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pipeline {
agent {
docker {
image 'nvcr.io/nvidia/pytorch:21.12-py3'
args '--device=/dev/nvidia0 --gpus all --user 0:128 -v /home/TestData:/home/TestData -v $HOME/.cache:/root/.cache --shm-size=8g'
}
}
options {
timeout(time: 2, unit: 'HOURS')
disableConcurrentBuilds()
}
stages {
stage('PyTorch version') {
steps {
sh 'python -c "import torch; print(torch.__version__)"'
sh 'python -c "import torchvision; print(torchvision.__version__)"'
}
}
stage('Install test requirements') {
steps {
sh 'apt-get update && apt-get install -y bc && pip install -r requirements/requirements_test.txt'
}
}
stage('Code formatting checks') {
steps {
sh 'python setup.py style'
}
}
stage('Copyright Headers check') {
steps {
sh 'python tests/check_copyright_header.py --dir .'
}
}
stage('Torch TTS unit tests') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
steps {
sh 'pip install ".[torch_tts]"'
sh 'pip list'
sh 'test $(pip list | grep -c lightning) -eq 0'
sh 'test $(pip list | grep -c omegaconf) -eq 0'
sh 'test $(pip list | grep -c hydra) -eq 0'
sh 'pytest -m "torch_tts" --cpu tests/collections/tts/test_torch_tts.py --relax_numba_compat'
}
}
stage('NeMo Installation') {
steps {
sh './reinstall.sh release'
}
}
stage('PyTorch Lightning version') {
steps {
sh 'python -c "import pytorch_lightning; print(pytorch_lightning.__version__)"'
}
}
stage('PyTorch Lightning DDP Checks') {
steps {
sh 'CUDA_VISIBLE_DEVICES="0,1" python "tests/core_ptl/check_for_ranks.py"'
}
}
stage('Basic Import Checks') {
steps {
sh 'python -c "import nemo.collections.asr as nemo_asr"'
sh 'python -c "import nemo.collections.nlp as nemo_nlp"'
sh 'python -c "import nemo.collections.tts as nemo_tts"'
}
}
stage('L0: Unit Tests GPU') {
steps {
sh 'NEMO_NUMBA_MINVER=0.55 pytest -m "not pleasefixme and not torch_tts" --with_downloads'
}
}
stage('L0: Unit Tests CPU') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
steps {
sh 'CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.55 pytest -m "not pleasefixme and not torch_tts" --cpu --with_downloads --relax_numba_compat'
}
}
stage('L0: TN/ITN Tests CPU') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('En TN grammars') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/text_normalization/normalize.py "1" --cache_dir /home/TestData/nlp/text_norm/ci/grammars/01-27.1'
}
}
stage('En ITN grammars') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/inverse_text_normalization/inverse_normalize.py --language en "twenty" --cache_dir /home/TestData/nlp/text_norm/ci/grammars/01-27.1'
}
}
stage('German ITN and non-deterministic TN') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" pytest tests/nemo_text_processing/de -m "not pleasefixme" --cpu --tn_cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-14'
}
}
stage('Spanish ITN') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" pytest tests/nemo_text_processing/es -m "not pleasefixme" --cpu --tn_cache_dir /home/TestData/nlp/text_norm/ci/grammars/Spanish/9-13'
}
}
stage('Test En non-deterministic TN & Run all En TN/ITN tests (restore grammars from cache)') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/text_normalization/normalize_with_audio.py --text "\$.01" --n_tagged 2 --cache_dir /home/TestData/nlp/text_norm/ci/grammars/01-27.1'
sh 'CUDA_VISIBLE_DEVICES="" pytest tests/nemo_text_processing/en/ -m "not pleasefixme" --cpu --tn_cache_dir /home/TestData/nlp/text_norm/ci/grammars/01-27.1'
}
}
stage('Run Ru ITN and non-deterministic TN & Run all Ru ITN tests') {
steps {
sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/inverse_text_normalization/inverse_normalize.py "двадцать" --cache_dir /home/TestData/nlp/text_norm/ci/grammars/9-13 --language ru'
sh 'CUDA_VISIBLE_DEVICES="" python nemo_text_processing/text_normalization/normalize_with_audio.py --text "25" --n_tagged 2 --cache_dir /home/TestData/nlp/text_norm/ci/grammars/9-13 --language ru'
sh 'CUDA_VISIBLE_DEVICES="" pytest tests/nemo_text_processing/ru/test_ru_inverse_normalization.py -m "not pleasefixme" --cpu --tn_cache_dir /home/TestData/nlp/text_norm/ci/grammars/9-13'
}
}
}
}
stage('L2: NeMo text processing') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: Eng TN') {
steps {
sh 'cd tools/text_processing_deployment && python pynini_export.py --output=/home/TestData/nlp/text_norm/output/ --grammars=tn_grammars --cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-15 --language=en && ls -R /home/TestData/nlp/text_norm/output/ && echo ".far files created "|| exit 1'
sh 'cd nemo_text_processing/text_normalization/ && python run_predict.py --input=/home/TestData/nlp/text_norm/ci/test.txt --input_case="lower_cased" --language=en --output=/home/TestData/nlp/text_norm/output/test.pynini.txt --verbose'
sh 'cat /home/TestData/nlp/text_norm/output/test.pynini.txt'
sh 'cmp --silent /home/TestData/nlp/text_norm/output/test.pynini.txt /home/TestData/nlp/text_norm/ci/test_goal_py_12-10.txt || exit 1'
sh 'rm -rf /home/TestData/nlp/text_norm/output/*'
}
}
stage('L2: Eng ITN export') {
steps {
sh 'cd tools/text_processing_deployment && python pynini_export.py --output=/home/TestData/nlp/text_denorm/output/ --grammars=itn_grammars --cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-15 --language=en && ls -R /home/TestData/nlp/text_denorm/output/ && echo ".far files created "|| exit 1'
sh 'cd nemo_text_processing/inverse_text_normalization/ && python run_predict.py --input=/home/TestData/nlp/text_denorm/ci/test.txt --language=en --output=/home/TestData/nlp/text_denorm/output/test.pynini.txt --verbose'
sh 'cmp --silent /home/TestData/nlp/text_denorm/output/test.pynini.txt /home/TestData/nlp/text_denorm/ci/test_goal_py.txt || exit 1'
sh 'rm -rf /home/TestData/nlp/text_denorm/output/*'
}
}
stage('L2: TN with Audio (audio and raw text)') {
steps {
sh 'cd nemo_text_processing/text_normalization && \
python normalize_with_audio.py --language=en --cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-15 --text "The total amounts to \\$4.76." \
--audio_data /home/TestData/nlp/text_norm/audio_based/audio.wav | tail -n2 | head -n1 > /tmp/out_raw.txt 2>&1 && \
cmp --silent /tmp/out_raw.txt /home/TestData/nlp/text_norm/audio_based/result.txt || exit 1'
}
}
stage('L2: TN with Audio (audio and text file)') {
steps {
sh 'cd nemo_text_processing/text_normalization && \
python normalize_with_audio.py --language=en --cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-15 --text /home/TestData/nlp/text_norm/audio_based/text.txt \
--audio_data /home/TestData/nlp/text_norm/audio_based/audio.wav | tail -n2 | head -n1 > /tmp/out_file.txt 2>&1 && \
cmp --silent /tmp/out_file.txt /home/TestData/nlp/text_norm/audio_based/result.txt || exit 1'
}
}
stage('L2: TN with Audio (manifest)') {
steps {
sh 'cd nemo_text_processing/text_normalization && \
python normalize_with_audio.py --language=en --audio_data /home/TestData/nlp/text_norm/audio_based/manifest.json --n_tagged=120 --cache_dir /home/TestData/nlp/text_norm/ci/grammars/12-15'
}
}
}
}
stage('L0: Computer Vision Integration') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage ('MNIST image classification with LeNet-5 Integration Test - on CPU') {
steps {
sh 'cd examples/cv && \
python mnist_lenet5_image_classification_pure_lightning.py trainer.gpus=0 \
trainer.accelerator=null \
trainer.fast_dev_run=true model.dataset.data_folder=/home/TestData \
&& rm -rf outputs'
}
}
}
}
// We have no integration tests, please enable this when one is added
// stage('L0: Integration Tests GPU') {
// steps {
// sh 'pytest -s -m "integration and not skipduringci and not pleasefixme"'
// }
// }
// stage('L0: Integration Tests CPU') {
// when {
// anyOf{
// branch 'main'
// changeRequest target: 'main'
// }
// }
// steps {
// sh 'pytest -s -m "integration and not pleasefixme" --cpu'
// }
// }
// We have no system tests, please enable this when one is added
// stage('L1: System Tests GPU') {
// steps {
// sh 'pytest -m "system and not skipduringci and not pleasefixme"'
// }
// }
// stage('L1: System Tests CPU') {
// when {
// anyOf{
// branch 'dev
// changeRequest target: 'main'
// }
// }
// steps {
// sh 'pytest -m "system and not pleasefixme" --cpu'
// }
// }
stage('L2: ASR dev run') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Speech to Text') {
steps {
sh 'python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.gpus=[0] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_results'
sh 'rm -rf examples/asr/speech_to_text_results'
}
}
stage('L2: Speech to Text WPE - CitriNet') {
steps {
sh 'python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/citrinet/" --config-name="config_bpe" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
model.tokenizer.type="wpe" \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_wpe_results'
sh 'rm -rf examples/asr/speech_to_text_wpe_results'
}
}
stage('L2: Speech Pre-training - CitriNet') {
steps {
sh 'python examples/asr/speech_pretraining/speech_pre_training.py \
--config-path="../conf/ssl/citrinet/" --config-name="citrinet_ssl_ci" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_pre_training_results'
sh 'rm -rf examples/asr/speech_pre_training_results'
}
}
stage('L2: Speech to Text WPE - Conformer') {
steps {
sh 'python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/conformer" --config-name="conformer_ctc_bpe" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
model.tokenizer.type="wpe" \
model.train_ds.batch_size=4 \
model.validation_ds.batch_size=4 \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_wpe_conformer_results'
sh 'rm -rf examples/asr/speech_to_text_wpe_conformer_results'
}
}
}
}
stage('L2: Speaker dev run') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Speaker Recognition') {
steps {
sh 'python examples/speaker_tasks/recognition/speaker_reco.py \
model.train_ds.batch_size=10 \
model.validation_ds.batch_size=2 \
model.train_ds.manifest_filepath=/home/TestData/an4_speaker/train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_speaker/dev.json \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/speaker_tasks/recognition/speaker_recognition_results'
sh 'rm -rf examples/speaker_tasks/recognition/speaker_recognition_results'
}
}
stage('Speech to Label') {
steps {
sh 'python examples/asr/speech_classification/speech_to_label.py \
model.train_ds.manifest_filepath=/home/TestData/speech_commands/train_manifest.json \
model.validation_ds.manifest_filepath=/home/TestData/speech_commands/test_manifest.json \
model.test_ds.manifest_filepath=/home/TestData/speech_commands/test_manifest.json \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
model.preprocessor._target_=nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor \
~model.preprocessor.window_size \
~model.preprocessor.window_stride \
~model.preprocessor.window \
~model.preprocessor.n_mels \
~model.preprocessor.n_mfcc \
~model.preprocessor.n_fft \
exp_manager.exp_dir=examples/asr/speech_to_label_results'
sh 'rm -rf examples/asr/speech_to_label_results'
}
}
stage('Speaker Diarization with ASR Inference') {
steps {
sh 'python examples/speaker_tasks/diarization/offline_diarization_with_asr.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.speaker_embeddings.model_path=/home/TestData/an4_diarizer/spkr.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.speaker_embeddings.parameters.window_length_in_sec=[1.5] \
diarizer.speaker_embeddings.parameters.shift_length_in_sec=[0.75] \
diarizer.speaker_embeddings.parameters.multiscale_weights=[1.0] \
diarizer.asr.model_path=QuartzNet15x5Base-En \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.out_dir=examples/speaker_tasks/diarization/speaker_diarization_asr_results'
sh 'rm -rf examples/speaker_tasks/diarization/speaker_diarization_asr_results'
}
}
stage('Speaker Diarization Inference') {
steps {
sh 'python examples/speaker_tasks/diarization/offline_diarization.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.speaker_embeddings.model_path=/home/TestData/an4_diarizer/spkr.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.speaker_embeddings.parameters.window_length_in_sec=1.5 \
diarizer.speaker_embeddings.parameters.shift_length_in_sec=0.75 \
diarizer.speaker_embeddings.parameters.multiscale_weights=null \
diarizer.vad.model_path=/home/TestData/an4_diarizer/MatchboxNet_VAD_3x2.nemo \
diarizer.out_dir=examples/speaker_tasks/diarization/speaker_diarization_results'
sh 'rm -rf examples/speaker_tasks/diarization/speaker_diarization_results'
}
}
}
}
// TODO: Enable test after 21.08 container is used.
// stage('L2: ASR DALI dev run') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// parallel {
// stage('Speech to Text - DALI AudioToMelSpectrogramPreprocessor') {
// steps {
// sh 'python examples/asr/asr_ctc/speech_to_text_ctc.py \
// model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
// +model.train_ds.use_dali=True \
// model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
// +model.validation_ds.use_dali=True \
// trainer.gpus=[0] \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_results'
// sh 'rm -rf examples/asr/speech_to_text_results'
// }
// }
// stage('Speech to Text BPE - DALI AudioToMelSpectrogramPreprocessor') {
// steps {
// sh 'python examples/asr/asr_ctc/speech_to_text_bpe.py \
// --config-path="../conf/citrinet/" --config-name="config_bpe" \
// model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
// model.tokenizer.type="wpe" \
// model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
// +model.train_ds.use_dali=True \
// model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
// +model.validation_ds.use_dali=True \
// trainer.gpus=[0] \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_wpe_results'
// sh 'rm -rf examples/asr/speech_to_text_wpe_results'
// }
// }
// // TODO: This would fail due to an unnecessary torchaudio import.
// // To be enabled once torchaudio is available in the container used for CI
// // stage('Speech to Text - DALI AudioToMFCCPreprocessor') {
// // steps {
// // sh 'python examples/asr/asr_ctc/speech_to_text_ctc.py \
// // model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
// // +model.train_ds.use_dali=True \
// // model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
// // +model.validation_ds.use_dali=True \
// // model.preprocessor._target_=nemo.collections.asr.modules.AudioToMFCCPreprocessor \
// // ~model.preprocessor.normalize \
// // ~model.preprocessor.features \
// // ~model.preprocessor.frame_splicing \
// // ~model.preprocessor.dither \
// // ~model.preprocessor.stft_conv \
// // +model.n_mels=64 \
// // +model.n_mfcc=64 \
// // trainer.gpus=[0] \
// // +trainer.fast_dev_run=True \
// // exp_manager.exp_dir=examples/asr/speech_to_text_results'
// // sh 'rm -rf examples/asr/speech_to_text_results'
// // }
// // }
// }
// }
// TODO: Add back once CI is updated
// stage('L2: ASR RNNT dev run') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// parallel {
// stage('Speech to Text - RNNT') {
// steps {
// sh 'STRICT_NUMBA_COMPAT_CHECK=false python examples/asr/asr_transducer/speech_to_text_rnnt.py \
// --config-path="../conf/contextnet_rnnt/" --config-name="config_rnnt.yaml" \
// model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
// model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
// model.train_ds.batch_size=2 \
// model.validation_ds.batch_size=2 \
// trainer.gpus=[0] \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_rnnt_results'
// sh 'rm -rf examples/asr/speech_to_text_rnnt_results'
// }
// }
// stage('L2: Speech to Text RNNT WPE') {
// steps {
// sh 'STRICT_NUMBA_COMPAT_CHECK=false python examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py \
// --config-path="../conf/contextnet_rnnt/" --config-name="config_rnnt_bpe.yaml" \
// model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
// model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
// model.train_ds.batch_size=2 \
// model.validation_ds.batch_size=2 \
// model.tokenizer.dir="/home/TestData/asr_tokenizers/an4_wpe_128/" \
// model.tokenizer.type="wpe" \
// trainer.gpus=[0] \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_rnnt_wpe_results'
// sh 'rm -rf examples/asr/speech_to_text_rnnt_wpe_results'
// }
// }
// }
// }
stage('L2: ASR Multi-dataloader dev run') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Speech to Text multi-dataloader') {
steps {
sh 'python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=[/home/TestData/an4_dataset/an4_val.json,/home/TestData/an4_dataset/an4_val.json] \
trainer.gpus=[0] \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
+trainer.num_sanity_val_steps=1 \
exp_manager.exp_dir=examples/asr/speech_to_text_results'
sh 'rm -rf examples/asr/speech_to_text_results'
}
}
stage('Speech to Label multi-dataloader') {
steps {
sh 'python examples/asr/speech_classification/speech_to_label.py \
model.train_ds.manifest_filepath=/home/TestData/speech_commands/train_manifest.json \
model.validation_ds.manifest_filepath=[/home/TestData/speech_commands/test_manifest.json,/home/TestData/speech_commands/test_manifest.json] \
trainer.gpus=[1] \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
+trainer.num_sanity_val_steps=1 \
model.preprocessor._target_=nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor \
~model.preprocessor.window_size \
~model.preprocessor.window_stride \
~model.preprocessor.window \
~model.preprocessor.n_mels \
~model.preprocessor.n_mfcc \
~model.preprocessor.n_fft \
exp_manager.exp_dir=examples/asr/speech_to_label_results'
sh 'rm -rf examples/asr/speech_to_label_results'
}
}
}
}
stage('L2: Speech Transcription') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Speech to Text Transcribe') {
steps {
sh 'python examples/asr/transcribe_speech.py \
pretrained_name="QuartzNet15x5Base-En" \
audio_dir="/home/TestData/an4_transcribe/test_subset/" \
output_filename="stt_test_res.json" \
cuda=0 \
amp=true'
sh 'rm -rf stt_test_res.json'
}
}
}
}
stage('L2: Segmentation Tool') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
stages {
stage('Install ctc_segmentation requirements') {
steps {
sh 'cd tools/ctc_segmentation && \
pip install -r requirements.txt && \
DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends ffmpeg'
}
}
stage('Parallel ctc_segmentation test') {
failFast true
parallel {
stage('L2: Eng CitriNet with .wav') {
steps {
sh 'cd tools/ctc_segmentation && \
TIME=`date +"%Y-%m-%d-%T"` && \
/bin/bash run_segmentation.sh \
--MODEL_NAME_OR_PATH="stt_en_citrinet_512_gamma_0_25" \
--DATA_DIR=/home/TestData/ctc_segmentation/eng \
--OUTPUT_DIR=/home/TestData/ctc_segmentation/eng/output${TIME} \
--LANGUAGE=en \
--USE_NEMO_NORMALIZATION="TRUE" && \
python /home/TestData/ctc_segmentation/verify_alignment.py \
-r /home/TestData/ctc_segmentation/eng/eng_valid_segments_1.7.txt \
-g /home/TestData/ctc_segmentation/eng/output${TIME}/verified_segments/nv_test_segments.txt && \
rm -rf /home/TestData/ctc_segmentation/eng/output${TIME}'
}
}
stage('L2: Ru QN with mp3') {
steps {
sh 'cd tools/ctc_segmentation && \
TIME=`date +"%Y-%m-%d-%T"` && \
/bin/bash run_segmentation.sh \
--MODEL_NAME_OR_PATH=/home/TestData/ctc_segmentation/QuartzNet15x5-Ru-e512-wer14.45.nemo \
--DATA_DIR=/home/TestData/ctc_segmentation/ru \
--OUTPUT_DIR=/home/TestData/ctc_segmentation/ru/output${TIME} \
--LANGUAGE=ru \
--ADDITIONAL_SPLIT_SYMBOLS=";" && \
python /home/TestData/ctc_segmentation/verify_alignment.py \
-r /home/TestData/ctc_segmentation/ru/valid_ru_segments_1.7.txt \
-g /home/TestData/ctc_segmentation/ru/output${TIME}/verified_segments/ru_segments.txt && \
rm -rf /home/TestData/ctc_segmentation/ru/output${TIME}'
}
}
}
}
}
}
// TODO: add test once megatron-bert is supported again
// stage('L2: Multi-GPU Megatron finetuning') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// parallel {
// stage('L2: Cased Megatron finetuning on MRPC') {
// steps {
// sh 'cd examples/nlp/glue_benchmark && \
// python glue_benchmark.py \
// model.dataset.data_dir=/home/TestData/nlp/glue_fake/MRPC \
// trainer.gpus=[0,1] \
// +trainer.fast_dev_run=true \
// model.dataset.use_cache=false \
// model.language_model.pretrained_model_name=megatron-bert-345m-cased \
// trainer.accelerator=ddp \
// exp_manager=null'
// }
// }
// }
// }
stage('L2: SGD-QA') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: SGD-QA') {
steps {
sh 'cd examples/nlp/dialogue_state_tracking && \
python sgd_qa.py \
model.dataset.data_dir=/home/TestData/nlp/sgd_small \
model.dataset.dialogues_example_dir=sgd_outputs \
model.dataset.task_name=debug_sample \
trainer.max_steps=1 \
trainer.max_epochs=1 \
model.train_ds.batch_size=2 \
model.validation_ds.batch_size=2 \
model.test_ds.batch_size=2 \
model.nemo_path=null \
trainer.val_check_interval=0.0 \
trainer.gpus=[0,1] \
model.dataset.use_cache=false \
model.language_model.pretrained_model_name=bert-base-cased \
trainer.accelerator=ddp \
exp_manager=null && \
rm -rf sgd_outputs'
}
}
stage('GLUE STS-b with AlBERT') {
steps {
sh 'python examples/nlp/glue_benchmark/glue_benchmark.py \
model.dataset.use_cache=false \
model.task_name=sts-b \
model.dataset.data_dir=/home/TestData/nlp/glue_fake/STS-B \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
model.language_model.pretrained_model_name=albert-base-v1 \
exp_manager=null'
}
}
stage('Test Restore with AlBERT') {
steps {
sh 'python examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py \
+do_training=false \
+do_testing=true \
pretrained_model=/home/TestData/nlp/pretrained_models/Punctuation_and_Capitalization_albert.nemo \
+model.test_ds.use_cache=false \
~model.train_ds \
~model.validation_ds \
model.test_ds.ds_item=/home/TestData/nlp/token_classification_punctuation/ \
trainer.gpus=[1] \
exp_manager=null'
}
}
stage('Test Restore with RoBERTa') {
steps {
sh 'python examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py \
+do_training=false \
+do_testing=true \
pretrained_model=/home/TestData/nlp/pretrained_models/Punctuation_and_Capitalization_roberta.nemo \
+model.test_ds.use_cache=false \
~model.train_ds \
~model.validation_ds \
model.test_ds.ds_item=/home/TestData/nlp/token_classification_punctuation/ \
trainer.gpus=[1] \
exp_manager=null'
}
}
}
}
stage('L2: Parallel BERT SQUAD v1.1 / v2.0') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('BERT SQUAD 1.1') {
// Cannot do fast_dev_run because squad needs whole dev dataset
steps {
sh 'cd examples/nlp/question_answering && \
python question_answering_squad.py \
model.train_ds.file=/home/TestData/nlp/squad_mini/v1.1/train-v1.1.json \
model.dataset.use_cache=false \
model.validation_ds.file=/home/TestData/nlp/squad_mini/v1.1/dev-v1.1.json \
model.test_ds.file=/home/TestData/nlp/squad_mini/v1.1/dev-v1.1.json \
model.train_ds.batch_size=2 \
model.train_ds.num_samples=2 \
model.validation_ds.batch_size=2 \
model.validation_ds.num_samples=2 \
model.test_ds.num_samples=2 \
model.test_ds.batch_size=2 \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
model.language_model.pretrained_model_name=bert-base-uncased \
model.dataset.version_2_with_negative=false \
trainer.precision=16 \
trainer.gpus=[0] \
exp_manager=null'
}
}
stage('BERT SQUAD 2.0') {
// Cannot do fast_dev_run because squad needs whole dev dataset
steps {
sh 'cd examples/nlp/question_answering && \
python question_answering_squad.py \
model.train_ds.file=/home/TestData/nlp/squad_mini/v2.0/train-v2.0.json \
model.dataset.use_cache=false \
model.train_ds.batch_size=2 \
model.train_ds.num_samples=2 \
model.validation_ds.batch_size=2 \
model.validation_ds.num_samples=2 \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
model.validation_ds.file=/home/TestData/nlp/squad_mini/v2.0/dev-v2.0.json \
model.language_model.pretrained_model_name=bert-base-uncased \
model.dataset.version_2_with_negative=true \
trainer.precision=16 \
trainer.gpus=[1] \
exp_manager=null'
}
}
stage('Duplex Text Normalization with Tarred dataset') {
steps {
sh 'cd examples/nlp/duplex_text_normalization && \
python duplex_text_normalization_train.py \
data.validation_ds.data_path=/home/TestData/nlp/duplex_text_norm/small_test.tsv \
mode=tn \
lang=en \
tagger_model.do_training=false \
decoder_model.transformer=t5-small \
data.validation_ds.batch_size=2 \
data.train_ds.use_cache=false \
data.validation_ds.use_cache=false \
data.test_ds.batch_size=2 \
data.train_ds.decoder_data_augmentation=false \
data.train_ds.num_workers=2 \
decoder_trainer.gpus=[0,1] \
data.train_ds.use_tarred_dataset=true \
+decoder_trainer.fast_dev_run=true \
decoder_exp_manager.create_checkpoint_callback=false \
data.train_ds.tar_metadata_file=/home/TestData/nlp/duplex_text_norm/tarred_small/metadata.json \
data.test_ds.use_cache=false \
data.test_ds.data_path=/home/TestData/nlp/duplex_text_norm/small_test.tsv'
}
}
}
}
// Runs out of memory on the 12G TITAN V (GPU 0 on main CI)
// TODO: add when megatron bert is supported again in NeMo
// stage('L2: MegaBERT Token Classification') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps {
// sh 'cd examples/nlp/token_classification && \
// python token_classification_train.py \
// model.dataset.data_dir=/home/TestData/nlp/token_classification_punctuation/ \
// model.language_model.pretrained_model_name=megatron-bert-345m-uncased \
// model.train_ds.batch_size=10 \
// model.dataset.max_seq_length=50 \
// model.dataset.use_cache=false \
// trainer.accelerator=ddp \
// trainer.precision=16 \
// trainer.gpus=[1] \
// +trainer.fast_dev_run=true \
// exp_manager=null'
// }
// }
stage('L2: Parallel SQUAD v1.1 & v2.0') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
// TODO: use megatron bert when supported again
stage('SQUAD v2.0 with DistilBERT Uncased') {
// stage('SQUAD v2.0 with Megatron with ckpt & config') {
// Cannot do fast_dev_run because squad needs whole dev dataset
// model.language_model.pretrained_model_name=megatron-bert-uncased \
// model.language_model.lm_checkpoint=/home/TestData/nlp/megatron_345m_uncased/model_optim_rng.pt \
// model.language_model.config_file=/home/TestData/nlp/megatron_345m_uncased/345m_config.json \
steps {
sh 'cd examples/nlp/question_answering && \
python question_answering_squad.py \
model.train_ds.file=/home/TestData/nlp/squad_mini/v2.0/train-v2.0.json \
model.dataset.use_cache=false \
model.train_ds.batch_size=1 \
model.train_ds.num_samples=1 \
model.validation_ds.batch_size=1 \
model.validation_ds.num_samples=1 \
trainer.accelerator=ddp \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
model.validation_ds.file=/home/TestData/nlp/squad_mini/v2.0/dev-v2.0.json \
model.language_model.pretrained_model_name=distilbert-base-uncased \
model.dataset.version_2_with_negative=true \
trainer.precision=16 \
trainer.gpus=[1] \
exp_manager=null'
}
}
stage('RoBERTa SQUAD 1.1') {
// Cannot do fast_dev_run because squad needs whole dev dataset
steps {
sh 'cd examples/nlp/question_answering && \
python question_answering_squad.py \
model.train_ds.file=/home/TestData/nlp/squad_mini/v1.1/train-v1.1.json \
model.dataset.use_cache=false \
model.train_ds.batch_size=2 \
model.train_ds.num_samples=2 \
model.validation_ds.batch_size=2 \
model.validation_ds.num_samples=2 \
trainer.max_epochs=1 \
+trainer.max_steps=1 \
model.validation_ds.file=/home/TestData/nlp/squad_mini/v1.1/dev-v1.1.json \
model.language_model.pretrained_model_name=roberta-base \
model.dataset.version_2_with_negative=false \
trainer.precision=16 \
trainer.gpus=[0] \
exp_manager=null'
}
}
stage ('Text Classification with BERT Test') {
steps {
sh 'cd examples/nlp/text_classification && \
python text_classification_with_bert.py \
model.dataset.num_classes=6 \
model.train_ds.file_path=/home/TestData/nlp/retail_text_classification/train.tsv \
model.validation_ds.file_path=/home/TestData/nlp/retail_text_classification/dev.tsv \
model.language_model.pretrained_model_name=distilbert-base-uncased \
model.train_ds.batch_size=10 \
model.dataset.max_seq_length=50 \
model.dataset.use_cache=false \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
exp_manager=null'
}
}
stage('L2: Intent and Slot Classification') {
steps {
sh 'cd examples/nlp/intent_slot_classification && \
python intent_slot_classification.py \
model.data_dir=/home/TestData/nlp/retail \
model.validation_ds.prefix=dev \
model.test_ds.prefix=dev \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
exp_manager.exp_dir=checkpoints'
sh 'rm -rf checkpoints'
}
}
}
}
// TODO: add when megatron-bert is supported again
// stage('L2: Model Parallel Size 2 Megatron Text Classification') {
// when {
// anyOf{
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps{
// sh 'cd examples/nlp/text_classification && \
// python text_classification_with_bert.py \
// trainer.gpus=[0,1] \
// trainer.num_nodes=1 \
// trainer.precision=16 \
// trainer.gradient_clip_val=1.0 \
// +trainer.fast_dev_run=true \
// model.dataset.num_classes=6 \
// model.train_ds.file_path=/home/TestData/nlp/retail_text_classification/train.tsv \
// model.train_ds.batch_size=4 \
// model.language_model.pretrained_model_name=megatron-bert-uncased \
// model.language_model.config_file=/home/TestData/nlp/mp_2_bert_toy/config.json \
// model.language_model.lm_checkpoint=/home/TestData/nlp/mp_2_bert_toy/iter_2000000 \
// model.nemo_path=null \
// ~model.infer_samples \
// exp_manager=null'
// }
// }
// stage('L2: Model Parallel Size 2 Megatron Autoresume') {
// when {
// anyOf{
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps{
// sh 'cd examples/nlp/text_classification && \
// python text_classification_with_bert.py \
// trainer.gpus=[0,1] \
// trainer.num_nodes=1 \
// trainer.precision=16 \
// trainer.gradient_clip_val=1.0 \
// trainer.max_epochs=1 \
// +trainer.fast_dev_run=true \
// model.dataset.num_classes=6 \
// model.train_ds.file_path=/home/TestData/nlp/retail_text_classification/train.tsv \
// model.train_ds.batch_size=4 \
// model.language_model.pretrained_model_name=megatron-bert-uncased \
// model.language_model.config_file=/home/TestData/nlp/mp_2_bert_toy/config.json \
// model.language_model.lm_checkpoint=/home/TestData/nlp/mp_2_bert_toy/iter_2000000 \
// model.nemo_path=null \
// ~model.infer_samples \
// +exp_manager.explicit_log_dir=/home/TestData/nlp/mp_autoresume \
// +exp_manager.resume_if_exists=true'
// }
// }
// stage('L2: Model Parallel Size 2 Megatron Evaluation from .nemo') {
// when {
// anyOf{
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps{