llama.cpp/.github/workflows/bench.yml

# Benchmark
name: Benchmark

on:
  workflow_dispatch:
    inputs:
      gpu-series:
        description: 'Azure GPU series to run with'
        required: true
        type: choice
        options:
          - Standard_NC4as_T4_v3
          - Standard_NC24ads_A100_v4
          - Standard_NC80adis_H100_v5
      sha:
        description: 'Commit SHA1 to build'
        required: false
        type: string
      duration:
        description: 'Duration of the bench'
        type: string
        default: 10m

  push:
    branches:
      - master
    paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
  pull_request:
    types: [opened, synchronize, reopened]
    paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
  schedule:
    -  cron: '04 2 * * *'

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

jobs:
  bench-server-baseline:
    runs-on: Standard_NC4as_T4_v3
    env:
      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
      N_USERS: 8
      DURATION: 10m
    if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v3
        with:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: Install python env
        id: pipenv
        run: |
          cd examples/server/bench
          python3 -m venv venv
          source venv/bin/activate
          pip install -r requirements.txt

      - name: Prometheus
        id: install_prometheus
        run: |
          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
          tar xzf prometheus*.tar.gz --strip-components=1
          ./prometheus --config.file=examples/server/bench/prometheus.yml &
          while ! nc -z localhost 9090; do
            sleep 0.1
          done

      - name: Install k6
        id: k6_installation
        run: |
          cd examples/server/bench
          wget --quiet https://github.com/grafana/k6/releases/download/v0.49.0/k6-v0.49.0-linux-amd64.tar.gz
          tar xzf k6*.tar.gz --strip-components=1

      - name: Build
        id: cmake_build
        run: |
          set -eux
          mkdir build
          cd build
          cmake .. \
              -DLLAMA_NATIVE=OFF \
              -DLLAMA_BUILD_SERVER=ON \
              -DLLAMA_CURL=ON \
              -DLLAMA_CUBLAS=ON \
              -DCUDAToolkit_ROOT=/usr/local/cuda \
              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
              -DCMAKE_CUDA_ARCHITECTURES=75 \
              -DLLAMA_FATAL_WARNINGS=OFF \
              -DLLAMA_ALL_WARNINGS=OFF \
              -DCMAKE_BUILD_TYPE=Release;
          cmake --build . --config Release -j $(nproc) --target server

      - name: Download the dataset
        id: download_dataset
        run: |
          cd examples/server/bench
          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

      - name: Server bench
        id: server_bench
        run: |
          set -eux

          cd examples/server/bench
          source venv/bin/activate
          BENCH_K6_BIN_PATH=./k6 python bench.py \
              --runner-label ${{ env.RUNNER_LABEL }} \
              --name ${{ github.job }} \
              --branch ${{ github.head_ref || github.ref_name }} \
              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
              --scenario script.js \
              --duration ${{ github.event.inputs.duration || env.DURATION }} \
              --hf-repo ggml-org/models	 \
              --hf-file phi-2/ggml-model-q4_0.gguf \
              --model-path-prefix /models \
              --parallel ${{ env.N_USERS }} \
              -ngl 33 \
              --batch-size 2048 \
              --ubatch-size	256 \
              --ctx-size 16384 \
              --n-prompts 1000 \
              --max-prompt-tokens 1024 \
              --max-tokens 2048

          cat results.github.env >> $GITHUB_ENV

          # Remove dataset as we do not want it in the artefact
          rm ShareGPT_V3_unfiltered_cleaned_split.json

      - uses: actions/upload-artifact@v4
        with:
          name: benchmark-results
          compression-level: 9
          path: |
            examples/server/bench/*.jpg
            examples/server/bench/*.json
            examples/server/bench/*.log

      - name: Commit status
        uses: Sibz/github-status-action@v1
        continue-on-error: true # If not authorized on external repo
        with:
          authToken: ${{secrets.GITHUB_TOKEN}}
          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
          context: bench-server-baseline
          description: |
            ${{ env.BENCH_RESULTS }}
          state: 'success'

      - name: Upload benchmark images
        uses: devicons/public-upload-to-imgur@v2.2.2
        continue-on-error: true # Important as it looks unstable: 503
        id: imgur_step
        with:
          client_id: ${{secrets.IMGUR_CLIENT_ID}}
          path: |
            examples/server/bench/prompt_tokens_seconds.jpg
            examples/server/bench/predicted_tokens_seconds.jpg
            examples/server/bench/kv_cache_usage_ratio.jpg
            examples/server/bench/requests_processing.jpg

      - name: Extract mermaid
        id: set_mermaid
        run: |
          set -eux

          cd examples/server/bench
          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV

          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV

          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV

          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
          echo "EOF" >> $GITHUB_ENV

      - name: Extract image url
        id: extract_image_url
        continue-on-error: true
        run: |
          set -eux

          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV

      - name: Comment PR
        uses: mshick/add-pr-comment@v2
        id: comment_pr
        if: ${{ github.event.pull_request != '' }}
        with:
          message-id: bench-${{ github.job }}-${{ env.RUNNER_LABEL }}
          message: |
            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_: **${{ env.BENCH_ITERATIONS}} iterations** 🚀

            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(90)=${{ env.HTTP_REQ_DURATION_P_90_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_TOKENS_AVG }}tk/s p(90)=${{ env.LLAMACPP_PROMPT_TOKENS_P_90_ }}tk/s **total=${{ env.LLAMACPP_PROMPT_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(90)=${{ env.LLAMACPP_TOKENS_SECOND_P_90_ }}tk/s **total=${{ env.LLAMACPP_COMPLETION_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
            - ${{ env.BENCH_GRAPH_XLABEL }}

            <details>

            <summary>Time series</summary>

            <p align="center">

            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />

            <details>

            <summary>More</summary>

            ```mermaid
            ${{ env.PROMPT_TOKENS_SECONDS }}
            ```

            </details>

            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>

            <details>
                <summary>More</summary>

            ```mermaid
            ${{ env.PREDICTED_TOKENS_SECONDS }}
            ```

            </details>

            </p>

            <details>

            <summary>Details</summary>

            <p align="center">

            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />

            <details>
                <summary>More</summary>

            ```mermaid
            ${{ env.KV_CACHE_USAGE_RATIO }}
            ```

            </details>

            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>

            <details>
                <summary>More</summary>

            ```mermaid
            ${{ env.REQUESTS_PROCESSING }}
            ```

            </details>

            </p>
            </details>
            </details>