Patch for Perf Bench (modelscope#506)

* * refine perf bench workflow
* fix wrong var in sphinx docs

* * refine perf bench workflow

* * fix wrong var in sphinx docs

* * set python version matrix to include only 3.9 and 3.10

* * hide unnecessary logs

* * update mem_required for image tagging models

* * enable unittests for 3 OPs due to dependency

* + add two dependencies by librosa

Author: HYLcool

.github/workflows/deploy_sphinx_docs.yml

@@ -12,13 +12,16 @@ on:
 jobs:
   pages:
     runs-on: ubuntu-20.04
+    strategy:
+      matrix:
+        python-version: [ "3.9", "3.10" ]
     steps:
     - name: Checkout
       uses: actions/checkout@v4
     - name: Setup Python ${{ matrix.python-version }}
       uses: actions/setup-python@master
       with:
-        python_version: ${{ matrix.python-version }}
+        python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/perf-bench.yml

@@ -16,8 +16,8 @@ env:
   ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true
 
 jobs:
-  unittest-single:
-    runs-on: [self-hosted, linux]
+  perf_bench:
+    runs-on: [GPU, unittest]
     environment: Testing
     steps:
     - uses: actions/checkout@v3
@@ -42,7 +42,7 @@ jobs:
     - name: Run performance benchmark standalone
       working-directory: dj-${{ github.run_id }}/.github/workflows/docker
       run: |
-        docker compose exec ray-head python tests/benchmark_performance/run.sh ${{ secrets.INTERNAL_WANDB_URL }} ${{ secrets.INTERNAL_WANDB_API_KEY }}
+        docker compose exec ray-head bash tests/benchmark_performance/run.sh ${{ secrets.INTERNAL_WANDB_URL }} ${{ secrets.INTERNAL_WANDB_API_KEY }}
 
     - name: Remove docker compose
       working-directory: dj-${{ github.run_id }}/.github/workflows/docker

configs/config_all.yaml

@@ -212,6 +212,7 @@ process:
       radius: 2                                               # radius of blur kernel
   - image_tagging_mapper:                                   # Mapper to generate image tags.
       tag_field_name: '__dj__image_tags__'                    # the field name to store the tags. It's "__dj__image_tags__" in default.
+      mem_required: '9GB'
   - nlpaug_en_mapper:                                       # simply augment texts in English based on the nlpaug library
       sequential: false                                       # whether combine all augmentation methods to a sequence. If it's True, a sample will be augmented by all opened augmentation methods sequentially. If it's False, each opened augmentation method would generate its augmented samples independently.
       aug_num: 1                                              # number of augmented samples to be generated. If `sequential` is True, there will be total aug_num augmented samples generated. If it's False, there will be (aug_num * #opened_aug_method) augmented samples generated.
@@ -382,6 +383,7 @@ process:
       frame_sampling_method: 'all_keyframes'                  # sampling method of extracting frame images from the videos. Should be one of ["all_keyframes", "uniform"]. The former one extracts all key frames and the latter one extract specified number of frames uniformly from the video. Default: "all_keyframes".
       frame_num: 3                                            # the number of frames to be extracted uniformly from the video. Only works when frame_sampling_method is "uniform". If it's 1, only the middle frame will be extracted. If it's 2, only the first and the last frames will be extracted. If it's larger than 2, in addition to the first and the last frames, other frames will be extracted uniformly within the video duration.
       tag_field_name: '__dj__video_frame_tags__'              # the field name to store the tags. It's "__dj__video_frame_tags__" in default.
+      mem_required: '9GB'
   - whitespace_normalization_mapper:                        # normalize different kinds of whitespaces to English whitespace.
 
   # Filter ops
@@ -614,6 +616,7 @@ process:
       frame_num: 3                                            # the number of frames to be extracted uniformly from the video. Only works when frame_sampling_method is "uniform". If it's 1, only the middle frame will be extracted. If it's 2, only the first and the last frames will be extracted. If it's larger than 2, in addition to the first and the last frames, other frames will be extracted uniformly within the video duration.
       tag_field_name: '__dj__video_frame_tags__'              # the field name to store the tags. It's "__dj__video_frame_tags__" in default.
       any_or_all: any                                         # keep this sample when any/all videos meet the filter condition
+      mem_required: '9GB'
   - words_num_filter:                                       # filter text with number of words out of specific range
       lang: en                                                # sample in which language
       tokenization: false                                     # whether to use model to tokenize documents

environments/minimal_requires.txt

@@ -4,7 +4,11 @@ pandas
 numpy
 av==13.1.0
 soundfile
+# need to install two dependencies by librosa to avoid lazy_loader error
 librosa>=0.10
+samplerate
+resampy
+# need to install two dependencies by librosa to avoid lazy_loader error
 loguru
 tabulate
 tqdm

tests/benchmark_performance/configs/video.yaml

@@ -14,6 +14,7 @@ process:
       score_threshold: 1.0
       mem_required: '1GB'
   - video_tagging_from_frames_mapper:
+      mem_required: '9GB'
   - video_duration_filter:
   - video_split_by_key_frame_mapper:
       keep_original_sample: false

tests/benchmark_performance/run.sh

@@ -11,7 +11,7 @@ MODALITIES=("text" "image" "video" "audio")
 cd $BENCH_PATH
 
 # 1. prepare dataset
-wget http://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/data_juicer/perf_bench_data/perf_bench_data.tar.gz && tar zxvf perf_bench_data.tar.gz
+wget -q http://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/data_juicer/perf_bench_data/perf_bench_data.tar.gz && tar zxf perf_bench_data.tar.gz
 
 # 2. run the benchmark
 for modality in ${MODALITIES[@]}

tests/ops/filter/test_audio_duration_filter.py

@@ -5,11 +5,8 @@
 
 from data_juicer.ops.filter.audio_duration_filter import AudioDurationFilter
 from data_juicer.utils.constant import Fields
-from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase, TEST_TAG, SKIPPED_TESTS
+from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase, TEST_TAG
 
-# skip due to conflicts when run lazy_load in multiprocessing in librosa
-# tests passed locally.
-@SKIPPED_TESTS.register_module()
 class AudioDurationFilterTest(DataJuicerTestCaseBase):
 
     data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',

tests/ops/filter/test_audio_nmf_snr_filter.py

@@ -5,11 +5,8 @@
 
 from data_juicer.ops.filter.audio_nmf_snr_filter import AudioNMFSNRFilter
 from data_juicer.utils.constant import Fields
-from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase, SKIPPED_TESTS
+from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase
 
-# skip due to conflicts when run lazy_load in multiprocessing in librosa
-# tests passed locally.
-@SKIPPED_TESTS.register_module()
 class AudioNMFSNRFilterTest(DataJuicerTestCaseBase):
 
     data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',

tests/ops/mapper/test_video_tagging_from_audio_mapper.py

@@ -6,11 +6,8 @@
     VideoTaggingFromAudioMapper
 from data_juicer.utils.constant import Fields
 from data_juicer.utils.mm_utils import SpecialTokens
-from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase, SKIPPED_TESTS
+from data_juicer.utils.unittest_utils import DataJuicerTestCaseBase
 
-# skip due to conflicts when run lazy_load in multiprocessing in librosa
-# tests passed locally.
-@SKIPPED_TESTS.register_module()
 class VideoTaggingFromAudioMapperTest(DataJuicerTestCaseBase):
     data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
                              'data')