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Jenkinsfile
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pipeline {
agent {
docker {
image 'nvcr.io/nvidia/pytorch:21.02-py3'
args '--device=/dev/nvidia0 --gpus all --user 0:128 -v /home/TestData:/home/TestData -v $HOME/.cache/torch:/root/.cache/torch --shm-size=8g'
}
}
options {
timeout(time: 1, unit: 'HOURS')
disableConcurrentBuilds()
}
stages {
stage('PyTorch version') {
steps {
sh 'python -c "import torch; print(torch.__version__)"'
sh 'python -c "import torchtext; print(torchtext.__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('Copyright Headers check') {
steps {
sh 'python /home/TestData/check_copyright_header.py --dir .'
}
}
stage('PyTorch STFT Patch check') {
steps {
sh 'python /home/TestData/check_stft_patch.py --dir .'
}
}
stage('Code formatting checks') {
steps {
sh 'python setup.py style'
}
}
stage('Installation') {
steps {
sh './reinstall.sh release'
}
}
stage('PyTorch Lightning version') {
steps {
sh 'python -c "import pytorch_lightning; print(pytorch_lightning.__version__)"'
}
}
stage('L0: Unit Tests GPU') {
steps {
sh 'pytest -m "unit and not skipduringci and not pleasefixme"'
}
}
stage('L0: Unit Tests CPU') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}
steps {
sh 'CUDA_VISIBLE_DEVICES="" pytest -m "unit and not pleasefixme" --cpu'
}
}
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/speech_to_text.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('Speech to Text - DALI AudioToMelSpectrogramPreprocessor') {
// steps {
// sh 'python examples/asr/speech_to_text.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 - DALI AudioToMFCCPreprocessor') {
// steps {
// sh 'python examples/asr/speech_to_text.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 \
// 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 Label') {
steps {
sh 'python examples/asr/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 Recognition') {
steps {
sh 'python examples/speaker_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.test_ds.manifest_filepath=/home/TestData/an4_speaker/test.json \
trainer.gpus=[1] \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/speaker_recognition/speaker_recognition_results'
sh 'rm -rf examples/speaker_recognition/speaker_recognition_results'
}
}
stage('Speaker Diarization Inference') {
steps {
sh 'python examples/speaker_recognition/speaker_diarize.py \
diarizer.paths2audio_files=/home/TestData/an4_diarizer/audio_files.scp \
diarizer.path2groundtruth_rttm_files=/home/TestData/an4_diarizer/rttm_files.scp \
diarizer.speaker_embeddings.model_path=/home/TestData/an4_diarizer/spkr.nemo \
diarizer.vad.model_path=/home/TestData/an4_diarizer/MatchboxNet_VAD_3x2.nemo \
diarizer.out_dir=examples/speaker_recognition/speaker_diarization_results'
sh 'rm -rf examples/speaker_recognition/speaker_diarization_results'
}
}
stage('L2: Speech to Text WPE - CitriNet') {
steps {
sh 'python examples/asr/speech_to_text_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 to Text WPE - Conformer') {
steps {
sh 'python examples/asr/speech_to_text_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=10 \
model.validation_ds.batch_size=10 \
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: 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/speech_to_text.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_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/" \
cuda=true \
amp=true'
sh 'rm -rf examples/asr/speech_to_text_transcriptions.txt'
}
}
}
}
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 QN with .wav') {
steps {
sh 'cd tools/ctc_segmentation && \
TIME=`date +"%Y-%m-%d-%T"` && \
/bin/bash run_sample.sh \
--MODEL_NAME_OR_PATH=QuartzNet15x5Base-En \
--DATA_DIR=/home/TestData/ctc_segmentation/eng \
--OUTPUT_DIR=/home/TestData/ctc_segmentation/eng/output${TIME} \
--LANGUAGE=eng \
--OFFSET=0 \
--CUT_PREFIX=0 \
--MIN_SEGMENT_LEN=0 \
--AUDIO_FORMAT=.wav && \
python /home/TestData/ctc_segmentation/verify_alignment.py \
-r /home/TestData/ctc_segmentation/eng/eng_valid_segments.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_sample.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${TIME} \
--LANGUAGE=ru \
--OFFSET=0 \
--CUT_PREFIX=0 \
--MIN_SEGMENT_LEN=0 \
--AUDIO_FORMAT=.mp3 \
--ADDITIONAL_SPLIT_SYMBOLS=";" && \
python /home/TestData/ctc_segmentation/verify_alignment.py \
-r /home/TestData/ctc_segmentation/ru/valid_ru_segments.txt \
-g /home/TestData/ctc_segmentation/ru/output${TIME}/verified_segments/ru_segments.txt && \
rm -rf /home/TestData/ctc_segmentation/ru/output${TIME}'
}
}
}
}
}
}
stage('L2: 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('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.amp_level=O1 \
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.amp_level=O1 \
trainer.gpus=[1] \
exp_manager=null'
}
}
}
}
// Runs out of memory on the 12G TITAN V (GPU 0 on main CI)
stage('L2: MegaBERT Token Classification') {
when {
anyOf{
branch 'v1.0.0b2'
changeRequest target: 'v1.0.0b2'
}
}
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.amp_level=O1 \
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 {
stage('SQUAD v2.0 with Megatron with ckpt & config') {
// 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=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=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 \
model.dataset.version_2_with_negative=true \
trainer.precision=16 \
trainer.amp_level=O1 \
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.amp_level=O1 \
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=bert-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: fix model parallel for PTL 1.2
// 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 \
// exp_manager.create_checkpoint_callback=false \
// exp_manager.exp_dir=exp_mp_2_megatron_bert \
// trainer.gpus=[0,1] \
// trainer.num_nodes=1 \
// trainer.precision=16 \
// trainer.gradient_clip_val=1.0 \
// ~trainer.amp_level \
// +trainer.replace_sampler_ddp=false \
// +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: Parallel NLP Examples 2') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage ('NER finetuning from pretrained Test') {
steps {
sh 'cd examples/nlp/token_classification && \
python token_classification_train.py \
pretrained_model=ner_en_bert \
model.dataset.data_dir=/home/TestData/nlp/ner/ \
model.train_ds.batch_size=2 \
model.dataset.use_cache=false \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
model.dataset.class_balancing="weighted_loss" \
exp_manager.exp_dir=null'
}
}
stage ('Punctuation and capitalization finetuning from pretrained test') {
steps {
sh 'cd examples/nlp/token_classification && \
python punctuation_capitalization_train.py \
pretrained_model=punctuation_en_bert \
model.dataset.data_dir=/home/TestData/nlp/token_classification_punctuation/ \
trainer.gpus=[1] \
+trainer.fast_dev_run=true \
model.dataset.use_cache=false \
exp_manager.exp_dir=null'
}
}
stage ('NER with TurkuNLP/bert-base-finnish-cased-v1') {
steps {
sh 'cd examples/nlp/token_classification && \
python token_classification_train.py \
model.dataset.data_dir=/home/TestData/nlp/token_classification_punctuation/ \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
model.dataset.use_cache=false \
model.language_model.pretrained_model_name="TurkuNLP/bert-base-finnish-cased-v1" \
exp_manager.exp_dir=null'
}
}
stage('Evaluation script for Token Classification') {
steps {
sh 'python examples/nlp/token_classification/token_classification_evaluate.py \
model.dataset.data_dir=/home/TestData/nlp/ner/ \
pretrained_model=/home/TestData/nlp/pretrained_models/NER_Model_with_BERT_base_uncased.nemo && \
rm -rf nemo_experiments'
}
}
stage('Evaluation script for Punctuation') {
steps {
sh 'python examples/nlp/token_classification/punctuation_capitalization_evaluate.py \
model.dataset.data_dir=/home/TestData/nlp/token_classification_punctuation/ \
pretrained_model=/home/TestData/nlp/pretrained_models/Punctuation_Capitalization_with_DistilBERT_base_uncased.nemo && \
rm -rf nemo_experiments'
}
}
stage('L2: Punctuation & Capitalization, 2GPUs with DistilBERT') {
steps {
sh 'cd examples/nlp/token_classification && \
python punctuation_capitalization_train.py \
model.dataset.data_dir=/home/TestData/nlp/token_classification_punctuation/ \
model.language_model.pretrained_model_name=distilbert-base-uncased \
model.dataset.use_cache=false \
trainer.gpus=[0,1] \
trainer.accelerator=ddp \
+trainer.fast_dev_run=true \
exp_manager=null'
}
}
}
}
stage('L2: Parallel Pretraining BERT pretraining from Text/Preprocessed') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: Pretraining BERT pretraining from Text') {
steps {
sh 'cd examples/nlp/language_modeling && \
python bert_pretraining.py \
--config-name=bert_pretraining_from_text_config.yaml \
trainer.gpus=[0] \
trainer.precision=16 \
trainer.amp_level=O1 \
+trainer.fast_dev_run=true \
model.train_ds.data_file=/home/TestData/nlp/wikitext-2/train.txt \
model.train_ds.batch_size=32 \
model.validation_ds.data_file=/home/TestData/nlp/wikitext-2/valid.txt \
model.validation_ds.batch_size=32 \
model.language_model.config_file=/home/TestData/nlp/bert_configs/bert_3200.json \
model.optim.lr=0.01 \
model.optim.sched.warmup_ratio=0.1 \
model.tokenizer.tokenizer_name=sentencepiece \
model.tokenizer.tokenizer_model=/home/TestData/nlp/wikitext-2/tokenizer_bpe_v3193/tokenizer.model \
model.mask_prob=0.15 \
model.short_seq_prob=0.1 \
exp_manager.exp_dir=PretrainingBERTFromText \
'
sh 'rm -f /home/TestData/nlp/wikitext-2/*.pkl'
sh 'rm -rf examples/nlp/language_modeling/PretrainingBERTFromText'
sh 'ls -lha examples/nlp/language_modeling'
}
}
stage('L2: Pretraining BERT from Preprocessed') {
steps {
sh 'cd examples/nlp/language_modeling && \
python bert_pretraining.py \
--config-name=bert_pretraining_from_preprocessed_config.yaml \
trainer.gpus=[1] \
trainer.precision=16 \
trainer.amp_level=O1 \
+trainer.fast_dev_run=true \
model.train_ds.data_file=/home/TestData/nlp/wiki_book_mini/training \
model.train_ds.batch_size=8 \
model.language_model.lm_checkpoint=/home/TestData/nlp/bert_ckpts/nemo1.0/bert_base_uncased_mlm_final_1074591_nemo1.0.pt \
model.language_model.config_file=/home/TestData/nlp/bert_configs/uncased_L-12_H-768_A-12.json \
model.optim.lr=0.875e-4 \
model.optim.weight_decay=0.01 \
model.optim.sched.warmup_ratio=0.01 \
exp_manager.exp_dir=PretrainingBERTFromPreprocessed \
exp_manager.create_checkpoint_callback=False \
'
sh 'rm -rf examples/nlp/language_modeling/PretrainingBERTFromPreprocessed'
sh 'ls -lha examples/nlp/language_modeling'
}
}
stage('L2: Pretraining BERT pretraining from Text with char tokenizer') {
steps {
sh 'cd examples/nlp/language_modeling && \
python bert_pretraining.py \
--config-name=bert_pretraining_from_text_config.yaml \
trainer.gpus=[0] \
trainer.precision=16 \
trainer.amp_level=O1 \
+trainer.fast_dev_run=true \
model.train_ds.data_file=/home/TestData/nlp/wikitext-2/train.txt \
model.train_ds.batch_size=32 \
model.validation_ds.data_file=/home/TestData/nlp/wikitext-2/valid.txt \
model.validation_ds.batch_size=32 \
model.language_model.config_file=/home/TestData/nlp/bert_configs/bert_3200.json \
model.optim.lr=0.01 \
model.optim.sched.warmup_ratio=0.1 \
model.tokenizer.tokenizer_name=char \
model.tokenizer.vocab_file=/home/TestData/nlp/vocabs/mini_vocab.txt \
model.mask_prob=0.15 \
model.short_seq_prob=0.1 \
exp_manager.exp_dir=PretrainingBERTFromTextchartok \
'
sh 'rm -rf examples/nlp/language_modeling/PretrainingBERTFromTextchartok'
}
}
stage('L2: Pretraining BERT pretraining from Text with word tokenizer') {
steps {
sh 'cd examples/nlp/language_modeling && \
python bert_pretraining.py \
--config-name=bert_pretraining_from_text_config.yaml \
trainer.gpus=[1] \
trainer.precision=16 \
trainer.amp_level=O1 \
+trainer.fast_dev_run=true \
model.train_ds.data_file=/home/TestData/nlp/wikitext-2/train.txt \
model.train_ds.batch_size=32 \
model.validation_ds.data_file=/home/TestData/nlp/wikitext-2/valid.txt \
model.validation_ds.batch_size=32 \
model.language_model.config_file=/home/TestData/nlp/bert_configs/bert_3200.json \
model.optim.lr=0.01 \
model.optim.sched.warmup_ratio=0.1 \
model.tokenizer.tokenizer_name=word \
model.tokenizer.vocab_file=/home/TestData/nlp/vocabs/mini_vocab.txt \
model.mask_prob=0.15 \
model.short_seq_prob=0.1 \
exp_manager.exp_dir=PretrainingBERTFromTextwordtok \
'
sh 'rm -rf examples/nlp/language_modeling/PretrainingBERTFromTextwordtok'
}
}
}
}
stage('L2: NMT Attention is All You Need') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: NMT Training Post-LN') {
steps {
sh 'cd examples/nlp/machine_translation && \
python enc_dec_nmt.py \
--config-path=conf \
--config-name=aayn_base \
model.train_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.train_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref \
model.validation_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.validation_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.encoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
model.decoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
exp_manager=null \
'
}
}
stage('L2: NMT Training Pre-LN') {
steps {
sh 'cd examples/nlp/machine_translation && \
python enc_dec_nmt.py \
--config-path=conf \
--config-name=aayn_base \
model.train_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.train_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref \
model.validation_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.validation_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.encoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
model.decoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
model.encoder.pre_ln=true \
model.decoder.pre_ln=true \
trainer.gpus=[1] \
+trainer.fast_dev_run=true \
exp_manager=null \
'
}
}
stage('L2: NMT Inference') {
steps{
sh 'cd examples/nlp/machine_translation && \
python nmt_transformer_infer.py \
--model=/home/TestData/nlp/nmt/toy_data/TransformerLargeDe-En.nemo \
--srctext=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.test.src \
--tgtout=/home/TestData/nlp/nmt/toy_data/out.txt \
--target_lang en \
--source_lang de \
'
}
}
}
}
stage('L2: NMT with HuggingFace') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('L2: NMT Pretrained HF Encoder') {
steps {
sh 'cd examples/nlp/machine_translation && \
python enc_dec_nmt.py \
--config-path=conf \
--config-name=huggingface \
model.shared_tokenizer=False \
model.encoder_tokenizer.library=huggingface \
model.encoder.library=huggingface \
model.encoder.model_name=bert-base-cased \
model.encoder.pretrained=true \
model.train_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.train_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref \
model.validation_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.validation_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.decoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
trainer.gpus=[0] \
+trainer.fast_dev_run=true \
exp_manager=null \
'
}
}
stage('L2: NMT Custom HF Encoder') {
steps {
sh 'cd examples/nlp/machine_translation && \
python enc_dec_nmt.py \
--config-path=conf \
--config-name=huggingface \
model.shared_tokenizer=True \
model.encoder_tokenizer.library=yttm \
model.encoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
model.encoder.library=huggingface \
model.encoder.model_name=null \
model.encoder.pretrained=false \
+model.encoder._target_=transformers.BertConfig \
+model.encoder.hidden_size=1536 \
model.train_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.train_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.ref \
model.validation_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.validation_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.src_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.test_ds.tgt_file_name=/home/TestData/nlp/nmt/toy_data/wmt14-de-en.src \
model.decoder_tokenizer.tokenizer_model=/home/TestData/nlp/nmt/toy_data/tt_tokenizer.BPE.4096.model \
trainer.gpus=[1] \
+trainer.fast_dev_run=true \
exp_manager=null \
'
}
}
}
}
stage('L2: NMT Tarred Dataset Creation') {
when {
anyOf{
branch 'main'
changeRequest target: 'main'
}
}