test(npu): remove obsolete npu pr nightly cases, move accuracy cases to full (#37990)

Co-authored-by: Sugar920 <Sugar920@users.noreply.github.com>
Co-authored-by: Claude Code <noreply@anthropic.com>

.github/workflows/_npu-pr-test-stage.yml

@@ -186,8 +186,6 @@ jobs:
             }
 
       - name: Install dependencies
-        # Only PR jobs install dependencies
-        if: ${{ inputs.is_nightly_pipeline_job != true }}
         env:
           TORCH_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/whl/cpu"
           PYPI_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
@@ -195,17 +193,31 @@ jobs:
           GITHUB_PROXY_URL: "https://gh-proxy.test.osinfra.cn/"
           RUSTUP_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local:8082"
         run: |
-          # speed up by using infra cache services
+          set -euo pipefail
+          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
+          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
+
           CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
           sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
           pip config set global.index-url http://${CACHING_URL}/pypi/simple
           pip config set global.trusted-host "${CACHING_URL}"
 
-          bash scripts/ci/npu/npu_ci_install_dependency.sh ${{ inputs.npu_device_type }}
+          if [[ "${{ inputs.is_nightly_pipeline_job }}" != "true" ]]; then
-          # copy required file from our daily cache
+            bash scripts/ci/npu/npu_ci_install_dependency.sh ${{ inputs.npu_device_type }}
-          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
+
-          # copy gsm8k dataset
+          else
-          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
+            sglang_pkg_path=/sgl-workspace/sglang/python
+            ascend_test_util_path=${sglang_pkg_path}/sglang/test/ascend
+            mkdir -p ${ascend_test_util_path}
+            mv ${ascend_test_util_path} ${ascend_test_util_path}_bak
+            cp -r $(pwd)/python/sglang/test/ascend ${ascend_test_util_path}
+
+            pip install sentence_transformers zss "wandb>=0.16.0" tenacity==8.3.0 loguru openpyxl latex2sympy2 zstandard transformers-stream-generator tqdm-multiprocess pycocoevalcap
+            pip install yt-dlp sentencepiece==0.1.99 nltk av ftfy sqlitedict==2.1.0 "sacrebleu>=1.5.0" pytablewriter "peft>=0.2.0" "accelerate>=0.29.1"
+            pip install jsonlines "evaluate>=0.4.0" numexpr dotenv
+
+            hf download MMMU/MMMU --repo-type dataset
+          fi
 
           # install sglang_router
           apt-get install -y libssl-dev

.github/workflows/full-test-npu.yml

@@ -18,16 +18,16 @@ on:
         required: false
         type: string
         default: 'all'
+      image_a2:
+        description: 'The a2 running docker image of the test task.'
+        required: false
+        type: string
+        default: 'swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:main-cann9.0.0-910b'
       image_a3:
-        description: 'The a3 running docker image of the test task.'
+        description: 'The a3 running docker image of the test task. Must be an image with sglang preinstalled (e.g. lmsysorg/sglang:*a3*).'
         required: false
         type: string
-        default: 'swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:9.0.0-a3-ubuntu22.04-py3.11'
+        default: 'swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:main-cann9.0.0-a3'
-      skip_install_flag:
-        description: 'Indicates whether to skip the installation of sglang, defaulting to false.'
-        required: false
-        type: string
-        default: 'false'
 
 concurrency:
   group: full-test-npu-${{ inputs.ref || github.ref }}
@@ -39,8 +39,8 @@ jobs:
     outputs:
       ref: ${{ steps.set-vars.outputs.ref }}
       job_filter: ${{ steps.set-vars.outputs.job_filter }}
+      image_a2: ${{ steps.set-vars.outputs.image_a2 }}
       image_a3: ${{ steps.set-vars.outputs.image_a3 }}
-      skip_install_flag: ${{ steps.set-vars.outputs.skip_install_flag }}
     steps:
       # When triggered by PR, no inputs parameters are used. The latest community code is tested by default.
       - name: Set image config
@@ -58,16 +58,16 @@ jobs:
             echo "job_filter=${{ inputs.job_filter }}" >> $GITHUB_OUTPUT
           fi
 
-          if [ -z "${{ inputs.image_a3 }}" ]; then
+          if [ -z "${{ inputs.image_a2 }}" ]; then
-            echo "image_a3=swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:8.5.0-a3-ubuntu22.04-py3.11" >> $GITHUB_OUTPUT
+            echo "image_a2=swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:main-cann9.0.0-910b" >> $GITHUB_OUTPUT
           else
-            echo "image_a3=${{ inputs.image_a3 }}" >> $GITHUB_OUTPUT
+            echo "image_a2=${{ inputs.image_a2 }}" >> $GITHUB_OUTPUT
           fi
 
-          if [ -z "${{ inputs.skip_install_flag }}" ]; then
+          if [ -z "${{ inputs.image_a3 }}" ]; then
-            echo "skip_install_flag=false" >> $GITHUB_OUTPUT
+            echo "image_a3=swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:main-cann9.0.0-a3" >> $GITHUB_OUTPUT
           else
-            echo "skip_install_flag=${{ inputs.skip_install_flag }}" >> $GITHUB_OUTPUT
+            echo "image_a3=${{ inputs.image_a3 }}" >> $GITHUB_OUTPUT
           fi
 
   nighly-test-npu:
@@ -79,269 +79,258 @@ jobs:
       ref:  ${{ needs.set-image-config.outputs.ref }}
       job_filter: ${{ needs.set-image-config.outputs.job_filter }}
       image_a3: ${{ needs.set-image-config.outputs.image_a3 }}
-      skip_install_flag: ${{ needs.set-image-config.outputs.skip_install_flag }}
     secrets: inherit
 
-  full-1-npu-a3:
+  full-4-npu-a2:
-    needs: [set-image-config]
+    name: full-4-npu-a2
     if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-    runs-on: linux-aarch64-a3-2
+    needs: [set-image-config]
-    container:
+    uses: ./.github/workflows/_npu-single-node-test-stage.yml
+    with:
+      runner: linux-aarch64-a2-4
+      test_type: 'perf'
+      test_suite: full-4-npu-a2
+      is_nightly_pipeline_job: true
+      skip_pr_test_health_check: 'true'
+      image: ${{ needs.set-image-config.outputs.image_a2 }}
+      device_type_for_deps: '910b'
+
+  full-1-npu-a3:
+    name: full-1-npu-a3
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
+    needs: [set-image-config]
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-1-npu-a3
+      runner_config: linux-aarch64-a3-2
       image: ${{ needs.set-image-config.outputs.image_a3 }}
-    steps:
+      run_timeout_minutes: '240'
-      - name: Checkout code
+      timeout_per_file: '3600'
-        uses: actions/checkout@v4
+      ref: ${{ needs.set-image-config.outputs.ref }}
-        with:
+      is_nightly_pipeline_job: true
-          ref: ${{ needs.set-image-config.outputs.ref || github.ref }}
+      skip_pr_test_health_check: 'true'
-
+    secrets: inherit
-      - name: Install dependencies
-        env:
-          TORCH_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/whl/cpu"
-          PYPI_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
-          UV_INDEX_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
-          GITHUB_PROXY_URL: "https://gh-proxy.test.osinfra.cn/"
-        run: |
-          # speed up by using infra cache services
-          CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
-          sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
-          pip config set global.index-url http://${CACHING_URL}/pypi/simple
-          pip config set global.trusted-host "${CACHING_URL}"
-
-          if [ ${{ needs.set-image-config.outputs.skip_install_flag }} != "true" ];then
-            bash scripts/ci/npu/npu_ci_install_dependency.sh a3
-          fi
-
-          # copy required file from our daily cache
-          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
-          # copy gsm8k dataset
-          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
-
-      - name: Print Log Information
-        run: |
-          bash scripts/ci/npu/npu_log_print.sh
-
-      - name: Run test
-        timeout-minutes: 240
-        env:
-          SGLANG_USE_MODELSCOPE: true
-          SGLANG_IS_IN_CI: true
-          HF_ENDPOINT: https://hf-mirror.com
-          TORCH_EXTENSIONS_DIR: /tmp/torch_extensions
-          PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
-          STREAMS_PER_DEVICE: 32
-        run: |
-          pip install sglang_router
-          hf download lmms-lab/MMMU --repo-type dataset
-          pip install sentence_transformers torchaudio==2.8.0
-          pip install protobuf==6.31.1 zss pre-commit wandb>=0.16.0 tenacity==8.3.0 loguru openpyxl latex2sympy2 zstandard transformers-stream-generator tqdm-multiprocess pycocoevalcap
-          pip install yt-dlp sentencepiece==0.1.99 nltk av ftfy sqlitedict==2.1.0 sacrebleu>=1.5.0 pytablewriter black==24.1.0 isort==5.13.2 peft>=0.2.0 accelerate>=0.29.1
-          pip install jsonlines httpx==0.25.0 evaluate>=0.4.0 datasets==2.16.1 numexpr xgrammar==0.2.1 numpy==1.26.4 dotenv
-          git clone --branch v0.3.3 --depth 1 https://github.com/EvolvingLMMs-Lab/lmms-eval.git
-          cd ./lmms-eval
-          nohup pip install . > lmmslog.txt 2>&1 &
-          sleep 120
-          export PYTHONPATH=$PYTHONPATH:$(pwd)
-          cd ../
-          cd test
-          python3 run_suite.py --hw npu --suite full-1-npu-a3 --nightly --continue-on-error --timeout-per-file 3600
 
   full-2-npu-a3:
-    needs: [set-image-config]
+    name: full-2-npu-a3
     if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-    runs-on: linux-aarch64-a3-2
+    needs: [set-image-config]
-    container:
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-2-npu-a3
+      runner_config: linux-aarch64-a3-2
       image: ${{ needs.set-image-config.outputs.image_a3 }}
-    steps:
+      run_timeout_minutes: '240'
-      - name: Checkout code
+      timeout_per_file: '3600'
-        uses: actions/checkout@v4
+      ref: ${{ needs.set-image-config.outputs.ref }}
-        with:
+      is_nightly_pipeline_job: true
-          ref: ${{ needs.set-image-config.outputs.ref || github.ref }}
+      skip_pr_test_health_check: 'true'
-
+    secrets: inherit
-      - name: Install dependencies
-        env:
-          TORCH_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/whl/cpu"
-          PYPI_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
-          UV_INDEX_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
-          GITHUB_PROXY_URL: "https://gh-proxy.test.osinfra.cn/"
-        run: |
-          # speed up by using infra cache services
-          CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
-          sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
-          pip config set global.index-url http://${CACHING_URL}/pypi/simple
-          pip config set global.trusted-host "${CACHING_URL}"
-
-          if [ ${{ needs.set-image-config.outputs.skip_install_flag }} != "true" ];then
-            bash scripts/ci/npu/npu_ci_install_dependency.sh a3
-          fi
-
-          # copy required file from our daily cache
-          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
-          # copy gsm8k dataset
-          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
-
-      - name: Print Log Information
-        run: |
-          bash scripts/ci/npu/npu_log_print.sh
-
-      - name: Run test
-        timeout-minutes: 240
-        env:
-          SGLANG_USE_MODELSCOPE: true
-          SGLANG_IS_IN_CI: true
-          HF_ENDPOINT: https://hf-mirror.com
-          TORCH_EXTENSIONS_DIR: /tmp/torch_extensions
-          PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
-          STREAMS_PER_DEVICE: 32
-        run: |
-          pip install sglang_router
-          hf download lmms-lab/MMMU --repo-type dataset
-          pip install sentence_transformers torchaudio==2.8.0
-          pip install protobuf==6.31.1 zss pre-commit wandb>=0.16.0 tenacity==8.3.0 loguru openpyxl latex2sympy2 zstandard transformers-stream-generator tqdm-multiprocess pycocoevalcap
-          pip install yt-dlp sentencepiece==0.1.99 nltk av ftfy sqlitedict==2.1.0 sacrebleu>=1.5.0 pytablewriter black==24.1.0 isort==5.13.2 peft>=0.2.0 accelerate>=0.29.1
-          pip install jsonlines httpx==0.25.0 evaluate>=0.4.0 datasets==2.16.1 numexpr xgrammar==0.2.1 numpy==1.26.4 dotenv
-          git clone --branch v0.3.3 --depth 1 https://github.com/EvolvingLMMs-Lab/lmms-eval.git
-          cd ./lmms-eval
-          nohup pip install . > lmmslog.txt 2>&1 &
-          sleep 120
-          export PYTHONPATH=$PYTHONPATH:$(pwd)
-          cd ../
-          cd test
-          python3 run_suite.py --hw npu --suite full-2-npu-a3 --nightly --continue-on-error --timeout-per-file 3600
 
   full-4-npu-a3:
-    needs: [set-image-config]
+    name: full-4-npu-a3
     if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-    runs-on: linux-aarch64-a3-4
+    needs: [set-image-config]
-    container:
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-4-npu-a3
+      runner_config: linux-aarch64-a3-4
       image: ${{ needs.set-image-config.outputs.image_a3 }}
-    steps:
+      run_timeout_minutes: '240'
-      - name: Checkout code
+      timeout_per_file: '3600'
-        uses: actions/checkout@v4
+      ref: ${{ needs.set-image-config.outputs.ref }}
-        with:
+      is_nightly_pipeline_job: true
-          ref: ${{ needs.set-image-config.outputs.ref || github.ref }}
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-      - name: Install dependencies
+  full-8-npu-a3:
-        env:
+    name: full-8-npu-a3
-          TORCH_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/whl/cpu"
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-          PYPI_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
+    needs: [set-image-config]
-          UV_INDEX_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
-          GITHUB_PROXY_URL: "https://gh-proxy.test.osinfra.cn/"
+    with:
-        run: |
+      self_name: full-8-npu-a3
-          # speed up by using infra cache services
+      runner_config: linux-aarch64-a3-8
-          CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
-          sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
+      run_timeout_minutes: '240'
-          pip config set global.index-url http://${CACHING_URL}/pypi/simple
+      timeout_per_file: '3600'
-          pip config set global.trusted-host "${CACHING_URL}"
+      ref: ${{ needs.set-image-config.outputs.ref }}
-
+      is_nightly_pipeline_job: true
-          if [ ${{ needs.set-image-config.outputs.skip_install_flag }} != "true" ];then
+      skip_pr_test_health_check: 'true'
-            bash scripts/ci/npu/npu_ci_install_dependency.sh a3
+    secrets: inherit
-          fi
-
-          # copy required file from our daily cache
-          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
-          # copy gsm8k dataset
-          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
-
-      - name: Print Log Information
-        run: |
-          bash scripts/ci/npu/npu_log_print.sh
-
-      - name: Run test
-        timeout-minutes: 240
-        env:
-          SGLANG_USE_MODELSCOPE: true
-          SGLANG_IS_IN_CI: true
-          HF_ENDPOINT: https://hf-mirror.com
-          TORCH_EXTENSIONS_DIR: /tmp/torch_extensions
-          PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
-          STREAMS_PER_DEVICE: 32
-        run: |
-          pip install sglang_router
-          hf download lmms-lab/MMMU --repo-type dataset
-          pip install sentence_transformers torchaudio==2.8.0
-          pip install protobuf==6.31.1 zss pre-commit wandb>=0.16.0 tenacity==8.3.0 loguru openpyxl latex2sympy2 zstandard transformers-stream-generator tqdm-multiprocess pycocoevalcap
-          pip install yt-dlp sentencepiece==0.1.99 nltk av ftfy sqlitedict==2.1.0 sacrebleu>=1.5.0 pytablewriter black==24.1.0 isort==5.13.2 peft>=0.2.0 accelerate>=0.29.1
-          pip install jsonlines httpx==0.25.0 evaluate>=0.4.0 datasets==2.16.1 numexpr xgrammar==0.2.1 numpy==1.26.4 dotenv
-          git clone --branch v0.3.3 --depth 1 https://github.com/EvolvingLMMs-Lab/lmms-eval.git
-          cd ./lmms-eval
-          nohup pip install . > lmmslog.txt 2>&1 &
-          sleep 120
-          export PYTHONPATH=$PYTHONPATH:$(pwd)
-          cd ../
-          cd test
-          python3 run_suite.py --hw npu --suite full-4-npu-a3 --nightly --continue-on-error --timeout-per-file 3600
 
   full-16-npu-a3:
-    needs: [set-image-config]
+    name: full-16-npu-a3
     if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-    runs-on: linux-aarch64-a3-16
+    needs: [set-image-config]
-    container:
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-16-npu-a3
+      runner_config: linux-aarch64-a3-16
       image: ${{ needs.set-image-config.outputs.image_a3 }}
-    steps:
+      run_timeout_minutes: '240'
-      - name: Checkout code
+      timeout_per_file: '3600'
-        uses: actions/checkout@v4
+      ref: ${{ needs.set-image-config.outputs.ref }}
-        with:
+      is_nightly_pipeline_job: true
-          ref: ${{ needs.set-image-config.outputs.ref || github.ref }}
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-      - name: Install dependencies
+  full-acc-2-npu-a3:
-        env:
+    name: full-acc-2-npu-a3
-          TORCH_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/whl/cpu"
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-          PYPI_CACHE_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
+    needs: [set-image-config]
-          UV_INDEX_URL: "http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple"
+    uses: ./.github/workflows/_npu-single-node-test-stage.yml
-          GITHUB_PROXY_URL: "https://gh-proxy.test.osinfra.cn/"
+    with:
-        run: |
+      runner: linux-aarch64-a3-2-
-          # speed up by using infra cache services
+      test_type: 'accuracy'
-          CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
+      test_suite: full-acc-2-npu-a3
-          sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
-          pip config set global.index-url http://${CACHING_URL}/pypi/simple
+      is_nightly_pipeline_job: true
-          pip config set global.trusted-host "${CACHING_URL}"
+      install_sglang_deps: true
+      device_type_for_deps: 'a3'
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-          if [ ${{ needs.set-image-config.outputs.skip_install_flag }} != "true" ];then
+  full-acc-4-npu-a3:
-            bash scripts/ci/npu/npu_ci_install_dependency.sh a3
+    name: full-acc-4-npu-a3
-          fi
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
+    needs: [set-image-config]
+    uses: ./.github/workflows/_npu-single-node-test-stage.yml
+    with:
+      runner: linux-aarch64-a3-4-
+      test_type: 'accuracy'
+      test_suite: full-acc-4-npu-a3
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      is_nightly_pipeline_job: true
+      install_sglang_deps: true
+      device_type_for_deps: 'a3'
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-          # copy required file from our daily cache
+  full-acc-16-npu-a3:
-          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
+    name: full-acc-16-npu-a3
-          # copy gsm8k dataset
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-          cp ~/.cache/modelscope/hub/datasets/tmp/test.jsonl /tmp
+    needs: [set-image-config]
+    uses: ./.github/workflows/_npu-single-node-test-stage.yml
+    with:
+      runner: linux-aarch64-a3-16-
+      test_type: 'accuracy'
+      test_suite: full-acc-16-npu-a3
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      is_nightly_pipeline_job: true
+      install_sglang_deps: true
+      device_type_for_deps: 'a3'
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-      - name: Print Log Information
+  full-perf-2-npu-a3:
-        run: |
+    name: full-perf-2-npu-a3
-          bash scripts/ci/npu/npu_log_print.sh
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
+    needs: [set-image-config]
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-perf-2-npu-a3
+      runner_config: linux-aarch64-a3-800t-2
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      run_timeout_minutes: '240'
+      timeout_per_file: '3600'
+      ref: ${{ needs.set-image-config.outputs.ref }}
+      is_nightly_pipeline_job: true
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
 
-      - name: Run test
+  full-perf-4-npu-a3:
-        timeout-minutes: 240
+    name: full-perf-4-npu-a3
-        env:
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-          SGLANG_USE_MODELSCOPE: true
+    needs: [set-image-config]
-          SGLANG_IS_IN_CI: true
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
-          HF_ENDPOINT: https://hf-mirror.com
+    with:
-          TORCH_EXTENSIONS_DIR: /tmp/torch_extensions
+      self_name: full-perf-4-npu-a3
-          PYTORCH_NPU_ALLOC_CONF: "expandable_segments:True"
+      runner_config: linux-aarch64-a3-800t-4
-          STREAMS_PER_DEVICE: 32
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
-        run: |
+      run_timeout_minutes: '240'
-          pip install sglang_router
+      timeout_per_file: '3600'
-          hf download lmms-lab/MMMU --repo-type dataset
+      ref: ${{ needs.set-image-config.outputs.ref }}
-          pip install sentence_transformers torchaudio==2.8.0
+      is_nightly_pipeline_job: true
-          pip install protobuf==6.31.1 zss pre-commit wandb>=0.16.0 tenacity==8.3.0 loguru openpyxl latex2sympy2 zstandard transformers-stream-generator tqdm-multiprocess pycocoevalcap
+      skip_pr_test_health_check: 'true'
-          pip install yt-dlp sentencepiece==0.1.99 nltk av ftfy sqlitedict==2.1.0 sacrebleu>=1.5.0 pytablewriter black==24.1.0 isort==5.13.2 peft>=0.2.0 accelerate>=0.29.1
+    secrets: inherit
-          pip install jsonlines httpx==0.25.0 evaluate>=0.4.0 datasets==2.16.1 numexpr xgrammar==0.2.1 numpy==1.26.4 dotenv
+
-          git clone --branch v0.3.3 --depth 1 https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+  full-perf-8-npu-a3:
-          cd ./lmms-eval
+    name: full-perf-8-npu-a3
-          nohup pip install . > lmmslog.txt 2>&1 &
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
-          sleep 120
+    needs: [set-image-config]
-          export PYTHONPATH=$PYTHONPATH:$(pwd)
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
-          cd ../
+    with:
-          cd test
+      self_name: full-perf-8-npu-a3
-          python3 run_suite.py --hw npu --suite full-16-npu-a3 --nightly --continue-on-error --timeout-per-file 3600
+      runner_config: linux-aarch64-a3-800t-8
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      run_timeout_minutes: '240'
+      timeout_per_file: '3600'
+      ref: ${{ needs.set-image-config.outputs.ref }}
+      is_nightly_pipeline_job: true
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
+
+  full-perf-16-npu-a3:
+    name: full-perf-16-npu-a3
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
+    needs: [set-image-config]
+    uses: ./.github/workflows/_npu-pr-test-stage.yml
+    with:
+      self_name: full-perf-16-npu-a3
+      runner_config: linux-aarch64-a3-800t-16
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      run_timeout_minutes: '240'
+      timeout_per_file: '3600'
+      ref: ${{ needs.set-image-config.outputs.ref }}
+      is_nightly_pipeline_job: true
+      skip_pr_test_health_check: 'true'
+    secrets: inherit
+
+  full-poc-multi-node-mix-tests:
+    name: multi-node-mix-poc
+    if: ${{ (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') }}
+    needs: [set-image-config]
+    strategy:
+      fail-fast: false
+      max-parallel: 1
+      matrix:
+        test_config:
+          # kimi_k2_6 performance tests
+          - name: kimi_k2_6_w4a8_16p_in64k_out1k_100ms_aime25
+            node_size: 2
+            test_case: test/registered/npu/accuracy/kimi_k2_6/test_npu_kimi_k2_6_w4a8_16p_in64k_out1k_100ms_aime25.py
+            test_type: 'accuracy'
+    uses: ./.github/workflows/nightly-test-npu-e2e-multi-node.yml
+    with:
+      runner: linux-amd64-cpu-4
+      test_type: ${{ matrix.test_config.test_type || 'perf' }}
+      test_config_name: ${{ matrix.test_config.name }}
+      node_size: ${{ matrix.test_config.node_size }}
+      test_case: ${{ matrix.test_config.test_case }}
+      image: ${{ needs.set-image-config.outputs.image_a3 }}
+      install_sglang_from_source: false
+      prefill_decode_deployment: 'mix'
+      transformers_version: ''
 
   check-all-jobs:
     if: github.repository == 'sgl-project/sglang' && always()
     needs:
       - nighly-test-npu
+      - full-4-npu-a2
       - full-1-npu-a3
       - full-2-npu-a3
       - full-4-npu-a3
+      - full-8-npu-a3
       - full-16-npu-a3
+      - full-acc-2-npu-a3
+      - full-acc-4-npu-a3
+      - full-acc-16-npu-a3
+      - full-perf-2-npu-a3
+      - full-perf-4-npu-a3
+      - full-perf-8-npu-a3
+      - full-perf-16-npu-a3
+      - full-poc-multi-node-mix-tests
     runs-on: ubuntu-latest
     container:
       image: docker.m.daocloud.io/ubuntu:22.04

.github/workflows/nightly-test-npu.yml

@@ -122,22 +122,6 @@ jobs:
           # Write to GITHUB_OUTPUT and print to the log in one command.
           echo "run_start_metadata=${RUN_START_METADATA}" | tee -a $GITHUB_OUTPUT
 
-  nightly-1-npu-a2:
-    name: nightly-1-npu-a2
-    if: ${{ !cancelled() }}
-    needs: [set-image-config]
-    uses: ./.github/workflows/_npu-single-node-test-stage.yml
-    with:
-      runner: linux-aarch64-a2-4
-      test_type: 'perf'
-      test_suite: nightly-1-npu-a2
-      is_nightly_pipeline_job: true
-      skip_pr_test_health_check: 'true'
-      image: ${{ needs.set-image-config.outputs.image_a2 }}
-      install_sglang_deps: false
-      device_type_for_deps: '910b'
-      run_start_metadata: ${{ needs.set-image-config.outputs.run_start_metadata }}
-
   nightly-1-npu-a3:
     name: nightly-1-npu-a3
     if: ${{ !cancelled() }}
@@ -247,22 +231,6 @@ jobs:
       device_type_for_deps: 'a3'
       run_start_metadata: ${{ needs.set-image-config.outputs.run_start_metadata }}
 
-  nightly-perf-8-npu-a3:
-    name: nightly-perf-8-npu-a3
-    if: ${{ !cancelled() }}
-    needs: [set-image-config]
-    uses: ./.github/workflows/_npu-single-node-test-stage.yml
-    with:
-      runner: linux-aarch64-a3-800t-8
-      test_type: 'perf'
-      test_suite: nightly-perf-8-npu-a3
-      is_nightly_pipeline_job: true
-      skip_pr_test_health_check: 'true'
-      image: ${{ needs.set-image-config.outputs.image_a3 }}
-      install_sglang_deps: false
-      device_type_for_deps: 'a3'
-      run_start_metadata: ${{ needs.set-image-config.outputs.run_start_metadata }}
-
   nightly-perf-16-npu-a3:
     name: nightly-perf-16-npu-a3
     if: ${{ !cancelled() }}
@@ -298,22 +266,6 @@ jobs:
       device_type_for_deps: 'a3'
       run_start_metadata: ${{ needs.set-image-config.outputs.run_start_metadata }}
 
-  nightly-acc-4-npu-a3:
-    name: nightly-acc-4-npu-a3
-    if: ${{ !cancelled() }}
-    needs: [set-image-config]
-    uses: ./.github/workflows/_npu-single-node-test-stage.yml
-    with:
-      runner: linux-aarch64-a3-4-
-      test_type: 'accuracy'
-      test_suite: nightly-acc-4-npu-a3
-      is_nightly_pipeline_job: true
-      skip_pr_test_health_check: 'true'
-      image: ${{ needs.set-image-config.outputs.image_a3 }}
-      install_sglang_deps: false
-      device_type_for_deps: 'a3'
-      run_start_metadata: ${{ needs.set-image-config.outputs.run_start_metadata }}
-
   nightly-acc-16-npu-a3:
     name: nightly-acc-16-npu-a3
     if: ${{ !cancelled() }}
@@ -333,7 +285,7 @@ jobs:
   nightly-poc-multi-node-tests:
     name: multi-node-poc
     if: ${{ !cancelled() }}
-    needs: [set-image-config, nightly-perf-2-npu-a3, nightly-perf-4-npu-a3, nightly-perf-16-npu-a3, nightly-acc-2-npu-a3, nightly-acc-4-npu-a3, nightly-acc-16-npu-a3]
+    needs: [set-image-config, nightly-perf-2-npu-a3, nightly-perf-4-npu-a3, nightly-perf-16-npu-a3, nightly-acc-2-npu-a3, nightly-acc-16-npu-a3]
     strategy:
       fail-fast: false
       max-parallel: 1
@@ -354,21 +306,6 @@ jobs:
             test_case: test/registered/npu/accuracy/glm5_1/test_npu_glm5_1_w4a8_1p1d_32p_in64k_out1k_50ms_aime26.py
             test_type: 'accuracy'
             prefill_decode_deployment: 'separation'
-          # mimo_v2_flash performance tests
-          - name: test_npu_mimo_v2_flash_1p1d_12p_in16k_out1_ttft_5s
-            prefill_size: 1
-            decode_size: 1
-            router_size: 1
-            test_case: test/registered/npu/performance/mimo_v2_flash/test_npu_mimo_v2_flash_1p1d_12p_in16k_out1_ttft_5s.py
-            test_type: 'perf'
-            prefill_decode_deployment: 'separation'
-          - name: test_npu_mimo_v2_flash_1p1d_12p_in16k_out1k_tpot_20ms
-            prefill_size: 1
-            decode_size: 1
-            router_size: 1
-            test_case: test/registered/npu/performance/mimo_v2_flash/test_npu_mimo_v2_flash_1p1d_12p_in16k_out1k_tpot_20ms.py
-            test_type: 'perf'
-            prefill_decode_deployment: 'separation'
           # deepseek_v4_flash performance tests
           - name: deepseek_v4_flash_w8a8_1p1d_16p_in8k_out1k_50ms
             prefill_size: 1
@@ -395,21 +332,12 @@ jobs:
   nightly-poc-multi-node-mix-tests:
     name: multi-node-mix-poc
     if: ${{ !cancelled() }}
-    needs: [set-image-config, nightly-perf-2-npu-a3, nightly-perf-4-npu-a3, nightly-perf-16-npu-a3, nightly-acc-2-npu-a3, nightly-acc-4-npu-a3, nightly-acc-16-npu-a3, nightly-poc-multi-node-tests]
+    needs: [set-image-config, nightly-perf-2-npu-a3, nightly-perf-4-npu-a3, nightly-perf-16-npu-a3, nightly-acc-2-npu-a3, nightly-acc-16-npu-a3, nightly-poc-multi-node-tests]
     strategy:
       fail-fast: false
       max-parallel: 1
       matrix:
         test_config:
-          # kimi_k2_6 performance tests
-          - name: kimi_k2_6_w4a8_16p_in64k_out1k_100ms
-            node_size: 2
-            test_case: test/registered/npu/performance/kimi_k2_6/test_npu_kimi_k2_6_w4a8_16p_in64k_out1k_100ms.py
-            test_type: 'perf'
-          - name: kimi_k2_6_w4a8_16p_in64k_out1k_100ms_aime25
-            node_size: 2
-            test_case: test/registered/npu/accuracy/kimi_k2_6/test_npu_kimi_k2_6_w4a8_16p_in64k_out1k_100ms_aime25.py
-            test_type: 'accuracy'
           # glm_5_2 accuracy tests
           - name: glm_5_2_w4a8_16p_gpqa
             node_size: 2
@@ -436,7 +364,6 @@ jobs:
   check-all-jobs:
     if: ${{ !cancelled() }}
     needs:
-      - nightly-1-npu-a2
       - nightly-1-npu-a3
       - nightly-2-npu-a3
       - nightly-4-npu-a3
@@ -444,10 +371,8 @@ jobs:
       - nightly-16-npu-a3
       - nightly-perf-2-npu-a3
       - nightly-perf-4-npu-a3
-      - nightly-perf-8-npu-a3
       - nightly-perf-16-npu-a3
       - nightly-acc-2-npu-a3
-      - nightly-acc-4-npu-a3
       - nightly-acc-16-npu-a3
       - nightly-poc-multi-node-tests
       - nightly-poc-multi-node-mix-tests
@@ -455,7 +380,6 @@ jobs:
     steps:
       - name: Generate results table
         run: |
-          single_result_a2="${{ needs.nightly-1-npu-a2.result }}"
           multi_result="${{ needs.nightly-poc-multi-node-tests.result }}"
           mix_result="${{ needs.nightly-poc-multi-node-mix-tests.result }}"
 
@@ -469,10 +393,8 @@ jobs:
             "${{ needs.nightly-16-npu-a3.result }}" \
             "${{ needs.nightly-perf-2-npu-a3.result }}" \
             "${{ needs.nightly-perf-4-npu-a3.result }}" \
-            "${{ needs.nightly-perf-8-npu-a3.result }}" \
             "${{ needs.nightly-perf-16-npu-a3.result }}" \
             "${{ needs.nightly-acc-2-npu-a3.result }}" \
-            "${{ needs.nightly-acc-4-npu-a3.result }}" \
             "${{ needs.nightly-acc-16-npu-a3.result }}"; do
             if [ "${r}" != "success" ] && [ "${r}" != "skipped" ]; then
               single_result="failure"
@@ -493,7 +415,6 @@ jobs:
           echo "" >> $GITHUB_STEP_SUMMARY
           echo "| Group | Status |" >> $GITHUB_STEP_SUMMARY
           echo "|-------|--------|" >> $GITHUB_STEP_SUMMARY
-          echo "| nightly-1-npu-a2 | $(group_icon ${single_result_a2}) ${single_result_a2} |" >> $GITHUB_STEP_SUMMARY
           for entry in \
             "nightly-1-npu-a3:${{ needs.nightly-1-npu-a3.result }}" \
             "nightly-2-npu-a3:${{ needs.nightly-2-npu-a3.result }}" \
@@ -502,10 +423,8 @@ jobs:
             "nightly-16-npu-a3:${{ needs.nightly-16-npu-a3.result }}" \
             "nightly-perf-2-npu-a3:${{ needs.nightly-perf-2-npu-a3.result }}" \
             "nightly-perf-4-npu-a3:${{ needs.nightly-perf-4-npu-a3.result }}" \
-            "nightly-perf-8-npu-a3:${{ needs.nightly-perf-8-npu-a3.result }}" \
             "nightly-perf-16-npu-a3:${{ needs.nightly-perf-16-npu-a3.result }}" \
             "nightly-acc-2-npu-a3:${{ needs.nightly-acc-2-npu-a3.result }}" \
-            "nightly-acc-4-npu-a3:${{ needs.nightly-acc-4-npu-a3.result }}" \
             "nightly-acc-16-npu-a3:${{ needs.nightly-acc-16-npu-a3.result }}"; do
             suite="${entry%%:*}"
             r="${entry##*:}"
@@ -516,7 +435,7 @@ jobs:
           echo "" >> $GITHUB_STEP_SUMMARY
 
           FAIL=0
-          for r in "${single_result_a2}" "${single_result}" "${multi_result}" "${mix_result}"; do
+          for r in "${single_result}" "${multi_result}" "${mix_result}"; do
             if [ "${r}" != "success" ] && [ "${r}" != "skipped" ]; then FAIL=1; fi
           done
           exit $FAIL

test/registered/npu/accuracy/deepseek_v3_2/test_npu_deepseek_v3_2_8p_aime25.py

@@ -8,7 +8,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-acc-16-npu-a3",
+    suite="full-acc-16-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/glm4_7_flash/test_npu_glm4_7_flash_1p_aime25.py

@@ -8,7 +8,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=6500,
-    suite="nightly-acc-2-npu-a3",
+    suite="full-acc-2-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/glm4_7_flash/test_npu_glm4_7_flash_1p_gsm8k.py

@@ -6,7 +6,7 @@ from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
 from sglang.test.ascend.e2e.test_npu_performance_utils import GLM_4_7_FLASH_MODEL_PATH
 from sglang.test.ci.ci_register import register_npu_ci
 
-register_npu_ci(est_time=6500, suite="nightly-acc-2-npu-a3", nightly=True)
+register_npu_ci(est_time=6500, suite="full-acc-2-npu-a3", nightly=True)
 
 ENVS = {
     "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",

test/registered/npu/accuracy/minimax_m2_5/test_npu_minimax_m2_5_w8a8_4p_in64k_out1k_prefix90_50ms_gpqa.py

@@ -12,7 +12,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=7200,
-    suite="nightly-acc-16-npu-a3",
+    suite="full-acc-16-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/minimax_m2_5/test_npu_minimax_m2_5_w8a8_8p_in3k5_out1k5_50ms_gpqa.py

@@ -12,7 +12,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-acc-16-npu-a3",
+    suite="full-acc-16-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3-8b/test_npu_qwen3_8b_w8a8_1p_in3k5_out1k5_50ms_gpqa.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=3700,
-    suite="nightly-acc-2-npu-a3",
+    suite="full-acc-2-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3-8b/test_npu_qwen3_8b_w8a8_1p_in6k_out1k5_bs16_gpqa.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=3700,
-    suite="nightly-acc-2-npu-a3",
+    suite="full-acc-2-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_30b_a3b/test_npu_qwen3_30b_w8a8_1p_in3k5_out1k5_50ms_aime25.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=2800,
-    suite="nightly-acc-2-npu-a3",
+    suite="full-acc-2-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_32b/test_npu_qwen3_32b_bf16_8p_gpqa.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-acc-16-npu-a3",
+    suite="full-acc-16-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_32b/test_npu_qwen3_32b_w8a8_2p_in3k5_out1k5_50ms_gpqa.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-acc-4-npu-a3",
+    suite="full-acc-4-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_32b/test_npu_qwen3_32b_w8a8_2p_in3k5_out1k5_50ms_gpqa_a2.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-1-npu-a2",
+    suite="full-4-npu-a2",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_5_9b/test_npu_qwen3_5_9b_bf16_1p_gsm8k.py

@@ -8,7 +8,7 @@ from sglang.test.ascend.e2e.test_npu_performance_utils import (
 )
 from sglang.test.ci.ci_register import register_npu_ci
 
-register_npu_ci(est_time=2800, suite="nightly-acc-2-npu-a3", nightly=True)
+register_npu_ci(est_time=2800, suite="full-acc-2-npu-a3", nightly=True)
 
 QWEN3_5_9B_ENVS = {
     "SGLANG_SET_CPU_AFFINITY": "1",

test/registered/npu/accuracy/qwen3_next_80b_a3b_instruct/test_npu_qwen3_next_80b_w8a8_2p_in6k_out1k5_bs16_aime25.py

@@ -11,7 +11,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=4800,
-    suite="nightly-acc-4-npu-a3",
+    suite="full-acc-4-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_vl_30b_a3b/test_npu_qwen3_vl_30b_a3b_bf16_2p_gsm8k.py

@@ -8,7 +8,7 @@ from sglang.test.ascend.e2e.test_npu_performance_utils import (
 )
 from sglang.test.ci.ci_register import register_npu_ci
 
-register_npu_ci(est_time=4800, suite="nightly-acc-4-npu-a3", nightly=True)
+register_npu_ci(est_time=4800, suite="full-acc-4-npu-a3", nightly=True)
 
 QWEN3_VL_30B_A3B_ENVS = {
     "SGLANG_SET_CPU_AFFINITY": "1",

test/registered/npu/accuracy/qwen3_vl_30b_a3b_thinking/test_npu_qwen3_vl_30b_a3b_thinking_1p_mmmu.py

@@ -10,7 +10,7 @@ from sglang.test.ci.ci_register import register_npu_ci
 
 register_npu_ci(
     est_time=8400,
-    suite="nightly-acc-2-npu-a3",
+    suite="full-acc-2-npu-a3",
     nightly=True,
 )
 

test/registered/npu/accuracy/qwen3_vl_8b/test_npu_qwen3_vl_8b_bf16_2p_gsm8k.py

@@ -8,7 +8,7 @@ from sglang.test.ascend.e2e.test_npu_performance_utils import (
 )
 from sglang.test.ci.ci_register import register_npu_ci
 
-register_npu_ci(est_time=4800, suite="nightly-acc-4-npu-a3", nightly=True)
+register_npu_ci(est_time=4800, suite="full-acc-4-npu-a3", nightly=True)
 
 QWEN3_VL_8B_ENVS = {
     "SGLANG_SET_CPU_AFFINITY": "1",

test/registered/npu/accuracy/qwen3_vl_8b_thinking/test_npu_qwen3_vl_8b_thinking_1p_mmmu.py

@@ -9,7 +9,7 @@ from sglang.test.ascend.e2e.test_npu_performance_utils import (
 )
 from sglang.test.ci.ci_register import register_npu_ci
 
-register_npu_ci(est_time=12000, suite="nightly-acc-2-npu-a3", nightly=True)
+register_npu_ci(est_time=12000, suite="full-acc-2-npu-a3", nightly=True)
 
 _is_pr_pipeline = os.environ.get("GITHUB_EVENT_NAME") == "pull_request"
 

test/registered/npu/embedding_models/test_npu_bge_large_en_v1_5.py

@@ -1,111 +0,0 @@
-import multiprocessing as mp
-import unittest
-from typing import Optional
-
-import torch
-from transformers import AutoConfig, AutoTokenizer
-
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.runners import HFRunner, SRTRunner
-from sglang.test.test_utils import CustomTestCase, get_similarities
-
-register_npu_ci(
-    est_time=400,
-    suite="full-1-npu-a3",
-    nightly=True,
-    disabled="embeddings are not all close",
-)
-
-DEFAULT_PROMPTS = [
-    "The capital of the United Kingdom is",
-    "Today is a sunny day and I like",
-    "AI is a field of computer science focused on",
-]
-
-MODELS = [
-    ("/root/.cache/modelscope/hub/models/bge-large-en-v1.5", 1, 1e-5),
-]
-TORCH_DTYPES = [torch.float16]
-
-
-class TestEmbeddingModels(CustomTestCase):
-    @classmethod
-    def setUpClass(cls):
-        mp.set_start_method("spawn", force=True)
-
-    def _truncate_prompts(self, prompts, model_path):
-        config = AutoConfig.from_pretrained(model_path)
-        max_length = getattr(config, "max_position_embeddings", 2048)
-
-        tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-        truncated_prompts = []
-        for prompt in prompts:
-            tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
-            if len(tokens.input_ids[0]) > max_length:
-                truncated_text = tokenizer.decode(
-                    tokens.input_ids[0][: max_length - 1], skip_special_tokens=True
-                )
-                truncated_prompts.append(truncated_text)
-            else:
-                truncated_prompts.append(prompt)
-        return truncated_prompts
-
-    def assert_close_prefill_logits(
-        self,
-        prompts,
-        model_path,
-        tp_size,
-        torch_dtype,
-        prefill_tolerance,
-        matryoshka_dim: Optional[int] = None,
-    ) -> None:
-        truncated_prompts = self._truncate_prompts(prompts, model_path)
-
-        with HFRunner(
-            model_path,
-            torch_dtype=torch_dtype,
-            model_type="embedding",
-            matryoshka_dim=matryoshka_dim,
-        ) as hf_runner:
-            hf_outputs = hf_runner.forward(truncated_prompts)
-
-        attention_backend = "ascend"
-        with SRTRunner(
-            model_path,
-            tp_size=tp_size,
-            torch_dtype=torch_dtype,
-            model_type="embedding",
-            attention_backend=attention_backend,
-            json_model_override_args=(
-                {"matryoshka_dimensions": [matryoshka_dim]} if matryoshka_dim else None
-            ),
-        ) as srt_runner:
-            srt_outputs = srt_runner.forward(
-                truncated_prompts, dimensions=matryoshka_dim
-            )
-
-        for i in range(len(prompts)):
-            hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
-            srt_logits = torch.Tensor(srt_outputs.embed_logits[i])
-
-            similarity = torch.tensor(get_similarities(hf_logits, srt_logits))
-            print("similarity diff", abs(similarity - 1))
-
-            if len(prompts[i]) <= 1000:
-                assert torch.all(abs(similarity - 1) < prefill_tolerance), (
-                    "embeddings are not all close"
-                )
-
-    def test_prefill_logits(self):
-        models_to_test = MODELS
-
-        for model, tp_size, prefill_tolerance in models_to_test:
-            for torch_dtype in TORCH_DTYPES:
-                self.assert_close_prefill_logits(
-                    DEFAULT_PROMPTS, model, tp_size, torch_dtype, prefill_tolerance
-                )
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_ascend_dbrx_instruct.py

@@ -1,26 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-8-npu-a3", nightly=True)
-
-
-class TestDbrx(GSM8KAscendMixin, CustomTestCase):
-    model = "/root/.cache/modelscope/hub/models/AI-ModelScope/dbrx-instruct"
-    accuracy = 0.735
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--tp-size",
-        "8",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_baichuan2_13b_chat.py

@@ -1,37 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import BAICHUAN2_13B_CHAT_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestBaichuan(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the baichuan-inc/Baichuan2-13B-Chat model on the GSM8K dataset is no less than 0.48.
-
-    [Test Category] Model
-    [Test Target] baichuan-inc/Baichuan2-13B-Chat
-    """
-
-    model = BAICHUAN2_13B_CHAT_WEIGHTS_PATH
-    accuracy = 0.48
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--max-running-requests",
-        "128",
-        "--disable-radix-cache",
-        "--chunked-prefill-size",
-        "-1",
-    ]
-    gsm8k_num_shots = 1
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_c4ai_command_r_v01.py

@@ -1,40 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import (
-    C4AI_COMMAND_R_V01_CHAT_TEMPLATE_PATH,
-    C4AI_COMMAND_R_V01_WEIGHTS_PATH,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-2-npu-a3", nightly=True)
-
-
-class TestC4AI(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the CohereForAI/c4ai-command-r-v01 model on the GSM8K dataset is no less than 0.55.
-
-    [Test Category] Model
-    [Test Target] CohereForAI/c4ai-command-r-v01
-    """
-
-    model = C4AI_COMMAND_R_V01_WEIGHTS_PATH
-    accuracy = 0.55
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--chat-template",
-        C4AI_COMMAND_R_V01_CHAT_TEMPLATE_PATH,
-        "--tp-size",
-        "2",
-        "--dtype",
-        "bfloat16",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_chatglm2_6b.py

@@ -1,33 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import CHATGLM2_6B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestChatGlm2(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the ZhipuAI/chatglm2-6b model on the GSM8K dataset is no less than 0.25.
-
-    [Test Category] Model
-    [Test Target] ZhipuAI/chatglm2-6b
-    """
-
-    model = CHATGLM2_6B_WEIGHTS_PATH
-    accuracy = 0.25
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--dtype",
-        "bfloat16",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_exaone_3.py

@@ -1,34 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import EXAONE_3_5_7_8B_INSTRUCT_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestEXAONE(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct model on the GSM8K dataset is no less than 0.8.
-
-    [Test Category] Model
-    [Test Target] LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
-    """
-
-    model = EXAONE_3_5_7_8B_INSTRUCT_WEIGHTS_PATH
-    # Allow 1% tolerance for the accuracy threshold
-    accuracy = round(0.8 * 0.99, 3)
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--dtype",
-        "bfloat16",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_glm4_9b_chat.py

@@ -1,27 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import GLM_4_9B_CHAT_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-1-npu-a3",
-    nightly=True,
-)
-
-
-class TestGLM49BChat(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the ZhipuAI/glm-4-9b-chat model on the GSM8K dataset is no less than 0.77.
-
-    [Test Category] Model
-    [Test Target] ZhipuAI/glm-4-9b-chat
-    """
-
-    model = GLM_4_9B_CHAT_WEIGHTS_PATH
-    accuracy = 0.77
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_granite_3_0_3b_a800m.py

@@ -1,25 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import (
-    GRANITE_3_0_3B_A800M_INSTRUCT_WEIGHTS_PATH,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestGranite(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the ibm-granite/granite-3.0-3b-a800m-instruct model on the GSM8K dataset is no less than 0.38.
-
-    [Test Category] Model
-    [Test Target] ibm-granite/granite-3.0-3b-a800m-instruct
-    """
-
-    model = GRANITE_3_0_3B_A800M_INSTRUCT_WEIGHTS_PATH
-    accuracy = 0.38
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_granite_3_1_8b.py

@@ -1,23 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import GRANITE_3_1_8B_INSTRUCT_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestGranite(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the ibm-granite/granite-3.1-8b-instruct model on the GSM8K dataset is no less than 0.695.
-
-    [Test Category] Model
-    [Test Target] ibm-granite/granite-3.1-8b-instruct
-    """
-
-    model = GRANITE_3_1_8B_INSTRUCT_WEIGHTS_PATH
-    accuracy = 0.695
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_grok_2.py

@@ -1,33 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-16-npu-a3",
-    nightly=True,
-)
-
-
-class TestGrok2(GSM8KAscendMixin, CustomTestCase):
-    model = "/root/.cache/modelscope/hub/models/huihui-ai/grok-2"
-    accuracy = 0.91
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-radix-cache",
-        "--disable-cuda-graph",
-        "--tokenizer-path",
-        "/root/.cache/modelscope/hub/models/huihui-ai/grok-2/tokenizer.tok.json",
-        "--tp-size",
-        "16",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_internlm2_7b.py

@@ -1,29 +0,0 @@
-import os
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import INTERNLM2_7B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-1-npu-a3",
-    nightly=True,
-)
-
-
-class TestInternlm2(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the Shanghai_AI_Laboratory/internlm2-7b model on the GSM8K dataset is no less than 0.585.
-
-    [Test Category] Model
-    [Test Target] Shanghai_AI_Laboratory/internlm2-7b
-    """
-
-    os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-    model = INTERNLM2_7B_WEIGHTS_PATH
-    accuracy = 0.585
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_llama_2_7b.py

@@ -1,23 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import LLAMA_2_7B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestLlama(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the LLM-Research/Llama-2-7B model on the GSM8K dataset is no less than 0.18.
-
-    [Test Category] Model
-    [Test Target] LLM-Research/Llama-2-7B
-    """
-
-    model = LLAMA_2_7B_WEIGHTS_PATH
-    accuracy = 0.18
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_llama_2_7b_communications_compression.py

@@ -1,37 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import LLAMA_2_7B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-2-npu-a3", nightly=True)
-
-
-class TestLlama(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the LLM-Research/Llama-2-7B model on the GSM8K dataset with tp communications quantization is no less than 0.18.
-
-    [Test Category] Model
-    [Test Target] LLM-Research/Llama-2-7B
-    """
-
-    model = LLAMA_2_7B_WEIGHTS_PATH
-    accuracy = 0.18
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        0.8,
-        "--max-running-requests",
-        32,
-        "--attention-backend",
-        "ascend",
-        "--cuda-graph-max-bs-decode",
-        32,
-        "--tp-size",
-        2,
-        "--enable-quant-communications",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_minicpm3_4b.py

@@ -1,37 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import MINICPM3_4B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestMiniCPM3(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the OpenBMB/MiniCPM3-4B model on the GSM8K dataset is no less than 0.69.
-
-    [Test Category] Model
-    [Test Target] OpenBMB/MiniCPM3-4B
-    """
-
-    model = MINICPM3_4B_WEIGHTS_PATH
-    accuracy = 0.69
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--disable-radix-cache",
-        "--disable-overlap-schedule",
-        "--max-running-requests",
-        "128",
-        "--chunked-prefill-size",
-        "-1",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_mistral_7b.py

@@ -1,23 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import MISTRAL_7B_INSTRUCT_V0_2_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestMistral7B(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the mistralai/Mistral-7B-Instruct-v0.2 model on the GSM8K dataset is no less than 0.375.
-
-    [Test Category] Model
-    [Test Target] mistralai/Mistral-7B-Instruct-v0.2
-    """
-
-    model = MISTRAL_7B_INSTRUCT_V0_2_WEIGHTS_PATH
-    accuracy = 0.375
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_persimmon_8b_chat.py

@@ -1,29 +0,0 @@
-import os
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import PERSIMMON_8B_CHAT_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-1-npu-a3",
-    nightly=True,
-)
-
-
-class TestPersimmon8BChat(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the Howeee/persimmon-8b-chat model on the GSM8K dataset is no less than 0.17.
-
-    [Test Category] Model
-    [Test Target] Howeee/persimmon-8b-chat
-    """
-
-    os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-    model = PERSIMMON_8B_CHAT_WEIGHTS_PATH
-    accuracy = 0.17
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_smollm_1_7b.py

@@ -1,33 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import SMOLLM_1_7B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestSmolLM(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the HuggingFaceTB/SmolLM-1.7B model on the GSM8K dataset is no less than 0.05.
-
-    [Test Category] Model
-    [Test Target] HuggingFaceTB/SmolLM-1.7B
-    """
-
-    model = SMOLLM_1_7B_WEIGHTS_PATH
-    accuracy = 0.05
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--dtype",
-        "bfloat16",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/llm_models/test_npu_stablelm_2_1_6b.py

@@ -1,34 +0,0 @@
-import unittest
-
-from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
-from sglang.test.ascend.test_ascend_utils import STABLELM_2_1_6B_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestStablelm(GSM8KAscendMixin, CustomTestCase):
-    """Testcase: Verify that the inference accuracy of the stabilityai/stablelm-2-1_6b model on the GSM8K dataset is no less than 0.195.
-
-    [Test Category] Model
-    [Test Target] stabilityai/stablelm-2-1_6b
-    """
-
-    model = STABLELM_2_1_6B_WEIGHTS_PATH
-    accuracy = 0.195
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--tp-size",
-        1,
-        "--enable-torch-compile",
-    ]
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/kimi_k2_6/test_npu_kimi_k2_6_w4a8_16p_in64k_out1k_100ms.py

@@ -1,113 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_multi_node_utils import NIC_NAME
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    KIMI_K2_6_EAGLE3_MODEL_PATH,
-    KIMI_K2_6_W4A8_MODEL_PATH,
-    TestNpuPerfMultiNodePdMixTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=1800,
-    suite="full-8-npu-a3",
-    nightly=True,
-    disabled="Currently it is executed by the npu performance workflow.",
-)
-
-ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "STREAMS_PER_DEVICE": "32",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "64",
-    "DEEPEP_HCCL_BUFFSIZE": "4400",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "HCCL_SOCKET_IFNAME": NIC_NAME,
-    "GLOO_SOCKET_IFNAME": NIC_NAME,
-}
-
-OTHER_ARGS = [
-    "--trust-remote-code",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--dtype",
-    "bfloat16",
-    "--tp-size",
-    32,
-    "--nnodes",
-    2,
-    "--mem-fraction-static",
-    0.68,
-    "--max-running-requests",
-    32,
-    "--chunked-prefill-size",
-    32768,
-    "--context-length",
-    75000,
-    "--enable-multimodal",
-    "--mm-attention-backend",
-    "ascend_attn",
-    "--sampling-backend",
-    "ascend",
-    "--enable-dp-attention",
-    "--dp-size",
-    32,
-    "--moe-a2a-backend",
-    "deepep",
-    "--deepep-mode",
-    "auto",
-    "--cuda-graph-bs",
-    1,
-    "--disable-radix-cache",
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    KIMI_K2_6_EAGLE3_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--reasoning-parser",
-    "kimi_k2",
-    "--tool-call-parser",
-    "kimi_k2",
-]
-
-MODEL_CONFIG = {
-    "model_path": KIMI_K2_6_W4A8_MODEL_PATH,
-    "other_args": OTHER_ARGS,
-    "node_envs": ENVS,
-}
-
-
-class TestNPUKimiK2_6_W4A8_16P_In64k_Out1k_100ms(TestNpuPerfMultiNodePdMixTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model_config = MODEL_CONFIG
-    dataset_name = "random"
-    max_concurrency = 32
-    num_prompts = 32
-    input_len = 64000
-    output_len = 1000
-    random_range_ratio = 1
-    seed = 1
-    tpot = 100
-    output_token_throughput = 160
-
-    def test_npu_kimi_k2_6_w4a8_16p_in64k_out1k_100ms(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/kimi_k2_6/test_npu_kimi_k2_6_w4a8_8p_in3k5_out1k5_20ms.py

@@ -1,126 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_multi_node_utils import NIC_NAME
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    KIMI_K2_6_EAGLE3_MODEL_PATH,
-    KIMI_K2_6_W4A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=1200, suite="nightly-perf-16-npu-a3", nightly=True)
-
-KIMI_K2_6_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "HCCL_SOCKET_IFNAME": NIC_NAME,
-    "GLOO_SOCKET_IFNAME": NIC_NAME,
-    "STREAMS_PER_DEVICE": "32",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "96",
-    "DEEPEP_HCCL_BUFFSIZE": "1200",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_NPU_USE_MLAPO": "1",
-    "SGLANG_NPU_USE_MULTI_STREAM": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "64",
-}
-
-KIMI_K2_6_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--dtype",
-    "bfloat16",
-    "--tp-size",
-    16,
-    "--mem-fraction-static",
-    0.865,
-    "--max-running-requests",
-    80,
-    "--chunked-prefill-size",
-    32768,
-    "--context-length",
-    6144,
-    "--max-prefill-tokens",
-    65536,
-    "--max-total-tokens",
-    32256,
-    "--enable-multimodal",
-    "--mm-attention-backend",
-    "ascend_attn",
-    "--sampling-backend",
-    "ascend",
-    "--enable-dp-attention",
-    "--dp-size",
-    16,
-    "--moe-a2a-backend",
-    "deepep",
-    "--deepep-mode",
-    "auto",
-    "--cuda-graph-bs-decode",
-    1,
-    2,
-    3,
-    4,
-    5,
-    "--disable-radix-cache",
-    "--model-loader-extra-config",
-    '{"enable_multithread_load": true}',
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    KIMI_K2_6_EAGLE3_MODEL_PATH,
-    "--speculative-num-steps",
-    4,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    5,
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--prefill-delayer-max-delay-passes",
-    200,
-    "--enable-prefill-delayer",
-    "--reasoning-parser",
-    "kimi_k2",
-    "--tool-call-parser",
-    "kimi_k2",
-]
-
-
-class TestKimiK25W4A8(
-    TestNpuPerformanceTestCaseBase,
-):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    max_attempts = 5
-    model = KIMI_K2_6_W4A8_MODEL_PATH
-    other_args = KIMI_K2_6_OTHER_ARGS
-    envs = KIMI_K2_6_ENVS
-    backend = "sglang"
-    dataset_name = "random"
-    max_concurrency = 64
-    num_prompts = 256
-    input_len = 3500
-    output_len = 1500
-    random_range_ratio = 1
-    warmup_requests = 0
-    seed = 1
-    tpot = 20
-    output_token_throughput = 1900
-
-    def test_kimi_k2_6_w4a8(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/mimo_v2_flash/test_npu_mimo_v2_flash_1p1d_12p_in16k_out1_ttft_5s.py

@@ -1,191 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    MIMO_V2_FLASH_MODEL_PATH,
-    TestNpuPerfMultiNodePdSepTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="",
-    nightly=True,
-    disabled="performance testcase",
-)
-
-PREFILL_ENVS = {
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "STREAMS_PER_DEVICE": "32",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "32",
-    "DEEPEP_HCCL_BUFFSIZE": "1024",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "SGLANG_NPU_PROFILING": "0",
-    "SGLANG_NPU_PROFILING_STAGE": "prefill",
-    "DEEPEP_NORMAL_LONG_SEQ_ROUND": "32",
-    "DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "3584",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "3600",
-    "SGLANG_DISAGGREGATION_WAITING_TIMEOUT": "3600",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "0",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_BF16_DISPATCH": "0",
-    "SGLANG_DISAGGREGATION_FORCE_QUERY_PREFILL_DP_RANK": "1",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_CONNECT_TIMEOUT": "1800",
-    "ASCEND_USE_FIA": "1",
-}
-
-DECODE_ENVS = {
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "STREAMS_PER_DEVICE": "32",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "128",
-    "DEEPEP_HCCL_BUFFSIZE": "800",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "SGLANG_NPU_PROFILING": "0",
-    "SGLANG_NPU_PROFILING_STAGE": "prefill",
-    "DEEPEP_NORMAL_LONG_SEQ_ROUND": "32",
-    "DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "3584",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "3600",
-    "SGLANG_DISAGGREGATION_WAITING_TIMEOUT": "3600",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "0",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_BF16_DISPATCH": "0",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_CONNECT_TIMEOUT": "1800",
-    "SGLANG_PROFILE_WITH_STACK": "True",
-    "ASCEND_USE_FIA": "1",
-}
-
-PREFILL_ARGS = [
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--tp-size",
-    8,
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--chunked-prefill-size",
-    8192,
-    "--trust-remote-code",
-    "--max-running-requests",
-    64,
-    "--mem-fraction-static",
-    0.8,
-    "--swa-full-tokens-ratio",
-    0.3,
-    "--disaggregation-mode",
-    "prefill",
-    "--disaggregation-transfer-backend",
-    "ascend",
-    "--disable-radix-cache",
-    "--disable-cuda-graph",
-    "--disable-piecewise-cuda-graph",
-    "--dp-size",
-    "2",
-]
-
-DECODE_ARGS = [
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--tp-size",
-    16,
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--trust-remote-code",
-    "--max-running-requests",
-    64,
-    "--mem-fraction-static",
-    0.8,
-    "--swa-full-tokens-ratio",
-    0.3,
-    "--cuda-graph-bs",
-    1,
-    2,
-    4,
-    8,
-    12,
-    16,
-    20,
-    24,
-    28,
-    32,
-    "--disaggregation-mode",
-    "decode",
-    "--disaggregation-transfer-backend",
-    "ascend",
-    "--speculative-algorithm",
-    "EAGLE",
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--enable-multi-layer-eagle",
-    "--disable-radix-cache",
-    "--dp-size",
-    "2",
-    "--enable-dp-attention",
-    "--enable-dp-lm-head",
-    "--moe-a2a-backend",
-    "deepep",
-    "--deepep-mode",
-    "low_latency",
-]
-
-ROUTER_ARGS = [
-    "--health-check-interval-secs",
-    "3600",
-    "--mini-lb",
-]
-
-MODEL_CONFIG = {
-    "model_path": MIMO_V2_FLASH_MODEL_PATH,
-    "prefill_args": PREFILL_ARGS,
-    "decode_args": DECODE_ARGS,
-    "prefill_envs": PREFILL_ENVS,
-    "decode_envs": DECODE_ENVS,
-    "router_args": ROUTER_ARGS,
-    "router_envs": {},
-}
-
-
-class TestNPUMimo_v2_flash_1P1D_16p_In16k_Out1_TTFT_5s(
-    TestNpuPerfMultiNodePdSepTestCaseBase
-):
-    """Test NPU performance for mimo_v2_flash 1P+1D 16p: input_len=16000, output_len=1, 0 cache, TTFT=5s"""
-
-    model_config = MODEL_CONFIG
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    dataset_name = "random"
-    max_concurrency = 64
-    num_prompts = 128
-    request_rate = 0.4
-    input_len = 16000
-    output_len = 1
-    random_range_ratio = 1
-    seed = 1
-    ttft = 5000
-
-    def test_npu_mimo_v2_flash_1p1d_16p_in16k_out1_ttft_5s(self):
-        """Run NPU performance test for 1P+1D 16p with 16k input, 1 output, 0 cache, TTFT=5s"""
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/mimo_v2_flash/test_npu_mimo_v2_flash_1p1d_12p_in16k_out1k_tpot_20ms.py

@@ -1,187 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    MIMO_V2_FLASH_MODEL_PATH,
-    TestNpuPerfMultiNodePdSepTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="",
-    nightly=True,
-    disabled="performance testcase",
-)
-
-PREFILL_ENVS = {
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "STREAMS_PER_DEVICE": "32",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "32",
-    "DEEPEP_HCCL_BUFFSIZE": "1024",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "SGLANG_NPU_PROFILING": "0",
-    "SGLANG_NPU_PROFILING_STAGE": "prefill",
-    "DEEPEP_NORMAL_LONG_SEQ_ROUND": "32",
-    "DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "3584",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "3600",
-    "SGLANG_DISAGGREGATION_WAITING_TIMEOUT": "3600",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "0",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_BF16_DISPATCH": "0",
-    "SGLANG_DISAGGREGATION_FORCE_QUERY_PREFILL_DP_RANK": "1",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_CONNECT_TIMEOUT": "1800",
-    "ASCEND_USE_FIA": "1",
-}
-
-DECODE_ENVS = {
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "STREAMS_PER_DEVICE": "32",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "128",
-    "DEEPEP_HCCL_BUFFSIZE": "800",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "SGLANG_NPU_PROFILING": "0",
-    "SGLANG_NPU_PROFILING_STAGE": "prefill",
-    "DEEPEP_NORMAL_LONG_SEQ_ROUND": "32",
-    "DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "3584",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "3600",
-    "SGLANG_DISAGGREGATION_WAITING_TIMEOUT": "3600",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "0",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_BF16_DISPATCH": "0",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_CONNECT_TIMEOUT": "1800",
-    "SGLANG_PROFILE_WITH_STACK": "True",
-    "ASCEND_USE_FIA": "1",
-}
-
-PREFILL_ARGS = [
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--tp-size",
-    8,
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--chunked-prefill-size",
-    8192,
-    "--trust-remote-code",
-    "--max-running-requests",
-    64,
-    "--mem-fraction-static",
-    0.8,
-    "--swa-full-tokens-ratio",
-    0.3,
-    "--disaggregation-mode",
-    "prefill",
-    "--disaggregation-transfer-backend",
-    "ascend",
-    "--disable-radix-cache",
-    "--disable-cuda-graph",
-    "--disable-piecewise-cuda-graph",
-    "--dp-size",
-    "2",
-]
-
-DECODE_ARGS = [
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--tp-size",
-    16,
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--trust-remote-code",
-    "--max-running-requests",
-    32,
-    "--mem-fraction-static",
-    0.8,
-    "--swa-full-tokens-ratio",
-    0.3,
-    "--cuda-graph-bs",
-    1,
-    2,
-    4,
-    8,
-    12,
-    16,
-    "--disaggregation-mode",
-    "decode",
-    "--disaggregation-transfer-backend",
-    "ascend",
-    "--speculative-algorithm",
-    "EAGLE",
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--enable-multi-layer-eagle",
-    "--disable-radix-cache",
-    "--dp-size",
-    "2",
-    "--enable-dp-attention",
-    "--enable-dp-lm-head",
-    "--moe-a2a-backend",
-    "deepep",
-    "--deepep-mode",
-    "low_latency",
-]
-
-ROUTER_ARGS = [
-    "--health-check-interval-secs",
-    "3600",
-    "--mini-lb",
-]
-
-MODEL_CONFIG = {
-    "model_path": MIMO_V2_FLASH_MODEL_PATH,
-    "prefill_args": PREFILL_ARGS,
-    "decode_args": DECODE_ARGS,
-    "prefill_envs": PREFILL_ENVS,
-    "decode_envs": DECODE_ENVS,
-    "router_args": ROUTER_ARGS,
-    "router_envs": {},
-}
-
-
-class TestNPUMimo_v2_flash_1P1D_16p_In16k_Out1k_TTFT_5s(
-    TestNpuPerfMultiNodePdSepTestCaseBase
-):
-    """Test NPU performance for mimo_v2_flash 1P+1D 16p: input_len=16000, output_len=1000, 0 cache, TPOT=20ms"""
-
-    model_config = MODEL_CONFIG
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    dataset_name = "random"
-    max_concurrency = 32
-    num_prompts = 128
-    request_rate = float("inf")
-    input_len = 16000
-    output_len = 1000
-    random_range_ratio = 1
-    seed = 1
-    tpot = 20
-
-    def test_npu_mimo_v2_flash_1p1d_16p_in16k_out1k_tpot_20ms(self):
-        """Run NPU performance test for 1P+1D 16p with 16k input, 1k output, 0 cache, TPOT=20ms"""
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/minimax_m2_5/test_npu_minimax_m2_5_w8a8_4p_in64k_out1k_prefix90_50ms.py

@@ -1,118 +0,0 @@
-import os
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    BENCHMARK_TOOL_DEFAULT,
-    MINIMAX_M2_5_EAGLE3_MODEL_PATH,
-    MINIMAX_M2_5_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-8-npu-a3", nightly=True)
-
-MINIMAX_M2_5_W8A8_4P_IN64K_OUT1K_PREFIX90_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "STREAMS_PER_DEVICE": "32",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "TASK_QUEUE_ENABLE": "1",
-    "ASCEND_USE_FIA": "1",
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "140000",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "DEEPEP_HCCL_BUFFSIZE": "1024",
-    "SGLANG_EXTERNAL_MODEL_PACKAGE": "custom_eagle3",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "999999999",
-    "PYTHONPATH": f"{MINIMAX_M2_5_EAGLE3_MODEL_PATH}:{os.environ.get('PYTHONPATH', '')}",
-}
-
-MINIMAX_M2_5_W8A8_4P_IN64K_OUT1K_PREFIX90_OTHER_ARGS = [
-    "--tp-size",
-    8,
-    "--mem-fraction-static",
-    0.63,
-    "--max-running-requests",
-    24,
-    "--reasoning-parser",
-    "minimax-append-think",
-    "--tool-call-parser",
-    "minimax-m2",
-    "--enable-prefill-delayer",
-    "--prefill-delayer-max-delay-passes",
-    30,
-    "--prefill-max-requests",
-    10,
-    "--chunked-prefill-size",
-    67072,
-    "--max-prefill-token",
-    67000,
-    "--cuda-graph-bs",
-    2,
-    4,
-    8,
-    12,
-    16,
-    18,
-    20,
-    22,
-    24,
-    "--moe-a2a-backend",
-    "ascend_fuseep",
-    "--fuseep-mode",
-    2,
-    "--deepep-mode",
-    "auto",
-    "--quantization",
-    "modelslim",
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    MINIMAX_M2_5_EAGLE3_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--dtype",
-    "bfloat16",
-    "--trust-remote-code",
-    "--reasoning-parser",
-    "minimax-append-think",
-    "--tool-call-parser",
-    "minimax-m2",
-]
-
-
-class TestNPUMiniMaxM2_5W8A8_4P_In64k_Out1k_Prefix90_50ms(
-    TestNpuPerformanceTestCaseBase
-):
-    """MiniMax-M2.5-w8a8 4p (4 cards) 64k input 1k output with 90% prefix cache performance test"""
-
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    model = MINIMAX_M2_5_W8A8_MODEL_PATH
-    other_args = MINIMAX_M2_5_W8A8_4P_IN64K_OUT1K_PREFIX90_OTHER_ARGS
-    envs = MINIMAX_M2_5_W8A8_4P_IN64K_OUT1K_PREFIX90_ENVS
-    dataset_name = "generated-shared-prefix"
-    max_concurrency = 24
-    num_prompts = 96
-    input_len = 65536
-    output_len = 1024
-    random_range_ratio = 1
-    seed = 1
-    repeat_rate = 0.9
-    tpot = 50
-    output_token_throughput = 390.5859
-    request_rate = float("inf")
-
-    def test_npu_minimax_m2_5_w8a8_4p_in64k_out1k_prefix90_50ms(self):
-        """Run MiniMax-M2.5-w8a8 4p 64k/1k prefix90 performance test"""
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/minimax_m2_5/test_npu_minimax_m2_5_w8a8_8p_in3k5_out1k5_50ms.py

@@ -1,117 +0,0 @@
-import os
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    MINIMAX_M2_5_EAGLE3_MODEL_PATH,
-    MINIMAX_M2_5_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="nightly-perf-16-npu-a3",
-    nightly=True,
-)
-
-MINIMAX_M2_5_HIGH_THROUGHPUT_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "STREAMS_PER_DEVICE": "32",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "TASK_QUEUE_ENABLE": "1",
-    "DEEPEP_HCCL_BUFFSIZE": "1024",
-    "ASCEND_USE_FIA": "1",
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "204800",
-    "PYTHONPATH": f"{MINIMAX_M2_5_EAGLE3_MODEL_PATH}:{os.environ.get('PYTHONPATH', '')}",
-    "SGLANG_EXTERNAL_MODEL_PACKAGE": "custom_eagle3",
-}
-
-MINIMAX_M2_5_HIGH_THROUGHPUT_OTHER_ARGS = [
-    "--tp-size",
-    16,
-    "--enable-dp-attention",
-    "--dp-size",
-    16,
-    "--mem-fraction-static",
-    0.75,
-    "--max-running-requests",
-    320,
-    "--disable-radix-cache",
-    "--reasoning-parser",
-    "minimax-append-think",
-    "--tool-call-parser",
-    "minimax-m2",
-    "--prefill-delayer-max-delay-passes",
-    500,
-    "--enable-prefill-delayer",
-    "--chunked-prefill-size",
-    196608,
-    "--max-prefill-token",
-    8192,
-    "--cuda-graph-bs",
-    1,
-    2,
-    4,
-    8,
-    12,
-    16,
-    20,
-    "--moe-a2a-backend",
-    "ascend_fuseep",
-    "--fuseep-mode",
-    2,
-    "--quantization",
-    "modelslim",
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    MINIMAX_M2_5_EAGLE3_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--dtype",
-    "bfloat16",
-    "--reasoning-parser",
-    "minimax-append-think",
-    "--tool-call-parser",
-    "minimax-m2",
-]
-
-
-class TestNPUMiniMaxM2_5_W8A8_8P_In3k5_Out1k5_HighThroughput(
-    TestNpuPerformanceTestCaseBase
-):
-    """Test NPU performance for MiniMax-M2.5-w8a8 8p single node high throughput in3k5 out1k5"""
-
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = MINIMAX_M2_5_W8A8_MODEL_PATH
-    other_args = MINIMAX_M2_5_HIGH_THROUGHPUT_OTHER_ARGS
-    envs = MINIMAX_M2_5_HIGH_THROUGHPUT_ENVS
-    dataset_name = "random"
-    max_concurrency = 320
-    num_prompts = 1280
-    input_len = 3500
-    output_len = 1500
-    random_range_ratio = 1
-    seed = 1
-    tpot = 50
-    output_token_throughput = 5717.58
-
-    def test_npu_minimax_m2_5_w8a8_8p_in3k5_out1k5_high_throughput(self):
-        """Run NPU performance test for MiniMax-M2.5-w8a8 high throughput"""
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3-8b/test_npu_qwen3_8b_w8a8_1p_in3k5_out1k5_50ms.py

@@ -1,105 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_8B_EAGLE_MODEL_PATH,
-    QWEN3_8B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-2-npu-a3", nightly=True)
-
-QWEN3_8B_ENVS = {
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "50",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "128",
-}
-
-QWEN3_8B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    70,
-    "--max-prefill-tokens",
-    16384,
-    "--disable-radix-cache",
-    "--chunked-prefill-size",
-    16384,
-    "--tp-size",
-    1,
-    "--mem-fraction-static",
-    0.85,
-    "--cuda-graph-bs",
-    8,
-    12,
-    24,
-    36,
-    48,
-    51,
-    55,
-    60,
-    63,
-    64,
-    66,
-    68,
-    70,
-    "--dtype",
-    "bfloat16",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_8B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen8B(TestNpuPerformanceTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_8B_W8A8_MODEL_PATH
-    other_args = QWEN3_8B_OTHER_ARGS
-    envs = QWEN3_8B_ENVS
-    dataset_name = "random"
-    max_concurrency = 64
-    num_prompts = 256
-    input_len = 3500
-    output_len = 1500
-    random_range_ratio = 1
-    seed = 1
-    tpot = 37
-    output_token_throughput = 1586
-
-    def test_qwen3_8b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3-8b/test_npu_qwen3_8b_w8a8_1p_in6k_out1k5_bs16.py

@@ -1,93 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    QWEN3_8B_EAGLE_MODEL_PATH,
-    QWEN3_8B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="nightly-perf-2-npu-a3",
-    nightly=True,
-)
-
-QWEN3_8B_ENVS = {
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-}
-
-QWEN3_8B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    16,
-    "--max-prefill-tokens",
-    16384,
-    "--disable-radix-cache",
-    "--chunked-prefill-size",
-    -1,
-    "--tp-size",
-    2,
-    "--mem-fraction-static",
-    0.894,
-    "--cuda-graph-bs",
-    1,
-    5,
-    15,
-    16,
-    "--dtype",
-    "bfloat16",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_8B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    4,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    5,
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen8B(TestNpuPerformanceTestCaseBase):
-    max_attempts = 5
-    model = QWEN3_8B_W8A8_MODEL_PATH
-    other_args = QWEN3_8B_OTHER_ARGS
-    envs = QWEN3_8B_ENVS
-    dataset_name = "random"
-    max_concurrency = 16
-    num_prompts = 16
-    input_len = 6144
-    output_len = 1500
-    random_range_ratio = 1
-    tpot = 11.79
-    output_token_throughput = 1040.96
-
-    def test_qwen3_8b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_235b_a22b/test_npu_qwen3_235b_w8a8_8p_in3k5_out1k5_50ms.py

@@ -1,122 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_235B_A22B_EAGLE_MODEL_PATH,
-    QWEN3_235B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-16-npu-a3", nightly=True)
-
-QWEN3_235B_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "DEEPEP_HCCL_BUFFSIZE": "570",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_ENABLE_SPEC_V2": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "50",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "16",
-    "SGLANG_NPU_PROFILING": "0",
-    "SGLANG_NPU_PROFILING_BS": "27",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "188416",
-}
-
-QWEN3_235B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    432,
-    "--context-length",
-    8192,
-    "--dtype",
-    "bfloat16",
-    "--chunked-prefill-size",
-    94208,
-    "--max-prefill-tokens",
-    458880,
-    "--sampling-backend",
-    "ascend",
-    "--ep-dispatch-algorithm",
-    "static",
-    "--init-expert-location",
-    "/root/.cache/modelscope/hub/models/hot_map/235B_3_5k_decode.pt",
-    "--disable-radix-cache",
-    "--moe-a2a-backend",
-    "ascend_fuseep",
-    "--fuseep-mode",
-    2,
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_235B_A22B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    "3",
-    "--speculative-eagle-topk",
-    "1",
-    "--speculative-num-draft-tokens",
-    "4",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--tp",
-    "16",
-    "--dp-size",
-    "16",
-    "--enable-dp-attention",
-    "--enable-dp-lm-head",
-    "--mem-fraction-static",
-    "0.8",
-    "--cuda-graph-bs",
-    "1",
-    "2",
-    "4",
-    "8",
-    "16",
-    "20",
-    "24",
-    "26",
-    "27",
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen25",
-]
-
-
-class TestQwen235B(TestNpuPerformanceTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_235B_W8A8_MODEL_PATH
-    other_args = QWEN3_235B_OTHER_ARGS
-    envs = QWEN3_235B_ENVS
-    dataset_name = "random"
-    max_concurrency = 432
-    num_prompts = int(max_concurrency) * 4
-    input_len = 3500
-    output_len = 1500
-    random_range_ratio = 1
-    seed = 1
-    tpot = 50.1
-    output_token_throughput = 6189
-
-    def test_qwen3_235b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_30b_a3b/test_npu_qwen3_30b_w8a8_1p_in3k5_out1k5_50ms.py

@@ -1,110 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_30B_A3B_W8A8_VLLM_MODEL_PATH,
-    QWEN3_A3B_EAGLE_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-2-npu-a3", nightly=True)
-
-QWEN3_30B_A3B_ENVS = {
-    "ASCEND_LAUNCH_BLOCKING": "0",
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "200",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "128",
-    "DEEPEP_HCCL_BUFFSIZE": "400",
-}
-
-QWEN3_30B_A3B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    162,
-    "--disable-radix-cache",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--chunked-prefill-size",
-    -1,
-    "--max-prefill-tokens",
-    35000,
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_A3B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--tp-size",
-    2,
-    "--mem-fraction-static",
-    0.87,
-    "--cuda-graph-bs",
-    1,
-    5,
-    15,
-    40,
-    70,
-    100,
-    120,
-    130,
-    140,
-    146,
-    150,
-    154,
-    156,
-    158,
-    160,
-    162,
-    "--dtype",
-    "bfloat16",
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen30B(TestNpuPerformanceTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_30B_A3B_W8A8_VLLM_MODEL_PATH
-    other_args = QWEN3_30B_A3B_OTHER_ARGS
-    envs = QWEN3_30B_A3B_ENVS
-    dataset_name = "random"
-    max_concurrency = 160
-    num_prompts = int(max_concurrency) * 4
-    input_len = 3500
-    output_len = 1500
-    random_range_ratio = 1
-    seed = 1
-    tpot = 50
-    output_token_throughput = 3200
-
-    def test_qwen3_30b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_32b/test_npu_qwen3_32b_bf16_8p_in18k_out4k_6ms.py

@@ -1,94 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    QWEN3_32B_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="nightly-perf-16-npu-a3",
-    nightly=True,
-)
-
-QWEN3_32B_ENVS = {
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "200",
-}
-
-QWEN3_32B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--max-running-requests",
-    1,
-    "--disable-radix-cache",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--chunked-prefill-size",
-    -1,
-    "--max-prefill-tokens",
-    65536,
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    4,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    5,
-    "--tp-size",
-    16,
-    "--mem-fraction-static",
-    0.72,
-    "--cuda-graph-bs",
-    1,
-    "--dtype",
-    "bfloat16",
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen32B(TestNpuPerformanceTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_32B_MODEL_PATH
-    other_args = QWEN3_32B_OTHER_ARGS
-    envs = QWEN3_32B_ENVS
-    dataset_name = "random"
-    max_concurrency = 1
-    num_prompts = 1
-    input_len = 18000
-    output_len = 4000
-    random_range_ratio = 1
-    seed = 1
-    tpot = 6
-    output_token_throughput = 171
-
-    def test_qwen3_32b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_32b/test_npu_qwen3_32b_w8a8_2p_in3k5_out1k5_50ms.py

@@ -1,107 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    QWEN3_32B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-4-npu-a3", nightly=True)
-
-QWEN3_32B_ENVS = {
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "100",
-    "SGLANG_PREFILL_DELAYER_MAX_PREFILL_BS_WINDOW_SIZE": "128",
-    "SGLANG_NPU_USE_DEEPGEMM": "1",
-}
-
-QWEN3_32B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    101,
-    "--disable-radix-cache",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--chunked-prefill-size",
-    -1,
-    "--max-prefill-tokens",
-    35000,
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--tp-size",
-    4,
-    "--mem-fraction-static",
-    0.845,
-    "--cuda-graph-bs",
-    16,
-    32,
-    64,
-    72,
-    88,
-    90,
-    92,
-    94,
-    96,
-    97,
-    98,
-    99,
-    100,
-    101,
-    "--dtype",
-    "bfloat16",
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen32B(TestNpuPerformanceTestCaseBase):
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_32B_W8A8_MODEL_PATH
-    other_args = QWEN3_32B_OTHER_ARGS
-    envs = QWEN3_32B_ENVS
-    dataset_name = "random"
-    max_concurrency = 100
-    num_prompts = 400
-    input_len = 3584
-    output_len = 1536
-    random_range_ratio = 1
-    seed = 1
-    tpot = 50
-    output_token_throughput = 1600
-
-    def test_qwen3_32b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_32b/test_npu_qwen3_32b_w8a8_2p_in3k5_out1k5_50ms_a2.py

@@ -1,110 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    DEFAULT_URL_FOR_TEST,
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    QWEN3_32B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="nightly-1-npu-a2",
-    nightly=True,
-)
-
-QWEN3_32B_ENVS = {
-    "SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "100",
-    "SGLANG_NPU_USE_DEEPGEMM": "1",
-}
-
-QWEN3_32B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--nnodes",
-    "1",
-    "--node-rank",
-    "0",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--max-running-requests",
-    101,
-    "--disable-radix-cache",
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--chunked-prefill-size",
-    -1,
-    "--max-prefill-tokens",
-    35000,
-    "--speculative-algorithm",
-    "EAGLE3",
-    "--speculative-draft-model-path",
-    QWEN3_32B_EAGLE_MODEL_PATH,
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--tp-size",
-    4,
-    "--mem-fraction-static",
-    0.845,
-    "--cuda-graph-bs",
-    16,
-    32,
-    64,
-    72,
-    88,
-    90,
-    92,
-    94,
-    96,
-    97,
-    98,
-    99,
-    100,
-    101,
-    "--dtype",
-    "bfloat16",
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen",
-]
-
-
-class TestQwen32B(TestNpuPerformanceTestCaseBase):
-    base_url = DEFAULT_URL_FOR_TEST
-    dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_32B_W8A8_MODEL_PATH
-    other_args = QWEN3_32B_OTHER_ARGS
-    envs = QWEN3_32B_ENVS
-    dataset_name = "random"
-    max_concurrency = 100
-    num_prompts = 400
-    input_len = 3584
-    output_len = 1536
-    random_range_ratio = 1
-    seed = 1
-    tpot = 55
-    output_token_throughput = 1500
-
-    def test_qwen3_32b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_6_27b/test_npu_qwen3_6_27b_w8a8_2p_in64k_out1k_50ms.py

@@ -1,100 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    AISBENCHMARK_DATASET_DEFAULT,
-    BENCHMARK_TOOL_DEFAULT,
-    QWEN3_6_27B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=3600,
-    suite="",
-    nightly=True,
-    disabled="performance testcase",
-)
-
-QWEN3_6_27B_64K_1K_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "STREAMS_PER_DEVICE": "32",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "SGLANG_SET_CPU_AFFINITY": "1",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE": "1",
-    "SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES": "30",
-    "ASCEND_USE_FIA": "1",
-}
-
-QWEN3_6_27B_64K_1K_OTHER_ARGS = [
-    "--tp-size",
-    4,
-    "--nnodes",
-    1,
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--chunked-prefill-size",
-    -1,
-    "--max-prefill-tokens",
-    50000,
-    "--disable-radix-cache",
-    "--trust-remote-code",
-    "--max-running-requests",
-    28,
-    "--max-mamba-cache-size",
-    50,
-    "--mem-fraction-static",
-    0.7,
-    "--cuda-graph-bs",
-    2,
-    4,
-    6,
-    "--enable-multimodal",
-    "--quantization",
-    "modelslim",
-    "--mm-attention-backend",
-    "ascend_attn",
-    "--dtype",
-    "bfloat16",
-    "--mamba-ssm-dtype",
-    "bfloat16",
-    "--speculative-algorithm",
-    "NEXTN",
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-]
-
-
-class TestNPUQwen3_6_27B_2P_In64k_Out1k_50ms(TestNpuPerformanceTestCaseBase):
-    """Test NPU performance for Qwen3.6-27B-w8a8 2p in64k out1k 50ms"""
-
-    benchmark_tool = BENCHMARK_TOOL_DEFAULT
-    aisbench_dataset_type = AISBENCHMARK_DATASET_DEFAULT
-    model = QWEN3_6_27B_W8A8_MODEL_PATH
-    other_args = QWEN3_6_27B_64K_1K_OTHER_ARGS
-    envs = QWEN3_6_27B_64K_1K_ENVS
-    dataset_name = "random"
-    max_concurrency = 6
-    num_prompts = 24
-    input_len = 64000
-    output_len = 1000
-    random_range_ratio = 1
-    seed = 1
-    tpot = 50
-    output_token_throughput = 122.6
-
-    def test_npu_qwen3_6_27b_2p_in64k_out1k_50ms(self):
-        """Run NPU performance test for Qwen3.6-27B-w8a8 in64k out1k 50ms"""
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/performance/qwen3_next_80b_a3b_instruct/test_npu_qwen3_next_80b_w8a8_2p_in6k_out1k5_bs16.py

@@ -1,110 +0,0 @@
-import unittest
-
-from sglang.test.ascend.e2e.test_npu_performance_utils import (
-    QWEN3_NEXT_80B_A3B_MODEL_PATH,
-    QWEN3_NEXT_80B_A3B_W8A8_MODEL_PATH,
-    TestNpuPerformanceTestCaseBase,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=3600, suite="nightly-perf-4-npu-a3", nightly=True)
-
-QWEN3_NEXT_80B_A3B_ENVS = {
-    "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
-    "STREAMS_PER_DEVICE": "32",
-    "HCCL_SOCKET_IFNAME": "lo",
-    "GLOO_SOCKET_IFNAME": "lo",
-    "DEEP_NORMAL_MODE_USE_INT8_QUANT": "1",
-    "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "400",
-    "DEEPEP_NORMAL_LONG_SEQ_ROUND": "10",
-    "DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "2048",
-    "HCCL_OP_EXPANSION_MODE": "AIV",
-    "TASK_QUEUE_ENABLE": "1",
-    "ASCEND_USE_FIA": "1",
-    "SGLANG_NPU_USE_MULTI_STREAM": "0",
-    "SGLANG_WARMUP_TIMEOUT": "3600",
-    "SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
-    "FORCE_DRAFT_MODEL_NON_QUANT": "1",
-    "DEEPEP_HCCL_BUFFSIZE": "2000",
-    "ZBCCL_LOCAL_MEM_SIZE": "60416",
-    "SGLANG_ENABLE_TP_MEMORY_INBALANCE_CHECK": "0",
-    "ZBCCL_BOOTSTRAP_URL": "tcp://127.0.0.1:24669",
-    "ZBCCL_NPU_ALLOC_CONF": "use_vmm_for_static_memory:True",
-    "ZBCCL_ENABLE_GRAPH": "1",
-}
-
-QWEN3_NEXT_80B_A3B_OTHER_ARGS = [
-    "--trust-remote-code",
-    "--attention-backend",
-    "ascend",
-    "--device",
-    "npu",
-    "--quantization",
-    "modelslim",
-    "--page-size",
-    128,
-    "--tp-size",
-    4,
-    "--watchdog-timeout",
-    9000,
-    "--mem-fraction-static",
-    0.85,
-    "--disable-radix-cache",
-    "--max-prefill-tokens",
-    28672,
-    "--context-length",
-    81920,
-    "--max-total-tokens",
-    122304,
-    "--dp-size",
-    2,
-    "--enable-dp-attention",
-    "--enable-dp-lm-head",
-    "--speculative-algorithm",
-    "NEXTN",
-    "--speculative-num-steps",
-    3,
-    "--speculative-eagle-topk",
-    1,
-    "--speculative-num-draft-tokens",
-    4,
-    "--speculative-draft-model-quantization",
-    "unquant",
-    "--chunked-prefill-size",
-    -1,
-    "--max-running-requests",
-    16,
-    "--cuda-graph-bs",
-    2,
-    4,
-    8,
-    "--mamba-ssm-dtype",
-    "bfloat16",
-    "--speculative-draft-model-path",
-    QWEN3_NEXT_80B_A3B_MODEL_PATH,
-    "--reasoning-parser",
-    "qwen3",
-    "--tool-call-parser",
-    "qwen3_coder",
-]
-
-
-class TestQwen3Next80BA3B(TestNpuPerformanceTestCaseBase):
-    max_attempts = 5
-    model = QWEN3_NEXT_80B_A3B_W8A8_MODEL_PATH
-    other_args = QWEN3_NEXT_80B_A3B_OTHER_ARGS
-    envs = QWEN3_NEXT_80B_A3B_ENVS
-    dataset_name = "random"
-    max_concurrency = 16
-    num_prompts = 16
-    input_len = 6144
-    output_len = 1500
-    random_range_ratio = 1
-    tpot = 15.62
-
-    def test_qwen3_next_80b_a3b(self):
-        self.run_throughput()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/rerank_models/test_npu_bge_reranker_v2_m3.py

@@ -1,98 +0,0 @@
-import multiprocessing as mp
-import unittest
-
-import torch
-
-from sglang.test.ascend.test_ascend_utils import BGE_RERANKER_V2_M3_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.runners import TEST_RERANK_QUERY_DOCS, HFRunner, SRTRunner
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-1-npu-a3",
-    nightly=True,
-)
-
-MODELS = [
-    (BGE_RERANKER_V2_M3_WEIGHTS_PATH, 1, 1e-2),
-]
-ATTENTION_BACKEND = ["ascend"]
-TORCH_DTYPES = [torch.bfloat16]
-
-
-class TestBgeReranker(CustomTestCase):
-    """Testcase: This test case validates that the cross-encoder scores from the BAAI/bge-reranker-v2-m3 model in the
-    SGLang framework are less than 1e-2 different from the Hugging Face implementation.
-
-    [Test Category] Model
-    [Test Target] BAAI/bge-reranker-v2-m3
-    """
-
-    @classmethod
-    def setUpClass(cls):
-        mp.set_start_method("spawn", force=True)
-
-    def assert_close_prefill_logits(
-        self,
-        prompts,
-        model_path,
-        tp_size,
-        torch_dtype,
-        score_tolerance,
-        attention_backend,
-    ) -> None:
-        with HFRunner(
-            model_path,
-            torch_dtype=torch_dtype,
-            model_type="cross_encoder",
-        ) as hf_runner:
-            hf_scores = hf_runner.forward(prompts).scores
-
-        with SRTRunner(
-            model_path,
-            tp_size=tp_size,
-            torch_dtype=torch_dtype,
-            model_type="cross_encoder",
-            attention_backend=attention_backend,
-            chunked_prefill_size=-1,
-            disable_radix_cache=True,
-        ) as srt_runner:
-            srt_scores = srt_runner.forward(prompts).scores
-
-        for i in range(len(srt_scores)):
-            score_difference = abs(hf_scores[i] - srt_scores[i])
-
-            assert score_difference < score_tolerance, (
-                "cross encoder scores are not all close"
-            )
-
-    def preprocess_prompts(self, prompt):
-        processed_prompts = []
-        query = prompt["query"]
-        documents = prompt["documents"]
-        for document in documents:
-            processed_prompts.append([query, document])
-
-        return processed_prompts
-
-    def test_prefill_logits(self):
-        models_to_test = MODELS
-
-        for model, tp_size, prefill_tolerance in models_to_test:
-            for attention_backend in ATTENTION_BACKEND:
-                for queryDocs in TEST_RERANK_QUERY_DOCS:
-                    prompts = self.preprocess_prompts(queryDocs)
-                    for torch_dtype in TORCH_DTYPES:
-                        self.assert_close_prefill_logits(
-                            prompts,
-                            model,
-                            tp_size,
-                            torch_dtype,
-                            prefill_tolerance,
-                            attention_backend,
-                        )
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/reward_models/test_npu_internlm2_7b_reward.py

@@ -1,65 +0,0 @@
-import os
-
-os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
-import multiprocessing as mp
-import unittest
-
-import torch
-
-from sglang.test.ascend.test_ascend_utils import INTERNLM2_7B_REWARD_WEIGHTS_PATH
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.runners import SRTRunner
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(
-    est_time=400,
-    suite="full-4-npu-a3",
-    nightly=True,
-)
-
-PROMPT = (
-    "What is the range of the numeric output of a sigmoid node in a neural network?"
-)
-RESPONSE1 = "The output of a sigmoid node is bounded between -1 and 1."
-RESPONSE2 = "The output of a sigmoid node is bounded between 0 and 1."
-
-CONVS = [
-    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE1}],
-    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE2}],
-]
-
-
-class TestInternlm2(CustomTestCase):
-    """Testcase: This test case verifies that the Shanghai_AI_Laboratory/internlm2-7b-reward model can successfully generate reward
-    scores for different conversational responses using the SGLang framework, without comparing to a reference implementation.
-
-    [Test Category] Model
-    [Test Target] Shanghai_AI_Laboratory/internlm2-7b-reward
-    """
-
-    model_path = INTERNLM2_7B_REWARD_WEIGHTS_PATH
-    torch_dtype = torch.float16
-
-    @classmethod
-    def setUpClass(cls):
-        mp.set_start_method("spawn", force=True)
-
-    def test_assert_close_reward_scores(self):
-        with SRTRunner(
-            self.model_path,
-            torch_dtype=self.torch_dtype,
-            model_type="reward",
-            trust_remote_code=True,
-            disable_cuda_graph=True,
-            tp_size=4,
-            mem_fraction_static=0.8,
-        ) as srt_runner:
-            prompts = srt_runner.tokenizer.apply_chat_template(CONVS, tokenize=False)
-            srt_outputs = srt_runner.forward(prompts)
-        srt_scores = torch.tensor(srt_outputs.scores)
-        print(f"accuracy: {srt_scores}")
-        self.assertIsInstance(srt_scores, torch.Tensor)
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/reward_models/test_npu_llama_3_1_8b_v0_2.py

@@ -1,86 +0,0 @@
-import multiprocessing as mp
-import unittest
-
-import torch
-
-from sglang.test.ascend.test_ascend_utils import (
-    SKYWORK_REWARD_LLAMA_3_1_8B_V0_2_WEIGHTS_PATH,
-)
-from sglang.test.ci.ci_register import register_npu_ci
-from sglang.test.runners import HFRunner, SRTRunner
-from sglang.test.test_utils import CustomTestCase
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-MODELS = [
-    (SKYWORK_REWARD_LLAMA_3_1_8B_V0_2_WEIGHTS_PATH, 1, 4e-2),
-]
-TORCH_DTYPES = [torch.float16]
-
-PROMPT = (
-    "What is the range of the numeric output of a sigmoid node in a neural network?"
-)
-RESPONSE1 = "The output of a sigmoid node is bounded between -1 and 1."
-RESPONSE2 = "The output of a sigmoid node is bounded between 0 and 1."
-
-CONVS = [
-    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE1}],
-    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE2}],
-]
-
-
-class TestLlama(CustomTestCase):
-    """Testcase: This test case validates that the reward scores from the Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 model
-    in the SGLang framework are less than 4e-2 different from the Hugging Face implementation.
-
-    [Test Category] Model
-    [Test Target] Skywork/Skywork-Reward-Llama-3.1-8B-v0.2
-    """
-
-    @classmethod
-    def setUpClass(cls):
-        mp.set_start_method("spawn", force=True)
-
-    def assert_close_reward_scores(
-        self,
-        convs,
-        model_path,
-        tp_size,
-        torch_dtype,
-        tolerance,
-    ) -> None:
-        with HFRunner(
-            model_path,
-            torch_dtype=torch_dtype,
-            model_type="reward",
-        ) as hf_runner:
-            hf_outputs = hf_runner.forward(convs)
-
-        with SRTRunner(
-            model_path,
-            tp_size=tp_size,
-            torch_dtype=torch_dtype,
-            model_type="reward",
-        ) as srt_runner:
-            prompts = srt_runner.tokenizer.apply_chat_template(convs, tokenize=False)
-            srt_outputs = srt_runner.forward(prompts)
-
-        hf_scores = torch.tensor(hf_outputs.scores)
-        srt_scores = torch.tensor(srt_outputs.scores)
-        print(f"{hf_scores=}")
-        print(f"{srt_scores=}")
-
-        assert torch.all(abs(hf_scores - srt_scores) < tolerance), (
-            "reward scores are not all close"
-        )
-
-    def test_reward_scores(self):
-        for model, tp_size, tolerance in MODELS:
-            for torch_dtype in TORCH_DTYPES:
-                self.assert_close_reward_scores(
-                    CONVS, model, tp_size, torch_dtype, tolerance
-                )
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/vlm_models/test_npu_deepseek_vl2.py

@@ -1,29 +0,0 @@
-import unittest
-
-from sglang.test.ascend.test_ascend_utils import DEEPSEEK_VL2_WEIGHTS_PATH
-from sglang.test.ascend.vlm_utils import TestVLMModels
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(
-    est_time=400,
-    suite="full-4-npu-a3",
-    nightly=True,
-)
-
-
-class TestDeepseekVl2(TestVLMModels):
-    """Testcase: Verify that the inference accuracy of the deepseek-ai/deepseek-vl2 model on the MMMU dataset is no less than 0.2.
-
-    [Test Category] Model
-    [Test Target] deepseek-ai/deepseek-vl2
-    """
-
-    model = DEEPSEEK_VL2_WEIGHTS_PATH
-    mmmu_accuracy = 0.2
-
-    def test_vlm_mmmu_benchmark(self):
-        self._run_vlm_mmmu_test()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/vlm_models/test_npu_llama_3_2_11b_vision_instruct.py

@@ -1,36 +0,0 @@
-import unittest
-
-from sglang.test.ascend.test_ascend_utils import (
-    LLAMA_3_2_11B_VISION_INSTRUCT_WEIGHTS_PATH,
-)
-from sglang.test.ascend.vlm_utils import TestVLMModels
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=400, suite="full-1-npu-a3", nightly=True)
-
-
-class TestLlama3211BVisionInstruct(TestVLMModels):
-    """Testcase: Verify that the inference accuracy of the LLM-Research/Llama-3.2-11B-Vision-Instruct model on the MMMU dataset is no less than 0.2.
-
-    [Test Category] Model
-    [Test Target] LLM-Research/Llama-3.2-11B-Vision-Instruct
-    """
-
-    model = LLAMA_3_2_11B_VISION_INSTRUCT_WEIGHTS_PATH
-    mmmu_accuracy = 0.2
-    other_args = [
-        "--trust-remote-code",
-        "--mem-fraction-static",
-        "0.8",
-        "--attention-backend",
-        "ascend",
-        "--disable-cuda-graph",
-        "--disable-radix-cache",
-    ]
-
-    def test_vlm_mmmu_benchmark(self):
-        self._run_vlm_mmmu_test()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/registered/npu/vlm_models/test_npu_minicpm_v_2_6.py

@@ -1,25 +0,0 @@
-import unittest
-
-from sglang.test.ascend.test_ascend_utils import MINICPM_V_2_6_WEIGHTS_PATH
-from sglang.test.ascend.vlm_utils import TestVLMModels
-from sglang.test.ci.ci_register import register_npu_ci
-
-register_npu_ci(est_time=400, suite="full-4-npu-a3", nightly=True)
-
-
-class TestMiniCPMModelsV(TestVLMModels):
-    """Testcase: Verify that the inference accuracy of the openbmb/MiniCPM-V-2_6 model on the MMMU dataset is no less than 0.2.
-
-    [Test Category] Model
-    [Test Target] openbmb/MiniCPM-V-2_6
-    """
-
-    model = MINICPM_V_2_6_WEIGHTS_PATH
-    mmmu_accuracy = 0.2
-
-    def test_vlm_mmmu_benchmark(self):
-        self._run_vlm_mmmu_test()
-
-
-if __name__ == "__main__":
-    unittest.main()

test/run_suite.py

@@ -153,25 +153,30 @@ NIGHTLY_SUITES = {
     ],
     HWBackend.CPU: [],
     HWBackend.NPU: [
-        "nightly-1-npu-a2",
         "nightly-1-npu-a3",
         "nightly-2-npu-a3",
         "nightly-4-npu-a3",
         "nightly-8-npu-a3",
         "nightly-16-npu-a3",
         "nightly-acc-2-npu-a3",
-        "nightly-acc-4-npu-a3",
         "nightly-acc-8-npu-a3",
         "nightly-acc-16-npu-a3",
         "nightly-perf-2-npu-a3",
         "nightly-perf-4-npu-a3",
-        "nightly-perf-8-npu-a3",
         "nightly-perf-16-npu-a3",
+        "full-4-npu-a2",
         "full-1-npu-a3",
         "full-2-npu-a3",
         "full-4-npu-a3",
         "full-8-npu-a3",
         "full-16-npu-a3",
+        "full-acc-2-npu-a3",
+        "full-acc-4-npu-a3",
+        "full-acc-16-npu-a3",
+        "full-perf-2-npu-a3",
+        "full-perf-4-npu-a3",
+        "full-perf-8-npu-a3",
+        "full-perf-16-npu-a3",
     ],
     HWBackend.XPU: [
         "nightly-xpu-1-gpu",