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pipeline {
agent {
docker {
image 'nvcr.io/nvidia/pytorch:23.04-py3'
args '--device=/dev/nvidia0 --gpus all --user 0:128 -v /home/TestData:/home/TestData -v $HOME/.cache:/root/.cache --shm-size=8g --env TRANSFORMERS_OFFLINE=1'
}
}
options {
timeout(time: 2, unit: 'HOURS')
disableConcurrentBuilds(abortPrevious: true)
}
stages {
stage('Add git safe directory'){
steps{
sh 'git config --global --add safe.directory /var/lib/jenkins/workspace/NeMo_$GIT_BRANCH'
sh 'git config --global --add safe.directory /raid/JenkinsWorkDir/workspace/NeMo_$GIT_BRANCH'
sh 'git config --global --add safe.directory /mnt/D3/JenkinsWorkDir/workspace/NeMo_$GIT_BRANCH'
}
}
stage('nvidia-smi'){
steps{
sh 'nvidia-smi'
}
}
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('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=0.53 pytest -m "not pleasefixme" --with_downloads'
}
}
stage('L0: Unit Tests CPU') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
steps {
sh 'CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat'
}
}
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.devices=[0] \
trainer.accelerator="gpu" \
+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.devices=[1] \
trainer.accelerator="gpu" \
+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.devices=[1] \
trainer.accelerator="gpu" \
+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 Pre-training - Wav2Vec') {
steps {
sh 'python examples/asr/speech_pretraining/speech_pre_training.py \
--config-path="../conf/ssl/wav2vec/" --config-name="wav2vec_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.devices=[1] \
trainer.accelerator="gpu" \
+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.devices=[1] \
trainer.accelerator="gpu" \
+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: ASR dev run - part two') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: Speech to Text WPE - Squeezeformer') {
steps {
sh 'python examples/asr/asr_ctc/speech_to_text_ctc_bpe.py \
--config-path="../conf/squeezeformer" --config-name="squeezeformer_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.encoder.d_model=144 \
model.train_ds.batch_size=4 \
model.validation_ds.batch_size=4 \
trainer.devices=[0] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_wpe_squeezeformer_results'
sh 'rm -rf examples/asr/speech_to_text_wpe_squeezeformer_results'
}
}
}
}
stage('L2: Speech to Text EMA') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
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.devices=2 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
+exp_manager.ema.enable=True \
exp_manager.exp_dir=examples/asr/speech_to_text_results'
sh 'rm -rf examples/asr/speech_to_text_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 \
model.decoder.num_classes=2 \
trainer.max_epochs=10 \
trainer.devices=[1] \
trainer.accelerator="gpu" \
+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('Speaker Diarization') {
steps {
sh 'python examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder.py \
model.diarizer.speaker_embeddings.model_path=titanet_large \
model.train_ds.batch_size=5 \
model.validation_ds.batch_size=5 \
model.train_ds.emb_dir=examples/speaker_tasks/diarization/speaker_diarization_results \
model.validation_ds.emb_dir=examples/speaker_tasks/diarization/speaker_diarization_results \
model.train_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_train/msdd_data.50step.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_diarizer/simulated_valid/msdd_data.50step.json \
trainer.devices=[1] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/speaker_tasks/diarization/speaker_diarization_results'
sh 'rm -rf examples/speaker_tasks/diarization/speaker_diarization_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.devices=[1] \
trainer.accelerator="gpu" \
+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/clustering_diarizer/offline_diar_with_asr_infer.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('Clustering Diarizer Inference') {
steps {
sh 'python examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_infer.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/clustering_diarizer_results'
sh 'rm -rf examples/speaker_tasks/diarization/clustering_diarizer_results'
}
}
stage('Neural Diarizer Inference') {
steps {
sh 'python examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py \
diarizer.manifest_filepath=/home/TestData/an4_diarizer/an4_manifest.json \
diarizer.msdd_model.model_path=/home/TestData/an4_diarizer/diar_msdd_telephonic.nemo \
diarizer.speaker_embeddings.parameters.save_embeddings=True \
diarizer.vad.model_path=/home/TestData/an4_diarizer/MatchboxNet_VAD_3x2.nemo \
diarizer.out_dir=examples/speaker_tasks/diarization/neural_diarizer_results'
sh 'rm -rf examples/speaker_tasks/diarization/neural_diarizer_results'
}
}
stage('Multispeaker ASR Data Simulation') {
steps {
sh 'python tools/speech_data_simulator/multispeaker_simulator.py \
--config-path=conf --config-name=data_simulator.yaml \
data_simulator.random_seed=42 \
data_simulator.manifest_filepath=/home/TestData/LibriSpeechShort/dev-clean-align-short.json \
data_simulator.outputs.output_dir=./test_simulator \
data_simulator.session_config.num_sessions=2 \
data_simulator.session_config.session_length=60'
sh 'rm -rf ./test_simulator'
}
}
}
}
// 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.devices=[0] \
// trainer.accelerator="gpu" \
// +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.devices=[0] \
// trainer.accelerator="gpu" \
// +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.devices=[1] \
// // trainer.accelerator="gpu" \
// // +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.devices=[0] \
// trainer.accelerator="gpu" \
// +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.devices=[0] \
// trainer.accelerator="gpu" \
// +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('L3: Speech to Text Hybrid Transducer-CTC WPE') {
// steps {
// sh 'STRICT_NUMBA_COMPAT_CHECK=false python examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py \
// --config-path="../conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc/" --config-name="conformer_hybrid_transducer_ctc_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.encoder.n_layers= 2 \
// 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.devices=[0] \
// trainer.accelerator="gpu" \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_hybrid_transducer_ctc_wpe_results'
// sh 'rm -rf examples/asr/speech_to_text_hybrid_transducer_ctc_wpe_results'
// }
// }
// }
// }
// stage('L2: Hybrid ASR RNNT-CTC dev run') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// parallel {
// stage('Speech to Text Hybrid Transducer-CTC WPE') {
// steps {
// sh 'STRICT_NUMBA_COMPAT_CHECK=false python examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py \
// --config-path="../conf/conformer/hybrid_transducer_ctc/conformer_hybrid_transducer_ctc/" --config-name="conformer_hybrid_transducer_ctc_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.encoder.n_layers= 2 \
// 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.devices=[0] \
// trainer.accelerator="gpu" \
// +trainer.fast_dev_run=True \
// exp_manager.exp_dir=examples/asr/speech_to_text_hybrid_transducer_ctc_wpe_results'
// sh 'rm -rf examples/asr/speech_to_text_hybrid_transducer_ctc_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.devices=[0] \
trainer.accelerator="gpu" \
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.devices=[1] \
trainer.accelerator="gpu" \
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: ASR Adapters') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Linear Adapters') {
steps {
sh 'python examples/asr/asr_adapters/train_asr_adapter.py \
model.pretrained_model="stt_en_conformer_ctc_small" \
model.adapter.adapter_name="an4" \
model.adapter.linear.in_features=176 \
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.max_steps=5 \
trainer.devices=[0] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_adapters_results'
sh 'rm -rf examples/asr/speech_to_text_adapters_results'
}
}
stage('RelPos MHA Adapters') {
steps {
sh 'python examples/asr/asr_adapters/train_asr_adapter.py \
model.pretrained_model="stt_en_conformer_ctc_small" \
model.adapter.adapter_name="encoder:an4" \
model.adapter.adapter_type="tiny_attn" \
model.adapter.tiny_attn.n_feat=176 \
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.max_steps=5 \
trainer.devices=[0] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_adapters_mha_results'
sh 'rm -rf examples/asr/speech_to_text_adapters_mha_results'
}
}
}
}
stage('L2: Megatron T5 Adapter PP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('T5 Adapter tuning & inference TP=1 PP=2') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_t5_adapter_tuning.py \
--config-name=megatron_t5_adapter_tuning_config \
name='test_tp1_pp2' \
exp_manager.exp_dir='examples/adapter_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp1_pp2.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_t5_adapter_eval.py \
--config-name=megatron_t5_adapter_inference \
adapter_model_file='examples/adapter_tuning/test_tp1_pp2.nemo' \
language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp1_pp2.nemo' \
trainer.devices=2 \
data.num_workers=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=2 \
data.global_batch_size=2 \
data.micro_batch_size=2 \
data.test_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
pred_file_path='examples/adapter_tuning/test_tp1_pp2/preds.txt'"
sh "rm -rf examples/adapter_tuning/test_tp1_pp2.nemo"
sh "rm -rf examples/adapter_tuning/test_tp1_pp2"
}
}
}
}
stage('L2: Megatron T5 Adapter TP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('T5 Adapter tuning & inference TP=2 PP=1') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_t5_adapter_tuning.py \
--config-name=megatron_t5_adapter_tuning_config \
name='test_tp2_pp1' \
exp_manager.exp_dir='examples/adapter_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp2.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_t5_adapter_eval.py \
--config-name=megatron_t5_adapter_inference \
adapter_model_file='examples/adapter_tuning/test_tp2_pp1.nemo' \
language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp2.nemo' \
trainer.devices=2 \
tensor_model_parallel_size=2 \
data.global_batch_size=2 \
data.micro_batch_size=2 \
data.num_workers=1 \
data.test_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
pred_file_path='examples/adapter_tuning/test_tp2_pp1/preds.txt'"
sh "rm -rf examples/adapter_tuning/test_tp2_pp1.nemo"
sh "rm -rf examples/adapter_tuning/test_tp2_pp1"
}
}
}
}
stage('L2: Megatron T5 IA3 PP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('T5 IA3 tuning & inference TP=1 PP=2') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_t5_ia3_tuning.py \
--config-name=megatron_t5_ia3_tuning_config \
name='test_tp1_pp2' \
exp_manager.exp_dir='examples/ia3_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp1_pp2.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_t5_ia3_eval.py \
--config-name=megatron_t5_ia3_inference \
adapter_model_file='examples/ia3_tuning/test_tp1_pp2.nemo' \
language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp1_pp2.nemo' \
trainer.devices=2 \
data.num_workers=1 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=2 \
data.global_batch_size=2 \
data.micro_batch_size=2 \
data.test_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
pred_file_path='examples/ia3_tuning/test_tp1_pp2/preds.txt'"
sh "rm -rf examples/ia3_tuning/test_tp1_pp2.nemo"
sh "rm -rf examples/ia3_tuning/test_tp1_pp2"
}
}
}
}
stage('L2: Megatron T5 IA3 TP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('T5 IA3 tuning & inference TP=2 PP=1') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_t5_ia3_tuning.py \
--config-name=megatron_t5_ia3_tuning_config \
name='test_tp2_pp1' \
exp_manager.exp_dir='examples/ia3_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp2.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_t5_ia3_eval.py \
--config-name=megatron_t5_ia3_inference \
adapter_model_file='examples/ia3_tuning/test_tp2_pp1.nemo' \
language_model_path='/home/TestData/nlp/megatron_t5/8m/megatron_t5_8m_tp2.nemo' \
trainer.devices=2 \
data.num_workers=1 \
tensor_model_parallel_size=2 \
data.global_batch_size=2 \
data.micro_batch_size=2 \
data.test_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
pred_file_path='examples/ia3_tuning/test_tp2_pp1/preds.txt'"
sh "rm -rf examples/ia3_tuning/test_tp2_pp1.nemo"
sh "rm -rf examples/ia3_tuning/test_tp2_pp1"
}
}
}
}
stage('L2: Megatron GPT Adapter TP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('GPT Adapter tuning & inference TP=2 PP=1') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_gpt_adapter_tuning.py \
--config-name=megatron_gpt_adapter_tuning_config \
name='test_tp2_pp1' \
exp_manager.exp_dir='examples/adapter_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_gpt/tiny/megatron_14m_gpt_tp2_pp1.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_gpt_adapter_eval.py \
--config-name=megatron_gpt_adapter_inference \
adapter_model_file='examples/adapter_tuning/test_tp2_pp1.nemo' \
gpt_model_file='/home/TestData/nlp/megatron_gpt/tiny/megatron_14m_gpt_tp2_pp1.nemo' \
inference.greedy=True \
num_workers=1 \
inference.add_BOS=False \
trainer.devices=2 \
tensor_model_parallel_size=2 \
data_paths=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl']"
sh "rm -rf examples/adapter_tuning/test_tp2_pp1.nemo"
sh "rm -rf examples/adapter_tuning/test_tp2_pp1"
}
}
}
}
stage('L2: Megatron GPT Adapter PP=2') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel{
stage('GPT Adapter tuning & inference TP=1 PP=2') {
steps {
sh "python examples/nlp/language_modeling/tuning/megatron_gpt_adapter_tuning.py \
--config-name=megatron_gpt_adapter_tuning_config \
name='test_tp1_pp2' \
exp_manager.exp_dir='examples/adapter_tuning' \
trainer.devices=2 \
trainer.max_steps=1 \
trainer.val_check_interval=1 \
trainer.max_epochs=null \
model.data.num_workers=1 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=2 \
model.language_model_path='/home/TestData/nlp/megatron_gpt/tiny/megatron_14m_gpt_tp1_pp2.nemo' \
model.existing_tasks=[] \
model.new_tasks=['rte'] \
model.data.train_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.data.validation_ds=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl'] \
model.global_batch_size=4"
sh "python examples/nlp/language_modeling/tuning/megatron_gpt_adapter_eval.py \
--config-name=megatron_gpt_adapter_inference \
adapter_model_file='examples/adapter_tuning/test_tp1_pp2.nemo' \
gpt_model_file='/home/TestData/nlp/megatron_gpt/tiny/megatron_14m_gpt_tp1_pp2.nemo' \
inference.greedy=True \
inference.add_BOS=False \
trainer.devices=2 \
num_workers=1 \
tensor_model_parallel_size=2 \
data_paths=['/home/TestData/nlp/prompt_learning/rte_CI_test.jsonl']"
sh "rm -rf examples/adapter_tuning/test_tp1_pp2.nemo"
sh "rm -rf examples/adapter_tuning/test_tp1_pp2"
}
}
}
}
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" \
amp=true'
sh 'rm -rf stt_test_res.json'
}
}
}
}
stage('L2: Transducer alignment') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Running pytest') {
steps {
sh 'pytest tests/collections/asr/decoding/rnnt_alignments_check.py --durations=-1'
}
}
}
}
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 && \
apt-get update && apt-get install libsox-fmt-all -y'
}
}
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}'
}
}
}
}
}
}
stage('L2: G2P Models') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('G2P Conformer training, evaluation and inference') {
steps {
sh 'cd examples/tts/g2p && \
TIME=`date +"%Y-%m-%d-%T"` && OUTPUT_DIR_CONFORMER=output_ctc_${TIME} && \
python g2p_train_and_evaluate.py \
train_manifest=/home/TestData/g2p/g2p.json \
validation_manifest=/home/TestData/g2p/g2p.json \
model.test_ds.manifest_filepath=/home/TestData/g2p/g2p.json \
model.tokenizer.dir=/home/TestData/g2p/tokenizer_spe_unigram_v512 \
trainer.max_epochs=1 \
model.max_source_len=64 \
trainer.devices=[0] \
do_training=True \
do_testing=True \
exp_manager.exp_dir=${OUTPUT_DIR_CONFORMER} \
+exp_manager.use_datetime_version=False\
+exp_manager.version=test \
--config-name=g2p_conformer_ctc && \
python g2p_inference.py \
pretrained_model=${OUTPUT_DIR_CONFORMER}/G2P-Conformer-CTC/test/checkpoints/G2P-Conformer-CTC.nemo \
manifest_filepath=/home/TestData/g2p/g2p.json \