokens
+    const uint32_t kv_idx_now = kv_idx + block_size * ty / 2;
+    int block_id = __ldg(&block_table_now[kv_idx_now / block_size]);
+    if (block_id < 0) block_id = 0;
+    const T* cache_v_scale_now = cache_v_scale + block_id * kv_num_heads * block_size + kv_head_idx * block_size;
+    const int kv_idx_this_thread = kv_idx + ty * 32 + tx;
+    if (kv_idx_this_thread < chunk_end) {
+      v_smem_scale[ty * 32 + tx] = cache_v_scale_now[(ty  % 2) * 32 + tx];
+    } else {
+      v_smem_scale[ty * 32 + tx] = 0;
+    }
+    __syncwarp();
+    const uint32_t row_id = tx % 4 * 2;
+    for (uint32_t fz = 0; fz < num_frags_z; fz++) {
+      cache_v_reg[fz * 4] = v_smem_scale[ty * 32 + fz * 16 + row_id];
+      cache_v_reg[fz * 4 + 1] = v_smem_scale[ty * 32 + fz * 16 + row_id + 1];
+      cache_v_reg[fz * 4 + 2] = v_smem_scale[ty * 32 + fz * 16 + row_id + 8];
+      cache_v_reg[fz * 4 + 3] = v_smem_scale[ty * 32 + fz * 16 + row_id + 9];
+    }
+  }
+}
+
 template <SharedMemFillMode fill_mode,
           uint32_t num_warps,
           uint32_t block_size,
@@ -816,7 +923,8 @@ template <uint32_t num_frags_x,
           typename T,
           typename CacheT,
           bool is_scale_channel_wise = false,
-          bool IsFP8=false>
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 __device__ __forceinline__ void compute_qk_c8(smem_t* q_smem,
                                               uint32_t* q_smem_offset_r,
                                               smem_t* k_smem,
@@ -860,20 +968,27 @@ __device__ __forceinline__ void compute_qk_c8(smem_t* q_smem,
         convert_c8<T,IsFP8>(b_frag_dq_T, b_frag[fy * 2]);
         convert_c8<T,IsFP8>(b_frag_dq_T + 4, b_frag[fy * 2 + 1]);
         // scale zp
-        if constexpr (is_scale_channel_wise) {
-          const int scale_col = (ky * 2 + fy) * 4;
-          b_frag_dq_T[0] *= cache_k_scale[scale_col];
-          b_frag_dq_T[1] *= cache_k_scale[scale_col + 1];
-          b_frag_dq_T[2] *= cache_k_scale[scale_col + 2];
-          b_frag_dq_T[3] *= cache_k_scale[scale_col + 3];
-          b_frag_dq_T[4] *= cache_k_scale[scale_col];
-          b_frag_dq_T[5] *= cache_k_scale[scale_col + 1];
-          b_frag_dq_T[6] *= cache_k_scale[scale_col + 2];
-          b_frag_dq_T[7] *= cache_k_scale[scale_col + 3];
+        if constexpr (!IsDynamicC8) {
+          if constexpr (is_scale_channel_wise) {
+            const int scale_col = (ky * 2 + fy) * 4;
+            b_frag_dq_T[0] *= cache_k_scale[scale_col];
+            b_frag_dq_T[1] *= cache_k_scale[scale_col + 1];
+            b_frag_dq_T[2] *= cache_k_scale[scale_col + 2];
+            b_frag_dq_T[3] *= cache_k_scale[scale_col + 3];
+            b_frag_dq_T[4] *= cache_k_scale[scale_col];
+            b_frag_dq_T[5] *= cache_k_scale[scale_col + 1];
+            b_frag_dq_T[6] *= cache_k_scale[scale_col + 2];
+            b_frag_dq_T[7] *= cache_k_scale[scale_col + 3];
+          } else {
+#pragma unroll
+            for (uint32_t b_i = 0; b_i < 8; ++b_i) {
+              b_frag_dq_T[b_i] *= cache_k_scale[0];
+            }
+          }
         } else {
 #pragma unroll
           for (uint32_t b_i = 0; b_i < 8; ++b_i) {
-            b_frag_dq_T[b_i] *= cache_k_scale[0];
+            b_frag_dq_T[b_i] *= cache_k_scale[fz * 2 + b_i / 4];
           }
         }
 #pragma unroll
@@ -929,7 +1044,7 @@ __device__ __forceinline__ void mask_s(const bool* attn_mask,
                                   8 * (reg_id / 4) + reg_id % 2;
           bool out_of_boundary;
           if (mask_offset) {
-            out_of_boundary = q_idx < qo_len ? (kv_idx > mask_offset[q_idx]) : true;
+            out_of_boundary = q_idx < qo_len ? (kv_idx >= mask_offset[q_idx * 2 + 1] || kv_idx < mask_offset[q_idx * 2]) : true;
           } else {
             out_of_boundary =
                 (causal
@@ -1093,7 +1208,9 @@ template <uint32_t num_frags_x,
           uint32_t block_size,
           typename T,
           typename CacheT,
-          bool is_scale_channel_wise = false, bool IsFP8=false>
+          bool is_scale_channel_wise = false,
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 __device__ __forceinline__ void compute_sfm_v_c8(
     smem_t* v_smem,
     uint32_t* v_smem_offset_r,
@@ -1135,16 +1252,28 @@ __device__ __forceinline__ void compute_sfm_v_c8(
         convert_c8<T,IsFP8>(b_frag_dq_T, b_frag[fz * 2]);
         convert_c8<T,IsFP8>(b_frag_dq_T + 4, b_frag[fz * 2 + 1]);
         // scale zp
-        if constexpr (is_scale_channel_wise) {
+        if constexpr (!IsDynamicC8) {
+          if constexpr (is_scale_channel_wise) {
 #pragma unroll
-          for (uint32_t b_i = 0; b_i < 8; ++b_i) {
-            b_frag_dq_T[b_i] *= cache_v_scale[b_i / 4 + fy * 2];
+            for (uint32_t b_i = 0; b_i < 8; ++b_i) {
+              b_frag_dq_T[b_i] *= cache_v_scale[b_i / 4 + fy * 2];
+            }
+          } else {
+#pragma unroll
+            for (uint32_t b_i = 0; b_i < 8; ++b_i) {
+              b_frag_dq_T[b_i] *= cache_v_scale[0];
+            }
           }
         } else {
-#pragma unroll
-          for (uint32_t b_i = 0; b_i < 8; ++b_i) {
-            b_frag_dq_T[b_i] *= cache_v_scale[0];
-          }
+          const int scale_col = (kz * 2 + fz) * 4;
+          b_frag_dq_T[0] *= cache_v_scale[scale_col];
+          b_frag_dq_T[1] *= cache_v_scale[scale_col + 1];
+          b_frag_dq_T[2] *= cache_v_scale[scale_col + 2];
+          b_frag_dq_T[3] *= cache_v_scale[scale_col + 3];
+          b_frag_dq_T[4] *= cache_v_scale[scale_col];
+          b_frag_dq_T[5] *= cache_v_scale[scale_col + 1];
+          b_frag_dq_T[6] *= cache_v_scale[scale_col + 2];
+          b_frag_dq_T[7] *= cache_v_scale[scale_col + 3];
         }
 #pragma unroll
         for (uint32_t fx = 0; fx < num_frags_x; ++fx) {  // m: num_frags_x * 16
@@ -1171,7 +1300,9 @@ template <uint32_t num_frags_x,
           uint32_t block_size,
           typename T,
           typename CacheT,
-          bool is_scale_channel_wise = false, bool IsFP8=false>
+          bool is_scale_channel_wise = false,
+          bool IsFP8 = false,
+          bool IsDynamicC8 = false>
 __device__ __forceinline__ void compute_sfm_v_c8_iter_sq_bvec(
     smem_t* v_smem,
     uint32_t* v_smem_offset_r,
@@ -1215,16 +1346,28 @@ __device__ __forceinline__ void compute_sfm_v_c8_iter_sq_bvec(
         convert_c8<T,IsFP8>(b_frag_dq_T, b_frag[fz * 2]);
         convert_c8<T,IsFP8>(b_frag_dq_T + 4, b_frag[fz * 2 + 1]);
         // scale zp
-        if constexpr (is_scale_channel_wise) {
+        if constexpr (!IsDynamicC8) {
+          if constexpr (is_scale_channel_wise) {
 #pragma unroll
-          for (uint32_t b_i = 0; b_i < 8; ++b_i) {
-            b_frag_dq_T[b_i] *= cache_v_scale[b_i / 4 + fy * 2];
+            for (uint32_t b_i = 0; b_i < 8; ++b_i) {
+              b_frag_dq_T[b_i] *= cache_v_scale[b_i / 4 + fy * 2];
+            }
+          } else {
+            #pragma unroll
+            for (uint32_t b_i = 0; b_i < 8; ++b_i) {
+              b_frag_dq_T[b_i] *= cache_v_scale[0];
+            }
           }
         } else {
-          #pragma unroll
-          for (uint32_t b_i = 0; b_i < 8; ++b_i) {
-            b_frag_dq_T[b_i] *= cache_v_scale[0];
-          }
+          const int scale_col = (kz * 2 + fz) * 4;
+          b_frag_dq_T[0] *= cache_v_scale[scale_col];
+          b_frag_dq_T[1] *= cache_v_scale[scale_col + 1];
+          b_frag_dq_T[2] *= cache_v_scale[scale_col + 2];
+          b_frag_dq_T[3] *= cache_v_scale[scale_col + 3];
+          b_frag_dq_T[4] *= cache_v_scale[scale_col];
+          b_frag_dq_T[5] *= cache_v_scale[scale_col + 1];
+          b_frag_dq_T[6] *= cache_v_scale[scale_col + 2];
+          b_frag_dq_T[7] *= cache_v_scale[scale_col + 3];
         }
 #pragma unroll
         for (uint32_t fx = 0; fx < num_frags_x; ++fx) {  // m: num_frags_x * 16

custom_ops/gpu_ops/append_attn/append_attention_kernel.h

@@ -103,6 +103,7 @@ void CascadeAppendAttentionC8Kernel(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -264,9 +265,10 @@ void CascadeAppendAttentionKernel(
                                                 causal,
                                                 is_decoder,
                                                 enable_prefill,
+                                                cache_quant_type_str,
                                                 stream,
                                                 out);
-    } else if (cache_quant_type_str == "cache_fp8") {
+    } else if (cache_quant_type_str == "cache_fp8" or cache_quant_type_str == "block_wise_fp8") {
         CascadeAppendAttentionC8Kernel<T, OutT, true>(meta_data,
                                                 qkv,
                                                 cache_k,
@@ -299,6 +301,7 @@ void CascadeAppendAttentionKernel(
                                                 causal,
                                                 is_decoder,
                                                 enable_prefill,
+                                                cache_quant_type_str,
                                                 stream,
                                                 out);
     } else if (cache_quant_type_str == "cache_int4_zp") {

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_impl.cuh

@@ -18,6 +18,53 @@
 #include "mma_tensor_op.cuh"
 #include "utils.cuh"
 
+
+// Note(ZKK)
+// This function is very easy!
+// just make HeadDim data to be new HeadDim data!
+
+template <typename T, int VecSize=8, int HEAD_DIM=128, int NUM_THREADS=32>
+__device__ __forceinline__ void apply_rope(
+  const T* input,
+  const float* cos_emb,
+  const float* sin_emb,
+  T* output,
+  const int thread_id) {
+
+  using LoadT = AlignedVector<T, VecSize>;
+  using LoadBiasT = AlignedVector<T, VecSize>;
+  using LoadOutScaleT = AlignedVector<float, VecSize>;
+  constexpr int HalfVecSize = VecSize / 2;
+  using LoadEmbT = AlignedVector<float, HalfVecSize>;
+
+  LoadT src_vec;
+  LoadBiasT out_vec;
+  LoadEmbT cos_emb_vec;
+  LoadEmbT sin_emb_vec;
+
+#pragma unroll
+    for (uint32_t head_bias = thread_id * VecSize; head_bias < HEAD_DIM; head_bias +=  NUM_THREADS * VecSize) {
+      Load<T, VecSize>(&input[head_bias], &src_vec);
+      const uint32_t emb_idx = head_bias / 2;
+      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+#pragma unroll
+      for (int i = 0; i < HalfVecSize; i++) {
+
+        float input_left = static_cast<float>(src_vec[2 * i]);
+        float input_right = static_cast<float>(src_vec[2 * i + 1]);
+
+        const float cos_tmp = cos_emb_vec[i];
+        const float sin_tmp = sin_emb_vec[i];
+        out_vec[2 * i] =
+            static_cast<T>(input_left * cos_tmp - input_right * sin_tmp);
+        out_vec[2 * i + 1] =
+            static_cast<T>(input_right * cos_tmp + input_left * sin_tmp);
+      }
+      Store<T, VecSize>(out_vec, &output[head_bias]);
+    }
+}
+
 template <typename T, int VecSize = 1>
 __global__ void append_decode_cache_T_rope_qk_norm_kernel(
     const T* __restrict__ quant_qkv,  // [bsz, num_heads + 2 * kv_num_heads,
@@ -28,7 +75,7 @@ __global__ void append_decode_cache_T_rope_qk_norm_kernel(
                                   // head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -120,7 +167,6 @@ __global__ void append_decode_cache_T_rope_qk_norm_kernel(
       float row_variance =
           max(warp_m2 / head_size, 0.0f);
       float row_inv_var = Rsqrt(row_variance + rms_norm_eps);
-
       if (hi < num_heads) { // q
         Load<float, VecSize>(&q_norm_weight[threadIdx.x * VecSize], &q_norm_vec);
         #pragma unroll
@@ -129,6 +175,7 @@ __global__ void append_decode_cache_T_rope_qk_norm_kernel(
         }
       } else { // k
         Load<float, VecSize>(&k_norm_weight[threadIdx.x * VecSize], &k_norm_vec);
+        #pragma unroll
         for (int i = 0; i < VecSize; i++) {
           out_vec[i] = static_cast<T>(tmp_vec[i] * row_inv_var * k_norm_vec[i]);
         }
@@ -164,7 +211,7 @@ __global__ void append_decode_cache_T_rope_kernel(
                                   // head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -270,7 +317,7 @@ __global__ void append_decode_cache_T_rope_kernel(
                                   // head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -391,7 +438,6 @@ __global__ void append_decode_cache_T_neox_rope_kernel(
                                   // head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -505,7 +551,6 @@ __global__ void append_decode_cache_T_neox_rope_kernel(
                                   // head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -629,6 +674,293 @@ __global__ void append_decode_cache_T_neox_rope_kernel(
   }
 }
 
+template <typename T, int VecSize = 4, int RoundType = 0, int HeadDim = 128, bool is_scale_channel_wise=false, bool IsFP8=true>
+__global__ void append_decode_cache_int8_rope_qk_norm_kernel(
+    const T* __restrict__ quant_qkv,    // [bsz, num_heads + 2 * kv_num_heads,
+                                        // head_size]
+    uint8_t* __restrict__ key_cache,    // [num_blocks, kv_num_heads,
+                                        // block_size, head_size // 2]
+    uint8_t* __restrict__ value_cache,  // [num_blocks, kv_num_heads,
+                                        // block_size, head_size // 2]
+    T* __restrict__ qkv_out,
+    const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
+    const int* __restrict__ cu_seqlens_q,
+    const int* __restrict__ seq_lens,          // [bsz]
+    const int* __restrict__ seq_lens_encoder,  // [bsz]
+    const float* __restrict__ cos_emb,
+    const float* __restrict__ sin_emb,
+    T* __restrict__ cache_k_scale,
+    T* __restrict__ cache_v_scale,
+    const float* q_norm_weight,
+    const float* k_norm_weight,
+    const int max_seq_len,
+    const int max_blocks_per_seq,
+    const int num_heads,
+    const int block_size,
+    const float max_bound,
+    const float min_bound,
+    const int kv_num_heads,
+    const bool rope_3d,
+    const float rms_norm_eps) {
+  static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
+  static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
+  constexpr int NUM_WARPS = 4;
+  const int tid = threadIdx.x;
+  const int wid = tid / 32;
+  const int lane_id = tid % 32;
+  const int bid = blockIdx.x, head_idx = blockIdx.y * NUM_WARPS + wid;
+  int q_head_idx, k_head_idx, v_idx;
+  const int64_t hidden_size = (num_heads + 2 * kv_num_heads) * HeadDim;
+  constexpr int half_head_size = HeadDim / 2;
+  const int start_token_idx = cu_seqlens_q[bid];
+  if (seq_lens_encoder[bid] > 0) return;
+  const int write_seq_id = seq_lens[bid];
+  if (write_seq_id == 0) return;
+  const int* block_table_now = nullptr;
+
+  block_table_now = block_tables + bid * max_blocks_per_seq;
+  const int block_idx = __ldg(&block_table_now[write_seq_id / block_size]);
+  const int block_offset = write_seq_id % block_size;
+
+  int cache_offset;
+  if (head_idx < num_heads) {
+    cache_offset = 0;
+  } else if (head_idx < num_heads + 2 * kv_num_heads) {
+    cache_offset = block_idx * kv_num_heads * block_size + (head_idx - num_heads) % kv_num_heads * block_size + block_offset;
+  }
+  T *cache_k_scale_now = cache_k_scale + cache_offset;
+  T *cache_v_scale_now = cache_v_scale + cache_offset;
+
+  float thread_m2 = 0.0f;
+  float warp_m2 = 0.0f;
+
+  if (head_idx < num_heads) {
+    // q
+    using LoadT = AlignedVector<T, VecSize>;
+    using LoadBiasT = AlignedVector<T, VecSize>;
+    using LoadOutScaleT = AlignedVector<float, VecSize>;
+    constexpr int HalfVecSize = VecSize / 2;
+    using LoadEmbT = AlignedVector<float, HalfVecSize>;
+
+    LoadT src_vec;
+    LoadBiasT out_vec;
+    LoadEmbT cos_emb_vec;
+    LoadEmbT sin_emb_vec;
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
+    T* qkv_out_now = qkv_out + start_token_idx * hidden_size;
+#pragma unroll
+    for (uint32_t head_bias = lane_id * VecSize; head_bias < HeadDim;
+         head_bias += 32 * VecSize) {
+      const int bias_idx = head_idx * HeadDim + head_bias;
+      Load<T, VecSize>(&qkv_now[bias_idx], &src_vec);
+      // q rope
+      const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
+      const uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
+#pragma unroll
+      for (int i = 0; i < HalfVecSize; i++) {
+        // dequant + add_bias + rope
+        float input_left = static_cast<float>(src_vec[2 * i]);
+        float input_right = static_cast<float>(src_vec[2 * i + 1]);
+
+        const float cos_tmp = cos_emb_vec[i];
+        const float sin_tmp = sin_emb_vec[i];
+        float tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+        float tmp2 = input_right * cos_tmp + input_left * sin_tmp;
+        thread_m2 += tmp1 * tmp1 + tmp2 * tmp2;
+        out_vec[2 * i] =
+            static_cast<T>(tmp1);
+        out_vec[2 * i + 1] =
+            static_cast<T>(tmp2);
+      }
+      // qk norm
+      if (q_norm_weight) {
+        WelfordWarpAllReduce<float, 32>(thread_m2, &warp_m2);
+        float row_variance =
+            max(warp_m2 / HeadDim, 0.0f);
+        float row_inv_var = Rsqrt(row_variance + rms_norm_eps);
+        LoadOutScaleT q_norm_vec;
+        Load<float, VecSize>(&q_norm_weight[lane_id * VecSize], &q_norm_vec);
+        #pragma unroll
+        for (int i = 0; i < VecSize; i++) {
+          out_vec[i] = static_cast<T>(static_cast<float>(out_vec[i]) * row_inv_var * q_norm_vec[i]);
+        }
+      }
+      Store<T, VecSize>(out_vec, &qkv_out_now[bias_idx]);
+    }
+  } else if (head_idx < num_heads + 2 * kv_num_heads) {
+    // k
+    constexpr int KV_VEC_SIZE = 16 / sizeof(uint8_t);  // 16
+    using LoadPadKVT = AlignedVector<uint8_t, KV_VEC_SIZE>;
+    const uint32_t kv_head_idx = (head_idx - num_heads) % kv_num_heads;
+    if (block_offset == 0) {
+      // pad zero for this kv_head_idx for this block
+      LoadPadKVT pad_cache_vec;
+      *(reinterpret_cast<uint4*>(pad_cache_vec.val)) = make_uint4(0, 0, 0, 0);
+      if (head_idx < num_heads + kv_num_heads) {
+        constexpr int num_vecs_per_head_dim = HeadDim / KV_VEC_SIZE;
+        constexpr int num_token_each_time = 32 / num_vecs_per_head_dim;
+        const uint32_t tgt_idx =
+            (block_idx * kv_num_heads + kv_head_idx) * block_size * HeadDim +
+            lane_id % num_vecs_per_head_dim * KV_VEC_SIZE;
+        for (int block_i = lane_id / num_vecs_per_head_dim;
+             block_i < block_size;
+             block_i += num_token_each_time) {
+          Store<uint8_t, KV_VEC_SIZE>(pad_cache_vec,
+                                      &key_cache[tgt_idx + block_i * HeadDim]);
+        }
+      } else {
+        const int num_vecs_per_head_dim = block_size / KV_VEC_SIZE;
+        const int num_token_each_time = 32 / num_vecs_per_head_dim;
+        const uint32_t tgt_idx =
+            (block_idx * kv_num_heads + kv_head_idx) * HeadDim * block_size +
+            lane_id % num_vecs_per_head_dim * KV_VEC_SIZE;
+        for (int block_i = lane_id / num_vecs_per_head_dim; block_i < HeadDim;
+             block_i += num_token_each_time) {
+          Store<uint8_t, KV_VEC_SIZE>(
+              pad_cache_vec, &value_cache[tgt_idx + block_i * block_size]);
+        }
+      }
+      __syncwarp();
+    }
+
+    constexpr int K_VEC_SIZE = 4;
+    constexpr int HALF_K_VEC_SIZE = 2;
+    using LoadKVResT = AlignedVector<uint8_t, K_VEC_SIZE>;
+    using LoadKVT = AlignedVector<uint8_t, HALF_K_VEC_SIZE>;
+    using LoadT = AlignedVector<T, HALF_K_VEC_SIZE>;
+    using LoadBiasT = AlignedVector<T, HALF_K_VEC_SIZE>;
+    using LoadOutScaleT = AlignedVector<float, HALF_K_VEC_SIZE>;
+    using LoadEmbT = AlignedVector<float, 1>;
+    LoadKVResT cache_vec;
+    LoadT src_vec1, src_vec2;
+    LoadBiasT out_vec1, out_vec2;
+    LoadEmbT cos_emb_vec1, cos_emb_vec2;
+    LoadEmbT sin_emb_vec1, sin_emb_vec2;
+
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
+    const int head_bias = lane_id / 4 * 16 + lane_id % 4 * 2;
+    const int bias_idx = head_idx * HeadDim + head_bias;
+    Load<T, HALF_K_VEC_SIZE>(&qkv_now[bias_idx], &src_vec1);
+    Load<T, HALF_K_VEC_SIZE>(&qkv_now[bias_idx + 8], &src_vec2);
+    T scale = T(1.0f);
+    const int k_head_idx = head_idx - num_heads;
+    const int v_head_idx = head_idx - num_heads - kv_num_heads;
+    if (head_idx < num_heads + kv_num_heads) {
+      const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
+      const uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, 1>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+      Load<float, 1>(&cos_emb[new_emb_idx + 4], &cos_emb_vec2);
+      Load<float, 1>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+      Load<float, 1>(&sin_emb[new_emb_idx + 4], &sin_emb_vec2);
+    }
+
+    float input_left = static_cast<float>(src_vec1[0]);
+    float input_right = static_cast<float>(src_vec1[1]);
+    if (head_idx < num_heads + kv_num_heads) {
+      float cos_tmp = cos_emb_vec1[0];
+      float sin_tmp = sin_emb_vec1[0];
+      float tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      float tmp2 = input_right * cos_tmp + input_left * sin_tmp;
+      thread_m2 += tmp1 * tmp1 + tmp2 * tmp2;
+      out_vec1[0] =
+          static_cast<T>(tmp1);
+      out_vec1[1] =
+          static_cast<T>(tmp2);
+    } else {
+      out_vec1[0] = src_vec1[0];
+      out_vec1[1] = src_vec1[1];
+    }
+
+    // rope
+    input_left = static_cast<float>(src_vec2[0]);
+    input_right = static_cast<float>(src_vec2[1]);
+    if (head_idx < num_heads + kv_num_heads) {
+      float cos_tmp = cos_emb_vec2[0];
+      float sin_tmp = sin_emb_vec2[0];
+      float tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      float tmp2 = input_right * cos_tmp + input_left * sin_tmp;
+      thread_m2 += tmp1 * tmp1 + tmp2 * tmp2;
+      out_vec2[0] = static_cast<T>(tmp1);
+      out_vec2[1] = static_cast<T>(tmp2);
+    } else {
+      out_vec2[0] = src_vec2[0];
+      out_vec2[1] = src_vec2[1];
+    }
+    if (k_norm_weight) {
+      if (head_idx < num_heads + kv_num_heads) {
+        LoadOutScaleT k_norm_vec1, k_norm_vec2;
+        Load<float, HALF_K_VEC_SIZE>(&k_norm_weight[head_bias], &k_norm_vec1);
+        Load<float, HALF_K_VEC_SIZE>(&k_norm_weight[head_bias + 8], &k_norm_vec2);
+        // qk norm
+        WelfordWarpAllReduce<float, 32>(thread_m2, &warp_m2);
+        float row_variance =
+            max(warp_m2 / HeadDim, 0.0f);
+        float row_inv_var = Rsqrt(row_variance + rms_norm_eps);
+
+        for (int i = 0; i < HALF_K_VEC_SIZE; i++) {
+          out_vec1[i] = static_cast<T>(static_cast<float>(out_vec1[i]) * row_inv_var * k_norm_vec1[i]);
+          out_vec2[i] = static_cast<T>(static_cast<float>(out_vec2[i]) * row_inv_var * k_norm_vec2[i]);
+        }
+      }
+    }
+    // reduce max, 1 head per warp
+    T local_max = -INFINITY;
+#pragma unroll
+    for (int i = 0; i < HALF_K_VEC_SIZE; i++) {
+      local_max = __hmax(local_max, __habs(out_vec1[i]));
+      local_max = __hmax(local_max, __habs(out_vec2[i]));
+    }
+#pragma unroll
+    for (int m_offset = 16; m_offset > 1; m_offset /= 2) {
+      local_max = __hmax(local_max, __shfl_xor_sync(0xffffffff, local_max, m_offset));
+    }
+
+    scale = __hdiv(448, local_max);
+
+    if (lane_id == 0) {
+      if (head_idx < num_heads + kv_num_heads) {
+        cache_k_scale_now[0] = __hdiv(1, scale);
+      } else {
+        cache_v_scale_now[0] = __hdiv(1, scale);
+      }
+    }
+
+#pragma unroll
+    for (uint32_t i = 0; i < HALF_K_VEC_SIZE; i++) {
+      cache_vec[i] = QuantToC8<T,true, IsFP8, RoundType>(scale, out_vec1[i], max_bound, min_bound);
+      cache_vec[i + HALF_K_VEC_SIZE] = QuantToC8<T,true, IsFP8, RoundType>(scale, out_vec2[i], max_bound, min_bound);
+    }
+    if (head_idx < num_heads + kv_num_heads) {
+      const int start_block_16 =
+          block_offset / 16 * 16 + block_offset % 8 + lane_id / 4 % 2 * 8;
+      const uint32_t tgt_cache_idx =
+          block_idx * kv_num_heads * block_size * HeadDim +
+          kv_head_idx * block_size * HeadDim + start_block_16 * HeadDim +
+          lane_id / 4 / 2 * 32 + (block_offset % 16) / 8 * 16 + lane_id % 4 * 4;
+      Store<uint8_t, K_VEC_SIZE>(cache_vec, &key_cache[tgt_cache_idx]);
+    } else {
+      const uint32_t base_tgt_cache_idx =
+          block_idx * kv_num_heads * HeadDim * block_size +
+          kv_head_idx * HeadDim * block_size +
+          (lane_id / 4 * 16 + lane_id % 4 * 2) * block_size +
+          block_offset / 16 % 2 * 8 * block_size + block_offset / 16 / 2 * 32;
+      const uint32_t tgt_cache_idx1 = base_tgt_cache_idx +
+                                      block_offset % 8 / 2 * 4     // per 4
+                                      + block_offset % 16 / 8 * 2  // per 2
+                                      + block_offset % 2;          // per 1
+      const uint32_t tgt_cache_idx2 = tgt_cache_idx1 + block_size;
+      const uint32_t tgt_cache_idx3 = tgt_cache_idx1 + 16;
+      const uint32_t tgt_cache_idx4 = tgt_cache_idx3 + block_size;
+      value_cache[tgt_cache_idx1] = cache_vec[0];
+      value_cache[tgt_cache_idx2] = cache_vec[1];
+      value_cache[tgt_cache_idx3] = cache_vec[2];
+      value_cache[tgt_cache_idx4] = cache_vec[3];
+    }
+  }
+}
+
 template <typename T, int VecSize = 4, int RoundType = 0, int HeadDim = 128, bool is_scale_channel_wise=false, bool IsFP8=false>
 __global__ void append_decode_cache_int8_rope_kernel(
     const T* __restrict__ quant_qkv,    // [bsz, num_heads + 2 * kv_num_heads,
@@ -639,7 +971,6 @@ __global__ void append_decode_cache_int8_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -677,44 +1008,18 @@ __global__ void append_decode_cache_int8_rope_kernel(
 
   if (head_idx < num_heads) {
     // q
-    using LoadT = AlignedVector<T, VecSize>;
-    using LoadBiasT = AlignedVector<T, VecSize>;
-    using LoadOutScaleT = AlignedVector<float, VecSize>;
-    constexpr int HalfVecSize = VecSize / 2;
-    using LoadEmbT = AlignedVector<float, HalfVecSize>;
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size + head_idx * HeadDim;
+    T* qkv_out_now = qkv_out + start_token_idx * hidden_size + head_idx * HeadDim;
 
-    LoadT src_vec;
-    LoadBiasT out_vec;
-    LoadEmbT cos_emb_vec;
-    LoadEmbT sin_emb_vec;
-    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
-    T* qkv_out_now = qkv_out + start_token_idx * hidden_size;
-#pragma unroll
-    for (uint32_t head_bias = lane_id * VecSize; head_bias < HeadDim;
-         head_bias += 32 * VecSize) {
-      const int bias_idx = head_idx * HeadDim + head_bias;
-      Load<T, VecSize>(&qkv_now[bias_idx], &src_vec);
+    uint32_t emb_offset = write_seq_id * half_head_size;
+    emb_offset += rope_3d ? bid * max_seq_len * HeadDim : 0;
+    apply_rope<T, VecSize, HeadDim, 32>(
+      qkv_now,
+      cos_emb + emb_offset,
+      sin_emb + emb_offset,
+      qkv_out_now,
+      lane_id);
 
-      // q rope
-      const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
-      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
-#pragma unroll
-      for (int i = 0; i < HalfVecSize; i++) {
-        // dequant + add_bias + rope
-        float input_left = static_cast<float>(src_vec[2 * i]);
-        float input_right = static_cast<float>(src_vec[2 * i + 1]);
-
-        const float cos_tmp = cos_emb_vec[i];
-        const float sin_tmp = sin_emb_vec[i];
-        out_vec[2 * i] =
-            static_cast<T>(input_left * cos_tmp - input_right * sin_tmp);
-        out_vec[2 * i + 1] =
-            static_cast<T>(input_right * cos_tmp + input_left * sin_tmp);
-      }
-      Store<T, VecSize>(out_vec, &qkv_out_now[bias_idx]);
-    }
   } else if (head_idx < num_heads + 2 * kv_num_heads) {
     // k
     constexpr int KV_VEC_SIZE = 16 / sizeof(uint8_t);  // 16
@@ -889,7 +1194,6 @@ __global__ void append_decode_cache_int8_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -1194,7 +1498,6 @@ __global__ void append_decode_cache_int8_neox_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -1496,7 +1799,7 @@ __global__ void append_decode_cache_int8_neox_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -1893,7 +2196,7 @@ __global__ void append_decode_cache_int4_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -1934,44 +2237,18 @@ __global__ void append_decode_cache_int4_rope_kernel(
 
   if (head_idx < num_heads) {
     // q
-    using LoadT = AlignedVector<T, VecSize>;
-    using LoadBiasT = AlignedVector<T, VecSize>;
-    using LoadOutScaleT = AlignedVector<float, VecSize>;
-    constexpr int HalfVecSize = VecSize / 2;
-    using LoadEmbT = AlignedVector<float, HalfVecSize>;
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size + head_idx * HeadDim;
+    T* qkv_out_now = qkv_out + start_token_idx * hidden_size + head_idx * HeadDim;
 
-    LoadT src_vec;
-    LoadBiasT out_vec;
-    LoadEmbT cos_emb_vec;
-    LoadEmbT sin_emb_vec;
-    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
-    T* qkv_out_now = qkv_out + start_token_idx * hidden_size;
-#pragma unroll
-    for (uint32_t head_bias = lane_id * VecSize; head_bias < HeadDim;
-         head_bias += 32 * VecSize) {
-      const int bias_idx = head_idx * HeadDim + head_bias;
-      Load<T, VecSize>(&qkv_now[bias_idx], &src_vec);
+    uint32_t emb_offset = write_seq_id * half_head_size;
+    emb_offset += rope_3d ? bid * max_seq_len * HeadDim : 0;
+    apply_rope<T, VecSize, HeadDim, 32>(
+      qkv_now,
+      cos_emb + emb_offset,
+      sin_emb + emb_offset,
+      qkv_out_now,
+      lane_id);
 
-      // q rope
-      const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
-      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
-#pragma unroll
-      for (int i = 0; i < HalfVecSize; i++) {
-        // dequant + add_bias + rope
-        float input_left = static_cast<float>(src_vec[2 * i]);
-        float input_right = static_cast<float>(src_vec[2 * i + 1]);
-
-        const float cos_tmp = cos_emb_vec[i];
-        const float sin_tmp = sin_emb_vec[i];
-        out_vec[2 * i] =
-            static_cast<T>(input_left * cos_tmp - input_right * sin_tmp);
-        out_vec[2 * i + 1] =
-            static_cast<T>(input_right * cos_tmp + input_left * sin_tmp);
-      }
-      Store<T, VecSize>(out_vec, &qkv_out_now[bias_idx]);
-    }
   } else if (head_idx < num_heads + 2 * kv_num_heads) {
     // k
     constexpr int KV_VEC_SIZE = 16 / sizeof(uint8_t);  // 16
@@ -2191,7 +2468,7 @@ __global__ void append_decode_cache_int4_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -2522,7 +2799,7 @@ __global__ void append_decode_cache_int4_neox_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]
@@ -2895,7 +3172,7 @@ __global__ void append_decode_cache_int4_neox_rope_kernel(
                                         // block_size, head_size // 2]
     T* __restrict__ qkv_out,
     const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
-    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+
     const int* __restrict__ cu_seqlens_q,
     const int* __restrict__ seq_lens,          // [bsz]
     const int* __restrict__ seq_lens_encoder,  // [bsz]

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_kernel.cu

@@ -21,7 +21,6 @@ void append_decode_cache_rope_qk_norm(const QKV_TYPE* qkv,
                               T* value_cache,
                               T* qkv_out,
                               const int* block_tables,
-                              const int* batch_id_per_token,
                               const int* cu_seqlens_q,
                               const int* seq_lens,
                               const int* seq_lens_encoder,
@@ -59,7 +58,6 @@ void append_decode_cache_rope_qk_norm(const QKV_TYPE* qkv,
                                             value_cache,
                                             qkv_out,
                                             block_tables,
-                                            batch_id_per_token,
                                             cu_seqlens_q,
                                             seq_lens,
                                             seq_lens_encoder,
@@ -84,7 +82,6 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
                               T* value_cache,
                               T* qkv_out,
                               const int* block_tables,
-                              const int* batch_id_per_token,
                               const int* cu_seqlens_q,
                               const int* seq_lens,
                               const int* seq_lens_encoder,
@@ -120,7 +117,6 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -143,7 +139,6 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
                                                 value_cache,
                                                 qkv_out,
                                                 block_tables,
-                                                batch_id_per_token,
                                                 cu_seqlens_q,
                                                 seq_lens,
                                                 seq_lens_encoder,
@@ -167,7 +162,6 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -190,7 +184,6 @@ void append_decode_cache_rope(const QKV_TYPE* qkv,
                                                 value_cache,
                                                 qkv_out,
                                                 block_tables,
-                                                batch_id_per_token,
                                                 cu_seqlens_q,
                                                 seq_lens,
                                                 seq_lens_encoder,
@@ -214,7 +207,6 @@ void append_decode_cache_int8_rope(const QKV_TYPE* qkv,
                                    uint8_t* value_cache,
                                    T* qkv_out,
                                    const int* block_tables,
-                                   const int* batch_id_per_token,
                                    const int* cu_seqlens_q,
                                    const int* seq_lens,
                                    const int* seq_lens_encoder,
@@ -247,7 +239,6 @@ void append_decode_cache_int8_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -273,7 +264,6 @@ void append_decode_cache_int8_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -299,7 +289,6 @@ void append_decode_cache_int8_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -325,7 +314,6 @@ void append_decode_cache_int8_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -351,7 +339,6 @@ void append_decode_cache_int4_rope(const QKV_TYPE* qkv,
                                    uint8_t* value_cache,
                                    T* qkv_out,
                                    const int* block_tables,
-                                   const int* batch_id_per_token,
                                    const int* cu_seqlens_q,
                                    const int* seq_lens,
                                    const int* seq_lens_encoder,
@@ -386,7 +373,6 @@ void append_decode_cache_int4_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -414,7 +400,6 @@ void append_decode_cache_int4_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -442,7 +427,6 @@ void append_decode_cache_int4_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -470,7 +454,6 @@ void append_decode_cache_int4_rope(const QKV_TYPE* qkv,
               value_cache,
               qkv_out,
               block_tables,
-              batch_id_per_token,
               cu_seqlens_q,
               seq_lens,
               seq_lens_encoder,
@@ -497,7 +480,6 @@ void DecoderWriteCacheWithRoPEKernel(
     const paddle::Tensor& qkv,
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,
@@ -549,7 +531,6 @@ void DecoderWriteCacheWithRoPEKernel(
           reinterpret_cast<DataType_*>(value_cache_out->data<T>()),
           reinterpret_cast<DataType_*>(qkv_out->data<T>()),
           block_tables.data<int>(),
-          batch_id_per_token.data<int>(),
           cu_seqlens_q.data<int>(),
           seq_lens.data<int>(),
           seq_lens_encoder.data<int>(),
@@ -572,9 +553,39 @@ void DecoderWriteCacheWithRoPEKernel(
           q_norm_weight ? q_norm_weight.get().data<float>() : nullptr,
           k_norm_weight ? k_norm_weight.get().data<float>() : nullptr,
           rms_norm_eps);
+    } else if (cache_quant_type_str == "block_wise_fp8") {
+      constexpr int num_warps = 4;
+      const int all_warps =
+          ((num_heads + 2 * kv_num_heads) + num_warps - 1) / num_warps * num_warps;
+      dim3 grids(bsz, all_warps / num_warps);
+      append_decode_cache_int8_rope_qk_norm_kernel<DataType_, 4, 0, 128, false, true>
+          <<<grids, num_warps * 32, 0, stream>>>(
+              reinterpret_cast<const DataType_*>(qkv_ptr),
+              key_cache_out->data<uint8_t>(),
+              value_cache_out->data<uint8_t>(),
+              reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+              block_tables.data<int>(),
+              cu_seqlens_q.data<int>(),
+              seq_lens.data<int>(),
+              seq_lens_encoder.data<int>(),
+              cos_emb,
+              sin_emb,
+              const_cast<DataType_*>(reinterpret_cast<const DataType_*>(cache_k_scale.get().data<T>())),
+              const_cast<DataType_*>(reinterpret_cast<const DataType_*>((cache_v_scale.get().data<T>()))),
+              q_norm_weight.get().data<float>(),
+              k_norm_weight.get().data<float>(),
+              max_seq_len,
+              max_blocks_per_seq,
+              num_heads,
+              block_size,
+              127.0f,
+              -127.0f,
+              kv_num_heads,
+              rope_3d,
+              rms_norm_eps);
     } else {
       PD_THROW(
-          "append_decode_cache_rope_qk_norm not support cachekv quant yet");
+          "append_decode_cache_rope_qk_norm just supports cache_quant_type none/block_wise_fp8");
     }
   } else {
     if (cache_quant_type_str == "none") {
@@ -584,7 +595,6 @@ void DecoderWriteCacheWithRoPEKernel(
           reinterpret_cast<DataType_*>(value_cache_out->data<T>()),
           reinterpret_cast<DataType_*>(qkv_out->data<T>()),
           block_tables.data<int>(),
-          batch_id_per_token.data<int>(),
           cu_seqlens_q.data<int>(),
           seq_lens.data<int>(),
           seq_lens_encoder.data<int>(),
@@ -616,7 +626,6 @@ void DecoderWriteCacheWithRoPEKernel(
           value_cache_out->data<uint8_t>(),
           reinterpret_cast<DataType_*>(qkv_out->data<T>()),
           block_tables.data<int>(),
-          batch_id_per_token.data<int>(),
           cu_seqlens_q.data<int>(),
           seq_lens.data<int>(),
           seq_lens_encoder.data<int>(),
@@ -649,7 +658,6 @@ void DecoderWriteCacheWithRoPEKernel(
             value_cache_out->data<uint8_t>(),
             reinterpret_cast<DataType_*>(qkv_out->data<T>()),
             block_tables.data<int>(),
-            batch_id_per_token.data<int>(),
             cu_seqlens_q.data<int>(),
             seq_lens.data<int>(),
             seq_lens_encoder.data<int>(),
@@ -683,7 +691,6 @@ void DecoderWriteCacheWithRoPEKernel(
             value_cache_out->data<uint8_t>(),
             reinterpret_cast<DataType_*>(qkv_out->data<T>()),
             block_tables.data<int>(),
-            batch_id_per_token.data<int>(),
             cu_seqlens_q.data<int>(),
             seq_lens.data<int>(),
             seq_lens_encoder.data<int>(),
@@ -709,6 +716,36 @@ void DecoderWriteCacheWithRoPEKernel(
             stream,
             use_neox_rotary_style,
             rope_3d);
+    } else if (cache_quant_type_str == "block_wise_fp8") {
+      constexpr int num_warps = 4;
+      const int all_warps =
+          ((num_heads + 2 * kv_num_heads) + num_warps - 1) / num_warps * num_warps;
+      dim3 grids(bsz, all_warps / num_warps);
+      append_decode_cache_int8_rope_qk_norm_kernel<DataType_, 4, 0, 128, false, true>
+          <<<grids, num_warps * 32, 0, stream>>>(
+              reinterpret_cast<const DataType_*>(qkv_ptr),
+              key_cache_out->data<uint8_t>(),
+              value_cache_out->data<uint8_t>(),
+              reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+              block_tables.data<int>(),
+              cu_seqlens_q.data<int>(),
+              seq_lens.data<int>(),
+              seq_lens_encoder.data<int>(),
+              cos_emb,
+              sin_emb,
+              const_cast<DataType_*>(reinterpret_cast<const DataType_*>(cache_k_scale.get().data<T>())),
+              const_cast<DataType_*>(reinterpret_cast<const DataType_*>((cache_v_scale.get().data<T>()))),
+              nullptr,
+              nullptr,
+              max_seq_len,
+              max_blocks_per_seq,
+              num_heads,
+              block_size,
+              127.0f,
+              -127.0f,
+              kv_num_heads,
+              rope_3d,
+              rms_norm_eps);
     } else if (cache_quant_type_str == "cache_int4_zp") {
       append_decode_cache_int4_rope(
           reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
@@ -716,7 +753,6 @@ void DecoderWriteCacheWithRoPEKernel(
           value_cache_out->data<uint8_t>(),
           reinterpret_cast<DataType_*>(const_cast<T*>(qkv_out->data<T>())),
           block_tables.data<int>(),
-          batch_id_per_token.data<int>(),
           cu_seqlens_q.data<int>(),
           seq_lens.data<int>(),
           seq_lens_encoder.data<int>(),
@@ -764,7 +800,6 @@ template void DecoderWriteCacheWithRoPEKernel<paddle::bfloat16, int>(
               // kv_num_heads, head_dim] if GQA)
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,
@@ -794,7 +829,6 @@ DecoderWriteCacheWithRoPEKernel<paddle::bfloat16, paddle::bfloat16>(
               // kv_num_heads, head_dim] if GQA)
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,
@@ -823,7 +857,6 @@ template void DecoderWriteCacheWithRoPEKernel<paddle::float16, int>(
               // kv_num_heads, head_dim] if GQA)
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,
@@ -852,7 +885,6 @@ template void DecoderWriteCacheWithRoPEKernel<paddle::float16, paddle::float16>(
               // kv_num_heads, head_dim] if GQA)
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_kernel.h

@@ -23,7 +23,6 @@ void DecoderWriteCacheWithRoPEKernel(
               // kv_num_heads, head_dim] if GQA)
     const paddle::Tensor& seq_lens,
     const paddle::Tensor& seq_lens_encoder,
-    const paddle::Tensor& batch_id_per_token,
     const paddle::Tensor& cu_seqlens_q,
     const paddle::Tensor& block_tables,
     const paddle::optional<paddle::Tensor>& rotary_embs,

custom_ops/gpu_ops/append_attn/encoder_write_cache_with_rope_impl.cuh

@@ -449,8 +449,8 @@ __global__ void GQAVariableLengthRotaryQKNormKernel(
   const int half_lastdim = last_dim / 2;
   const int offset = (q_num_head + kv_num_head) * last_dim;
   const int all_head_num = elem_cnt / last_dim;
-  for (int gloabl_hi = global_warp_idx; gloabl_hi < all_head_num; gloabl_hi += all_warp_num) {
-    int64_t linear_index = gloabl_hi * last_dim + threadIdx.x * VecSize;
+  for (int global_hi = global_warp_idx; global_hi < all_head_num; global_hi += all_warp_num) {
+    int64_t linear_index = global_hi * last_dim + threadIdx.x * VecSize;
     const int token_idx = linear_index / offset;
     const int ori_bi = batch_id_per_token[token_idx];
     if (seq_lens[ori_bi] == 0) continue;
@@ -1232,6 +1232,411 @@ __global__ void append_write_cache_kv_c8_qkv(
   }
 }
 
+template <typename T,
+          uint32_t num_frags_y,
+          uint32_t num_frags_z,
+          uint32_t HEAD_DIM,
+          uint32_t BLOCK_SIZE,
+          uint32_t NUM_WARPS,
+          bool is_need_kv_quant,
+          bool IsFP8 = true>
+__global__ void append_write_cache_kv_c8_qkv_dynamic(
+    uint8_t *__restrict__ cache_k,
+    uint8_t *__restrict__ cache_v,
+    const T *__restrict__ qkv_input,
+    T *__restrict__ cache_k_scales, // [block_num, num_heads, block_size]
+    T *__restrict__ cache_v_scales, // [block_num, num_heads, block_size]
+    const int *__restrict__ batch_ids,
+    const int *__restrict__ tile_ids,
+    const int *__restrict__ seq_lens_this_time,
+    const int *__restrict__ seq_lens_decoder,
+    const int *__restrict__ batch_id_per_token,
+    const int *__restrict__ cu_seqlens_q,
+    const int *__restrict__ block_tables,
+    const int max_seq_len,
+    const int max_blocks_per_seq,
+    const int num_heads,
+    const int kv_num_heads) {
+  constexpr uint32_t num_vecs_per_head = HEAD_DIM / num_elems_per_128b<T>();
+  constexpr uint32_t pad_len = BLOCK_SIZE;
+  const uint32_t btid = blockIdx.x, kv_head_idx = blockIdx.z;
+  const T cache_k_scale = cache_k_scales[kv_head_idx];
+  const T cache_v_scale = cache_v_scales[kv_head_idx];
+  const uint32_t tid = threadIdx.x, wid = threadIdx.y;
+  const uint32_t batch_id = batch_ids[btid];
+  const uint32_t tile_id = tile_ids[btid];
+  const uint32_t seq_len_this_time = seq_lens_this_time[batch_id];
+  if (seq_len_this_time <= 0) {
+    return;
+  }
+  const int *block_table_now = nullptr;
+
+  block_table_now = block_tables + batch_id * max_blocks_per_seq;
+
+  const uint32_t num_rows_per_block =
+      NUM_WARPS * num_frags_z * 16;  // BLOCK_SIZE
+  const uint32_t start_len = seq_lens_decoder[batch_id];
+  const uint32_t bf_pad_len = start_len % pad_len;
+  const uint32_t start_len_pad = start_len - bf_pad_len;
+  const uint32_t end_len = start_len + seq_len_this_time;
+
+  const uint32_t tile_start = start_len_pad + tile_id * num_rows_per_block;
+  int block_id = __ldg(&block_table_now[tile_start / BLOCK_SIZE]);
+  uint32_t chunk_start = tile_start + wid * num_frags_z * 16 + tid / 8;
+
+  const uint32_t start_token_idx = cu_seqlens_q[batch_id];
+  const uint32_t kv_batch_stride = (num_heads + 2 * kv_num_heads) * HEAD_DIM;
+  const uint32_t kv_h_stride = HEAD_DIM;
+  __shared__ T k_smem_ori[num_rows_per_block * HEAD_DIM];
+  __shared__ T v_smem_ori[num_rows_per_block * HEAD_DIM];
+  __shared__ T v_scale_smem[BLOCK_SIZE];
+  if (tile_start >= start_len) {
+    constexpr int KV_VEC_SIZE = 16 / sizeof(uint8_t);  // 16
+    using LoadPadKVT = AlignedVector<uint8_t, KV_VEC_SIZE>;
+    // pad zero for this kv_head_idx for this block
+    LoadPadKVT pad_cache_vec;
+    *(reinterpret_cast<uint4*>(pad_cache_vec.val)) = make_uint4(0, 0, 0, 0);
+    // reset k
+    constexpr int num_vecs_per_head_k = HEAD_DIM / KV_VEC_SIZE;
+    constexpr int num_token_each_time_k = 32 / num_vecs_per_head_k;
+    uint32_t tgt_idx =
+        (block_id * kv_num_heads + kv_head_idx) * BLOCK_SIZE * HEAD_DIM +
+        tid % num_vecs_per_head_k * KV_VEC_SIZE;
+    for (int block_i = tid / num_vecs_per_head_k;
+          block_i < BLOCK_SIZE;
+          block_i += num_token_each_time_k) {
+      Store<uint8_t, KV_VEC_SIZE>(pad_cache_vec,
+                                  &cache_k[tgt_idx + block_i * HEAD_DIM]);
+    }
+
+    // reset v
+    const int num_vecs_per_head_v = BLOCK_SIZE / KV_VEC_SIZE;
+    const int num_token_each_time_v = 32 / num_vecs_per_head_v;
+    tgt_idx =
+        (block_id * kv_num_heads + kv_head_idx) * HEAD_DIM * BLOCK_SIZE +
+        tid % num_vecs_per_head_v * KV_VEC_SIZE;
+    for (int block_i = tid / num_vecs_per_head_v; block_i < HEAD_DIM;
+          block_i += num_token_each_time_v) {
+      Store<uint8_t, KV_VEC_SIZE>(
+          pad_cache_vec, &cache_v[tgt_idx + block_i * BLOCK_SIZE]);
+    }
+  }
+  smem_t k_smem(k_smem_ori);
+  smem_t v_smem(v_smem_ori);
+
+  uint32_t kv_smem_offset_w = smem_t::get_permuted_offset<num_vecs_per_head>(
+      wid * num_frags_z * 16 + tid / 8, tid % 8);  // 4 * 8 per warp
+
+  /*
+   0 | 1
+   2 | 3
+  */
+  uint32_t k_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
+      wid * num_frags_z * 16 + 8 * (tid / 16) + tid % 8, (tid % 16) / 8);
+
+  constexpr uint32_t num_frags_v = num_frags_y / NUM_WARPS;
+  /*
+   0 | 2
+   1 | 3
+  */
+  uint32_t v_smem_offset_r = smem_t::get_permuted_offset<num_vecs_per_head>(
+      tid % 16, wid * num_frags_v * 2 + tid / 16);
+
+  // load kv gmem to smem
+  const uint32_t real_start_token_idx = start_token_idx - bf_pad_len +
+                                        tile_id * num_rows_per_block +
+                                        wid * num_frags_z * 16 + tid / 8;
+  uint32_t k_read_idx = real_start_token_idx * kv_batch_stride +
+                        (num_heads + kv_head_idx) * kv_h_stride +
+                        tid % 8 * num_elems_per_128b<T>();
+  uint32_t v_read_idx = real_start_token_idx * kv_batch_stride +
+                        (num_heads + kv_num_heads + kv_head_idx) * kv_h_stride +
+                        tid % 8 * num_elems_per_128b<T>();
+#pragma unroll
+  for (uint32_t fz = 0; fz < num_frags_z; ++fz) {
+#pragma unroll
+    for (uint32_t j = 0; j < 4; ++j) {
+#pragma unroll
+      for (uint32_t fy = 0; fy < num_frags_y / 4;
+           ++fy) {  // (num_frags_y * 16) / (8 *  num_elems_per_128b<T>())
+        if (chunk_start >= start_len && chunk_start < end_len) {
+          k_smem.load_128b_async<SharedMemFillMode::kNoFill>(
+              kv_smem_offset_w, qkv_input + k_read_idx, chunk_start < end_len);
+          v_smem.load_128b_async<SharedMemFillMode::kNoFill>(
+              kv_smem_offset_w, qkv_input + v_read_idx, chunk_start < end_len);
+        }
+        kv_smem_offset_w =
+            k_smem.advance_offset_by_column<8>(kv_smem_offset_w, fy);
+        k_read_idx += 8 * num_elems_per_128b<T>();
+        v_read_idx += 8 * num_elems_per_128b<T>();
+      }
+      kv_smem_offset_w =
+          k_smem.advance_offset_by_row<4, num_vecs_per_head>(kv_smem_offset_w) -
+          2 * num_frags_y;
+      chunk_start += 4;
+      k_read_idx +=
+          4 * kv_batch_stride - 2 * num_frags_y * num_elems_per_128b<T>();
+      v_read_idx +=
+          4 * kv_batch_stride - 2 * num_frags_y * num_elems_per_128b<T>();
+    }
+  }
+  commit_group();
+  wait_group<0>();
+  __syncthreads();
+
+  // reduce scale
+  // 16 rows per warp
+  uint32_t kv_reduce_frag[4];
+  T *kv_reduce_frag_T = reinterpret_cast<T*>(kv_reduce_frag);
+
+   T k_local_max_value[num_frags_z * 2];
+   T v_local_max_value[num_frags_z * 2];
+#pragma unroll
+  for (int i = 0; i < num_frags_z * 2; i++) {
+    k_local_max_value[i] = -INFINITY;
+  }
+#pragma unroll
+  for (int i = 0; i < num_frags_z * 2; i++) {
+    v_local_max_value[i] = -INFINITY;
+  }
+  const int num_kv_heads = gridDim.z;
+  const int scale_offset = block_id * num_kv_heads * BLOCK_SIZE + kv_head_idx * BLOCK_SIZE;
+  T *cache_k_scale_now = cache_k_scales + scale_offset;
+  T *cache_v_scale_now = cache_v_scales + scale_offset;
+  // k scale
+#pragma unroll
+  for (uint32_t fz = 0; fz < num_frags_z; ++fz) {
+#pragma unroll
+    for (uint32_t fy = 0; fy < num_frags_y; ++fy) {
+      // reduce per thread, 4 threads each row
+      k_smem.ldmatrix_m8n8x4(k_smem_offset_r, kv_reduce_frag);
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        k_local_max_value[fz * 2] = __hmax(__habs(kv_reduce_frag_T[i]), k_local_max_value[fz * 2]);
+      }
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        k_local_max_value[fz * 2 + 1] = __hmax(__habs(kv_reduce_frag_T[i + 4]), k_local_max_value[fz * 2 + 1]);
+      }
+      k_smem_offset_r = k_smem.advance_offset_by_column<2>(k_smem_offset_r, fy);
+    }
+    // reduce per row
+    for (int i = 0; i < 2; i++) {
+      T local_max_value = __habs(k_local_max_value[fz * 2 + i]);
+      local_max_value = __hmax(local_max_value, __shfl_xor_sync(0xffffffff, local_max_value, 2));
+      local_max_value = __hmax(local_max_value, __shfl_xor_sync(0xffffffff, local_max_value, 1));
+      // used for quant
+      k_local_max_value[fz * 2 + i] = __hdiv(448, local_max_value);
+    }
+    // store
+    if (tid % 4 == 0) {
+      const int offset_now = wid * num_frags_z * 16 + tid / 4;
+      // used for dequant
+      if (tile_start + offset_now >= start_len) {
+        if (tile_start + offset_now < end_len) {
+          cache_k_scale_now[offset_now] = __hdiv(1, k_local_max_value[fz * 2]);
+        } else {
+          cache_k_scale_now[offset_now] = 0;
+        }
+      }
+      if (tile_start + offset_now + 8 >= start_len) {
+        if (tile_start + offset_now + 8 < end_len) {
+          cache_k_scale_now[offset_now + 8] = __hdiv(1, k_local_max_value[fz * 2 + 1]);
+        } else {
+          cache_k_scale_now[offset_now + 8] = 0;
+        }
+      }
+    }
+    __syncthreads();
+    k_smem_offset_r -= 2 * num_frags_y; // num_frags_z = 1
+  }
+  // v scale
+  #pragma unroll
+  for (uint32_t fz = 0; fz < num_frags_z; ++fz) {
+#pragma unroll
+    for (uint32_t fy = 0; fy < num_frags_y; ++fy) {
+      // reduce per thread, 4 threads each row
+      v_smem.ldmatrix_m8n8x4(k_smem_offset_r, kv_reduce_frag);
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        v_local_max_value[fz * 2] = __hmax(__habs(kv_reduce_frag_T[i]), v_local_max_value[fz * 2]);
+      }
+#pragma unroll
+      for (int i = 0; i < 4; i++) {
+        v_local_max_value[fz * 2 + 1] = __hmax(__habs(kv_reduce_frag_T[i + 4]), v_local_max_value[fz * 2 + 1]);
+      }
+      k_smem_offset_r = v_smem.advance_offset_by_column<2>(k_smem_offset_r, fy);
+    }
+    // reduce per row
+    for (int i = 0; i < 2; i++) {
+      T local_max_value = __habs(v_local_max_value[fz * 2 + i]);
+      local_max_value = __hmax(local_max_value, __shfl_xor_sync(0xffffffff, local_max_value, 2));
+      local_max_value = __hmax(local_max_value, __shfl_xor_sync(0xffffffff, local_max_value, 1));
+      v_local_max_value[fz * 2 + i] = __hdiv(448, local_max_value);
+    }
+    // store
+    if (tid % 4 == 0) {
+      const int offset_now = wid * num_frags_z * 16 + tid / 4;
+      // used for dequant
+      if (tile_start + offset_now >= start_len) {
+        if (tile_start + offset_now < end_len) {
+          cache_v_scale_now[offset_now] = __hdiv(1, v_local_max_value[fz * 2]);
+          v_scale_smem[offset_now] = v_local_max_value[fz * 2];
+        } else {
+          cache_v_scale_now[offset_now] = 0;
+          v_scale_smem[offset_now] = 0;
+        }
+      }
+      if (tile_start + offset_now + 8 >= start_len) {
+        if (tile_start + offset_now + 8 < end_len) {
+          cache_v_scale_now[offset_now + 8] = __hdiv(1, v_local_max_value[fz * 2 + 1]);
+          v_scale_smem[offset_now + 8] = v_local_max_value[fz * 2 + 1];
+        } else {
+          cache_v_scale_now[offset_now + 8] = 0;
+          v_scale_smem[offset_now + 8] = 0;
+        }
+      }
+    }
+    __syncthreads();
+    k_smem_offset_r -= 2 * num_frags_y; // num_frags_z = 1
+  }
+  __syncthreads();
+
+  // mask, quant, store
+  using LoadKVT = AlignedVector<uint8_t, 4>;
+  LoadKVT cache_vec1;
+  LoadKVT cache_vec2;
+
+  uint32_t chunk_start_k = tile_start + wid * num_frags_z * 16 + tid / 4;
+  uint32_t kv_frag[4];
+  const uint32_t write_n_stride = kv_num_heads * BLOCK_SIZE * HEAD_DIM;
+  const uint32_t write_h_stride = BLOCK_SIZE * HEAD_DIM;
+  const uint32_t write_b_stride = HEAD_DIM;
+  const uint32_t write_d_stride = BLOCK_SIZE;
+  uint32_t k_write_idx = block_id * write_n_stride +
+                         kv_head_idx * write_h_stride +
+                         (wid * num_frags_z * 16 + tid / 4) * write_b_stride +
+                         tid % 4 * 4;  // 4 * int8 = 8 * int4 = 32bit
+#pragma unroll
+  for (uint32_t fz = 0; fz < num_frags_z; ++fz) {
+    uint32_t k_write_idx_now_z = k_write_idx + fz * 16 * write_b_stride;
+#pragma unroll
+    for (uint32_t fy = 0; fy < num_frags_y; ++fy) {
+      uint32_t k_write_idx_now = k_write_idx_now_z +
+                                 fy % 2 * 8 * write_b_stride +
+                                 fy / 2 * 32;  // + fy % 2 * 16;
+      // load
+      k_smem.ldmatrix_m8n8x4(k_smem_offset_r, kv_frag);
+      // quant
+      T *k_frag_T = reinterpret_cast<T *>(kv_frag);
+      if (bf_pad_len != 0) {
+        Load<uint8_t, 4>(cache_k + k_write_idx_now, &cache_vec1);
+        Load<uint8_t, 4>(cache_k + k_write_idx_now + 16, &cache_vec2);
+      }
+#pragma unroll
+      for (uint32_t v_id = 0; v_id < 8; ++v_id) {
+        uint8_t uint_quant_value;
+        if (chunk_start_k + (v_id / 4) * 8 >= start_len &&
+            chunk_start_k + (v_id / 4) * 8 < end_len) {
+          uint_quant_value = QuantToC8<T, is_need_kv_quant, IsFP8>(k_local_max_value[fz * 2 + v_id / 4], k_frag_T[v_id], 127.0f, -127.0f);
+        } else {
+          uint_quant_value = 0;
+        }
+        if (bf_pad_len != 0) {
+          if (v_id < 4) {
+            cache_vec1[v_id] |= uint_quant_value;
+          } else {
+            cache_vec2[v_id % 4] |= uint_quant_value;
+          }
+        } else {
+          if (v_id < 4) {
+            cache_vec1[v_id] = uint_quant_value;
+          } else {
+            cache_vec2[v_id - 4] = uint_quant_value;
+          }
+        }
+      }
+      // store
+      Store<uint8_t, 4>(cache_vec1, cache_k + k_write_idx_now);
+      Store<uint8_t, 4>(cache_vec2, cache_k + k_write_idx_now + 16);
+      k_smem_offset_r = k_smem.advance_offset_by_column<2>(k_smem_offset_r, fy);
+    }
+    k_smem_offset_r =
+        k_smem.advance_offset_by_row<16, num_vecs_per_head>(k_smem_offset_r) -
+        2 * num_frags_y;
+    chunk_start_k += 16;
+  }
+
+  uint32_t chunk_start_v = tile_start + tid % 4 * 2;
+  uint32_t v_write_idx = block_id * write_n_stride +
+                         kv_head_idx * write_h_stride +
+                         (wid * num_frags_v * 16 + tid / 4) * write_d_stride +
+                         tid % 4 * 4;  // 4 * int8 = 8 * int4 = 32bit
+  const uint32_t num_frags_z_v = num_frags_z * NUM_WARPS;
+  T v_scales[num_frags_z_v * 4];
+  for (int v_i = 0; v_i < num_frags_z_v; v_i++) {
+    const int offset = v_i * 16;
+    const int t_offset = tid % 4 * 2;
+    v_scales[v_i * 4] = v_scale_smem[offset + t_offset];
+    v_scales[v_i * 4 + 1] = v_scale_smem[offset + t_offset + 1];
+    v_scales[v_i * 4 + 2] = v_scale_smem[offset + t_offset + 8];
+    v_scales[v_i * 4 + 3] = v_scale_smem[offset + t_offset + 9];
+  }
+
+#pragma unroll
+  for (uint32_t fy = 0; fy < num_frags_v; ++fy) {
+    uint32_t v_write_idx_now_v = v_write_idx + fy * 16 * write_d_stride;
+#pragma unroll
+    for (uint32_t fz = 0; fz < num_frags_z_v; ++fz) {
+      uint32_t v_write_idx_now = v_write_idx_now_v +
+                                 fz % 2 * 8 * write_d_stride +
+                                 fz / 2 * 32;  // + fz % 2 * 16;
+      // load
+      v_smem.ldmatrix_m8n8x4_trans(v_smem_offset_r, kv_frag);
+      // quant
+      T *v_frag_T = reinterpret_cast<T *>(kv_frag);
+      if (bf_pad_len != 0) {
+        Load<uint8_t, 4>(cache_v + v_write_idx_now, &cache_vec1);
+        Load<uint8_t, 4>(cache_v + v_write_idx_now + 16, &cache_vec2);
+      }
+#pragma unroll
+      for (uint32_t v_id = 0; v_id < 8; ++v_id) {
+        uint8_t uint_quant_value;
+        if (chunk_start_v + v_id % 2 + (v_id % 4) / 2 * 8 >= start_len &&
+            chunk_start_v + v_id % 2 + (v_id % 4) / 2 * 8 < end_len) {
+          uint_quant_value = QuantToC8<T, is_need_kv_quant, IsFP8>(v_scales[fz * 4 + v_id % 4], v_frag_T[v_id], 127.0f, -127.0f);
+          // store now
+        } else {
+          uint_quant_value = 0;
+        }
+        if (bf_pad_len != 0) {
+          if (v_id < 4) {
+            cache_vec1[v_id] |= uint_quant_value;
+          } else {
+            cache_vec2[v_id % 4] |= uint_quant_value;
+          }
+        } else {
+          if (v_id < 4) {
+            cache_vec1[v_id] = uint_quant_value;
+          } else {
+            cache_vec2[v_id % 4] = uint_quant_value;
+          }
+        }
+      }
+      // store
+      Store<uint8_t, 4>(cache_vec1, cache_v + v_write_idx_now);
+      Store<uint8_t, 4>(cache_vec2, cache_v + v_write_idx_now + 16);
+      chunk_start_v += 16;
+      v_smem_offset_r =
+          k_smem.advance_offset_by_row<16, num_vecs_per_head>(v_smem_offset_r);
+    }
+    v_smem_offset_r = k_smem.advance_offset_by_column<2>(
+                          v_smem_offset_r, wid * num_frags_v + fy) -
+                      16 * num_frags_z_v * num_vecs_per_head;
+    chunk_start_v -= 16 * num_frags_z_v;
+  }
+}
+
 // Write Cache KV in Append
 template <typename T,
           uint32_t num_frags_y,
@@ -2006,10 +2411,11 @@ void CascadeAppendWriteCacheKVC8QKV(
     int num_blocks_x_cpu,
     int max_seq_len,
     bool is_scale_channel_wise,
-    const bool is_fp8,
+    const std::string& cache_quant_type,
     cudaStream_t &stream,
     paddle::Tensor *cache_k_out,
     paddle::Tensor *cache_v_out) {
+  using NV_TYPE = typename cascade_attn_type_traits<T>::type;
   auto max_blocks_per_seq = meta_data.max_blocks_per_seq;
   auto num_tokens = meta_data.token_nums;
   auto num_heads = meta_data.q_num_heads;
@@ -2027,49 +2433,77 @@ void CascadeAppendWriteCacheKVC8QKV(
   dim3 blocks(32, num_warps);
 
   const uint32_t smem_size = (BLOCK_SIZE * HEAD_DIM) * sizeof(T) * 2;
-  auto kernel_fn = append_write_cache_kv_c8_qkv<T,
-                                                num_frags_y,
-                                                num_frags_z,
-                                                HEAD_DIM,
-                                                BLOCK_SIZE,
-                                                num_warps,
-                                                true, false>;
-  if (is_fp8) {
-    kernel_fn = append_write_cache_kv_c8_qkv<T,
-                                                num_frags_y,
-                                                num_frags_z,
-                                                HEAD_DIM,
-                                                BLOCK_SIZE,
-                                                num_warps,
-                                                true, true>;
+  if (cache_quant_type != "block_wise_fp8") {
+    auto kernel_fn = append_write_cache_kv_c8_qkv<T,
+                                                  num_frags_y,
+                                                  num_frags_z,
+                                                  HEAD_DIM,
+                                                  BLOCK_SIZE,
+                                                  num_warps,
+                                                  true, false>;
+    if (cache_quant_type == "cache_fp8") {
+      kernel_fn = append_write_cache_kv_c8_qkv<T,
+                                              num_frags_y,
+                                              num_frags_z,
+                                              HEAD_DIM,
+                                              BLOCK_SIZE,
+                                              num_warps,
+                                              true, true>;
+    }
+    if (is_scale_channel_wise) {
+      kernel_fn = append_write_cache_kv_c8_qkv<T,
+                                              num_frags_y,
+                                              num_frags_z,
+                                              HEAD_DIM,
+                                              BLOCK_SIZE,
+                                              num_warps,
+                                              false>;
+    }
+    cudaFuncSetAttribute(
+        kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
+    kernel_fn<<<grids, blocks, 0, stream>>>(cache_k_out->data<uint8_t>(),
+                                            cache_v_out->data<uint8_t>(),
+                                            qkv.data<T>(),
+                                            cache_k_scale.data<T>(),
+                                            cache_v_scale.data<T>(),
+                                            batch_ids.data<int>(),
+                                            tile_ids_per_batch.data<int>(),
+                                            seq_lens_this_time.data<int>(),
+                                            seq_lens_decoder.data<int>(),
+                                            batch_id_per_token.data<int>(),
+                                            cu_seqlens_q.data<int>(),
+                                            block_table.data<int>(),
+                                            max_seq_len,
+                                            max_blocks_per_seq,
+                                            num_heads,
+                                            kv_num_heads);
+  } else {
+    auto kernel_fn = append_write_cache_kv_c8_qkv_dynamic<NV_TYPE,
+                                                          num_frags_y,
+                                                          num_frags_z,
+                                                          HEAD_DIM,
+                                                          BLOCK_SIZE,
+                                                          num_warps,
+                                                          true, true>;
+    cudaFuncSetAttribute(
+        kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
+    kernel_fn<<<grids, blocks, 0, stream>>>(cache_k_out->data<uint8_t>(),
+                                            cache_v_out->data<uint8_t>(),
+                                            reinterpret_cast<const NV_TYPE*>(qkv.data<T>()),
+                                            const_cast<NV_TYPE*>(reinterpret_cast<const NV_TYPE*>(cache_k_scale.data<T>())),
+                                            const_cast<NV_TYPE*>(reinterpret_cast<const NV_TYPE*>(cache_v_scale.data<T>())),
+                                            batch_ids.data<int>(),
+                                            tile_ids_per_batch.data<int>(),
+                                            seq_lens_this_time.data<int>(),
+                                            seq_lens_decoder.data<int>(),
+                                            batch_id_per_token.data<int>(),
+                                            cu_seqlens_q.data<int>(),
+                                            block_table.data<int>(),
+                                            max_seq_len,
+                                            max_blocks_per_seq,
+                                            num_heads,
+                                            kv_num_heads);
   }
-  if (is_scale_channel_wise) {
-    kernel_fn = append_write_cache_kv_c8_qkv<T,
-                                             num_frags_y,
-                                             num_frags_z,
-                                             HEAD_DIM,
-                                             BLOCK_SIZE,
-                                             num_warps,
-                                             false>;
-  }
-  cudaFuncSetAttribute(
-      kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
-  kernel_fn<<<grids, blocks, 0, stream>>>(cache_k_out->data<uint8_t>(),
-                                          cache_v_out->data<uint8_t>(),
-                                          qkv.data<T>(),
-                                          cache_k_scale.data<T>(),
-                                          cache_v_scale.data<T>(),
-                                          batch_ids.data<int>(),
-                                          tile_ids_per_batch.data<int>(),
-                                          seq_lens_this_time.data<int>(),
-                                          seq_lens_decoder.data<int>(),
-                                          batch_id_per_token.data<int>(),
-                                          cu_seqlens_q.data<int>(),
-                                          block_table.data<int>(),
-                                          max_seq_len,
-                                          max_blocks_per_seq,
-                                          num_heads,
-                                          kv_num_heads);
 }
 
 template <typename T, uint32_t HEAD_DIM, uint32_t BLOCK_SIZE>

custom_ops/gpu_ops/append_attn/encoder_write_cache_with_rope_kernel.h

@@ -167,7 +167,7 @@ void EncoderWriteCacheWithRopeKernel(
                                     stream,
                                     key_cache_out,
                                     value_cache_out);
-  } else if (cache_quant_type_str == "cache_int8" or cache_quant_type_str == "cache_fp8") {
+  } else if (cache_quant_type_str == "cache_int8" or cache_quant_type_str == "cache_fp8" or cache_quant_type_str == "block_wise_fp8") {
     DISPATCH_HEAD_DIM(
         head_dim, HEAD_DIM, {DISPATCH_BLOCK_SIZE(block_size, BLOCK_SIZE, {
           CascadeAppendWriteCacheKVC8QKV<T, HEAD_DIM, BLOCK_SIZE>(
@@ -187,7 +187,7 @@ void EncoderWriteCacheWithRopeKernel(
               num_blocks,
               max_seq_len,
               is_scale_channel_wise,
-              cache_quant_type_str == "cache_fp8",
+              cache_quant_type_str,
               stream,
               key_cache_out,
               value_cache_out);

custom_ops/gpu_ops/append_attn/get_block_shape_and_split_kv_block.cu

@@ -191,14 +191,21 @@ get_max_len_kv_ernel(int *max_seq_lens_out, const int *seq_lens_this_time,
   }
 }
 
-std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
+void GetBlockShapeAndSplitKVBlock(
     const paddle::Tensor &seq_lens_encoder,
     const paddle::Tensor &seq_lens_decoder,
     const paddle::Tensor &seq_lens_this_time,
     paddle::Tensor &decoder_batch_ids,          // Inplace
     paddle::Tensor &decoder_tile_ids_per_batch, // Inplace
     paddle::Tensor &decoder_num_blocks_x_cpu,   // Inplace, Pinned Memory
-    paddle::Tensor &max_len_tensor_cpu,         // Inplace, Pinned Memory
+    paddle::Tensor &max_len_tensor_cpu,         // Inplace, CPU
+    paddle::Tensor &encoder_batch_ids,          // Inplace
+    paddle::Tensor &encoder_tile_ids_per_batch, // Inplace
+    paddle::Tensor &encoder_num_blocks_x_cpu,   // Inplace, CPU
+    paddle::Tensor &kv_batch_ids,               // Inplace
+    paddle::Tensor &kv_tile_ids_per_batch,      // Inplace
+    paddle::Tensor &kv_num_blocks_x_cpu,        // Inplace, CPU
+    paddle::Tensor &max_len_kv_cpu,             // Inplace, CPU
     const int encoder_block_shape_q,
     const int decoder_block_shape_q,
     const int group_size,
@@ -223,13 +230,7 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
   int max_system_len = max_len_cpu_ptr[6];
   int max_just_dec_len_without_system = max_len_cpu_ptr[7];
 
-  paddle::Tensor encoder_batch_ids;
-  paddle::Tensor encoder_tile_ids_per_batch;
-  paddle::Tensor encoder_num_blocks_x_cpu;  /*cpu*/
-  paddle::Tensor kv_batch_ids;
-  paddle::Tensor kv_tile_ids_per_batch;
-  paddle::Tensor kv_num_blocks_x_cpu;       /*cpu*/
-  paddle::Tensor max_len_kv_cpu;            /*cpu*/
+
 
   auto max_len_kv =
       GetEmptyTensor({1}, paddle::DataType::INT32, seq_lens_decoder.place());
@@ -237,17 +238,14 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
       max_len_kv.data<int>(), seq_lens_this_time.data<int>(),
       seq_lens_decoder.data<int>(), bsz);
 
-  max_len_kv_cpu = max_len_kv.copy_to(paddle::CPUPlace(), false);
+
+  max_len_kv_cpu.copy_(max_len_kv, max_len_kv_cpu.place(), false);
 
   if (max_enc_len_this_time > 0) {
-    const uint32_t max_tile_size_per_bs_kv =
-        div_up(max_enc_dec_len_this_time, block_size);
-    kv_batch_ids =
-        GetEmptyTensor({bsz * max_tile_size_per_bs_kv}, paddle::DataType::INT32,
-                       seq_lens_encoder.place());
-    kv_tile_ids_per_batch =
-        GetEmptyTensor({bsz * max_tile_size_per_bs_kv}, paddle::DataType::INT32,
-                       seq_lens_encoder.place());
+    const uint32_t max_tile_size_per_bs_kv = div_up(max_enc_dec_len_this_time, block_size);
+    const uint32_t kv_batch_shape = bsz * max_tile_size_per_bs_kv;
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaMemsetAsync(kv_batch_ids.data<int>(), 0, kv_batch_shape * sizeof(int32_t), stream));
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaMemsetAsync(kv_tile_ids_per_batch.data<int>(), 0, kv_batch_shape * sizeof(int32_t), stream));
     auto kv_num_blocks_x =
         GetEmptyTensor({1}, paddle::DataType::INT32, seq_lens_encoder.place());
 
@@ -258,16 +256,12 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
         kv_tile_ids_per_batch.data<int>(), kv_num_blocks_x.data<int>(), bsz,
         block_size, block_size);
 
-    kv_num_blocks_x_cpu = kv_num_blocks_x.copy_to(paddle::CPUPlace(), false);
-
-    const uint32_t encoder_max_tile_size_per_bs_q =
-        div_up((max_enc_dec_len_this_time * group_size), encoder_block_shape_q);
-    encoder_batch_ids =
-        GetEmptyTensor({bsz * encoder_max_tile_size_per_bs_q},
-                       paddle::DataType::INT32, seq_lens_encoder.place());
-    encoder_tile_ids_per_batch =
-        GetEmptyTensor({bsz * encoder_max_tile_size_per_bs_q},
-                       paddle::DataType::INT32, seq_lens_encoder.place());
+    kv_num_blocks_x_cpu.copy_(kv_num_blocks_x, kv_num_blocks_x_cpu.place(), false);
+    // Clear buffer
+    const uint32_t encoder_max_tile_size_per_bs_q = div_up((max_enc_dec_len_this_time * group_size), encoder_block_shape_q);
+    const uint32_t encoder_batch_shape = bsz * encoder_max_tile_size_per_bs_q;
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaMemsetAsync(encoder_batch_ids.data<int>(), 0, encoder_batch_shape * sizeof(int32_t), stream));
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaMemsetAsync(encoder_tile_ids_per_batch.data<int>(), 0, encoder_batch_shape * sizeof(int32_t), stream));
     auto encoder_num_blocks_x =
         GetEmptyTensor({1}, paddle::DataType::INT32, seq_lens_encoder.place());
     split_q_block<<<1, 32, 0, stream>>>(seq_lens_encoder.data<int>(), nullptr,
@@ -275,21 +269,7 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
                                         encoder_tile_ids_per_batch.data<int>(),
                                         encoder_num_blocks_x.data<int>(), bsz,
                                         encoder_block_shape_q, group_size);
-    encoder_num_blocks_x_cpu =
-        encoder_num_blocks_x.copy_to(paddle::CPUPlace(), false);
-  } else {
-    encoder_batch_ids =
-        GetEmptyTensor({0}, paddle::DataType::INT32, seq_lens_encoder.place());
-    encoder_tile_ids_per_batch =
-        GetEmptyTensor({0}, paddle::DataType::INT32, seq_lens_encoder.place());
-    encoder_num_blocks_x_cpu =
-        GetEmptyTensor({0}, paddle::DataType::INT32, paddle::CPUPlace());
-    kv_batch_ids =
-        GetEmptyTensor({0}, paddle::DataType::INT32, seq_lens_encoder.place());
-    kv_tile_ids_per_batch =
-        GetEmptyTensor({0}, paddle::DataType::INT32, seq_lens_encoder.place());
-    kv_num_blocks_x_cpu =
-        GetEmptyTensor({0}, paddle::DataType::INT32, paddle::CPUPlace());
+    encoder_num_blocks_x_cpu.copy_(encoder_num_blocks_x, encoder_num_blocks_x_cpu.place(), false);
   }
 
   if (max_just_dec_len_this_time > 0) {
@@ -314,15 +294,6 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
     decoder_num_blocks_x_cpu.copy_(decoder_num_blocks_x, decoder_num_blocks_x_cpu.place(), false);
   }
 
-  return {
-    encoder_batch_ids,
-    encoder_tile_ids_per_batch,
-    encoder_num_blocks_x_cpu, /*cpu*/
-    kv_batch_ids,
-    kv_tile_ids_per_batch,
-    kv_num_blocks_x_cpu,      /*cpu*/
-    max_len_kv_cpu,           /*cpu*/
-  };
 }
 
 PD_BUILD_STATIC_OP(get_block_shape_and_split_kv_block)
@@ -333,16 +304,17 @@ PD_BUILD_STATIC_OP(get_block_shape_and_split_kv_block)
       "decoder_batch_ids",
       "decoder_tile_ids_per_batch",
       "decoder_num_blocks_x_cpu",
-      "max_len_tensor_cpu"
+      "max_len_tensor_cpu",
+      "encoder_batch_ids",
+      "encoder_tile_ids_per_batch",
+      "encoder_num_blocks_x_cpu",
+      "kv_batch_ids",
+      "kv_tile_ids_per_batch",
+      "kv_num_blocks_x_cpu",
+      "max_len_kv_cpu"
     })
     .Outputs({
-      paddle::Optional("encoder_batch_ids"),
-      paddle::Optional("encoder_tile_ids_per_batch"),
-      paddle::Optional("encoder_num_blocks_x_cpu"),
-      paddle::Optional("kv_batch_ids"),
-      paddle::Optional("kv_tile_ids_per_batch"),
-      paddle::Optional("kv_num_blocks_x_cpu"),
-      "max_len_kv_cpu"
+
     })
     .Attrs({
       "encoder_block_shape_q: int",

custom_ops/gpu_ops/append_attn/gqa_rope_write_cache.cu

@@ -217,7 +217,7 @@ __global__ void append_cache_kv_c16(
 
   // load k_smem 64 rows 128 cols
   for (int fz = 0; fz < 4; fz++) { // 4 rows pre warp once, 16 rows all 4 warps once, need 4 iter
-    for (int fy = 0; fy < 2; fy++) { // 8 * 128b = 64 * bf16 noce, need 2 iter
+    for (int fy = 0; fy < 2; fy++) { // 8 * 128b = 64 * bf16 once, need 2 iter
       k_smem.load_128b_async<SharedMemFillMode::kNoFill>(
             k_smem_offset_w, cur_cache_k + k_read_idx, end_idx > 0);
       k_smem_offset_w =
@@ -235,7 +235,7 @@ __global__ void append_cache_kv_c16(
   // deal k_smem 64 rows 128 cols
   for (int fz = 0; fz < 1; fz++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 1 iter
     uint32_t row_idx = wid * 16 + tid / 4;
-    for (int fy = 0; fy < 8; fy++) { // 2 * 128b = 16 * bf16 noce, need 8 iter
+    for (int fy = 0; fy < 8; fy++) { // 2 * 128b = 16 * bf16 once, need 8 iter
       uint32_t col_idx = fy * 16 + tid % 4 * 2;
       k_smem.ldmatrix_m8n8x4(k_smem_offset_r, kv_frag);
       // layout
@@ -278,7 +278,7 @@ __global__ void append_cache_kv_c16(
 
   // load v_smem 64 rows 128 cols
   for (int fz = 0; fz < 4; fz++) { // // 4 rows pre warp once, 16 rows all 4 warps once, need 4 iter
-    for (int fy = 0; fy < 2; fy++) { // 8 * 128b = 64 * bf16 noce, need 2 iter
+    for (int fy = 0; fy < 2; fy++) { // 8 * 128b = 64 * bf16 once, need 2 iter
       v_smem.load_128b_async<SharedMemFillMode::kNoFill>(
             v_smem_offset_w, cur_cache_v + v_read_idx, end_idx > 0);
       v_smem_offset_w =
@@ -296,7 +296,7 @@ __global__ void append_cache_kv_c16(
   // deal v_smem 64 rows 128 cols
   for (int fz = 0; fz < 1; fz++) { //  16 rows pre warp once, 64 rows all 4 warps once, need 1 iter
     uint32_t row_idx = wid * 16 + tid / 4;
-    for (int fy = 0; fy < 8; fy++) { // 2 * 128b = 16 * bf16 noce, need 8 iter
+    for (int fy = 0; fy < 8; fy++) { // 2 * 128b = 16 * bf16 once, need 8 iter
       uint32_t col_idx = fy * 16 + tid % 4 * 2;
       v_smem.ldmatrix_m8n8x4(v_smem_offset_r, kv_frag);
       // layout
@@ -400,7 +400,7 @@ __global__ void append_cache_kv_c8(
 
   // load v_smem 64 rows, 128 cols
   for (int fz = 0; fz < 4; fz++) { // 4 rows pre warp once, 16 rows all 4 warps once, need 4 iter
-    for (int fy = 0; fy < 1; fy++) { // 8 * 128b = 128 * uint8 noce, need 1 iter
+    for (int fy = 0; fy < 1; fy++) { // 8 * 128b = 128 * uint8 once, need 1 iter
       k_smem.load_128b_async<SharedMemFillMode::kNoFill>(
             k_smem_offset_w, cur_cache_k + k_read_idx, end_idx > 0);
       k_smem_offset_w =
@@ -418,7 +418,7 @@ __global__ void append_cache_kv_c8(
   // deal k_smem 64 rows, 128 cols
   for (int fz = 0; fz < 1; fz++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 1 iter
     uint32_t row_idx = wid * 16 + tid / 4;
-    for (int fy = 0; fy < 4; fy++) { // 2 * 128b = 32 * uint8 noce, need 4 iter
+    for (int fy = 0; fy < 4; fy++) { // 2 * 128b = 32 * uint8 once, need 4 iter
       uint32_t col_idx = fy * 32 + tid % 4 * 2;
       k_smem.ldmatrix_m8n8x4(k_smem_offset_r, k_frag);
       // layout
@@ -466,7 +466,7 @@ __global__ void append_cache_kv_c8(
                           tid % 4 * num_elems_per_128b<CacheT>();
   // load v_smem 128 rows 64 cols
   for (int fy = 0; fy < 4; fy++) { // 8 rows pre warp once, 32 rows all 4 warps once, need 4 iter
-    for (int fz = 0; fz < 1; fz++) { // 4 * 128b = 64 * uint8 noce, need 1 iter
+    for (int fz = 0; fz < 1; fz++) { // 4 * 128b = 64 * uint8 once, need 1 iter
       v_smem.load_128b_async<SharedMemFillMode::kNoFill>(
               v_smem_offset_w, cur_cache_v + v_read_idx, end_idx > 0);
       v_smem_offset_w =
@@ -485,7 +485,7 @@ __global__ void append_cache_kv_c8(
   // deal v_smem 128 rows 64 cols
   for (int fy = 0; fy < 2; fy++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 2 iter
     uint32_t dim_idx = fy * NUM_WARPS * 16 + wid * 16 + tid / 4;
-    for (int fz = 0; fz < 2; fz++) { // 2 * 128b = 32 * uint8 noce, need 2 iter
+    for (int fz = 0; fz < 2; fz++) { // 2 * 128b = 32 * uint8 once, need 2 iter
       uint32_t kv_idx = fz * 32 + tid % 4 * 2;
       v_smem.ldmatrix_m8n8x4(v_smem_offset_r, v_frag);
       // layout
@@ -614,7 +614,7 @@ __global__ void append_cache_kv_c4(
 
   // load k_smem 64 rows 128 cols
   for (int fz = 0; fz < 2; fz++) { // 4 rows pre warp once, 16 rows all 4 warps once, need 4 iter
-    for (int fy = 0; fy < 1; fy++) { // 4 * 128b = 128 * int4 noce, need 1 iter
+    for (int fy = 0; fy < 1; fy++) { // 4 * 128b = 128 * int4 once, need 1 iter
       k_smem.load_128b_async<SharedMemFillMode::kNoFill>(
             k_smem_offset_w, cur_cache_k + k_read_idx, end_idx > 0);
       k_smem_offset_w =
@@ -632,7 +632,7 @@ __global__ void append_cache_kv_c4(
   // deal k_smem 64 rows 128 cols
   for (int fz = 0; fz < 1; fz++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 1 iter
     uint32_t row_idx = wid * 16 + tid / 4;
-    for (int fy = 0; fy < 2; fy++) { // 2 * 128b = 64 * int4 noce, need 2 iter
+    for (int fy = 0; fy < 2; fy++) { // 2 * 128b = 64 * int4 once, need 2 iter
       uint32_t col_idx = fy * 64 + tid % 4 * 2;
       k_smem.ldmatrix_m8n8x4(k_smem_offset_r, k_frag);
 
@@ -685,7 +685,7 @@ __global__ void append_cache_kv_c4(
                           tid % 2 * num_elems_per_128b<CacheT>();
   // load v_smem 128 rows 64 rows
   for (int fy = 0; fy < 2; fy++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 2 iter
-    for (int fz = 0; fz < 1; fz++) { // 2 * 128b = 64 * int4 noce, need 1 iter
+    for (int fz = 0; fz < 1; fz++) { // 2 * 128b = 64 * int4 once, need 1 iter
       v_smem.load_128b_async<SharedMemFillMode::kNoFill>(
               v_smem_offset_w, cur_cache_v + v_read_idx, end_idx > 0);
       v_smem_offset_w =
@@ -704,7 +704,7 @@ __global__ void append_cache_kv_c4(
   // deal v_smem 128 rows 64 cols
   for (int fy = 0; fy < 2; fy++) { // 16 rows pre warp once, 64 rows all 4 warps once, need 2 iter
     uint32_t dim_idx = fy * NUM_WARPS * 16 + wid * 16 + tid / 4;
-    for (int fz = 0; fz < 1; fz++) { // 2 * 128b = 64 * int4 noce, need 1 iter
+    for (int fz = 0; fz < 1; fz++) { // 2 * 128b = 64 * int4 once, need 1 iter
       uint32_t kv_idx = fz * 64 + tid % 4 * 2;
       v_smem.ldmatrix_m8n8x4(v_smem_offset_r, v_frag);
       // layout
@@ -1000,7 +1000,7 @@ std::vector<paddle::Tensor> GQARopeWriteCacheKernel(
       stream,
       const_cast<paddle::Tensor*>(&key_cache),
       const_cast<paddle::Tensor*>(&value_cache));
-  } else if (cache_quant_type == "cache_int8" || cache_quant_type == "cache_fp8") {
+  } else if (cache_quant_type == "cache_int8" || cache_quant_type == "cache_fp8" || cache_quant_type == "block_wise_fp8") {
     CascadeAppendWriteCacheKVC8QKV<data_t, 128, 64>(
         meta_data,
         *const_cast<paddle::Tensor*>(&key_cache),
@@ -1018,7 +1018,7 @@ std::vector<paddle::Tensor> GQARopeWriteCacheKernel(
         kv_num_blocks_data,
         max_seq_len,
         false, // is_scale_channel_wise
-        cache_quant_type == "cache_fp8", // is_fp8
+        cache_quant_type,
         stream,
         const_cast<paddle::Tensor*>(&key_cache),
         const_cast<paddle::Tensor*>(&value_cache));

custom_ops/gpu_ops/append_attn/speculate_write_cache_with_rope_impl.cuh

@@ -18,6 +18,166 @@
 #include "mma_tensor_op.cuh"
 #include "utils.cuh"
 
+template <typename T, int VecSize = 1, typename InT = T>
+__global__ void append_speculate_cache_T_rope_qk_norm_kernel(
+    const InT* __restrict__ qkv,  // [token_num, num_heads + 2 * gqa_group_size,
+                                  // head_size]
+    T* __restrict__ key_cache,    // [num_blocks, gqa_group_size, block_size,
+                                  // head_size // 2]
+    T* __restrict__ value_cache,  // [num_blocks, gqa_group_size, block_size,
+                                  // head_size // 2]
+    T* __restrict__ q_out,
+    const int* __restrict__ block_tables,     // [bsz, max_blocks_per_seq]
+    const int* __restrict__ batch_id_per_token,  // [num_tokens]
+    const int* __restrict__ cu_seqlens_q,
+    const int* __restrict__ seq_lens_decoder,  // [bsz]
+    const float* __restrict__ cos_emb,
+    const float* __restrict__ sin_emb,
+    const float*
+        qkv_out_scales,   // [(num_heads + 2 * gqa_group_size) * head_size]
+    const T* qkv_biases,  // [num_head + 2 * gqa_group_size, dim_head]
+    const int max_seq_len,
+    const int max_blocks_per_seq,
+    const int num_heads,
+    const int output_inner_dim,
+    const int head_size,
+    const int block_size,
+    const int elem_cnt,
+    const int gqa_group_size,
+    const float* q_norm_weight,
+    const float* k_norm_weight,
+    const float rms_norm_eps) {
+  using LoadT = AlignedVector<T, VecSize>;
+  using LoadFloat = AlignedVector<float, VecSize>;
+  using LoadInT = AlignedVector<InT, VecSize>;
+  constexpr int HalfVecSize = VecSize / 2;
+  using LoadEmbT = AlignedVector<float, HalfVecSize>;
+  LoadInT src_vec;
+  LoadFloat scale_vec;
+  LoadT bias_vec;
+  LoadEmbT cos_emb_vec;
+  LoadEmbT sin_emb_vec;
+  LoadFloat tmp_vec;
+  LoadFloat q_norm_vec;
+  LoadFloat k_norm_vec;
+
+  int64_t global_warp_idx = blockDim.y * blockIdx.x + threadIdx.y;
+  int64_t all_warp_num = gridDim.x * blockDim.y;
+  int64_t all_head_dim = elem_cnt / head_size;
+
+  const int64_t hidden_size = (num_heads + 2 * gqa_group_size) * head_size;
+  const int half_head_size = head_size / 2;
+  for (int global_hi = global_warp_idx; global_hi < all_head_dim; global_hi += all_warp_num) {
+    int64_t linear_index = global_hi * head_size + threadIdx.x * VecSize;
+    const int token_id = linear_index / hidden_size;
+    const int ori_bi = batch_id_per_token[token_id];
+    if (seq_lens_decoder[ori_bi] == 0) continue;
+    const int bias = linear_index % hidden_size;
+    const int hi = bias / head_size;  // q + k + v
+    const int h_bias = bias % head_size;
+    const int start_token_idx = cu_seqlens_q[ori_bi];
+    const int write_seq_id =
+        seq_lens_decoder[ori_bi] + token_id - start_token_idx;
+    if (write_seq_id == 0) continue;
+
+    const int* block_table_now = block_tables + ori_bi * max_blocks_per_seq;
+    const int block_idx = block_table_now[write_seq_id / block_size];
+    if (block_idx < 0) {
+      printf(
+          "Fatal Error!!!, block idx %d when write_seq_id is %d\n some key var "
+          "%d %d %d %d\n",
+          block_idx,
+          write_seq_id,
+          ori_bi,
+          seq_lens_decoder[ori_bi],
+          token_id,
+          cu_seqlens_q[ori_bi]);
+    }
+    const int block_offset = write_seq_id % block_size;
+
+    const int write_q_idx =
+        token_id * output_inner_dim * head_size + hi * head_size + h_bias;
+
+    const int bias_idx = hi * head_size + h_bias;
+    Load<InT, VecSize>(&qkv[linear_index], &src_vec);
+    if (qkv_biases) {
+      Load<T, VecSize>(&qkv_biases[bias_idx], &bias_vec);
+    }
+    if (qkv_out_scales) {
+      Load<float, VecSize>(&qkv_out_scales[bias_idx], &scale_vec);
+    }
+    if (hi < num_heads + gqa_group_size) {
+      // q k rope
+      const int64_t emb_idx = write_seq_id * half_head_size + h_bias / 2;
+      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+    }
+    float thread_m2 = 0.0f;
+    float warp_m2 = 0.0f;
+#pragma unroll
+    for (int i = 0; i < HalfVecSize; i++) {
+      // add_bias + rope
+      float input_left = static_cast<float>(src_vec[2 * i]);
+      float input_right = static_cast<float>(src_vec[2 * i + 1]);
+      if (qkv_out_scales) {
+        input_left *= scale_vec[2 * i];
+        input_right *= scale_vec[2 * i + 1];
+      }
+      if (qkv_biases) {
+        input_left = input_left + static_cast<float>(bias_vec[2 * i]);
+        input_right = input_right + static_cast<float>(bias_vec[2 * i + 1]);
+      }
+      if (hi < num_heads + gqa_group_size) {
+        const float cos_tmp = cos_emb_vec[i];
+        const float sin_tmp = sin_emb_vec[i];
+        float tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+        float tmp2 = input_right * cos_tmp + input_left * sin_tmp;
+        thread_m2 += tmp1 * tmp1 + tmp2 * tmp2;
+        tmp_vec[2 * i] = tmp1;
+        tmp_vec[2 * i + 1] = tmp2;
+      } else {
+        bias_vec[2 * i] = static_cast<T>(input_left);
+        bias_vec[2 * i + 1] = static_cast<T>(input_right);
+      }
+    }
+    if (hi < (num_heads + gqa_group_size)) {
+      WelfordWarpAllReduce<float, 32>(thread_m2, &warp_m2);
+      float row_variance =
+          max(warp_m2 / head_size, 0.0f);
+      float row_inv_var = Rsqrt(row_variance + rms_norm_eps);
+      if (hi < num_heads) {
+        Load<float, VecSize>(&q_norm_weight[threadIdx.x * VecSize], &q_norm_vec);
+        #pragma unroll
+        for (int i = 0; i < VecSize; i++) {
+          bias_vec[i] = static_cast<T>(tmp_vec[i] * row_inv_var * q_norm_vec[i]);
+        }
+      } else {
+        Load<float, VecSize>(&k_norm_weight[threadIdx.x * VecSize], &k_norm_vec);
+        #pragma unroll
+        for (int i = 0; i < VecSize; i++) {
+          bias_vec[i] = static_cast<T>(tmp_vec[i] * row_inv_var * k_norm_vec[i]);
+        }
+      }
+    }
+    if (hi < num_heads) {
+      // write q
+      Store<T, VecSize>(bias_vec, &q_out[write_q_idx]);
+    } else {
+      //  write k/v
+      const int kv_head_idx = (hi - num_heads) % gqa_group_size;
+      const int tgt_idx = (block_idx * gqa_group_size * block_size * head_size +
+                           kv_head_idx * block_size * head_size +
+                           block_offset * head_size + h_bias);
+      // write
+      if (hi < num_heads + gqa_group_size) {
+        Store<T, VecSize>(bias_vec, &key_cache[tgt_idx]);
+      } else {
+        Store<T, VecSize>(bias_vec, &value_cache[tgt_idx]);
+      }
+    }
+  }
+}
+
 template <int VecSize = 4, int HeadDim = 128>
 __global__ void append_clear_cache_int8_block(
     uint8_t* __restrict__ key_cache,    // [num_blocks, gqa_group_size,
@@ -193,7 +353,8 @@ __global__ void append_speculate_cache_rope_kernel(
     const int head_size,
     const int block_size,
     const int elem_cnt,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   using LoadT = AlignedVector<T, VecSize>;
   using LoadFloat = AlignedVector<float, VecSize>;
   using LoadInT = AlignedVector<InT, VecSize>;
@@ -253,8 +414,9 @@ __global__ void append_speculate_cache_rope_kernel(
     if (hi < num_heads + gqa_group_size) {
       // q k rope
       const int64_t emb_idx = write_seq_id * half_head_size + h_bias / 2;
-      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      int64_t new_emb_idx = rope_3d ? emb_idx + ori_bi * max_seq_len * head_size : emb_idx;
+      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
     }
 #pragma unroll
     for (int i = 0; i < HalfVecSize; i++) {
@@ -326,7 +488,8 @@ __global__ void append_speculate_cache_neox_rope_kernel(
     const int head_size,
     const int block_size,
     const int elem_cnt,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   using LoadT = AlignedVector<T, VecSize>;
   using LoadFloat = AlignedVector<float, VecSize>;
   using LoadInT = AlignedVector<InT, VecSize>;
@@ -390,8 +553,9 @@ __global__ void append_speculate_cache_neox_rope_kernel(
     if (hi < num_heads + gqa_group_size) {
       // q k rope
       const int64_t emb_idx = write_seq_id * head_size + h_bias;
-      Load<float, VecSize>(&cos_emb[emb_idx], &cos_emb_vec);
-      Load<float, VecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      int64_t new_emb_idx = rope_3d ? emb_idx + ori_bi * max_seq_len * head_size * 2: emb_idx;
+      Load<float, VecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, VecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
     }
 #pragma unroll
     for (int i = 0; i < VecSize; i++) {
@@ -476,7 +640,8 @@ __global__ void append_speculate_cache_int8_rope_kernel(
     const int block_size,
     const float max_bound,
     const float min_bound,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
   static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
   constexpr int NUM_WARPS = 4;
@@ -522,8 +687,9 @@ __global__ void append_speculate_cache_int8_rope_kernel(
 
       // q rope
       const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
       if (qkv_out_scales) {
         Load<float, VecSize>(&qkv_out_scales[bias_idx], &out_scale_vec);
       }
@@ -583,10 +749,11 @@ __global__ void append_speculate_cache_int8_rope_kernel(
     T scale;
     if (head_idx < num_heads + gqa_group_size) {
       const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      Load<float, 1>(&cos_emb[emb_idx], &cos_emb_vec1);
-      Load<float, 1>(&cos_emb[emb_idx + 4], &cos_emb_vec2);
-      Load<float, 1>(&sin_emb[emb_idx], &sin_emb_vec1);
-      Load<float, 1>(&sin_emb[emb_idx + 4], &sin_emb_vec2);
+      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, 1>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+      Load<float, 1>(&cos_emb[new_emb_idx + 4], &cos_emb_vec2);
+      Load<float, 1>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+      Load<float, 1>(&sin_emb[new_emb_idx + 4], &sin_emb_vec2);
       scale = __ldg(&cache_k_scales[kv_head_idx]);
     } else {
       scale = __ldg(&cache_v_scales[kv_head_idx]);
@@ -708,7 +875,8 @@ __global__ void append_speculate_cache_int8_neox_rope_kernel(
     const int block_size,
     const float max_bound,
     const float min_bound,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
   static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
   constexpr int NUM_WARPS = 4;
@@ -757,8 +925,9 @@ __global__ void append_speculate_cache_int8_neox_rope_kernel(
 
       // q rope
       const uint32_t emb_idx = write_seq_id * HeadDim + head_bias;
-      Load<float, VecSize>(&cos_emb[emb_idx], &cos_emb_vec);
-      Load<float, VecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim * 2 : emb_idx;
+      Load<float, VecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, VecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
       if (qkv_out_scales) {
         Load<float, VecSize>(&qkv_out_scales[bias_idx_left],
                              &left_out_scale_vec);
@@ -853,10 +1022,11 @@ __global__ void append_speculate_cache_int8_neox_rope_kernel(
 
         T scale;
         const uint32_t emb_idx = write_seq_id * HeadDim + head_bias;
-        Load<float, HALF_K_VEC_SIZE>(&cos_emb[emb_idx], &cos_emb_vec1);
-        Load<float, HALF_K_VEC_SIZE>(&cos_emb[emb_idx + 8], &cos_emb_vec2);
-        Load<float, HALF_K_VEC_SIZE>(&sin_emb[emb_idx], &sin_emb_vec1);
-        Load<float, HALF_K_VEC_SIZE>(&sin_emb[emb_idx + 8], &sin_emb_vec2);
+        uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim * 2 : emb_idx;
+        Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+        Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx + 8], &cos_emb_vec2);
+        Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+        Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx + 8], &sin_emb_vec2);
         scale = __ldg(&cache_k_scales[kv_head_idx]);
 #pragma unroll
         for (int i = 0; i < HALF_K_VEC_SIZE; i++) {
@@ -1088,7 +1258,8 @@ __global__ void append_speculate_cache_int4_rope_kernel(
     const int block_size,
     const float max_bound,
     const float min_bound,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
   static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
   constexpr int NUM_WARPS = 4;
@@ -1145,8 +1316,9 @@ __global__ void append_speculate_cache_int4_rope_kernel(
       // Load<float, VecSize>(&qkv_out_scales[bias_idx], &out_scale_vec);
       // q rope
       const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      Load<float, HalfVecSize>(&cos_emb[emb_idx], &cos_emb_vec);
-      Load<float, HalfVecSize>(&sin_emb[emb_idx], &sin_emb_vec);
+      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, HalfVecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, HalfVecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
 #pragma unroll
       for (int i = 0; i < HalfVecSize; i++) {
         // dequant + add_bias + rope
@@ -1235,10 +1407,11 @@ __global__ void append_speculate_cache_int4_rope_kernel(
     //                              &out_scale_vec2);
     if (head_idx < num_heads + gqa_group_size) {
       const uint32_t emb_idx = write_seq_id * half_head_size + head_bias / 2;
-      Load<float, 1>(&cos_emb[emb_idx], &cos_emb_vec1);
-      Load<float, 1>(&cos_emb[emb_idx + 4], &cos_emb_vec2);
-      Load<float, 1>(&sin_emb[emb_idx], &sin_emb_vec1);
-      Load<float, 1>(&sin_emb[emb_idx + 4], &sin_emb_vec2);
+      uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, 1>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+      Load<float, 1>(&cos_emb[new_emb_idx + 4], &cos_emb_vec2);
+      Load<float, 1>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+      Load<float, 1>(&sin_emb[new_emb_idx + 4], &sin_emb_vec2);
       Load<T, HALF_K_VEC_SIZE>(&cache_k_scales[cache_idx], &scale_vec1);
       Load<T, HALF_K_VEC_SIZE>(&cache_k_scales[cache_idx + 8], &scale_vec2);
       Load<T, HALF_K_VEC_SIZE>(&cache_k_zero_points[cache_idx], &zp_vec1);
@@ -1431,7 +1604,8 @@ __global__ void append_speculate_cache_int4_neox_rope_kernel(
     const int block_size,
     const float max_bound,
     const float min_bound,
-    const int gqa_group_size) {
+    const int gqa_group_size,
+    const bool rope_3d) {
   static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
   static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
   constexpr int NUM_WARPS = 4;
@@ -1581,10 +1755,11 @@ __global__ void append_speculate_cache_int4_neox_rope_kernel(
                                      &right_out_scale_vec2);
 
         const uint32_t emb_idx = write_seq_id * HeadDim + head_bias;
-        Load<float, HALF_K_VEC_SIZE>(&cos_emb[emb_idx], &cos_emb_vec1);
-        Load<float, HALF_K_VEC_SIZE>(&cos_emb[emb_idx + 8], &cos_emb_vec2);
-        Load<float, HALF_K_VEC_SIZE>(&sin_emb[emb_idx], &sin_emb_vec1);
-        Load<float, HALF_K_VEC_SIZE>(&sin_emb[emb_idx + 8], &sin_emb_vec2);
+        uint32_t new_emb_idx = rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+        Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+        Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx + 8], &cos_emb_vec2);
+        Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+        Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx + 8], &sin_emb_vec2);
         Load<T, HALF_K_VEC_SIZE>(&cache_k_scales[left_cache_idx],
                                  &left_scale_vec1);
         Load<T, HALF_K_VEC_SIZE>(&cache_k_scales[left_cache_idx + 8],

custom_ops/gpu_ops/append_attn/speculate_write_cache_with_rope_kernel.cu

@@ -15,6 +15,77 @@
 #include "speculate_write_cache_with_rope_kernel.h"
 #include "utils.cuh"
 
+template <typename T, typename QKV_TYPE>
+void append_speculate_cache_rope_qk_norm(const QKV_TYPE* qkv,
+                              T* key_cache,
+                              T* value_cache,
+                              T* qkv_out,
+                              const int* block_tables,
+                              const int* batch_id_per_token,
+                              const int* cu_seqlens_q,
+                              const int* seq_lens,
+                              const int* seq_lens_encoder,
+                              const float* cos_emb,
+                              const float* sin_emb,
+                              const float* qkv_out_scales,
+                              const T* qkv_biases,
+                              const int max_seq_len,
+                              const int max_blocks_per_seq,
+                              const int num_heads,
+                              const int kv_num_heads,
+                              const int dim_head,
+                              const int block_size,
+                              const int bsz,
+                              const int token_num,
+                              const cudaStream_t& stream,
+                              const bool use_neox_style,
+                              const float* q_norm_weight,
+                              const float* k_norm_weight,
+                              const float rms_norm_eps) {
+  int output_inner_dim = num_heads + 2 * kv_num_heads;
+  const uint32_t elem_nums =
+      use_neox_style ? token_num * (num_heads + 2 * kv_num_heads) * dim_head / 2
+                     : token_num * (num_heads + 2 * kv_num_heads) * dim_head;
+  constexpr int HEAD_DIM = 128;
+
+  constexpr int PackSize = HEAD_DIM / kWarpSize;
+  const int pack_num = elem_nums / PackSize;
+  const int blocksize = 128;
+  int grid_size = 1;
+  GetNumBlocks<128>(pack_num, &grid_size);
+
+  if (use_neox_style) {
+      PD_THROW(
+          "append_speculate_cache_rope_qk_norm not support neox rope yet");
+  } else {
+    dim3 block_dim(kWarpSize, blocksize / kWarpSize, 1);
+    append_speculate_cache_T_rope_qk_norm_kernel<T, PackSize>
+      <<<grid_size, block_dim, 0, stream>>>(qkv,
+                                            key_cache,
+                                            value_cache,
+                                            qkv_out,
+                                            block_tables,
+                                            batch_id_per_token,
+                                            cu_seqlens_q,
+                                            seq_lens,
+                                            cos_emb,
+                                            sin_emb,
+                                            qkv_out_scales,
+                                            qkv_biases,
+                                            max_seq_len,
+                                            max_blocks_per_seq,
+                                            num_heads,
+                                            output_inner_dim,
+                                            dim_head,
+                                            block_size,
+                                            elem_nums,
+                                            kv_num_heads,
+                                            q_norm_weight,
+                                            k_norm_weight,
+                                            rms_norm_eps);
+  }
+}
+
 // rope + write
 template <typename T, typename QKV_TYPE>
 void append_speculate_cache_rope(const QKV_TYPE* qkv,
@@ -39,7 +110,8 @@ void append_speculate_cache_rope(const QKV_TYPE* qkv,
                                  const int bsz,
                                  const int token_num,
                                  const cudaStream_t& stream,
-                                 const bool use_neox_style) {
+                                 const bool use_neox_style,
+                                 const bool rope_3d) {
   int output_inner_dim = num_heads + 2 * kv_num_heads;
 
   const uint32_t elem_nums =
@@ -73,7 +145,8 @@ void append_speculate_cache_rope(const QKV_TYPE* qkv,
             dim_head,
             block_size,
             elem_nums,
-            kv_num_heads);
+            kv_num_heads,
+            rope_3d);
   } else {
     append_speculate_cache_rope_kernel<T, PackSize>
         <<<grid_size, threads_per_block, 0, stream>>>(
@@ -96,7 +169,8 @@ void append_speculate_cache_rope(const QKV_TYPE* qkv,
             dim_head,
             block_size,
             elem_nums,
-            kv_num_heads);
+            kv_num_heads,
+            rope_3d);
   }
 }
 
@@ -125,7 +199,8 @@ void append_speculate_cache_int8_rope(const QKV_TYPE* qkv,
                                       const int bsz,
                                       const int token_num,
                                       const cudaStream_t& stream,
-                                      const bool use_neox_style) {
+                                      const bool use_neox_style,
+                                      const bool rope_3d) {
   constexpr int num_warps = 4;
   const int all_warps =
       ((num_heads + 2 * kv_num_heads) + num_warps - 1) / num_warps * num_warps;
@@ -167,7 +242,8 @@ void append_speculate_cache_int8_rope(const QKV_TYPE* qkv,
                                                block_size,
                                                127.0f,
                                                -127.0f,
-                                               kv_num_heads);
+                                               kv_num_heads,
+                                               rope_3d);
   } else {
     append_speculate_cache_int8_rope_kernel<T, 4, 0, 128, QKV_TYPE, IsFP8>
         <<<grids, num_warps * 32, 0, stream>>>(qkv,
@@ -191,7 +267,8 @@ void append_speculate_cache_int8_rope(const QKV_TYPE* qkv,
                                                block_size,
                                                127.0f,
                                                -127.0f,
-                                               kv_num_heads);
+                                               kv_num_heads,
+                                               rope_3d);
   }
 }
 
@@ -222,7 +299,8 @@ void append_speculate_cache_int4_rope(const QKV_TYPE* qkv,
                                       const int bsz,
                                       const int token_num,
                                       const cudaStream_t& stream,
-                                      const bool use_neox_style) {
+                                      const bool use_neox_style,
+                                      const bool rope_3d) {
   constexpr int num_warps = 4;
   const int all_warps =
       ((num_heads + 2 * kv_num_heads) + num_warps - 1) / num_warps * num_warps;
@@ -266,7 +344,8 @@ void append_speculate_cache_int4_rope(const QKV_TYPE* qkv,
                                                block_size,
                                                7.0f,
                                                -8.0f,
-                                               kv_num_heads);
+                                               kv_num_heads,
+                                               rope_3d);
   } else {
     append_speculate_cache_int4_rope_kernel<T, 4>
         <<<grids, num_warps * 32, 0, stream>>>(qkv,
@@ -292,7 +371,8 @@ void append_speculate_cache_int4_rope(const QKV_TYPE* qkv,
                                                block_size,
                                                7.0f,
                                                -8.0f,
-                                               kv_num_heads);
+                                               kv_num_heads,
+                                               rope_3d);
   }
 }
 template <typename T, typename QKV_TYPE>
@@ -313,11 +393,15 @@ void SpeculateWriteCacheWithRoPEKernel(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out) {
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps) {
   typedef cascade_attn_type_traits<T> traits_;
   typedef cascade_attn_type_traits<QKV_TYPE> qkt_nv_type_;
   typedef typename traits_::type DataType_;
@@ -342,142 +426,184 @@ void SpeculateWriteCacheWithRoPEKernel(
             ? rotary_embs.get().data<float>() + max_seq_len * dim_head
             : rotary_embs.get().data<float>() + max_seq_len * dim_head / 2;
   }
-  if (cache_quant_type_str == "none") {
-    append_speculate_cache_rope(
-        reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
-        reinterpret_cast<DataType_*>(key_cache_out->data<T>()),
-        reinterpret_cast<DataType_*>(value_cache_out->data<T>()),
-        reinterpret_cast<DataType_*>(qkv_out->data<T>()),
-        block_tables.data<int>(),
-        batch_id_per_token.data<int>(),
-        cu_seqlens_q.data<int>(),
-        seq_lens.data<int>(),
-        seq_lens_encoder.data<int>(),
-        cos_emb,
-        sin_emb,
-        qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
-        qkv_biases ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(qkv_biases.get().data<T>()))
-                   : nullptr,
-        max_seq_len,
-        max_blocks_per_seq,
-        num_heads,
-        kv_num_heads,
-        dim_head,
-        block_size,
-        bsz,
-        token_nums,
-        stream,
-        use_neox_rotary_style);
-  } else if (cache_quant_type_str == "cache_int8") {
-    append_speculate_cache_int8_rope(
-        reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
-        key_cache_out->data<uint8_t>(),
-        value_cache_out->data<uint8_t>(),
-        reinterpret_cast<DataType_*>(qkv_out->data<T>()),
-        block_tables.data<int>(),
-        batch_id_per_token.data<int>(),
-        cu_seqlens_q.data<int>(),
-        seq_lens.data<int>(),
-        seq_lens_encoder.data<int>(),
-        cos_emb,
-        sin_emb,
-        qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
-        qkv_biases ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(qkv_biases.get().data<T>()))
-                   : nullptr,
-        cache_k_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_k_scale.get().data<T>()))
-                      : nullptr,
-        cache_v_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_v_scale.get().data<T>()))
-                      : nullptr,
-        max_seq_len,
-        max_blocks_per_seq,
-        num_heads,
-        kv_num_heads,
-        dim_head,
-        block_size,
-        bsz,
-        token_nums,
-        stream,
-        use_neox_rotary_style);
-  } else if (cache_quant_type_str == "cache_fp8") {
-    append_speculate_cache_int8_rope<DataType_, QKV_TYPE, true>(
-        reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
-        key_cache_out->data<uint8_t>(),
-        value_cache_out->data<uint8_t>(),
-        reinterpret_cast<DataType_*>(qkv_out->data<T>()),
-        block_tables.data<int>(),
-        batch_id_per_token.data<int>(),
-        cu_seqlens_q.data<int>(),
-        seq_lens.data<int>(),
-        seq_lens_encoder.data<int>(),
-        cos_emb,
-        sin_emb,
-        qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
-        qkv_biases ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(qkv_biases.get().data<T>()))
-                   : nullptr,
-        cache_k_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_k_scale.get().data<T>()))
-                      : nullptr,
-        cache_v_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_v_scale.get().data<T>()))
-                      : nullptr,
-        max_seq_len,
-        max_blocks_per_seq,
-        num_heads,
-        kv_num_heads,
-        dim_head,
-        block_size,
-        bsz,
-        token_nums,
-        stream,
-        use_neox_rotary_style);
-  } else if (cache_quant_type_str == "cache_int4_zp") {
-    append_speculate_cache_int4_rope(
-        reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
-        key_cache_out->data<uint8_t>(),
-        value_cache_out->data<uint8_t>(),
-        reinterpret_cast<DataType_*>(const_cast<T*>(qkv_out->data<T>())),
-        block_tables.data<int>(),
-        batch_id_per_token.data<int>(),
-        cu_seqlens_q.data<int>(),
-        seq_lens.data<int>(),
-        seq_lens_encoder.data<int>(),
-        cos_emb,
-        sin_emb,
-        qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
-        qkv_biases ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(qkv_biases.get().data<T>()))
-                   : nullptr,
-        cache_k_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_k_scale.get().data<T>()))
-                      : nullptr,
-        cache_v_scale ? reinterpret_cast<DataType_*>(
-                            const_cast<T*>(cache_v_scale.get().data<T>()))
-                      : nullptr,
-        cache_k_zp ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(cache_k_zp.get().data<T>()))
-                   : nullptr,
-        cache_v_zp ? reinterpret_cast<DataType_*>(
-                         const_cast<T*>(cache_v_zp.get().data<T>()))
-                   : nullptr,
-        max_seq_len,
-        max_blocks_per_seq,
-        num_heads,
-        kv_num_heads,
-        dim_head,
-        block_size,
-        bsz,
-        token_nums,
-        stream,
-        use_neox_rotary_style);
+
+  if (q_norm_weight && k_norm_weight) {
+    if (cache_quant_type_str == "none") {
+      append_speculate_cache_rope_qk_norm(
+          reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
+          reinterpret_cast<DataType_*>(key_cache_out->data<T>()),
+          reinterpret_cast<DataType_*>(value_cache_out->data<T>()),
+          reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+          block_tables.data<int>(),
+          batch_id_per_token.data<int>(),
+          cu_seqlens_q.data<int>(),
+          seq_lens.data<int>(),
+          seq_lens_encoder.data<int>(),
+          cos_emb,
+          sin_emb,
+          qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
+          qkv_biases ? reinterpret_cast<DataType_*>(
+                          const_cast<T*>(qkv_biases.get().data<T>()))
+                    : nullptr,
+          max_seq_len,
+          max_blocks_per_seq,
+          num_heads,
+          kv_num_heads,
+          dim_head,
+          block_size,
+          bsz,
+          token_nums,
+          stream,
+          use_neox_rotary_style,
+          reinterpret_cast<const float*>(q_norm_weight.get().data<float>()),
+          reinterpret_cast<const float*>(k_norm_weight.get().data<float>()),
+          rms_norm_eps);
+    } else {
+      PD_THROW(
+          "append_decode_cache_rope_qk_norm not support cachekv quant yet");
+    }
   } else {
-    PD_THROW(
-        "cache_quant_type_str should be one of [none, cache_int8, "
-        "cache_int4_zp]");
+    if (cache_quant_type_str == "none") {
+        append_speculate_cache_rope(
+            reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
+            reinterpret_cast<DataType_*>(key_cache_out->data<T>()),
+            reinterpret_cast<DataType_*>(value_cache_out->data<T>()),
+            reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+            block_tables.data<int>(),
+            batch_id_per_token.data<int>(),
+            cu_seqlens_q.data<int>(),
+            seq_lens.data<int>(),
+            seq_lens_encoder.data<int>(),
+            cos_emb,
+            sin_emb,
+            qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
+            qkv_biases ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(qkv_biases.get().data<T>()))
+                    : nullptr,
+            max_seq_len,
+            max_blocks_per_seq,
+            num_heads,
+            kv_num_heads,
+            dim_head,
+            block_size,
+            bsz,
+            token_nums,
+            stream,
+            use_neox_rotary_style,
+            rope_3d);
+    } else if (cache_quant_type_str == "cache_int8") {
+        append_speculate_cache_int8_rope(
+            reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
+            key_cache_out->data<uint8_t>(),
+            value_cache_out->data<uint8_t>(),
+            reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+            block_tables.data<int>(),
+            batch_id_per_token.data<int>(),
+            cu_seqlens_q.data<int>(),
+            seq_lens.data<int>(),
+            seq_lens_encoder.data<int>(),
+            cos_emb,
+            sin_emb,
+            qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
+            qkv_biases ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(qkv_biases.get().data<T>()))
+                    : nullptr,
+            cache_k_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_k_scale.get().data<T>()))
+                        : nullptr,
+            cache_v_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_v_scale.get().data<T>()))
+                        : nullptr,
+            max_seq_len,
+            max_blocks_per_seq,
+            num_heads,
+            kv_num_heads,
+            dim_head,
+            block_size,
+            bsz,
+            token_nums,
+            stream,
+            use_neox_rotary_style,
+            rope_3d);
+    } else if (cache_quant_type_str == "cache_fp8") {
+        append_speculate_cache_int8_rope<DataType_, QKV_TYPE, true>(
+            reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
+            key_cache_out->data<uint8_t>(),
+            value_cache_out->data<uint8_t>(),
+            reinterpret_cast<DataType_*>(qkv_out->data<T>()),
+            block_tables.data<int>(),
+            batch_id_per_token.data<int>(),
+            cu_seqlens_q.data<int>(),
+            seq_lens.data<int>(),
+            seq_lens_encoder.data<int>(),
+            cos_emb,
+            sin_emb,
+            qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
+            qkv_biases ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(qkv_biases.get().data<T>()))
+                    : nullptr,
+            cache_k_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_k_scale.get().data<T>()))
+                        : nullptr,
+            cache_v_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_v_scale.get().data<T>()))
+                        : nullptr,
+            max_seq_len,
+            max_blocks_per_seq,
+            num_heads,
+            kv_num_heads,
+            dim_head,
+            block_size,
+            bsz,
+            token_nums,
+            stream,
+            use_neox_rotary_style,
+            rope_3d);
+    } else if (cache_quant_type_str == "cache_int4_zp") {
+        append_speculate_cache_int4_rope(
+            reinterpret_cast<const QKV_TYPE*>(qkv_ptr),
+            key_cache_out->data<uint8_t>(),
+            value_cache_out->data<uint8_t>(),
+            reinterpret_cast<DataType_*>(const_cast<T*>(qkv_out->data<T>())),
+            block_tables.data<int>(),
+            batch_id_per_token.data<int>(),
+            cu_seqlens_q.data<int>(),
+            seq_lens.data<int>(),
+            seq_lens_encoder.data<int>(),
+            cos_emb,
+            sin_emb,
+            qkv_out_scales ? qkv_out_scales.get().data<float>() : nullptr,
+            qkv_biases ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(qkv_biases.get().data<T>()))
+                    : nullptr,
+            cache_k_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_k_scale.get().data<T>()))
+                        : nullptr,
+            cache_v_scale ? reinterpret_cast<DataType_*>(
+                                const_cast<T*>(cache_v_scale.get().data<T>()))
+                        : nullptr,
+            cache_k_zp ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(cache_k_zp.get().data<T>()))
+                    : nullptr,
+            cache_v_zp ? reinterpret_cast<DataType_*>(
+                            const_cast<T*>(cache_v_zp.get().data<T>()))
+                    : nullptr,
+            max_seq_len,
+            max_blocks_per_seq,
+            num_heads,
+            kv_num_heads,
+            dim_head,
+            block_size,
+            bsz,
+            token_nums,
+            stream,
+            use_neox_rotary_style,
+            rope_3d);
+    } else {
+        PD_THROW(
+            "cache_quant_type_str should be one of [none, cache_int8, "
+            "cache_int4_zp]");
+    }
   }
 }
 
@@ -500,11 +626,15 @@ template void SpeculateWriteCacheWithRoPEKernel<paddle::bfloat16, int>(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out);
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps);
 
 template void
 SpeculateWriteCacheWithRoPEKernel<paddle::bfloat16, paddle::bfloat16>(
@@ -526,11 +656,15 @@ SpeculateWriteCacheWithRoPEKernel<paddle::bfloat16, paddle::bfloat16>(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out);
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps);
 
 template void SpeculateWriteCacheWithRoPEKernel<paddle::float16, int>(
     const AppendAttnMetaData& meta_data,
@@ -551,11 +685,15 @@ template void SpeculateWriteCacheWithRoPEKernel<paddle::float16, int>(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out);
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps);
 
 
 template void
@@ -578,8 +716,12 @@ SpeculateWriteCacheWithRoPEKernel<paddle::float16, paddle::float16>(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out);
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps);

custom_ops/gpu_ops/append_attn/speculate_write_cache_with_rope_kernel.h

@@ -35,8 +35,12 @@ void SpeculateWriteCacheWithRoPEKernel(
     const paddle::optional<paddle::Tensor>& cache_v_zp,
     const std::string& cache_quant_type_str,
     const bool use_neox_rotary_style,
+    const bool rope_3d,
     const int max_seq_len,
     cudaStream_t& stream,
     paddle::Tensor* qkv_out,
     paddle::Tensor* key_cache_out,
-    paddle::Tensor* value_cache_out);
+    paddle::Tensor* value_cache_out,
+    const paddle::optional<paddle::Tensor>& q_norm_weight,
+    const paddle::optional<paddle::Tensor>& k_norm_weight,
+    const float rms_norm_eps);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_bfloat16_bfloat16_kernel.cu

@@ -56,6 +56,7 @@ CascadeAppendAttentionC8Kernel<paddle::bfloat16, paddle::bfloat16, false>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -103,5 +104,6 @@ CascadeAppendAttentionC8Kernel<paddle::bfloat16, paddle::bfloat16, true>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_bfloat16_fp8_kernel.cu

@@ -54,6 +54,7 @@ template void CascadeAppendAttentionC8Kernel<paddle::bfloat16, paddle::float8_e4
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -98,5 +99,6 @@ template void CascadeAppendAttentionC8Kernel<paddle::bfloat16, paddle::float8_e4
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_bfloat16_int8_kernel.cu

@@ -54,6 +54,7 @@ template void CascadeAppendAttentionC8Kernel<paddle::bfloat16, int8_t, false>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -100,5 +101,6 @@ template void CascadeAppendAttentionC8Kernel<paddle::bfloat16, int8_t, true>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_float16_float16_kernel.cu

@@ -54,6 +54,7 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, paddle::float16, f
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -100,5 +101,6 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, paddle::float16, t
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_float16_fp8_kerne.cu

@@ -54,6 +54,7 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, paddle::float8_e4m
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -99,5 +100,6 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, paddle::float8_e4m
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/template_instantiation/append_attention_c8_float16_int8_kerne.cu

@@ -54,6 +54,7 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, int8_t, false>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);
 
@@ -99,5 +100,6 @@ template void CascadeAppendAttentionC8Kernel<paddle::float16, int8_t, true>(
     const bool causal,
     const bool is_decoder,
     const bool enable_prefill,
+    const std::string& cache_quant_type_str,
     cudaStream_t& stream,
     paddle::Tensor* out);

custom_ops/gpu_ops/append_attn/utils.cuh

@@ -441,6 +441,15 @@ __forceinline__ __host__ __device__ void vec_cast<nv_bfloat16, float>(
     PD_THROW("not support the group_size", group_size);      \
   }
 
+#define DISPATCH_DyCfp8(is_dynamic_cfp8, IsDynamicC8, ...) \
+  if (is_dynamic_cfp8) {                                   \
+    constexpr bool IsDynamicC8 = true;                     \
+    __VA_ARGS__                                            \
+  } else {                                                 \
+    constexpr bool IsDynamicC8 = false;                    \
+    __VA_ARGS__                                            \
+  }
+
 #define DISPATCH_MLA_GROUP_SIZE(group_size, GROUP_SIZE, ...) \
   if (group_size == 8) {                              \
     constexpr size_t GROUP_SIZE = 8;                         \

custom_ops/gpu_ops/cpp_extensions.cc

@@ -255,7 +255,8 @@ paddle::Tensor MoeExpertFFNFunc(
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
     const std::string& quant_method, const bool used_in_ep_low_latency,
-    const int estimate_total_token_nums);
+    const int estimate_total_token_nums,
+    const int hadamard_block_size);
 
 paddle::Tensor MoeExpertFFNWint2Func(
     const paddle::Tensor& permute_input,
@@ -298,7 +299,7 @@ paddle::Tensor OpenShmAndGetMetaSignalFunc(const int rank, const int device_id,
 paddle::Tensor InitSignalLayerwiseFunc(const paddle::Tensor &kv_signal_metadata,
                                        const int layer_id);
 
-std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
+void GetBlockShapeAndSplitKVBlock(
     const paddle::Tensor &seq_lens_encoder,
     const paddle::Tensor &seq_lens_decoder,
     const paddle::Tensor &seq_lens_this_time,
@@ -306,6 +307,13 @@ std::vector<paddle::Tensor> GetBlockShapeAndSplitKVBlock(
     paddle::Tensor &decoder_tile_ids_per_batch, // Inplace
     paddle::Tensor &decoder_num_blocks_x_cpu,   // Inplace, Pinned Memory
     paddle::Tensor &max_len_tensor_cpu,         // Inplace, Pinned Memory
+    paddle::Tensor &encoder_batch_ids,          // Inplace
+    paddle::Tensor &encoder_tile_ids_per_batch, // Inplace
+    paddle::Tensor &encoder_num_blocks_x_cpu,   // Inplace, Pinned Memory
+    paddle::Tensor &kv_batch_ids,               // Inplace
+    paddle::Tensor &kv_tile_ids_per_batch,      // Inplace
+    paddle::Tensor &kv_num_blocks_x_cpu,        // Inplace, Pinned Memory
+    paddle::Tensor &max_len_kv_cpu,             // Inplace, Pinned Memory
     const int encoder_block_shape_q,
     const int decoder_block_shape_q,
     const int group_size,
@@ -378,9 +386,11 @@ void RecoverDecodeTask(const paddle::Tensor &stop_flags,
                    const paddle::Tensor &step_seq_lens_decoder,
                    const paddle::Tensor &block_tables,
                    const paddle::Tensor &is_block_step,
-                   const int block_size);
-
-
+                   const paddle::optional<paddle::Tensor> &draft_tokens,
+                   const paddle::optional<paddle::Tensor> &step_draft_tokens,
+                   const paddle::optional<paddle::Tensor> &step_seq_lens_this_time,
+                   const int block_size,
+                   const int max_draft_tokens);
 
 paddle::Tensor
 GroupSwigluWithMasked(const paddle::Tensor &fc1_out_tensor,
@@ -707,6 +717,22 @@ void SpeculateSaveWithOutputMsgStatic(const paddle::Tensor& accept_tokens,
 void SpeculateClearAcceptNums(const paddle::Tensor& accept_num,
                               const paddle::Tensor& seq_lens_decoder);
 
+void SpeculateScheduleCache(const paddle::Tensor &draft_tokens,
+                            const paddle::Tensor &block_tables,
+                            const paddle::Tensor &stop_flags,
+                            const paddle::Tensor &seq_lens_this_time,
+                            const paddle::Tensor &seq_lens_decoder,
+                            const paddle::Tensor &step_seq_lens_decoder,
+                            const paddle::Tensor &step_draft_tokens,
+                            const paddle::Tensor &step_seq_lens_this_time,
+                            const paddle::Tensor &accept_num,
+                            const paddle::Tensor &accept_tokens,
+                            const paddle::Tensor &is_block_step,
+                            const paddle::Tensor &not_need_stop,
+                            const paddle::Tensor &stop_nums,
+                            const int block_size,
+                            const int max_draft_tokens);
+
 void NgramMatch(const paddle::Tensor &input_ids,
         const paddle::Tensor &input_ids_len,
         const paddle::Tensor &pre_ids,
@@ -750,6 +776,7 @@ void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
                           const paddle::Tensor& seq_lens_decoder,
                           const paddle::Tensor& step_idx,
                           const paddle::Tensor& not_need_stop,
+                          const paddle::Tensor& is_block_step,
                           const paddle::Tensor& batch_drop,
                           const paddle::Tensor& pre_ids,
                           const paddle::Tensor& accept_tokens,
@@ -763,7 +790,8 @@ void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
                           const paddle::Tensor& base_model_draft_tokens,
                           const int max_draft_token,
                           const bool truncate_first_token,
-                          const bool splitwise_prefill);
+                          const bool splitwise_prefill,
+                          const bool kvcache_scheduler_v1);
 
 
 void DraftModelUpdate(const paddle::Tensor& inter_next_tokens,
@@ -980,7 +1008,7 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
 
   m.def("per_token_quant_padding", &PerTokenQuantPadding, py::arg("input"),
         py::arg("block_size"),
-        "per token per block quant and padding tranpose scale");
+        "per token per block quant and padding transpose scale");
 
   m.def("masked_per_token_quant", &MaskedPerTokenQuant, py::arg("input"),
         py::arg("recv_expert_count"), py::arg("block_size"),
@@ -1023,7 +1051,7 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
   m.def("moe_expert_ffn", &MoeExpertFFNFunc, "moe export ffn function");
 
   /**
-   * moe/fused_moe/moe_ffn_wint2.cu
+   * moe/fused_moe/moe_expert_ffn_wint2.cu
    * moe_expert_ffn_wint2
    */
   m.def("moe_expert_ffn_wint2", &MoeExpertFFNWint2Func, "moe export ffn wint2 function");
@@ -1228,6 +1256,8 @@ PYBIND11_MODULE(fastdeploy_ops, m) {
 
   m.def("speculate_clear_accept_nums",&SpeculateClearAcceptNums, "speculate_clear_accept_nums function");
 
+  m.def("speculate_schedule_cache",&SpeculateScheduleCache, "SpeculateScheduleCache function");
+
   m.def("ngram_match", &NgramMatch, "ngram_match function");
 
   m.def("hybird_mtp_ngram", &HybridMtpNgram, "ngram_match_mixed function");

custom_ops/gpu_ops/cutlass_extensions/gemm/threadblock/wint2x_mma_base.h

@@ -89,11 +89,11 @@ public:
       GemmShape<Shape::kM / WarpGemm::kM, Shape::kN / WarpGemm::kN,
                 Shape::kK / WarpGemm::kK>;
 
-  /// Number of warp-level GEMM oeprations
+  /// Number of warp-level GEMM operations
   static int const kWarpGemmIterations =
       (WarpGemm::kK / Operator::Policy::MmaShape::kK);
 
-  /// Number of warp-level GEMM oeprations per load for B
+  /// Number of warp-level GEMM operations per load for B
   static constexpr int kWarpGemmIterationsPerLoadForB =
       Operator::IteratorB::InstructionShape::kRow / Operator::InstructionShape::kK;
   static_assert(!(kWarpGemmIterations % kWarpGemmIterationsPerLoadForB), "");

custom_ops/gpu_ops/cutlass_kernels/fp8_gemm_fused/dual_gemm/thread/left_gelu_and_mul.h

@@ -117,7 +117,7 @@ class LeftGELUAndMul {
     CUTLASS_HOST_DEVICE
     FragmentOutput operator()(FragmentAccumulator const &lhs,
                               FragmentAccumulator const &rhs) const {
-        // Convert source to interal compute numeric type
+        // Convert source to internal compute numeric type
         NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
             accumulator_to_compute;
 

custom_ops/gpu_ops/cutlass_kernels/fp8_gemm_fused/dual_gemm/thread/left_silu_and_mul.h

@@ -117,7 +117,7 @@ class LeftSiLUAndMul {
     CUTLASS_HOST_DEVICE
     FragmentOutput operator()(FragmentAccumulator const &lhs,
                               FragmentAccumulator const &rhs) const {
-        // Convert source to interal compute numeric type
+        // Convert source to internal compute numeric type
         NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
             accumulator_to_compute;
 

custom_ops/gpu_ops/cutlass_kernels/fp8_gemm_fused/dual_gemm/threadblock/dual_mma_base.h

@@ -92,7 +92,7 @@ class DualMmaBase {
                               Shape::kN / WarpGemm::kN,
                               Shape::kK / WarpGemm::kK>;
 
-  /// Number of warp-level GEMM oeprations
+  /// Number of warp-level GEMM operations
   static int const kWarpGemmIterations =
       (WarpGemm::kK / Operator0::Policy::MmaShape::kK);
 

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/epilogue/threadblock/epilogue_per_row_per_col_scale_nf4.h

@@ -219,7 +219,7 @@ class EpilogueVisitorPerRowPerColNf4 {
       iterator_C_.clear_mask();
     }
     // NOTE(wangbojun) Currently, this kernel don't hanve implantention for
-    // adding elementwise beta, we keep this here for future useage beta_ =
+    // adding elementwise beta, we keep this here for future usage beta_ =
     // (params.elementwise.beta_ptr ? *params.elementwise.beta_ptr :
     // params.elementwise.beta); if (beta_ == ElementAccumulator()) {
     //     iterator_C_.clear_mask();

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/epilogue/threadblock/epilogue_tensor_op_int32.h

@@ -176,7 +176,7 @@ struct Nf4DefaultIteratorsTensorOp<cutlass::bfloat16_t,
 ///
 /// Satisfies: ReadableTileIterator
 ///
-template <typename ThreadMap_  ///< Thread map (conept: OutputTileThreadMap)
+template <typename ThreadMap_  ///< Thread map (concept: OutputTileThreadMap)
           >
 class SharedLoadIteratorMixed<ThreadMap_, int32_t, 32, 16, 8, 8> {
  public:

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/gemm/threadblock/default_mma_nf4_int8_interleaved.h

@@ -64,7 +64,7 @@ template <
     typename InstructionShape_,
     /// Number of stages used in the pipelined mainloop
     int Stages,
-    /// Operation perfomed by GEMM
+    /// Operation performed by GEMM
     typename Operator,
     /// Store the accumulators in row major or column major.  Row major is used
     /// when output layout is interleaved.

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/gemm/threadblock/int8_mma_base.h

@@ -133,7 +133,7 @@ public:
     /// Shape describing the number of warps filling the CTA
     using WarpCount = GemmShape<Shape::kM / WarpGemm::kM, Shape::kN / WarpGemm::kN, Shape::kK / WarpGemm::kK>;
 
-    /// Number of warp-level GEMM oeprations
+    /// Number of warp-level GEMM operations
     static int const kWarpGemmIterations = (WarpGemm::kK / Operator::Policy::MmaShape::kK);
     static_assert(Operator::IteratorB::InstructionShape::kRow>=Operator::InstructionShape::kK,"");
     static constexpr int kNumKIterationsPerWarpBLoad =

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/gemm/threadblock/int8_mma_multistage.h

@@ -509,7 +509,7 @@ public:
                     this->warp_tile_iterator_B_.load(warp_frag_B[(warp_tileB_k_load_offset + 1) % 2]);
                     ++this->warp_tile_iterator_B_;
                 }
-                // TOOD(wangbojun) lds_converter can be remove for int8 B input
+                // TODO(wangbojun) lds_converter can be remove for int8 B input
                 typename TransformBAfterLDS::result_type converted_frag_B =
                     lds_converter(warp_frag_B[warp_tileB_k_load_offset % 2]);
 

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/gemm/threadblock/nf4_int8_mma_base.h

@@ -96,7 +96,7 @@ public:
     /// Shape describing the number of warps filling the CTA
     using WarpCount = GemmShape<Shape::kM / WarpGemm::kM, Shape::kN / WarpGemm::kN, Shape::kK / WarpGemm::kK>;
 
-    /// Number of warp-level GEMM oeprations
+    /// Number of warp-level GEMM operations
     static int const kWarpGemmIterations = (WarpGemm::kK / Operator::Policy::MmaShape::kK);
     static_assert(Operator::IteratorB::InstructionShape::kRow>=Operator::InstructionShape::kK,"");
     static constexpr int kNumKIterationsPerWarpBLoad =

custom_ops/gpu_ops/cutlass_kernels/w4a8_moe/cutlass_extensions/gemm/threadblock/nf4_int8_mma_multistage.h

@@ -646,7 +646,7 @@ public:
                     //             );
                     // }
                 }
-                // TOOD(wangbojun) lds_converter can be remove for int8 B input
+                // TODO(wangbojun) lds_converter can be remove for int8 B input
                 // int4
                 // typename TransformBAfterLDS::result_type converted_frag_B =
                 //     lds_converter(warp_frag_B[warp_tileB_k_load_offset % 2]);

custom_ops/gpu_ops/int8_gemm_with_cutlass/epilogue_tensor_op_int32.h

@@ -171,7 +171,7 @@ struct DefaultIteratorsTensorOp<cutlass::bfloat16_t,
 ///
 /// Satisfies: ReadableTileIterator
 ///
-template <typename ThreadMap_  ///< Thread map (conept: OutputTileThreadMap)
+template <typename ThreadMap_  ///< Thread map (concept: OutputTileThreadMap)
           >
 class SharedLoadIteratorMixed<ThreadMap_, int32_t, 32, 16, 8, 8> {
     public:

custom_ops/gpu_ops/moba_attn/moba_decoder_attn/moba_decoder_attn.cu

@@ -383,7 +383,7 @@ __global__ __launch_bounds__(Kernel_traits::kNThreads) void moba_decoder_attenti
 
 
 template<typename Kernel_traits, typename ParamType>
-inline __device__ float caluate_logit_scale(const int partition_num, const int pack_max_partition_num, ParamType &params, char * shared_mem, const int seq_len, const int *qk_gate_topk_idx_ptr) {
+inline __device__ float calculate_logit_scale(const int partition_num, const int pack_max_partition_num, ParamType &params, char * shared_mem, const int seq_len, const int *qk_gate_topk_idx_ptr) {
     constexpr int32_t kNFloatPacksize = 16 / sizeof(float);
     constexpr int32_t kNReduceThreads = Kernel_traits::kNReduceThreads;
     const int32_t bi = blockIdx.z;
@@ -524,7 +524,7 @@ __global__ void __launch_bounds__(Kernel_traits::kNReduceThreads) moba_decoder_a
     const int kv_head_idx = head_idx / Kernel_traits::kGqaGroupSize;
     const int * qk_gate_topk_idx_ptr = params.qk_gate_topk_idx_ptr + (bi * params.kv_head_num + kv_head_idx) * Kernel_traits::kMaxN;
 
-    float inv_global_exp_sum = caluate_logit_scale<Kernel_traits>(partition_num, pack_max_partition_num, params, shared_mem, seq_len, qk_gate_topk_idx_ptr);
+    float inv_global_exp_sum = calculate_logit_scale<Kernel_traits>(partition_num, pack_max_partition_num, params, shared_mem, seq_len, qk_gate_topk_idx_ptr);
 
 
     using T_vec = Vec<cuteType, kNReducePacksize>;

custom_ops/gpu_ops/moba_attn/moba_encoder_attn/moba_encoder_write_cache.cu

@@ -40,7 +40,7 @@ __global__ void write_encoder_cachekv_c16(
 
     if (seq_len == 0) return;
 
-    const int ramian_tokens = seq_len - block_idx;
+    const int remain_tokens = seq_len - block_idx;
 
     const int32_t *block_table_now = block_tables + bidb * max_blocks_per_seq;
     const uint32_t physical_block_number = block_table_now[blockIdx.x + seq_len_decoder[bidb] / kBlockSize];
@@ -51,7 +51,7 @@ __global__ void write_encoder_cachekv_c16(
 
         #pragma unroll
         for (int i = row_idx; i < kBlockSize; i += 128 / (kHeadDim / kPackSize)) {
-            if (i < ramian_tokens) {
+            if (i < remain_tokens) {
                 *reinterpret_cast<float4*>(cache + i * kHeadDim) = *reinterpret_cast<const float4*>(k_input + base_load_idx + i * kv_head_num * kHeadDim);
             }
         }
@@ -62,7 +62,7 @@ __global__ void write_encoder_cachekv_c16(
 
         #pragma unroll
         for (int i = row_idx; i < kBlockSize; i += 128 / (kHeadDim / kPackSize)) {
-            if (i < ramian_tokens) {
+            if (i < remain_tokens) {
                 *reinterpret_cast<float4*>(cache + i * kHeadDim) = *reinterpret_cast<const float4*>(v_input + base_load_idx + i * kv_head_num * kHeadDim);
             }
         }

custom_ops/gpu_ops/moba_attn/moba_process/moba_get_kv_from_cache.cu

@@ -50,14 +50,14 @@ __global__ void get_kv_from_cache_c16_kernel(
     const int physical_block_number = block_tables[bidb * max_blocks_per_seq + block_idx];
 
 
-    const int ramian_tokens = seq_len - base_token_idx;
+    const int remain_tokens = seq_len - base_token_idx;
 
     if (bidh < kv_head_num) {
         const int cache_offset = physical_block_number * kv_head_num * kBlockSize * kHeadDim + bidh * kBlockSize * kHeadDim + col_idx;
         const int base_store_idx = (base_token_idx + cu_seq_k[bidb]) * kv_head_num * kHeadDim + bidh * kHeadDim + col_idx;
         #pragma unroll
         for (int i = row_idx; i < kBlockSize; i += 128 / (kHeadDim / kPackSize)) {
-            if (i < ramian_tokens) {
+            if (i < remain_tokens) {
                 *reinterpret_cast<float4*>(k_input + base_store_idx + i * kv_head_num * kHeadDim) = *reinterpret_cast<const float4*>(cache_k + cache_offset + i * kHeadDim);
             }
         }
@@ -67,7 +67,7 @@ __global__ void get_kv_from_cache_c16_kernel(
         const int base_store_idx = (base_token_idx + cu_seq_k[bidb]) * kv_head_num * kHeadDim + bidh * kHeadDim + col_idx;
         #pragma unroll
         for (int i = row_idx; i < kBlockSize; i += 128 / (kHeadDim / kPackSize)) {
-            if (i < ramian_tokens) {
+            if (i < remain_tokens) {
                 *reinterpret_cast<float4*>(v_input + base_store_idx + i * kv_head_num * kHeadDim) = *reinterpret_cast<const float4*>(cache_v + cache_offset + i * kHeadDim);
             }
         }

custom_ops/gpu_ops/moe/fast_hardamard_kernel.cu

@@ -872,16 +872,14 @@ void MoeFastHardamardWrapper(const T *x_data,
                           const int64_t dim,
                           const int num_max_tokens_per_expert,
                           bool used_in_ep_low_latency,
+                          const int hadamard_block_size,
                           OutT* out,
                           cudaStream_t &stream) {
   bool FLAGS_hardamard_use_diagonal_block_matrix = true;
 
-  static const char* FLAGS_hardamard_moe_block_size = std::getenv("FLAGS_hardamard_moe_block_size");
-  static const int32_t hardamard_moe_block_size = FLAGS_hardamard_moe_block_size != nullptr ?
-    stoi(std::string(FLAGS_hardamard_moe_block_size)) : 512;
   constexpr int kThreads = 128;
   if (FLAGS_hardamard_use_diagonal_block_matrix) {
-    const int VecSize = hardamard_moe_block_size / kThreads; // 128 / 128 = 1
+    const int VecSize = hadamard_block_size / kThreads;
     const int logN = int(ceil(std::log2(kThreads * VecSize)));
     constexpr int kNChunks = 1;
     DISPATCH_SP_VS(VecSize, VEC_SIZE, {
@@ -991,6 +989,7 @@ template void MoeFastHardamardWrapper<phi::dtype::float16, phi::dtype::float16>(
   const int64_t dim,
   const int num_max_tokens_per_expert,
   bool used_in_ep_low_latency,
+  const int hadamard_block_size,
   phi::dtype::float16 *out,
   cudaStream_t &stream
 );
@@ -1009,6 +1008,7 @@ template void MoeFastHardamardWrapper<phi::dtype::float16, int8_t>(
   const int64_t dim,
   const int num_max_tokens_per_expert,
   bool used_in_ep_low_latency,
+  const int hadamard_block_size,
   int8_t *out,
   cudaStream_t &stream
 );
@@ -1027,6 +1027,7 @@ template void MoeFastHardamardWrapper<phi::dtype::bfloat16, phi::dtype::bfloat16
   const int64_t dim,
   const int num_max_tokens_per_expert,
   bool used_in_ep_low_latency,
+  const int hadamard_block_size,
   phi::dtype::bfloat16 *out,
   cudaStream_t &stream
 );
@@ -1045,6 +1046,7 @@ template void MoeFastHardamardWrapper<phi::dtype::bfloat16, int8_t>(
   const int64_t dim,
   const int num_max_tokens_per_expert,
   bool used_in_ep_low_latency,
+  const int hadamard_block_size,
   int8_t *out,
   cudaStream_t &stream
 );

custom_ops/gpu_ops/moe/fast_hardamard_kernel.h

@@ -32,5 +32,6 @@ void MoeFastHardamardWrapper(const T *x_data,
                             const int64_t dim,
                             const int num_max_tokens_per_expert,
                             bool used_in_ep_low_latency,
+                            const int hadamard_block_size,
                             OutT* out,
                             cudaStream_t &stream);

custom_ops/gpu_ops/moe/moe_dispatch.cu

@@ -80,7 +80,7 @@ void MoeDispatchKernel(
   if (group_moe) {
     paddle::Tensor softmax_max_prob_tensor =
         GetEmptyTensor({num_rows, moe_topk}, paddle::DataType::FLOAT32, place);
-    // (TODO: check fill sucess ?)
+    // (TODO: check fill success ?)
     paddle::experimental::fill(softmax_max_prob_tensor, 0.f);
     softmax_max_prob = softmax_max_prob_tensor.data<float>();
   }

custom_ops/gpu_ops/moe/moe_ffn.cu

@@ -35,7 +35,8 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
                   const std::string& quant_method,
                   paddle::Tensor ffn_out,
                   bool used_in_ep_low_latency,
-                  const int estimate_total_token_nums) {
+                  const int estimate_total_token_nums,
+                  const int hadamard_block_size) {
     using namespace phi;
     typedef PDTraits<T> traits_;
     typedef typename traits_::DataType DataType_;
@@ -291,6 +292,7 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
             inter_size / 2,
             num_max_tokens_per_expert,
             used_in_ep_low_latency,
+            hadamard_block_size,
             reinterpret_cast<int8_t *>(int8_act_out->ptr()),
             stream
         );
@@ -340,6 +342,7 @@ void MoeFFNKernel(const paddle::Tensor& permute_input,
             inter_size / 2,
             num_max_tokens_per_expert,
             used_in_ep_low_latency,
+            hadamard_block_size,
             act_out_tensor.data<data_t>(),
             stream
         );
@@ -403,7 +406,7 @@ paddle::Tensor MoeExpertFFNFunc(
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
     const std::string& quant_method, const bool used_in_ep_low_latency,
-    const int estimate_total_token_nums) {
+    const int estimate_total_token_nums, const int hadamard_block_size) {
 
 const auto t_type = (quant_method == "w4a8") ? up_gate_proj_scale.get().dtype() :
                     (quant_method == "w4afp8") ? paddle::DataType::BFLOAT16 :
@@ -424,7 +427,8 @@ const auto t_type = (quant_method == "w4a8") ? up_gate_proj_scale.get().dtype()
                                                      quant_method,
                                                      ffn_out,
                                                      used_in_ep_low_latency,
-                                                     estimate_total_token_nums);
+                                                     estimate_total_token_nums,
+                                                     hadamard_block_size);
             break;
         case paddle::DataType::FLOAT16:
             MoeFFNKernel<paddle::DataType::FLOAT16>(permute_input,
@@ -439,7 +443,8 @@ const auto t_type = (quant_method == "w4a8") ? up_gate_proj_scale.get().dtype()
                                                     quant_method,
                                                     ffn_out,
                                                     used_in_ep_low_latency,
-                                                    estimate_total_token_nums);
+                                                    estimate_total_token_nums,
+                                                    hadamard_block_size);
             break;
         default:
             PD_THROW("Unsupported data type for MoeExpertFFN");
@@ -458,7 +463,8 @@ std::vector<paddle::Tensor> MoeExpertFFN(
     const paddle::optional<paddle::Tensor>& down_proj_in_scale,
     const paddle::optional<paddle::Tensor>& expert_idx_per_token,
     const std::string& quant_method, const bool used_in_ep_low_latency,
-    const int estimate_total_token_nums) {
+    const int estimate_total_token_nums,
+    const int hadamard_block_size) {
     return {MoeExpertFFNFunc(permute_input,
                              tokens_expert_prefix_sum,
                              up_gate_proj_weight,
@@ -470,7 +476,8 @@ std::vector<paddle::Tensor> MoeExpertFFN(
                              expert_idx_per_token,
                              quant_method,
                              used_in_ep_low_latency,
-                             estimate_total_token_nums)};
+                             estimate_total_token_nums,
+                             hadamard_block_size)};
 }
 
 std::vector<std::vector<int64_t>> MoeExpertFFNInferShape(
@@ -485,7 +492,8 @@ std::vector<std::vector<int64_t>> MoeExpertFFNInferShape(
     const paddle::optional<std::vector<int64_t>>& expert_idx_per_token_shape,
     const std::string& quant_method,
     const bool used_in_ep_low_latency,
-    const int estimate_total_token_nums) {
+    const int estimate_total_token_nums,
+    const int hadamard_block_size) {
     return {permute_input_shape};
 }
 
@@ -499,7 +507,7 @@ std::vector<paddle::DataType> MoeExpertFFNInferDtype(
     const paddle::optional<paddle::DataType> &down_proj_scale_dtype,
     const paddle::optional<paddle::DataType> &down_proj_in_scale_dtype,
     const std::string &quant_method, const bool used_in_ep_low_latency,
-    const int estimate_total_token_nums) {
+    const int estimate_total_token_nums, const int hadamard_block_size) {
   if (quant_method == "w4a8" || quant_method == "w4afp8") {
     return {up_gate_proj_scale_dtype.get()};
   } else {
@@ -555,6 +563,8 @@ std::vector<paddle::DataType> MoeExpertFFNInferDtype(
  *                 Options: "none", "weight_only_int4", "weight_only_int8", "w4a8"
  *   - used_in_ep_low_latency: Whether running in low latency mode
  *                            Affects activation function implementation
+ *   - estimate_total_token_nums: estimate total token numbers
+ *   - hadamard_block_size: hadamard block size for w4a8/w4afp8 quantization
  *
  * Note:
  * - w4a8 mode requires additional workspace memory allocation
@@ -571,7 +581,7 @@ PD_BUILD_STATIC_OP(moe_expert_ffn)
              paddle::Optional("down_proj_in_scale"),
              paddle::Optional("expert_idx_per_token")})
     .Outputs({"output_tensor"})
-    .Attrs({"quant_method:std::string", "used_in_ep_low_latency:bool", "estimate_total_token_nums:int"})
+    .Attrs({"quant_method:std::string", "used_in_ep_low_latency:bool", "estimate_total_token_nums:int", "hadamard_block_size:int"})
     .SetKernelFn(PD_KERNEL(MoeExpertFFN))
     .SetInferShapeFn(PD_INFER_SHAPE(MoeExpertFFNInferShape))
     .SetInferDtypeFn(PD_INFER_DTYPE(MoeExpertFFNInferDtype));

custom_ops/gpu_ops/recover_decode_task.cu

@@ -15,31 +15,72 @@
 #include "helper.h"
 
 __global__ void recover_decode_task(bool *stop_flags,
-                                   int *seq_lens_this_time,
-                                   int *seq_lens_encoder,
-                                   int *seq_lens_decoder,
-                                   int *step_seq_lens_decoder,
-                                   int *block_tables,
-                                   bool *is_block_step,
-                                   const int bsz,
-                                   const int block_num_per_seq,
-                                   const int block_size) {
+                                    int *seq_lens_this_time,
+                                    int *seq_lens_encoder,
+                                    int *seq_lens_decoder,
+                                    int *step_seq_lens_decoder,
+                                    int *block_tables,
+                                    bool *is_block_step,
+                                    const int bsz,
+                                    const int block_num_per_seq,
+                                    const int block_size) {
     int thread_idx = threadIdx.x;
     if (thread_idx < bsz) {
         if(is_block_step[thread_idx] == true) {
             int *block_table_now = block_tables + thread_idx * block_num_per_seq;
             if (block_table_now[step_seq_lens_decoder[thread_idx] / block_size] != -1) {
-                    // can be recovered for decoding
-                    is_block_step[thread_idx] = false;
-                    seq_lens_this_time[thread_idx]= 1;
-                    stop_flags[thread_idx] = false;
-                    seq_lens_encoder[thread_idx] = 0;
-                    seq_lens_decoder[thread_idx] = step_seq_lens_decoder[thread_idx];
-                }
+                // can be recovered for decoding
+                is_block_step[thread_idx] = false;
+                seq_lens_this_time[thread_idx]= 1;
+                stop_flags[thread_idx] = false;
+                seq_lens_encoder[thread_idx] = 0;
+                seq_lens_decoder[thread_idx] = step_seq_lens_decoder[thread_idx];
+
+            }
         }
     }
 }
 
+__global__ void recover_spec_decode_task(bool *stop_flags,
+                                        int *seq_lens_this_time,
+                                        int *seq_lens_encoder,
+                                        int *seq_lens_decoder,
+                                        int *step_seq_lens_decoder,
+                                        int *block_tables,
+                                        bool *is_block_step,
+                                        int64_t *draft_tokens,
+                                        const int64_t *step_draft_tokens,
+                                        const int *step_seq_lens_this_time,
+                                        const int bsz,
+                                        const int block_num_per_seq,
+                                        const int block_size,
+                                        const int draft_tokens_len,
+                                        const int num_extra_tokens) {
+    int thread_idx = threadIdx.x;
+    if (thread_idx < bsz) {
+        if(is_block_step[thread_idx] == true) {
+            int *block_table_now = block_tables + thread_idx * block_num_per_seq;
+            int max_possible_block_idx = (step_seq_lens_decoder[thread_idx] + num_extra_tokens) / block_size;
+            max_possible_block_idx = min(max_possible_block_idx, block_num_per_seq);
+            if (block_table_now[max_possible_block_idx] != -1) {
+                // can be recovered for decoding
+                int64_t *draft_tokens_now = draft_tokens + thread_idx * draft_tokens_len;
+                const int64_t *step_draft_tokens_now = step_draft_tokens + thread_idx * draft_tokens_len;
+                is_block_step[thread_idx] = false;
+                seq_lens_this_time[thread_idx] = step_seq_lens_this_time[thread_idx];
+                stop_flags[thread_idx] = false;
+                seq_lens_encoder[thread_idx] = 0;
+                seq_lens_decoder[thread_idx] = step_seq_lens_decoder[thread_idx];
+                for (int i = 0; i < seq_lens_this_time[thread_idx]; i++) {
+                    draft_tokens_now[i] = step_draft_tokens_now[i];
+                }
+
+            }
+        }
+    }
+}
+
+
 void RecoverDecodeTask(const paddle::Tensor &stop_flags,
                    const paddle::Tensor &seq_lens_this_time,
                    const paddle::Tensor &seq_lens_encoder,
@@ -47,7 +88,11 @@ void RecoverDecodeTask(const paddle::Tensor &stop_flags,
                    const paddle::Tensor &step_seq_lens_decoder,
                    const paddle::Tensor &block_tables,
                    const paddle::Tensor &is_block_step,
-                   const int block_size) {
+                   const paddle::optional<paddle::Tensor> &draft_tokens,
+                   const paddle::optional<paddle::Tensor> &step_draft_tokens,
+                   const paddle::optional<paddle::Tensor> &step_seq_lens_this_time,
+                   const int block_size,
+                   const int max_draft_tokens) {
 #ifdef PADDLE_WITH_CUSTOM_DEVICE
     auto dev_ctx = static_cast<const phi::CustomContext*>(paddle::experimental::DeviceContextPool::Instance().Get(seq_lens_this_time.place()));
     auto cu_stream = dev_ctx->stream();
@@ -56,17 +101,38 @@ void RecoverDecodeTask(const paddle::Tensor &stop_flags,
 #endif
     const int bsz = seq_lens_this_time.shape()[0];
     const int block_num_per_seq = block_tables.shape()[1];
-    recover_decode_task<<<1, 1024, 0, cu_stream>>>(
-        const_cast<bool *>(stop_flags.data<bool>()),
-        const_cast<int *>(seq_lens_this_time.data<int>()),
-        const_cast<int *>(seq_lens_encoder.data<int>()),
-        const_cast<int *>(seq_lens_decoder.data<int>()),
-        const_cast<int *>(step_seq_lens_decoder.data<int>()),
-        const_cast<int *>(block_tables.data<int>()),
-        const_cast<bool *>(is_block_step.data<bool>()),
-        bsz,
-        block_num_per_seq,
-        block_size);
+    if (draft_tokens) {
+        const int draft_tokens_len = draft_tokens.get_ptr()->shape()[1];
+        recover_spec_decode_task<<<1, 1024, 0, cu_stream>>>(
+            const_cast<bool *>(stop_flags.data<bool>()),
+            const_cast<int *>(seq_lens_this_time.data<int>()),
+            const_cast<int *>(seq_lens_encoder.data<int>()),
+            const_cast<int *>(seq_lens_decoder.data<int>()),
+            const_cast<int *>(step_seq_lens_decoder.data<int>()),
+            const_cast<int *>(block_tables.data<int>()),
+            const_cast<bool *>(is_block_step.data<bool>()),
+            const_cast<int64_t *>(draft_tokens.get_ptr()->data<int64_t>()),
+            step_draft_tokens.get_ptr()->data<int64_t>(),
+            step_seq_lens_this_time.get_ptr()->data<int>(),
+            bsz,
+            block_num_per_seq,
+            block_size,
+            draft_tokens_len,
+            max_draft_tokens * 2 + 1);
+
+    } else {
+        recover_decode_task<<<1, 1024, 0, cu_stream>>>(
+            const_cast<bool *>(stop_flags.data<bool>()),
+            const_cast<int *>(seq_lens_this_time.data<int>()),
+            const_cast<int *>(seq_lens_encoder.data<int>()),
+            const_cast<int *>(seq_lens_decoder.data<int>()),
+            const_cast<int *>(step_seq_lens_decoder.data<int>()),
+            const_cast<int *>(block_tables.data<int>()),
+            const_cast<bool *>(is_block_step.data<bool>()),
+            bsz,
+            block_num_per_seq,
+            block_size);
+    }
 }
 
 PD_BUILD_STATIC_OP(recover_decode_task)
@@ -76,8 +142,11 @@ PD_BUILD_STATIC_OP(recover_decode_task)
              "seq_lens_decoder",
              "step_seq_lens_decoder",
              "block_tables",
-             "is_block_step"})
-    .Attrs({"block_size: int"})
+             "is_block_step",
+             paddle::Optional("draft_tokens"),
+             paddle::Optional("step_draft_tokens"),
+             paddle::Optional("step_seq_lens_this_time")})
+    .Attrs({"block_size: int", "max_draft_tokens: int"})
     .Outputs({"seq_lens_this_time_out",
               "seq_lens_encoder_out",
               "seq_lens_decoder_out",

custom_ops/gpu_ops/save_output_msg_with_topk.cc

@@ -75,7 +75,7 @@ void SaveOutMmsgTopK(const paddle::Tensor& x,
         std::string inference_msg_id_env_str(inference_msg_id_env_p);
         inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
         if (inference_msg_id_from_env == 2) {
-            // 2 and -2 is perserve for no-output indication.
+            // 2 and -2 is preserve for no-output indication.
             throw std::runtime_error(
                 " INFERENCE_MSG_ID cannot be 2, please use other number.");
         }

custom_ops/gpu_ops/save_with_output_msg.cc

@@ -45,7 +45,7 @@ void save_kernel(const paddle::Tensor& x,
         std::string inference_msg_id_env_str(inference_msg_id_env_p);
         inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
         if (inference_msg_id_from_env == 2) {
-            // 2 and -2 is perserve for no-output indication.
+            // 2 and -2 is preserve for no-output indication.
             throw std::runtime_error(
                 " INFERENCE_MSG_ID cannot be 2, please use other number.");
         }

custom_ops/gpu_ops/set_value_by_flags_and_idx.cu

@@ -34,7 +34,7 @@ __global__ void set_value_by_flag_and_id(const bool *stop_flags,
         const int64_t *input_ids_now = input_ids + tid * length_input_ids;
         const int seq_len_dec = seq_lens_decoder[tid];
         const int seq_len_enc = seq_lens_encoder[tid];
-        if (seq_len_dec == 0 && seq_len_enc == 0) return;  // stoped
+        if (seq_len_dec == 0 && seq_len_enc == 0) return;  // stopped
         if (step_idx[tid] >= 0) {
             if (seq_len_enc > 0) { // encoder, get last token accord to seq_lens_encoder
                 pre_ids_all_now[step_idx[tid]] = input_ids_now[seq_len_enc - 1];

custom_ops/gpu_ops/speculate_decoding/draft_model/draft_model_preprocess.cu

@@ -15,7 +15,48 @@
 #include "helper.h"
 #include "paddle/extension.h"
 
-template <int THREADBLOCK_SIZE, bool TRCUNCATE_FIRST_TOKEN>
+
+#define DISPATCH_BLOCKSIZE(BLOCK_SIZE, ...) \
+  do {                                     \
+    constexpr int BlockSize = BLOCK_SIZE;  \
+    __VA_ARGS__;                           \
+  } while (0)
+
+#define DISPATCH_TRUNCATE_FIRST_TOKEN(truncate_first_token, TRUNCATE_FIRST_TOKEN, ...) \
+  do {                                                                                 \
+    if (truncate_first_token) {                                                        \
+      constexpr bool TRUNCATE_FIRST_TOKEN = true;                                     \
+      __VA_ARGS__;                                                                     \
+    } else {                                                                           \
+      constexpr bool TRUNCATE_FIRST_TOKEN = false;                                    \
+      __VA_ARGS__;                                                                     \
+    }                                                                                  \
+  } while (0)
+
+#define DISPATCH_KVCACHE_SCHEDULER(kvcache_scheduler_v1, KVCACHE_SCHEDULER_V1, ...) \
+  do {                                                                              \
+    if (kvcache_scheduler_v1) {                                                     \
+      constexpr bool KVCACHE_SCHEDULER_V1 = true;                                   \
+      __VA_ARGS__;                                                                  \
+    } else {                                                                        \
+      constexpr bool KVCACHE_SCHEDULER_V1 = false;                                  \
+      __VA_ARGS__;                                                                  \
+    }                                                                               \
+  } while (0)
+
+#define DISPATCH_SPLITWISE_PREFILL(splitwise_prefill, SPLITWISE_PREFILL, ...) \
+  do {                                                                        \
+    if (splitwise_prefill) {                                                  \
+      constexpr bool SPLITWISE_PREFILL = true;                                \
+      __VA_ARGS__;                                                            \
+    } else {                                                                  \
+      constexpr bool SPLITWISE_PREFILL = false;                               \
+      __VA_ARGS__;                                                            \
+    }                                                                         \
+  } while (0)
+
+
+template <int THREADBLOCK_SIZE, bool TRUNCATE_FIRST_TOKEN, bool KVCACHE_SCHEDULER_V1>
 __global__ void process_splitwise_prefill(
     int64_t* draft_tokens,
     int64_t* input_ids,
@@ -25,6 +66,7 @@ __global__ void process_splitwise_prefill(
     int* seq_lens_decoder,
     int64_t* step_idx,
     bool* not_need_stop,
+    bool* is_block_step,
     bool* batch_drop,
     int64_t* pre_ids,
     const int64_t* accept_tokens,
@@ -58,7 +100,7 @@ __global__ void process_splitwise_prefill(
       stop_flags[tid] = false;
       int64_t base_model_first_token = accept_tokens_now[0];
       int position = seq_len_encoder;
-      if (TRCUNCATE_FIRST_TOKEN) {
+      if (TRUNCATE_FIRST_TOKEN) {
         input_ids_now[position - 1] = base_model_first_token;
         seq_lens_this_time[tid] = seq_len_encoder;
       } else {
@@ -84,7 +126,7 @@ __global__ void process_splitwise_prefill(
 
 
 
-template <int THREADBLOCK_SIZE, bool TRCUNCATE_FIRST_TOKEN>
+template <int THREADBLOCK_SIZE, bool TRUNCATE_FIRST_TOKEN, bool KVCACHE_SCHEDULER_V1>
 __global__ void draft_model_preprocess_kernel(
     int64_t* draft_tokens,
     int64_t* input_ids,
@@ -94,6 +136,7 @@ __global__ void draft_model_preprocess_kernel(
     int* seq_lens_decoder,
     int64_t* step_idx,
     bool* not_need_stop,
+    bool* is_block_step,
     bool* batch_drop,
     int64_t* pre_ids,
     const int64_t* accept_tokens,
@@ -134,14 +177,26 @@ __global__ void draft_model_preprocess_kernel(
       base_model_draft_tokens_now[i] = -1;
     }
 
-    if (base_model_stop_flags[tid] && base_model_is_block_step[tid]) {
-      batch_drop[tid] = true;
-      stop_flags[tid] = true;
+    // 1. process block_step situation
+    //    -- In v0 mode, block_step will drop mtp query.
+    //    -- In v1 mode, block_step will continue to infer.
+    if constexpr(KVCACHE_SCHEDULER_V1) {
+      if (base_model_stop_flags[tid] && base_model_is_block_step[tid]) {
+        stop_flags[tid] = true;
+        is_block_step[tid] = true;
+        // Need to continue infer
+      }
+    } else {
+      if (base_model_stop_flags[tid] && base_model_is_block_step[tid]) {
+        batch_drop[tid] = true;
+        stop_flags[tid] = true;
+      }
     }
 
+    // 2. process normal query, not in any special case.
     if (!(base_model_stop_flags[tid] || batch_drop[tid])) {
       not_stop_flag = 1;
-      // 1. first token
+      // prefill generation
       if (seq_lens_encoder[tid] > 0) {
         // Can be extended to first few tokens
         int seq_len_encoder = seq_lens_encoder[tid];
@@ -149,14 +204,20 @@ __global__ void draft_model_preprocess_kernel(
         int64_t base_model_first_token = accept_tokens_now[0];
         pre_ids_now[0] = base_model_first_token;
         int position = seq_len_encoder;
-        if (TRCUNCATE_FIRST_TOKEN) {
+        if (TRUNCATE_FIRST_TOKEN) {
           input_ids_now[position - 1] = base_model_first_token;
           seq_lens_this_time[tid] = seq_len_encoder;
         } else {
           input_ids_now[position] = base_model_first_token;
           seq_lens_this_time[tid] = seq_len_encoder + 1;
         }
-      } else {
+      } else {  // decode generation
+        if constexpr (KVCACHE_SCHEDULER_V1) {
+        // 3. try to recover mtp infer in V1 mode
+          if (!base_model_is_block_step[tid] && is_block_step[tid]) {
+            is_block_step[tid] = false;
+          }
+        }
         if (stop_flags[tid]) {
           stop_flags[tid] = false;
           // TODO: check
@@ -189,99 +250,8 @@ __global__ void draft_model_preprocess_kernel(
   }
 }
 
-template <bool TRCUNCATE_FIRST_TOKEN>
-void DispatchRunner(
-    const cudaStream_t& stream,
-    int64_t* draft_tokens,
-    int64_t* input_ids,
-    bool* stop_flags,
-    int* seq_lens_this_time,
-    int* seq_lens_encoder,
-    int* seq_lens_decoder,
-    int64_t* step_idx,
-    bool* not_need_stop,
-    bool* batch_drop,
-    int64_t* pre_ids,
-    const int64_t* accept_tokens,
-    const int* accept_num,
-    const int* base_model_seq_lens_this_time,
-    const int* base_model_seq_lens_encoder,
-    const int* base_model_seq_lens_decoder,
-    const int64_t* base_model_step_idx,
-    const bool* base_model_stop_flags,
-    const bool* base_model_is_block_step,
-    int64_t* base_model_draft_tokens,
-    const int bsz,
-    const int num_model_step,
-    const int accept_tokens_len,
-    const int draft_tokens_len,
-    const int input_ids_len,
-    const int base_model_draft_tokens_len,
-    const int pre_ids_len,
-    const bool splitwise_prefill) {
-  constexpr int BlockSize = 512;
-  if (splitwise_prefill) {
-    process_splitwise_prefill<BlockSize, TRCUNCATE_FIRST_TOKEN>
-          <<<1, BlockSize, 0, stream>>>(
-              draft_tokens,
-              input_ids,
-              stop_flags,
-              seq_lens_this_time,
-              seq_lens_encoder,
-              seq_lens_decoder,
-              step_idx,
-              not_need_stop,
-              batch_drop,
-              pre_ids,
-              accept_tokens,
-              accept_num,
-              base_model_seq_lens_this_time,
-              base_model_seq_lens_encoder,
-              base_model_seq_lens_decoder,
-              base_model_step_idx,
-              base_model_stop_flags,
-              base_model_is_block_step,
-              base_model_draft_tokens,
-              bsz,
-              num_model_step,
-              accept_tokens_len,
-              draft_tokens_len,
-              input_ids_len,
-              base_model_draft_tokens_len,
-              pre_ids_len);
-  } else {
-      draft_model_preprocess_kernel<BlockSize, TRCUNCATE_FIRST_TOKEN>
-          <<<1, BlockSize, 0, stream>>>(
-              draft_tokens,
-              input_ids,
-              stop_flags,
-              seq_lens_this_time,
-              seq_lens_encoder,
-              seq_lens_decoder,
-              step_idx,
-              not_need_stop,
-              batch_drop,
-              pre_ids,
-              accept_tokens,
-              accept_num,
-              base_model_seq_lens_this_time,
-              base_model_seq_lens_encoder,
-              base_model_seq_lens_decoder,
-              base_model_step_idx,
-              base_model_stop_flags,
-              base_model_is_block_step,
-              base_model_draft_tokens,
-              bsz,
-              num_model_step,
-              accept_tokens_len,
-              draft_tokens_len,
-              input_ids_len,
-              base_model_draft_tokens_len,
-              pre_ids_len);
-  }
-}
 
-void DispatchTokenMode(
+void DispatchRunner(
     const cudaStream_t &stream,
     int64_t* draft_tokens,
     int64_t* input_ids,
@@ -291,6 +261,7 @@ void DispatchTokenMode(
     int* seq_lens_decoder,
     int64_t* step_idx,
     bool* not_need_stop,
+    bool* is_block_step,
     bool* batch_drop,
     int64_t* pre_ids,
     const int64_t* accept_tokens,
@@ -310,75 +281,79 @@ void DispatchTokenMode(
     const int base_model_draft_tokens_len,
     const int pre_ids_len,
     const bool truncate_first_token,
-    const bool splitwise_prefill) {
-  if (truncate_first_token) {
-    DispatchRunner<true>(
-              stream,
-              draft_tokens,
-              input_ids,
-              stop_flags,
-              seq_lens_this_time,
-              seq_lens_encoder,
-              seq_lens_decoder,
-              step_idx,
-              not_need_stop,
-              batch_drop,
-              pre_ids,
-              accept_tokens,
-              accept_num,
-              base_model_seq_lens_this_time,
-              base_model_seq_lens_encoder,
-              base_model_seq_lens_decoder,
-              base_model_step_idx,
-              base_model_stop_flags,
-              base_model_is_block_step,
-              base_model_draft_tokens,
-              bsz,
-              num_model_step,
-              accept_tokens_len,
-              draft_tokens_len,
-              input_ids_len,
-              base_model_draft_tokens_len,
-              pre_ids_len,
-              splitwise_prefill
-    );
-  } else {
-    DispatchRunner<false>(
-              stream,
-              draft_tokens,
-              input_ids,
-              stop_flags,
-              seq_lens_this_time,
-              seq_lens_encoder,
-              seq_lens_decoder,
-              step_idx,
-              not_need_stop,
-              batch_drop,
-              pre_ids,
-              accept_tokens,
-              accept_num,
-              base_model_seq_lens_this_time,
-              base_model_seq_lens_encoder,
-              base_model_seq_lens_decoder,
-              base_model_step_idx,
-              base_model_stop_flags,
-              base_model_is_block_step,
-              base_model_draft_tokens,
-              bsz,
-              num_model_step,
-              accept_tokens_len,
-              draft_tokens_len,
-              input_ids_len,
-              base_model_draft_tokens_len,
-              pre_ids_len,
-              splitwise_prefill
-    );
-  }
+    const bool splitwise_prefill,
+    const bool kvcache_scheduler_v1) {
+  DISPATCH_BLOCKSIZE(512, {
+    DISPATCH_TRUNCATE_FIRST_TOKEN(truncate_first_token, TRUNCATE_FIRST_TOKEN, {
+      DISPATCH_KVCACHE_SCHEDULER(kvcache_scheduler_v1, KVCACHE_SCHEDULER_V1, {
+        DISPATCH_SPLITWISE_PREFILL(splitwise_prefill, SPLITWISE_PREFILL, {
+          if constexpr (SPLITWISE_PREFILL) {
+            process_splitwise_prefill<BlockSize, TRUNCATE_FIRST_TOKEN, KVCACHE_SCHEDULER_V1>
+                <<<1, BlockSize, 0, stream>>>(
+                    draft_tokens,
+                    input_ids,
+                    stop_flags,
+                    seq_lens_this_time,
+                    seq_lens_encoder,
+                    seq_lens_decoder,
+                    step_idx,
+                    not_need_stop,
+                    is_block_step,
+                    batch_drop,
+                    pre_ids,
+                    accept_tokens,
+                    accept_num,
+                    base_model_seq_lens_this_time,
+                    base_model_seq_lens_encoder,
+                    base_model_seq_lens_decoder,
+                    base_model_step_idx,
+                    base_model_stop_flags,
+                    base_model_is_block_step,
+                    base_model_draft_tokens,
+                    bsz,
+                    num_model_step,
+                    accept_tokens_len,
+                    draft_tokens_len,
+                    input_ids_len,
+                    base_model_draft_tokens_len,
+                    pre_ids_len);
+          } else {
+            draft_model_preprocess_kernel<BlockSize, TRUNCATE_FIRST_TOKEN, KVCACHE_SCHEDULER_V1>
+                <<<1, BlockSize, 0, stream>>>(
+                    draft_tokens,
+                    input_ids,
+                    stop_flags,
+                    seq_lens_this_time,
+                    seq_lens_encoder,
+                    seq_lens_decoder,
+                    step_idx,
+                    not_need_stop,
+                    is_block_step,
+                    batch_drop,
+                    pre_ids,
+                    accept_tokens,
+                    accept_num,
+                    base_model_seq_lens_this_time,
+                    base_model_seq_lens_encoder,
+                    base_model_seq_lens_decoder,
+                    base_model_step_idx,
+                    base_model_stop_flags,
+                    base_model_is_block_step,
+                    base_model_draft_tokens,
+                    bsz,
+                    num_model_step,
+                    accept_tokens_len,
+                    draft_tokens_len,
+                    input_ids_len,
+                    base_model_draft_tokens_len,
+                    pre_ids_len);
+          }
+        });
+      });
+    });
+  });
 }
 
-
-
-
 void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
                           const paddle::Tensor& input_ids,
                           const paddle::Tensor& stop_flags,
@@ -387,6 +362,7 @@ void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
                           const paddle::Tensor& seq_lens_decoder,
                           const paddle::Tensor& step_idx,
                           const paddle::Tensor& not_need_stop,
+                          const paddle::Tensor& is_block_step,
                           const paddle::Tensor& batch_drop,
                           const paddle::Tensor& pre_ids,
                           const paddle::Tensor& accept_tokens,
@@ -400,7 +376,8 @@ void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
                           const paddle::Tensor& base_model_draft_tokens,
                           const int num_model_step,
                           const bool truncate_first_token,
-                          const bool splitwise_prefill) {
+                          const bool splitwise_prefill,
+                          const bool kvcache_scheduler_v1) {
   int real_bsz = seq_lens_this_time.shape()[0];
   int accept_tokens_len = accept_tokens.shape()[1];
   int input_ids_len = input_ids.shape()[1];
@@ -412,36 +389,38 @@ void DraftModelPreprocess(const paddle::Tensor& draft_tokens,
   auto not_need_stop_gpu =
       not_need_stop.copy_to(seq_lens_this_time.place(), false);
 
-  DispatchTokenMode(
-              cu_stream,
-              const_cast<int64_t*>(draft_tokens.data<int64_t>()),
-              const_cast<int64_t*>(input_ids.data<int64_t>()),
-              const_cast<bool*>(stop_flags.data<bool>()),
-              const_cast<int*>(seq_lens_this_time.data<int>()),
-              const_cast<int*>(seq_lens_encoder.data<int>()),
-              const_cast<int*>(seq_lens_decoder.data<int>()),
-              const_cast<int64_t*>(step_idx.data<int64_t>()),
-              const_cast<bool*>(not_need_stop_gpu.data<bool>()),
-              const_cast<bool*>(batch_drop.data<bool>()),
-              const_cast<int64_t*>(pre_ids.data<int64_t>()),
-              accept_tokens.data<int64_t>(),
-              accept_num.data<int>(),
-              base_model_seq_lens_this_time.data<int>(),
-              base_model_seq_lens_encoder.data<int>(),
-              base_model_seq_lens_decoder.data<int>(),
-              base_model_step_idx.data<int64_t>(),
-              base_model_stop_flags.data<bool>(),
-              base_model_is_block_step.data<bool>(),
-              const_cast<int64_t*>(base_model_draft_tokens.data<int64_t>()),
-              real_bsz,
-              num_model_step,
-              accept_tokens_len,
-              draft_tokens_len,
-              input_ids_len,
-              base_model_draft_tokens_len,
-              pre_ids_len,
-              truncate_first_token,
-              splitwise_prefill);
+  DispatchRunner(
+      cu_stream,
+      const_cast<int64_t*>(draft_tokens.data<int64_t>()),
+      const_cast<int64_t*>(input_ids.data<int64_t>()),
+      const_cast<bool*>(stop_flags.data<bool>()),
+      const_cast<int*>(seq_lens_this_time.data<int>()),
+      const_cast<int*>(seq_lens_encoder.data<int>()),
+      const_cast<int*>(seq_lens_decoder.data<int>()),
+      const_cast<int64_t*>(step_idx.data<int64_t>()),
+      const_cast<bool*>(not_need_stop_gpu.data<bool>()),
+      const_cast<bool*>(is_block_step.data<bool>()),
+      const_cast<bool*>(batch_drop.data<bool>()),
+      const_cast<int64_t*>(pre_ids.data<int64_t>()),
+      accept_tokens.data<int64_t>(),
+      accept_num.data<int>(),
+      base_model_seq_lens_this_time.data<int>(),
+      base_model_seq_lens_encoder.data<int>(),
+      base_model_seq_lens_decoder.data<int>(),
+      base_model_step_idx.data<int64_t>(),
+      base_model_stop_flags.data<bool>(),
+      base_model_is_block_step.data<bool>(),
+      const_cast<int64_t*>(base_model_draft_tokens.data<int64_t>()),
+      real_bsz,
+      num_model_step,
+      accept_tokens_len,
+      draft_tokens_len,
+      input_ids_len,
+      base_model_draft_tokens_len,
+      pre_ids_len,
+      truncate_first_token,
+      splitwise_prefill,
+      kvcache_scheduler_v1);
 
   auto not_need_stop_cpu =
       not_need_stop_gpu.copy_to(not_need_stop.place(), false);
@@ -459,6 +438,7 @@ PD_BUILD_STATIC_OP(draft_model_preprocess)
              "seq_lens_decoder",
              "step_idx",
              "not_need_stop",
+             "is_block_step",
              "batch_drop",
              "pre_ids",
              "accept_tokens",
@@ -480,7 +460,7 @@ PD_BUILD_STATIC_OP(draft_model_preprocess)
               "not_need_stop_out",
               "batch_drop_out",
               "pre_ids_out"})
-    .Attrs({"num_model_step: int", "truncate_first_token: bool", "splitwise_prefill: bool"})
+    .Attrs({"num_model_step: int", "truncate_first_token: bool", "splitwise_prefill: bool", "kvcache_scheduler_v1: bool"})
     .SetInplaceMap({{"draft_tokens", "draft_tokens_out"},
                     {"input_ids", "input_ids_out"},
                     {"stop_flags", "stop_flags_out"},

custom_ops/gpu_ops/speculate_decoding/draft_model/eagle_get_hidden_states.cu

@@ -63,7 +63,7 @@ __global__ void ComputeOrderKernel(
           position_map[in_offset++] = out_offset++;
         }
         in_offset += cur_base_model_seq_lens_this_time - accept_num;
-// (liuzichang): Temperary Reserved for debug
+// (liuzichang): Temporary Reserved for debug
 //         if (accept_num <= actual_draft_token_num) /*Accept partial draft tokens*/ {
 // #ifdef DEBUG_EAGLE_KERNEL
 //         printf("batch %d: accept_num <= actual_draft_token_num \n", i);

custom_ops/gpu_ops/speculate_decoding/speculate_get_padding_offset.cu

@@ -139,6 +139,7 @@ std::vector<paddle::DataType> SpeculateGetPaddingOffsetInferDtype(
 PD_BUILD_STATIC_OP(speculate_get_padding_offset)
     .Inputs({"input_ids",
              "draft_tokens",
+             "cum_offsets"
              "token_num",
              "seq_len",
              "seq_lens_encoder"})

custom_ops/gpu_ops/speculate_decoding/speculate_schedule_cache.cu

@@ -0,0 +1,176 @@
+// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "helper.h"
+
+template <int THREADBLOCK_SIZE>
+__global__ void speculate_schedula_cache(
+                                    const int64_t *draft_tokens,
+                                    int *block_tables,
+                                    bool *stop_flags,
+                                    int *seq_lens_this_time,
+                                    int *seq_lens_decoder,
+                                    int *step_seq_lens_decoder,
+                                    int64_t *step_draft_tokens,
+                                    int *step_seq_lens_this_time,
+                                    int *accept_num,
+                                    int64_t *accept_tokens,
+                                    bool *is_block_step,
+                                    bool *not_need_stop,
+                                    const int64_t *stop_nums,
+                                    const int real_bsz,
+                                    const int max_bsz,
+                                    const int max_next_step_tokens,
+                                    const int draft_tokens_len,
+                                    const int accept_tokens_len,
+                                    const int block_size,
+                                    const int block_num_per_seq) {
+    const int bid = threadIdx.x;
+    int stop_flag_now_int = 0;
+    if (bid < real_bsz) {
+        if (!stop_flags[bid]) {
+            const int64_t *draft_tokens_now = draft_tokens + bid * draft_tokens_len;
+            int64_t *step_draft_tokens_now = step_draft_tokens + bid * draft_tokens_len;
+            int *block_table_now = block_tables + bid * block_num_per_seq;
+            int64_t *accept_tokens_now = accept_tokens + bid * accept_tokens_len;
+            const int max_possible_block_idx = (seq_lens_decoder[bid] + max_next_step_tokens) / block_size;
+            if (max_possible_block_idx < block_num_per_seq && block_table_now[max_possible_block_idx] == -1) {
+                is_block_step[bid] = true;
+                step_seq_lens_this_time[bid] = seq_lens_this_time[bid];
+                seq_lens_this_time[bid] = 0;
+                stop_flags[bid] = true;
+                stop_flag_now_int = 1;
+                step_seq_lens_decoder[bid] = seq_lens_decoder[bid];
+                seq_lens_decoder[bid] = 0;
+                accept_num[bid] = 0;
+                for (int i = 0; i < accept_tokens_len; i++) {
+                    accept_tokens_now[i] = -1;
+                }
+                for (int i = 0; i < draft_tokens_len; i++) {
+                    step_draft_tokens_now[i] = draft_tokens_now[i];
+                }
+            }
+        } else {
+            stop_flag_now_int = 1;
+        }
+    } else if (bid >= real_bsz && bid < max_bsz) {
+        stop_flag_now_int = 1;
+    }
+    __syncthreads();
+    typedef cub::BlockReduce<int64_t, THREADBLOCK_SIZE> BlockReduce;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+
+    // printf("stop_flag_now_int %d \n", stop_flag_now_int);
+    int64_t stop_sum = BlockReduce(temp_storage).Sum(stop_flag_now_int);
+
+    if (threadIdx.x == 0) {
+        // printf("stop_sum %d \n", stop_sum);
+        not_need_stop[0] = stop_sum < stop_nums[0];
+    }
+}
+
+void SpeculateScheduleCache(const paddle::Tensor &draft_tokens,
+                            const paddle::Tensor &block_tables,
+                            const paddle::Tensor &stop_flags,
+                            const paddle::Tensor &seq_lens_this_time,
+                            const paddle::Tensor &seq_lens_decoder,
+                            const paddle::Tensor &step_seq_lens_decoder,
+                            const paddle::Tensor &step_draft_tokens,
+                            const paddle::Tensor &step_seq_lens_this_time,
+                            const paddle::Tensor &accept_num,
+                            const paddle::Tensor &accept_tokens,
+                            const paddle::Tensor &is_block_step,
+                            const paddle::Tensor &not_need_stop,
+                            const paddle::Tensor &stop_nums,
+                            const int block_size,
+                            const int max_draft_tokens) {
+    const int real_bsz = seq_lens_this_time.shape()[0];
+    const int max_bsz = stop_flags.shape()[0];
+    const int accept_tokens_len = accept_tokens.shape()[1];
+    const int draft_token_len = draft_tokens.shape()[1];
+    const int block_num_per_seq = block_tables.shape()[1];
+
+    constexpr int BlockSize = 512;
+    const int max_next_step_tokens = 2 * max_draft_tokens + 2;
+
+    auto not_need_stop_gpu = not_need_stop.copy_to(stop_flags.place(), false);
+    speculate_schedula_cache<BlockSize><<<1, BlockSize, 0, seq_lens_this_time.stream()>>>(
+        draft_tokens.data<int64_t>(),
+        const_cast<int *>(block_tables.data<int>()),
+        const_cast<bool *>(stop_flags.data<bool>()),
+        const_cast<int *>(seq_lens_this_time.data<int>()),
+        const_cast<int *>(seq_lens_decoder.data<int>()),
+        const_cast<int *>(step_seq_lens_decoder.data<int>()),
+        const_cast<int64_t *>(step_draft_tokens.data<int64_t>()),
+        const_cast<int *>(step_seq_lens_this_time.data<int>()),
+        const_cast<int *>(accept_num.data<int>()),
+        const_cast<int64_t *>(accept_tokens.data<int64_t>()),
+        const_cast<bool *>(is_block_step.data<bool>()),
+        const_cast<bool *>(not_need_stop_gpu.data<bool>()),
+        stop_nums.data<int64_t>(),
+        real_bsz,
+        max_bsz,
+        max_next_step_tokens,
+        draft_token_len,
+        accept_tokens_len,
+        block_size,
+        block_num_per_seq
+    );
+
+    auto not_need_stop_cpu =
+        not_need_stop_gpu.copy_to(not_need_stop.place(), true);
+    bool *not_need_stop_data = const_cast<bool *>(not_need_stop.data<bool>());
+    not_need_stop_data[0] = not_need_stop_cpu.data<bool>()[0];
+}
+
+PD_BUILD_STATIC_OP(speculate_schedule_cache)
+    .Inputs({"draft_tokens",
+             "block_tables",
+             "stop_flags",
+             "seq_lens_this_time",
+             "seq_lens_decoder",
+             "step_seq_lens_decoder",
+             "step_draft_tokens",
+             "step_seq_lens_this_time",
+             "accept_num",
+             "accept_tokens",
+             "is_block_step",
+             "not_need_stop",
+             "stop_nums"})
+    .Attrs({"block_size: int", "max_draft_tokens: int"})
+    .Outputs({"draft_tokens_out",
+              "block_tables_out",
+              "stop_flags_out",
+              "seq_lens_this_time_out",
+              "seq_lens_decoder_out",
+              "step_seq_lens_decoder_out",
+              "step_draft_tokens_out",
+              "step_seq_lens_this_time_out",
+              "accept_num_out",
+              "accept_tokens_out",
+              "is_block_step_out",
+              "not_need_stop_out"})
+    .SetInplaceMap({{"draft_tokens", "draft_tokens_out"},
+                    {"block_tables", "block_tables_out"},
+                    {"stop_flags", "stop_flags_out"},
+                    {"seq_lens_this_time", "seq_lens_this_time_out"},
+                    {"seq_lens_decoder", "seq_lens_decoder_out"},
+                    {"step_seq_lens_decoder", "step_seq_lens_decoder_out"},
+                    {"step_draft_tokens", "step_draft_tokens_out"},
+                    {"step_seq_lens_this_time", "step_seq_lens_this_time_out"},
+                    {"accept_num", "accept_num_out"},
+                    {"accept_tokens", "accept_tokens_out"},
+                    {"is_block_step", "is_block_step_out"},
+                    {"not_need_stop", "not_need_stop_out"},})
+    .SetKernelFn(PD_KERNEL(SpeculateScheduleCache));

custom_ops/gpu_ops/speculate_decoding/speculate_set_value_by_flags_and_idx.cu

@@ -35,7 +35,7 @@ __global__ void speculate_set_value_by_flag_and_id(int64_t *pre_ids_all,
             accept_tokens + tid * max_draft_tokens;
         const int seq_len_dec = seq_lens_decoder[tid];
         const int seq_len_enc = seq_lens_encoder[tid];
-        if (seq_len_dec == 0 && seq_len_enc == 0) return;  // stoped
+        if (seq_len_dec == 0 && seq_len_enc == 0) return;  // stopped
         // printf("step_idx[tid] %d\n", step_idx[tid]);
         if (step_idx[tid] >= 0) {
             for (int i = 0; i < accept_num[tid]; i++) {

custom_ops/gpu_ops/speculate_decoding/speculate_step_reschedule.cu

@@ -295,7 +295,7 @@ void SpeculateStepSchedule(const paddle::Tensor &stop_flags,
             std::string inference_msg_id_env_str(inference_msg_id_env_p);
             inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
             if (inference_msg_id_from_env == 2) {
-                // 2 and -2 is perserve for no-output indication.
+                // 2 and -2 is preserve for no-output indication.
                 throw std::runtime_error(
                     " INFERENCE_MSG_ID cannot be 2, please use other number.");
             }

custom_ops/gpu_ops/step_reschedule.cu

@@ -283,7 +283,7 @@ void Schedule(const paddle::Tensor &stop_flags,
             std::string inference_msg_id_env_str(inference_msg_id_env_p);
             inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
             if (inference_msg_id_from_env == 2) {
-                // 2 and -2 is perserve for no-output indication.
+                // 2 and -2 is preserve for no-output indication.
                 throw std::runtime_error(
                     " INFERENCE_MSG_ID cannot be 2, please use other number.");
             }

custom_ops/gpu_ops/token_transfer.hpp

@@ -58,7 +58,7 @@ class TokenTransfer {
     }
 
     // once copy: cpu --> cpu
-    // arrary length should be (1 + MAX_BATCH)
+    // array length should be (1 + MAX_BATCH)
     bool GetBatchToken(int64_t *array) {
         if (Empty()) {
             return false;

custom_ops/gpu_ops/update_split_fuse_input.cu

@@ -75,10 +75,10 @@ void UpdateSplitFuseInputes(const paddle::Tensor& split_fuse_seq_lens,
                             const int max_seq_len,
                             const int max_batch_size,
                             const int split_fuse_size) {
-    dim3 girds;
-    girds.x = max_batch_size;
+    dim3 grids;
+    grids.x = max_batch_size;
     const int block_size = 128;
-    update_split_fuse_inputs_kernel<<<girds,
+    update_split_fuse_inputs_kernel<<<grids,
                                       block_size,
                                       0,
                                       input_ids.stream()>>>(

custom_ops/gpu_ops/w4afp8_gemm/w4afp8_gemm.cu

@@ -75,12 +75,8 @@ void DisPatchW4AFp8Gemm(
         const int64_t K,
         cudaStream_t stream) {
 
-    int kBlockN = (max_tokens + 15) / 16 * 16;
+    int kBlockN = 256;
     int TailN = 0;
-    if (kBlockN > 256) {
-        TailN = kBlockN % 256;
-        kBlockN = 256;
-    }
     if constexpr (std::is_same_v<OutputType, cutlass::bfloat16_t>) {
         GEMM_SWITCH_BF16(
             M, K, batch_size, token_padding_size, kBlockN, TailN,

custom_ops/iluvatar_ops/moe_dispatch.cu

@@ -110,7 +110,7 @@ void MoeDispatchKernel(const paddle::Tensor& input,
   if (group_moe) {
     paddle::Tensor softmax_max_prob_tensor =
         GetEmptyTensor({num_rows, moe_topk}, paddle::DataType::FLOAT32, place);
-    // (TODO: check fill sucess ?)
+    // (TODO: check fill success ?)
     paddle::experimental::fill(softmax_max_prob_tensor, 0.f);
     softmax_max_prob = softmax_max_prob_tensor.data<float>();
   }

custom_ops/setup_ops.py

@@ -204,7 +204,7 @@ if paddle.is_compiled_with_rocm():
         "gpu_ops/get_output_msg_with_topk.cc",
         "gpu_ops/save_output_msg_with_topk.cc",
         "gpu_ops/transfer_output.cc",
-        "gpu_ops/set_value_by_flags.cu",
+        "gpu_ops/set_value_by_flags_and_idx.cu",
         "gpu_ops/token_penalty_multi_scores.cu",
         "gpu_ops/stop_generation.cu",
         "gpu_ops/stop_generation_multi_ends.cu",
@@ -223,7 +223,7 @@ if paddle.is_compiled_with_rocm():
         "gpu_ops/speculate_decoding/speculate_get_output_padding_offset.cu",
         "gpu_ops/speculate_decoding/speculate_get_seq_lens_output.cu",
         "gpu_ops/speculate_decoding/speculate_save_output.cc",
-        "gpu_ops/speculate_decoding/speculate_set_value_by_flags.cu",
+        "gpu_ops/speculate_decoding/speculate_set_value_by_flags_and_idx.cu",
         "gpu_ops/speculate_decoding/speculate_step.cu",
         "gpu_ops/speculate_decoding/speculate_step_system_cache.cu",
         "gpu_ops/speculate_decoding/speculate_update_v3.cu",
@@ -261,7 +261,7 @@ elif paddle.is_compiled_with_cuda():
         "gpu_ops/save_output_msg_with_topk.cc",
         "gpu_ops/transfer_output.cc",
         "gpu_ops/set_mask_value.cu",
-        "gpu_ops/set_value_by_flags.cu",
+        "gpu_ops/set_value_by_flags_and_idx.cu",
         "gpu_ops/ngram_mask.cu",
         "gpu_ops/gather_idx.cu",
         "gpu_ops/get_output_ep.cc",
@@ -276,7 +276,7 @@ elif paddle.is_compiled_with_cuda():
         "gpu_ops/recover_decode_task.cu",
         "gpu_ops/step.cu",
         "gpu_ops/step_reschedule.cu",
-        "gpu_ops/fused_get_rope.cu",
+        "gpu_ops/fused_get_rotary_embedding.cu",
         "gpu_ops/get_padding_offset.cu",
         "gpu_ops/update_inputs.cu",
         "gpu_ops/update_inputs_beam.cu",
@@ -507,7 +507,7 @@ elif paddle.is_compiled_with_cuda():
         sources += find_end_files(fp8_auto_gen_directory, ".cu")
 
     if cc >= 90 and nvcc_version >= 12.0:
-        # Hopper optmized mla
+        # Hopper optimized mla
         sources += find_end_files("gpu_ops/mla_attn", ".cu")
         sources += ["gpu_ops/flash_mask_attn/flash_mask_attn.cu"]
         sources += find_end_files("gpu_ops/moba_attn/moba_decoder_attn/", ".cu")
@@ -560,7 +560,7 @@ elif paddle.is_compiled_with_custom_device("iluvatar_gpu"):
                 "gpu_ops/save_output_msg_with_topk.cc",
                 "gpu_ops/transfer_output.cc",
                 "gpu_ops/get_padding_offset.cu",
-                "gpu_ops/set_value_by_flags.cu",
+                "gpu_ops/set_value_by_flags_and_idx.cu",
                 "gpu_ops/rebuild_padding.cu",
                 "gpu_ops/update_inputs.cu",
                 "gpu_ops/stop_generation_multi_ends.cu",
@@ -609,7 +609,7 @@ elif paddle.device.is_compiled_with_custom_device("metax_gpu"):
         "gpu_ops/transfer_output.cc",
         "gpu_ops/save_with_output.cc",
         "gpu_ops/set_mask_value.cu",
-        "gpu_ops/set_value_by_flags.cu",
+        "gpu_ops/set_value_by_flags_and_idx.cu",
         "gpu_ops/ngram_mask.cu",
         "gpu_ops/gather_idx.cu",
         "gpu_ops/get_output_ep.cc",
@@ -618,7 +618,7 @@ elif paddle.device.is_compiled_with_custom_device("metax_gpu"):
         "gpu_ops/stop_generation.cu",
         "gpu_ops/stop_generation_multi_ends.cu",
         "gpu_ops/set_flags.cu",
-        "gpu_ops/fused_get_rope.cu",
+        "gpu_ops/fused_get_rotary_embedding.cu",
         "gpu_ops/get_padding_offset.cu",
         "gpu_ops/update_inputs.cu",
         "gpu_ops/update_inputs_beam.cu",

custom_ops/utils/auto_gen_w4afp8_gemm_kernel.py

@@ -88,6 +88,8 @@ gemm_case = [
     [8192, 3584, 8, 2048],  # eb45T ffn1
     [7168, 8192, 8, 0],  # eb45T ffn2
     [7168, 8192, 8, 2048],  # eb45T ffn2
+    [1792, 8192, 64, 0],  # eb45t ffn1
+    [8192, 896, 64, 0],  # eb45t ffn2
 ]
 
 dtype = ["BF16"]

custom_ops/xpu_ops/src/download_dependencies.sh

@@ -12,8 +12,8 @@ rm -rf "$THIRDPARTY_DIR"
 mkdir -p "$THIRDPARTY_DIR" || exit 1
 
 if [ "$1" == "stable" ]; then
-    version_xvllm="20250710"
-    version_xtdk="3.2.40.1"
+    version_xvllm="20250902"
+    version_xtdk="3.4.40.1"
 else
     version_xvllm="latest"
     version_xtdk="latest"

custom_ops/xpu_ops/src/ops/moe_layer.cc

@@ -67,7 +67,7 @@ std::vector<paddle::Tensor> MoeLayerKernel(
     const auto xtype = x.dtype();
     auto x_dims = x.shape();
     auto up_gate_proj_dims = up_gate_proj_weight.shape();
-    PD_CHECK(x_dims.size() == 2, "x_dims.size() shoud be 2.");
+    PD_CHECK(x_dims.size() == 2, "x_dims.size() should be 2.");
     PD_CHECK(up_gate_proj_dims.size() == 3, "up_gate_proj_dims.size() should be 3.");
     PD_CHECK(down_proj_in_scale.get_ptr() == nullptr, "down_proj_in_scale not support.");
     if (quant_method == "weight_only_int4") {

custom_ops/xpu_ops/src/ops/mtp_ops/speculate_step_reschedule.cc

@@ -122,7 +122,7 @@ void SpeculateStepSchedule(
       std::string inference_msg_id_env_str(inference_msg_id_env_p);
       inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
       if (inference_msg_id_from_env == 2) {
-        // 2 and -2 is perserve for no-output indication.
+        // 2 and -2 is preserve for no-output indication.
         throw std::runtime_error(
             " INFERENCE_MSG_ID cannot be 2, please use other number.");
       }

custom_ops/xpu_ops/src/ops/save_with_output_msg.cc

@@ -59,7 +59,7 @@ void SaveOutMmsg(const paddle::Tensor &x, const paddle::Tensor &not_need_stop,
         std::string inference_msg_id_env_str(inference_msg_id_env_p);
         inference_msg_id_from_env = std::stoi(inference_msg_id_env_str);
         if (inference_msg_id_from_env == 2) {
-            // 2 and -2 is perserve for no-output indication.
+            // 2 and -2 is preserve for no-output indication.
             throw std::runtime_error(
                 " INFERENCE_MSG_ID cannot be 2, please use other number.");
         }

custom_ops/xpu_ops/src/plugin/src/kernel/kunlun3cpp/eb_adjust_batch.xpu

@@ -4,7 +4,7 @@
 namespace xpu3 {
 namespace plugin {
 #define MAX_LM_SIZE 28672
-// One core has 32KB LM（gropu LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
+// One core has 32KB LM（group LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
 // the stack space
 #define MAX_BATCH 512
 #define ALIGNMENT 64

custom_ops/xpu_ops/src/plugin/src/kernel/kunlun3cpp/eb_gather_next_token.xpu

@@ -4,7 +4,7 @@
 namespace xpu3 {
 namespace plugin {
 #define MAX_LM_SIZE 28672
-// One core has 32KB LM（gropu LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
+// One core has 32KB LM（group LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
 // the stack space
 #define MAX_BATCH 512
 #define ALIGNMENT 64

custom_ops/xpu_ops/src/plugin/src/kernel/kunlun3cpp/quant2d_per_channel.xpu

@@ -8,7 +8,7 @@
 namespace xpu3 {
 namespace plugin {
 #define MAX_SM_SIZE 32768
-// One core has 32KB LM（gropu LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
+// One core has 32KB LM（group LM), MAX_LM_SIZE = (32 - 4)KB / 2 = 30720, 4KB is
 // the stack space
 #define MAX_BATCH 512
 #define BANK_CONFLICT_M 128

custom_ops/xpu_ops/test/test_weight_quantize_xpu.py

@@ -79,7 +79,7 @@ qw_pd_trans = paddle.transpose(qw_pd, [1, 0])
 # print("wscale_pd:\n{}".format(wscale_pd))
 # print("wscale_np:\n{}".format(wscale_np))
 
-# comparation
+# comparison
 print(f"wscale_pd, mean={wscale_pd.mean()}, std={wscale_pd.std()}")
 print(f"wscale_np, mean={wscale_np.mean()}, std={wscale_np.std()}")
 print(f"qw_np, mean={qw_np.astype(np.float32).mean()}, std={qw_np.astype(np.float32).std()}")

dockerfiles/Dockerfile.gpu

@@ -1,6 +1,6 @@
-FROM ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.1.0
-ARG PADDLE_VERSION=3.1.1
-ARG FD_VERSION=2.1.0
+FROM ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.2.0
+ARG PADDLE_VERSION=3.2.0
+ARG FD_VERSION=2.2.0
 
 ENV DEBIAN_FRONTEND=noninteractive
 

docs/best_practices/ERNIE-4.5-0.3B-Paddle.md

@@ -19,22 +19,23 @@ The minimum number of GPUs required to deploy `ERNIE-4.5-0.3B` on the following
 ### 1.2 Install fastdeploy
 - Installation: For detail, please refer to [Fastdeploy Installation](../get_started/installation/README.md).
 
-- Model Download，For detail, please refer to [Supported Models](../supported_models.md). **Please note that models with Paddle suffix need to be used for Fastdeploy**：
+- Model Download，For detail, please refer to [Supported Models](../supported_models.md).
 
 ## 2.How to Use
 ### 2.1 Basic: Launching the Service
 Start the service by following command:
 ```bash
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
        --model baidu/ERNIE-4.5-0.3B-Paddle \
        --tensor-parallel-size 1 \
        --quantization wint4 \
        --max-model-len 32768 \
-       --max-num-seqs 128
+       --max-num-seqs 128 \
+       --load_choices "default_v1"
 ```
 - `--quantization`: indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).
 - `--max-model-len`: Indicates the maximum number of tokens supported by the currently deployed service. The larger the value, the longer the context length the model can support, but the more GPU memory is occupied, which may affect the concurrency.
+- `--load_choices`: indicates the version of the loader. "default_v1" means enabling the v1 version of the loader, which has faster loading speed and less memory usage.
 
 For more parameter meanings and default settings, see [FastDeploy Parameter Documentation](../parameters.md)。
 
@@ -42,17 +43,14 @@ For more parameter meanings and default settings, see [FastDeploy Parameter Docu
 #### 2.2.1 Correctly set parameters that match the application scenario
 Evaluate average input length, average output length, and maximum context length
 - Set max-model-len according to the maximum context length. For example, if the average input length is 1000 and the output length is 30000, then it is recommended to set it to 32768
-- **Enable the service management global block**
-
-```
-export ENABLE_V1_KVCACHE_SCHEDULER=1
-```
 
 #### 2.2.2 Prefix Caching
 **Idea:** The core idea of Prefix Caching is to avoid repeated calculations by caching the intermediate calculation results of the input sequence (KV Cache), thereby speeding up the response speed of multiple requests with the same prefix. For details, refer to [prefix-cache](../features/prefix_caching.md)
 
 **How to enable:**
-Add the following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine.
+Since version 2.2 (including the develop branch), Prefix Caching has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
 ```
 --enable-prefix-caching
 --swap-space 50
@@ -61,7 +59,10 @@ Add the following lines to the startup parameters, where `--enable-prefix-cachin
 #### 2.2.3 Chunked Prefill
 **Idea:** This strategy is adopted to split the prefill stage request into small-scale sub-chunks, and execute them in batches mixed with the decode request. This can better balance the computation-intensive (Prefill) and memory-intensive (Decode) operations, optimize GPU resource utilization, reduce the computational workload and memory usage of a single Prefill, thereby reducing the peak memory usage and avoiding the problem of insufficient memory. For details, please refer to [Chunked Prefill](../features/chunked_prefill.md)
 
-**How to enable:** Add the following lines to the startup parameters
+**How to enable:**
+Since version 2.2 (including the develop branch), Chunked Prefill has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding
 ```
 --enable-chunked-prefill
 ```
@@ -79,7 +80,7 @@ Notes:
 
 - Usually, no additional parameters need to be set, but CUDAGraph will generate some additional memory overhead, which may need to be adjusted in some scenarios with limited memory. For detailed parameter adjustments, please refer to [GraphOptimizationBackend](../features/graph_optimization.md) for related configuration parameter descriptions
 
-#### 2.2.6 Rejection Sampling
+#### 2.2.5 Rejection Sampling
 **Idea:**
 Rejection sampling is to generate samples from a proposal distribution that is easy to sample, avoiding explicit sorting to increase the sampling speed, which has a significant improvement on small-sized models.
 

docs/best_practices/ERNIE-4.5-21B-A3B-Paddle.md

@@ -19,22 +19,23 @@ The minimum number of GPUs required to deploy `ERNIE-4.5-21B-A3B` on the followi
 ### 1.2 Install fastdeploy and prepare the model
 - Installation: For detail, please refer to [Fastdeploy Installation](../get_started/installation/README.md).
 
-- Model Download，For detail, please refer to [Supported Models](../supported_models.md). **Please note that models with Paddle suffix need to be used for Fastdeploy**：
+- Model Download，For detail, please refer to [Supported Models](../supported_models.md).
 
 ## 2.How to Use
 ### 2.1 Basic: Launching the Service
 Start the service by following command:
 ```bash
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
        --model baidu/ERNIE-4.5-21B-A3B-Paddle \
        --tensor-parallel-size 1 \
        --quantization wint4 \
        --max-model-len 32768 \
-       --max-num-seqs 128
+       --max-num-seqs 128 \
+       --load_choices "default_v1"
 ```
 - `--quantization`: indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).
 - `--max-model-len`: Indicates the maximum number of tokens supported by the currently deployed service. The larger the value, the longer the context length the model can support, but the more GPU memory is occupied, which may affect the concurrency.
+- `--load_choices`: indicates the version of the loader. "default_v1" means enabling the v1 version of the loader, which has faster loading speed and less memory usage.
 
 For more parameter meanings and default settings, see [FastDeploy Parameter Documentation](../parameters.md)。
 
@@ -42,17 +43,14 @@ For more parameter meanings and default settings, see [FastDeploy Parameter Docu
 #### 2.2.1 Correctly set parameters that match the application scenario
 Evaluate average input length, average output length, and maximum context length
 - Set max-model-len according to the maximum context length. For example, if the average input length is 1000 and the output length is 30000, then it is recommended to set it to 32768
-- **Enable the service management global block**
-
-```
-export ENABLE_V1_KVCACHE_SCHEDULER=1
-```
 
 #### 2.2.2 Prefix Caching
 **Idea:** The core idea of Prefix Caching is to avoid repeated calculations by caching the intermediate calculation results of the input sequence (KV Cache), thereby speeding up the response speed of multiple requests with the same prefix. For details, refer to [prefix-cache](../features/prefix_caching.md)
 
 **How to enable:**
-Add the following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
+Since version 2.2 (including the develop branch), Prefix Caching has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
 ```
 --enable-prefix-caching
 --swap-space 50
@@ -61,7 +59,10 @@ Add the following lines to the startup parameters, where `--enable-prefix-cachin
 #### 2.2.3 Chunked Prefill
 **Idea:** This strategy is adopted to split the prefill stage request into small-scale sub-chunks, and execute them in batches mixed with the decode request. This can better balance the computation-intensive (Prefill) and memory-intensive (Decode) operations, optimize GPU resource utilization, reduce the computational workload and memory usage of a single Prefill, thereby reducing the peak memory usage and avoiding the problem of insufficient memory. For details, please refer to [Chunked Prefill](../features/chunked_prefill.md)
 
-**How to enable:** Add the following lines to the startup parameters
+**How to enable:**
+Since version 2.2 (including the develop branch), Chunked Prefill has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding
 ```
 --enable-chunked-prefill
 ```
@@ -77,7 +78,9 @@ Add the following lines to the startup parameters
 ```
 Notes:
 1. MTP currently does not support simultaneous use with Prefix Caching, Chunked Prefill, and CUDAGraph.
-2. MTP currently does not support service management global blocks, i.e. do not run with `export ENABLE_V1_KVCACHE_SCHEDULER=1`
+   - Use `export FD_DISABLE_CHUNKED_PREFILL=1` to disable Chunked Prefill.
+   - When setting `speculative-config`, Prefix Caching will be automatically disabled.
+2. MTP currently does not support service management global blocks, When setting `speculative-config`, service management global blocks will be automatically disabled.
 3. MTP currently does not support rejection sampling, i.e. do not run with `export FD_SAMPLING_CLASS=rejection`
 
 #### 2.2.5 CUDAGraph
@@ -110,7 +113,6 @@ export FD_SAMPLING_CLASS=rejection
 # prefill
 export CUDA_VISIBLE_DEVICES=0,1,2,3
 export INFERENCE_MSG_QUEUE_ID=1315
-export FLAGS_max_partition_size=2048
 export FD_ATTENTION_BACKEND=FLASH_ATTN
 export FD_LOG_DIR="prefill_log"
 
@@ -130,7 +132,6 @@ python -m fastdeploy.entrypoints.openai.api_server --model baidu/ERNIE-4.5-21B-A
 # decode
 export CUDA_VISIBLE_DEVICES=4,5,6,7
 export INFERENCE_MSG_QUEUE_ID=1215
-export FLAGS_max_partition_size=2048
 export FD_LOG_DIR="decode_log"
 
 quant_type=block_wise_fp8

docs/best_practices/ERNIE-4.5-21B-A3B-Thinking.md

@@ -0,0 +1,96 @@
+# ERNIE-4.5-21B-A3B
+## Environmental Preparation
+### 1.1 Hardware requirements
+The minimum number of GPUs required to deploy `ERNIE-4.5-21B-A3B` on the following hardware for each quantization is as follows:
+
+| | WINT8 | WINT4 | FP8 |
+|-----|-----|-----|-----|
+|H800 80GB| 1 | 1 | 1 |
+|A800 80GB| 1 | 1 | / |
+|H20 96GB| 1 | 1 | 1 |
+|L20 48GB| 1 | 1 | 1 |
+|A30 40GB| 2 | 1 | / |
+
+**Tips:**
+1. To modify the number of deployment GPUs, specify `--tensor-parallel-size 2` in starting command.
+2. For hardware not listed in the table, you can estimate whether it can be deployed based on the GPU memory.
+3. ERNIE-4.5-21B-A3B-Thinking requires FastDeploy version >= 2.2.
+
+### 1.2 Install fastdeploy and prepare the model
+- Installation: For detail, please refer to [Fastdeploy Installation](../get_started/installation/README.md).
+
+- Model Download，For detail, please refer to [Supported Models](../supported_models.md).
+
+## 2.How to Use
+### 2.1 Basic: Launching the Service
+Start the service by following command:
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model baidu/ERNIE-4.5-21B-A3B-Thinking \
+       --load_choices "default_v1" \
+       --tensor-parallel-size 1 \
+       --max-model-len 131072 \
+       --quantization wint8 \
+       --reasoning-parser ernie_x1 \
+       --tool-call-parser ernie_x1 \
+       --max-num-seqs 32
+```
+- `--quantization`: Indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).
+- `--max-model-len`: Indicates the maximum number of tokens supported by the currently deployed service. The larger the value, the longer the context length the model can support, but the more GPU memory is occupied, which may affect the concurrency.
+- `--load_choices`: Indicates the version of the loader. "default_v1" means enabling the v1 version of the loader, which has faster loading speed and less memory usage.
+- `--reasoning-parser`, `--tool-call-parser`: Indicates the corresponding reasoning content and tool call parser.
+
+For more parameter meanings and default settings, see [FastDeploy Parameter Documentation](../parameters.md)。
+
+### 2.2 Advanced: How to get better performance
+#### 2.2.1 Correctly set parameters that match the application scenario
+Evaluate average input length, average output length, and maximum context length
+- Set max-model-len according to the maximum context length. For example, if the average input length is 2000 and the output length is 80000, then it is recommended to set it to 131072
+
+#### 2.2.2 Prefix Caching
+**Idea:** The core idea of Prefix Caching is to avoid repeated calculations by caching the intermediate calculation results of the input sequence (KV Cache), thereby speeding up the response speed of multiple requests with the same prefix. For details, refer to [prefix-cache](../features/prefix_caching.md)
+
+**How to enable:**
+Since version 2.2 (including the develop branch), Prefix Caching has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
+```
+--enable-prefix-caching
+--swap-space 50
+```
+
+#### 2.2.3 Chunked Prefill
+**Idea:** This strategy is adopted to split the prefill stage request into small-scale sub-chunks, and execute them in batches mixed with the decode request. This can better balance the computation-intensive (Prefill) and memory-intensive (Decode) operations, optimize GPU resource utilization, reduce the computational workload and memory usage of a single Prefill, thereby reducing the peak memory usage and avoiding the problem of insufficient memory. For details, please refer to [Chunked Prefill](../features/chunked_prefill.md)
+
+**How to enable:**
+Since version 2.2 (including the develop branch), Chunked Prefill has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding
+```
+--enable-chunked-prefill
+```
+
+#### 2.2.4 CUDAGraph
+**Idea:**
+CUDAGraph is a GPU computing acceleration technology provided by NVIDIA. It achieves efficient execution and optimization of GPU tasks by capturing CUDA operation sequences into a graph structure. The core idea of CUDAGraph is to encapsulate a series of GPU computing and memory operations into a re-executable graph, thereby reducing CPU-GPU communication overhead, reducing kernel startup latency, and improving overall computing performance.
+
+**How to enable:**
+Add the following lines to the startup parameters
+```
+--use-cudagraph
+```
+Notes:
+- Usually, no additional parameters need to be set, but CUDAGraph will generate some additional memory overhead, which may need to be adjusted in some scenarios with limited memory. For detailed parameter adjustments, please refer to [GraphOptimizationBackend](../features/graph_optimization.md) for related configuration parameter descriptions
+
+#### 2.2.5 Rejection Sampling
+**Idea:**
+Rejection sampling is to generate samples from a proposal distribution that is easy to sample, avoiding explicit sorting to increase the sampling speed, which has a significant improvement on small-sized models.
+
+**How to enable:**
+Add the following environment variables before starting
+```
+export FD_SAMPLING_CLASS=rejection
+```
+
+## FAQ
+If you encounter any problems during use, you can refer to [FAQ](./FAQ.md).

docs/best_practices/ERNIE-4.5-300B-A47B-Paddle.md

@@ -16,22 +16,23 @@ The minimum number of GPUs required to deploy `ERNIE-4.5-300B-A47B` on the follo
 ### 1.2 Install fastdeploy
 - Installation: For detail, please refer to [Fastdeploy Installation](../get_started/installation/README.md).
 
-- Model Download，For detail, please refer to [Supported Models](../supported_models.md). **Please note that models with Paddle suffix need to be used for Fastdeploy**：
+- Model Download，For detail, please refer to [Supported Models](../supported_models.md).
 
 ## 2.How to Use
 ### 2.1 Basic: Launching the Service
 Start the service by following command:
 ```bash
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
        --model baidu/ERNIE-4.5-300B-A47B-Paddle \
        --tensor-parallel-size 8 \
        --quantization wint4 \
        --max-model-len 32768 \
-       --max-num-seqs 128
+       --max-num-seqs 128 \
+       --load_choices "default_v1"
 ```
 - `--quantization`: indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).
 - `--max-model-len`: Indicates the maximum number of tokens supported by the currently deployed service. The larger the value, the longer the context length the model can support, but the more GPU memory is occupied, which may affect the concurrency.
+- `--load_choices`: indicates the version of the loader. "default_v1" means enabling the v1 version of the loader, which has faster loading speed and less memory usage.
 
 For more parameter meanings and default settings, see [FastDeploy Parameter Documentation](../parameters.md)。
 
@@ -39,17 +40,14 @@ For more parameter meanings and default settings, see [FastDeploy Parameter Docu
 #### 2.2.1 Correctly set parameters that match the application scenario
 Evaluate average input length, average output length, and maximum context length
 - Set max-model-len according to the maximum context length. For example, if the average input length is 1000 and the output length is 30000, then it is recommended to set it to 32768
-- **Enable the service management global block**
-
-```
-export ENABLE_V1_KVCACHE_SCHEDULER=1
-```
 
 #### 2.2.2 Prefix Caching
 **Idea:** The core idea of Prefix Caching is to avoid repeated calculations by caching the intermediate calculation results of the input sequence (KV Cache), thereby speeding up the response speed of multiple requests with the same prefix. For details, refer to [prefix-cache](../features/prefix_caching.md)
 
 **How to enable:**
-Add the following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
+Since version 2.2 (including the develop branch), Prefix Caching has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding following lines to the startup parameters, where `--enable-prefix-caching` enables prefix caching, and `--swap-space` enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine. The recommended value is `(total machine memory - model size) * 20%`. If the service fails to start because other programs are occupying memory, try reducing the `--swap-space` value.
 ```
 --enable-prefix-caching
 --swap-space 50
@@ -58,7 +56,10 @@ Add the following lines to the startup parameters, where `--enable-prefix-cachin
 #### 2.2.3 Chunked Prefill
 **Idea:** This strategy is adopted to split the prefill stage request into small-scale sub-chunks, and execute them in batches mixed with the decode request. This can better balance the computation-intensive (Prefill) and memory-intensive (Decode) operations, optimize GPU resource utilization, reduce the computational workload and memory usage of a single Prefill, thereby reducing the peak memory usage and avoiding the problem of insufficient memory. For details, please refer to [Chunked Prefill](../features/chunked_prefill.md)
 
-**How to enable:** Add the following lines to the startup parameters
+**How to enable:**
+Since version 2.2 (including the develop branch), Chunked Prefill has been enabled by default.
+
+For versions 2.1 and earlier, you need to enable it manually by adding
 ```
 --enable-chunked-prefill
 ```
@@ -74,7 +75,9 @@ Add the following lines to the startup parameters
 ```
 Notes:
 1. MTP currently does not support simultaneous use with Prefix Caching, Chunked Prefill, and CUDAGraph.
-2. MTP currently does not support service management global blocks, i.e. do not run with `export ENABLE_V1_KVCACHE_SCHEDULER=1`
+   - Use `export FD_DISABLE_CHUNKED_PREFILL=1` to disable Chunked Prefill.
+   - When setting `speculative-config`, Prefix Caching will be automatically disabled.
+2. MTP currently does not support service management global blocks, When setting `speculative-config`, service management global blocks will be automatically disabled.
 3. MTP currently does not support rejection sampling, i.e. do not run with `export FD_SAMPLING_CLASS=rejection`
 
 #### 2.2.5 W4A8C8 Quantization
@@ -87,6 +90,9 @@ Just specify the corresponding model name in the startup command, `baidu/ERNIE-4
 --model baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle
 ```
 
+Note:
+- W4A8C8 quantized models are not supported when loaded via `--load_choices "default_v1"`.
+
 #### 2.2.6 Rejection Sampling
 **Idea:**
 Rejection sampling is to generate samples from a proposal distribution that is easy to sample, avoiding explicit sorting to increase the sampling speed, which has a significant improvement on small-sized models.

docs/best_practices/ERNIE-4.5-VL-28B-A3B-Paddle.md

@@ -18,15 +18,10 @@ The minimum number of cards required for deployment on the following hardware is
 
 Installation process reference documentation [FastDeploy GPU Install](../get_started/installation/nvidia_gpu.md)
 
-> ⚠️ Precautions:
-> - FastDeploy only supports models in Paddle format – please ensure to download models with the `-Paddle` file extension.
-> - The model name will trigger an automatic download. If the model has already been downloaded, you can directly use the absolute path to the model's download location.
-
 ## 2.How to Use
 ### 2.1 Basic: Launching the Service
 **Example 1:** Deploying a 32K Context Service on a Single RTX 4090 GPU
 ```shell
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
   --model baidu/ERNIE-4.5-VL-28B-A3B-Paddle \
   --port 8180 \
@@ -38,14 +33,11 @@ python -m fastdeploy.entrypoints.openai.api_server \
   --limit-mm-per-prompt '{"image": 100, "video": 100}' \
   --reasoning-parser ernie-45-vl \
   --gpu-memory-utilization 0.9 \
-  --enable-chunked-prefill \
   --max-num-batched-tokens 384 \
-  --quantization wint4 \
-  --enable-mm
+  --quantization wint4
 ```
 **Example 2:** Deploying a 128K Context Service on Dual H800 GPUs
 ```shell
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
   --model baidu/ERNIE-4.5-VL-28B-A3B-Paddle \
   --port 8180 \
@@ -57,14 +49,10 @@ python -m fastdeploy.entrypoints.openai.api_server \
   --limit-mm-per-prompt '{"image": 100, "video": 100}' \
   --reasoning-parser ernie-45-vl \
   --gpu-memory-utilization 0.9 \
-  --enable-chunked-prefill \
   --max-num-batched-tokens 384 \
-  --quantization wint4 \
-  --enable-mm
+  --quantization wint4
 ```
 
-> ⚠️ For versions 2.1 and above, the new scheduler needs to be enabled via an environment variable `ENABLE_V1_KVCACHE_SCHEDULER=1`. Otherwise, some requests may be truncated before reaching the maximum length or return empty results.
-
 An example is a set of configurations that can run stably while also delivering relatively good performance. If you have further requirements for precision or performance, please continue reading the content below.
 ### 2.2 Advanced: How to Achieve Better Performance
 
@@ -92,8 +80,8 @@ An example is a set of configurations that can run stably while also delivering
 
 #### 2.2.2 Chunked Prefill
 - **Parameters：** `--enable-chunked-prefill`
-- **Description：** Enabling `chunked prefill` can **reduce peak GPU memory usage** and **improve service throughput**.
-- **Other relevant configurations**:
+- **Description：** Enabling `chunked prefill` can reduce peak GPU memory usage and improve service throughput. Version 2.2 has **enabled by default**; for versions prior to 2.2, you need to enable it manually—refer to the best practices documentation for 2.1.
+- **Relevant configurations**:
 
     `--max-num-batched-tokens`：Limit the maximum number of tokens per chunk, with a recommended setting of 384.
 
@@ -115,12 +103,7 @@ An example is a set of configurations that can run stably while also delivering
 - **Description：** Rejection sampling involves generating samples from a proposal distribution that is easy to sample from, thereby avoiding explicit sorting and achieving an effect of improving sampling speed, which can enhance inference performance.
 - **Recommendation：** This is a relatively aggressive optimization strategy that affects the results, and we are still conducting comprehensive validation of its impact. If you have high performance requirements and can accept potential compromises in results, you may consider enabling this strategy.
 
-> **Attention Hyperparameter：**`FLAGS_max_partition_size=1024`
-- **Description：** The hyperparameters for the Append Attention (default) backend have been tested on commonly used datasets, and our results show that setting it to 1024 can significantly improve decoding speed, especially in long-text scenarios.
-- **Recommendation：** In the future, it will be modified to an automatic adjustment mechanism. If you have high performance requirements, you may consider enabling it.
-
 ## 3. FAQ
-**Note:** Deploying multimodal services requires adding parameters to the configuration `--enable-mm`.
 
 ### 3.1 Out of Memory
 If the service prompts "Out of Memory" during startup, please try the following solutions:

docs/best_practices/ERNIE-4.5-VL-424B-A47B-Paddle.md

@@ -15,15 +15,10 @@ The minimum number of cards required for deployment on the following hardware is
 
 Installation process reference documentation [FastDeploy GPU Install](../get_started/installation/nvidia_gpu.md)
 
-> ⚠️ Precautions:
-> - FastDeploy only supports models in Paddle format – please ensure to download models with the `-Paddle` file extension.
-> - The model name will trigger an automatic download. If the model has already been downloaded, you can directly use the absolute path to the model's download location.
-
 ## 2.How to Use
 ### 2.1 Basic: Launching the Service
 **Example 1:** Deploying a 128K context service on 8x H800 GPUs.
 ```shell
-export ENABLE_V1_KVCACHE_SCHEDULER=1
 python -m fastdeploy.entrypoints.openai.api_server \
   --model baidu/ERNIE-4.5-VL-424B-A47B-Paddle \
   --port 8180 \
@@ -34,15 +29,11 @@ python -m fastdeploy.entrypoints.openai.api_server \
   --max-num-seqs 16 \
   --limit-mm-per-prompt '{"image": 100, "video": 100}' \
   --reasoning-parser ernie-45-vl \
-  --gpu-memory-utilization 0.8 \
-  --enable-chunked-prefill \
+  --gpu-memory-utilization 0.85 \
   --max-num-batched-tokens 384 \
-  --quantization wint4 \
-  --enable-mm
+  --quantization wint4
 ```
 
-> ⚠️ For versions 2.1 and above, the new scheduler needs to be enabled via an environment variable `ENABLE_V1_KVCACHE_SCHEDULER=1`. Otherwise, some requests may be truncated before reaching the maximum length or return empty results.
-
 An example is a set of configurations that can run stably while also delivering relatively good performance. If you have further requirements for precision or performance, please continue reading the content below.
 ### 2.2 Advanced: How to Achieve Better Performance
 
@@ -70,8 +61,8 @@ An example is a set of configurations that can run stably while also delivering
 
 #### 2.2.2 Chunked Prefill
 - **Parameters：** `--enable-chunked-prefill`
-- **Description：** Enabling `chunked prefill` can **reduce peak GPU memory usage** and **improve service throughput**.
-- **Other relevant configurations**:
+- **Description：** Enabling `chunked prefill` can reduce peak GPU memory usage and improve service throughput. Version 2.2 has **enabled by default**; for versions prior to 2.2, you need to enable it manually—refer to the best practices documentation for 2.1.
+- **Relevant configurations**:
 
     `--max-num-batched-tokens`：Limit the maximum number of tokens per chunk, with a recommended setting of 384.
 
@@ -93,12 +84,7 @@ An example is a set of configurations that can run stably while also delivering
 - **Description：** Rejection sampling involves generating samples from a proposal distribution that is easy to sample from, thereby avoiding explicit sorting and achieving an effect of improving sampling speed, which can enhance inference performance.
 - **Recommendation：** This is a relatively aggressive optimization strategy that affects the results, and we are still conducting comprehensive validation of its impact. If you have high performance requirements and can accept potential compromises in results, you may consider enabling this strategy.
 
-> **Attention Hyperparameter：**`FLAGS_max_partition_size=1024`
-- **Description：** The hyperparameters for the Append Attention (default) backend have been tested on commonly used datasets, and our results show that setting it to 1024 can significantly improve decoding speed, especially in long-text scenarios.
-- **Recommendation：** In the future, it will be modified to an automatic adjustment mechanism. If you have high performance requirements, you may consider enabling it.
-
 ## 3. FAQ
-**Note:** Deploying multimodal services requires adding parameters to the configuration `--enable-mm`.
 
 ### 3.1 Out of Memory
 If the service prompts "Out of Memory" during startup, please try the following solutions:

docs/best_practices/README.md

@@ -3,5 +3,6 @@
 - [ERNIE-4.5-0.3B-Paddle.md](ERNIE-4.5-0.3B-Paddle.md)
 - [ERNIE-4.5-21B-A3B-Paddle.md](ERNIE-4.5-21B-A3B-Paddle.md)
 - [ERNIE-4.5-300B-A47B-Paddle.md](ERNIE-4.5-300B-A47B-Paddle.md)
+- [ERNIE-4.5-21B-A3B-Thinking.md](ERNIE-4.5-21B-A3B-Thinking.md)
 - [ERNIE-4.5-VL-28B-A3B-Paddle](ERNIE-4.5-VL-28B-A3B-Paddle.md)
 - [ERNIE-4.5-VL-424B-A47B-Paddle](ERNIE-4.5-VL-424B-A47B-Paddle.md)