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Jenkinsfile
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pipeline {
agent {
docker {
image 'nvcr.io/nvidia/pytorch:24.01-py3'
args '--device=/dev/nvidia0 --gpus all --user 0:128 -v /home/TestData:/home/TestData -v $HOME/.cache:/root/.cache --shm-size=8g --env TRANSFORMERS_OFFLINE=0 --env HYDRA_FULL_ERROR=1'
}
}
environment {
NVTE_FUSED_ATTN = 0
NVTE_FLASH_ATTN = 0
}
options {
timeout(time: 8, unit: 'HOURS')
disableConcurrentBuilds(abortPrevious: true)
}
stages {
stage('Add git safe directory'){
steps{
sh 'git config --global --add safe.directory /var/lib/jenkins/workspace/NeMo_$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 && pip install -r requirements/requirements_lightning.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'
}
}
// Transformer Engine 1.2.0
stage('Transformer Engine installation') {
steps {
sh 'git clone https://github.com/NVIDIA/TransformerEngine.git && \
cd TransformerEngine && \
git fetch origin 8c9abbb80dba196f086b8b602a7cf1bce0040a6a && \
git checkout FETCH_HEAD && \
git submodule init && git submodule update && \
NVTE_FRAMEWORK=pytorch NVTE_WITH_USERBUFFERS=1 MPI_HOME=/usr/local/mpi pip install .'
}
}
// Apex bugfix for PyTorch 23.11 container: https://github.com/NVIDIA/apex/pull/1760
stage('Apex installation') {
steps {
sh 'git clone https://github.com/NVIDIA/apex.git && \
cd apex && \
git checkout c07a4cf67102b9cd3f97d1ba36690f985bae4227 && \
cp -R apex /usr/local/lib/python3.10/dist-packages'
}
}
// pip package should be working with main, if not we can update the commit here
// until the pip package is updated
stage('Megatron Core installation') {
steps {
sh 'git clone https://github.com/NVIDIA/Megatron-LM.git && \
cd Megatron-LM && \
git checkout 5f9c870f9f24b482509699d206a9dbb00958f6fc && \
pip install .'
}
}
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('Import Checks'){
steps {
sh 'python tests/core_ptl/check_imports.py --domain "nlp"'
}
}
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: Multimodal Imagen Train') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
failFast true
steps {
sh "rm -rf /home/TestData/multimodal/imagen_train"
sh "pip install webdataset==0.2.48"
sh "python examples/multimodal/text_to_image/imagen/imagen_training.py \
trainer.precision=16 \
trainer.num_nodes=1 \
trainer.devices=1 \
++exp_manager.max_time_per_run=00:00:03:00 \
trainer.max_steps=20 \
model.conditioning.embed_dim=64 \
model.micro_batch_size=1 \
model.global_batch_size=1 \
model.data.synthetic_data=True \
exp_manager.exp_dir=/home/TestData/multimodal/imagen_train \
model.inductor=False \
model.unet.flash_attention=False \
"
sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
sh "rm -rf /home/TestData/multimodal/imagen_train"
}
}
stage('L2: Multimodal Stable Diffusion Train') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
failFast true
steps {
sh "rm -rf /home/TestData/multimodal/stable_diffusion_train"
sh "pip install webdataset==0.2.48"
sh "python examples/multimodal/text_to_image/stable_diffusion/sd_train.py \
trainer.precision=16 \
trainer.num_nodes=1 \
trainer.devices=1 \
++exp_manager.max_time_per_run=00:00:03:00 \
trainer.max_steps=20 \
model.micro_batch_size=1 \
model.global_batch_size=1 \
model.data.synthetic_data=True \
exp_manager.exp_dir=/home/TestData/multimodal/stable_diffusion_train \
model.inductor=False \
model.cond_stage_config._target_=nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder \
++model.cond_stage_config.version=openai/clip-vit-large-patch14 \
++model.cond_stage_config.max_length=77 \
~model.cond_stage_config.restore_from_path \
~model.cond_stage_config.freeze \
~model.cond_stage_config.layer \
model.unet_config.from_pretrained=null \
model.first_stage_config.from_pretrained=null \
model.unet_config.use_flash_attention=False \
model.unet_config.attention_resolutions=[1] \
model.unet_config.channel_mult=[1] \
"
sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
sh "rm -rf /home/TestData/multimodal/stable_diffusion_train"
}
}
// stage('L2: Multimodal ControlNet Train') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps {
// sh "rm -rf /home/TestData/multimodal/controlnet_train"
// sh "pip install webdataset==0.2.48"
// sh "python examples/multimodal/text_to_image/controlnet/controlnet_train.py \
// trainer.precision=16 \
// trainer.num_nodes=1 \
// trainer.devices=1 \
// ++exp_manager.max_time_per_run=00:00:03:00 \
// trainer.max_steps=20 \
// model.micro_batch_size=1 \
// model.global_batch_size=1 \
// model.data.synthetic_data=True \
// exp_manager.exp_dir=/home/TestData/multimodal/controlnet_train \
// model.inductor=False \
// model.image_logger.max_images=0 \
// model.control_stage_config.params.from_pretrained_unet=null \
// model.unet_config.from_pretrained=null \
// model.first_stage_config.from_pretrained=null \
// model.unet_config.use_flash_attention=False \
// "
// sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
// sh "rm -rf /home/TestData/multimodal/controlnet_train"
// }
// }
// stage('L2: Multimodal DreamBooth Train') {
// when {
// anyOf {
// branch 'main'
// changeRequest target: 'main'
// }
// }
// failFast true
// steps {
// sh "rm -rf /home/TestData/multimodal/dreambooth_train"
// sh "pip install webdataset==0.2.48"
// sh "python examples/multimodal/text_to_image/dreambooth/dreambooth.py \
// trainer.precision=16 \
// trainer.num_nodes=1 \
// trainer.devices=1 \
// ++exp_manager.max_time_per_run=00:00:03:00 \
// trainer.max_steps=20 \
// model.micro_batch_size=1 \
// model.global_batch_size=1 \
// exp_manager.exp_dir=/home/TestData/multimodal/dreambooth_train \
// model.inductor=False \
// model.cond_stage_config._target_=nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder \
// ++model.cond_stage_config.version=openai/clip-vit-large-patch14 \
// ++model.cond_stage_config.max_length=77 \
// ~model.cond_stage_config.restore_from_path \
// ~model.cond_stage_config.freeze \
// ~model.cond_stage_config.layer \
// model.unet_config.from_pretrained=null \
// model.first_stage_config.from_pretrained=null \
// model.data.instance_dir=/home/TestData/multimodal/tiny-dreambooth \
// model.unet_config.use_flash_attention=False \
// "
// sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
// sh "rm -rf /home/TestData/multimodal/dreambooth_train"
// }
// }
stage('L2: Vision ViT Pretrain TP=1') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
failFast true
steps {
sh "rm -rf /home/TestData/vision/vit_pretrain_tp1"
sh "pip install webdataset==0.2.48"
sh "python examples/vision/vision_transformer/megatron_vit_classification_pretrain.py \
trainer.precision=16 \
model.megatron_amp_O2=False \
trainer.num_nodes=1 \
trainer.devices=1 \
trainer.val_check_interval=5 \
++exp_manager.max_time_per_run=00:00:03:00 \
trainer.max_steps=20 \
model.micro_batch_size=2 \
model.global_batch_size=4 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
model.data.num_workers=0 \
exp_manager.create_checkpoint_callback=False \
model.data.data_path=[/home/TestData/multimodal/tiny-imagenet/train,/home/TestData/multimodal/tiny-imagenet/val] \
exp_manager.exp_dir=/home/TestData/vision/vit_pretrain_tp1 "
sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
sh "rm -rf /home/TestData/vision/vit_pretrain_tp1"
}
}
stage('L2: Multimodal CLIP Pretrain TP=1') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
failFast true
steps {
sh "rm -rf /home/TestData/multimodal/clip_pretrain_tp1"
sh "pip install webdataset==0.2.48"
sh "python examples/multimodal/vision_language_foundation/clip/megatron_clip_pretrain.py \
trainer.precision=16 \
model.megatron_amp_O2=False \
trainer.num_nodes=1 \
trainer.devices=1 \
trainer.val_check_interval=10 \
++exp_manager.max_time_per_run=00:00:03:00 \
trainer.max_steps=20 \
model.micro_batch_size=1 \
model.global_batch_size=1 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
exp_manager.create_checkpoint_callback=False \
model.data.num_workers=0 \
model.vision.num_layers=2 \
model.text.num_layers=2 \
model.vision.patch_dim=32 \
model.vision.encoder_seq_length=49 \
model.vision.class_token_length=7 \
model.data.train.dataset_path=[/home/TestData/multimodal/tiny-clip/00000.tar] \
model.data.validation.dataset_path=[/home/TestData/multimodal/tiny-clip/00000.tar] \
model.data.webdataset.local_root_path=/ \
exp_manager.exp_dir=/home/TestData/multimodal/clip_pretrain_tp1 "
sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
sh "rm -rf /home/TestData/multimodal/clip_pretrain_tp1"
}
}
stage('L2: Multimodal NeVA Pretrain TP=1') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
failFast true
steps {
sh "rm -rf /home/TestData/multimodal/neva_pretrain_tp1"
sh "pip install webdataset==0.2.48"
sh "python examples/multimodal/multimodal_llm/neva/neva_pretrain.py \
trainer.precision=16 \
model.megatron_amp_O2=False \
trainer.num_nodes=1 \
trainer.devices=1 \
trainer.val_check_interval=10 \
trainer.limit_val_batches=5 \
trainer.log_every_n_steps=1 \
++exp_manager.max_time_per_run=00:00:03:00 \
trainer.max_steps=20 \
model.micro_batch_size=2 \
model.global_batch_size=4 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
exp_manager.create_checkpoint_callback=False \
model.data.data_path=/home/TestData/multimodal/tiny-neva/dummy.json \
model.data.image_folder=/home/TestData/multimodal/tiny-neva/images \
model.tokenizer.library=sentencepiece \
model.tokenizer.model=/home/TestData/multimodal/tiny-neva/tokenizer_add_special.model \
model.num_layers=2 \
model.hidden_size=5120 \
model.ffn_hidden_size=13824 \
model.num_attention_heads=40 \
model.normalization=rmsnorm \
model.data.num_workers=0 \
model.data.conv_template=llama_2 \
model.mm_cfg.vision_encoder.from_pretrained='openai/clip-vit-large-patch14' \
model.mm_cfg.llm.from_pretrained=null \
model.use_flash_attention=false \
exp_manager.exp_dir=/home/TestData/multimodal/neva_pretrain_tp1 "
sh "pip install 'webdataset>=0.1.48,<=0.1.62'"
sh "rm -rf /home/TestData/multimodal/neva_pretrain_tp1"
}
}
// TODO: this requires TE >= v0.11 which is not available in 23.06.
// please uncomment this test once mcore CI is ready.
stage('L2: Community LLM Checkpoints tests') {
when {
anyOf {
branch 'main'
changeRequest target: 'main'
}
}
failFast true
parallel {
stage('Llama') {
steps {
sh 'CUDA_VISIBLE_DEVICES=0 python scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py \
--in-file=/home/TestData/nlp/megatron_llama/llama-ci-hf \
--out-file=/home/TestData/nlp/megatron_llama/ci.nemo \
--precision=16'
sh 'rm -f /home/TestData/nlp/megatron_llama/ci.nemo'
}
}
stage('StarCoder') {
steps {
sh 'python scripts/nlp_language_modeling/convert_starcoder_hf_to_nemo.py \
--config examples/nlp/language_modeling/conf/megatron_gpt_config.yaml \
--input /home/TestData/nlp/megatron_gpt/starcoder-ci-hf \
--output /home/TestData/nlp/megatron_gpt/starcoder-ci-hf'
sh 'rm -f /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/megatron_starcoder_tp1_pp1.nemo'
}
}
stage('Falcon') {
steps {
sh 'python scripts/nlp_language_modeling/convert_hf_falcon_to_nemo.py \
--config examples/nlp/language_modeling/conf/megatron_falcon_config.yaml \
--input /home/TestData/nlp/megatron_gpt/falcon-ci-hf \
--output /home/TestData/nlp/megatron_gpt/falcon-ci-hf/falcon_ci.nemo'
sh 'rm -f /home/TestData/nlp/megatron_gpt/falcon-ci-hf/falcon_ci.nemo'
}
}
stage('Baichuan2') {
steps {
sh 'python scripts/nlp_language_modeling/convert_hf_baichuan2_to_nemo.py \
--in-file=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base \
--out-file=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo'
sh 'rm -f /home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo'
}
}
}
}
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('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('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('Speech To Text Finetuning') {
steps {
sh 'python examples/asr/speech_to_text_finetune.py \
--config-path="conf/asr_finetune" --config-name="speech_to_text_finetune" \
model.train_ds.manifest_filepath=/home/TestData/an4_dataset/an4_train.json \
model.validation_ds.manifest_filepath=/home/TestData/an4_dataset/an4_val.json \
init_from_nemo_model=/home/TestData/asr/stt_en_fastconformer_transducer_large.nemo \
model.tokenizer.update_tokenizer=False \
trainer.devices=[1] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_finetuning_results'
sh 'rm -rf examples/asr/speech_finetuning_results'
}
}
stage('Speech To Text HF Finetuning') {
steps {
sh 'python examples/asr/speech_to_text_finetune.py \
--config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \
~model.train_ds.hf_data_cfg \
model.train_ds.num_workers=1 \
model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \
model.train_ds.streaming=true \
+model.train_ds.hf_data_cfg.path="librispeech_asr" \
+model.train_ds.hf_data_cfg.name=null \
+model.train_ds.hf_data_cfg.split="test.clean" \
+model.train_ds.hf_data_cfg.streaming=true \
~model.validation_ds.hf_data_cfg \
model.validation_ds.streaming=true \
+model.validation_ds.hf_data_cfg.path="librispeech_asr" \
+model.validation_ds.hf_data_cfg.name=null \
+model.validation_ds.hf_data_cfg.split="test.clean" \
+model.validation_ds.hf_data_cfg.streaming=true \
~model.test_ds \
init_from_nemo_model=/home/TestData/asr/stt_en_fastconformer_transducer_large.nemo \
model.tokenizer.update_tokenizer=False \
model.optim.sched.warmup_steps=0 \
+model.optim.sched.max_steps=3 \
trainer.max_epochs=null \
trainer.devices=[1] \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_finetuning_results'
sh 'rm -rf examples/asr/speech_finetuning_results'
}
}
// TODO: Please Fix Me
// Error locating target 'nemo.collections.asr.modules.wav2vec_modules.ConvFeatureEncoder', see chained exception above.
// 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: Speech to Text AED') {
when {
anyOf {
branch 'r1.23.0'
changeRequest target: 'r1.23.0'
}
}
steps {
sh 'python examples/asr/speech_multitask/speech_to_text_aed.py \
model.prompt_format=canary \
model.model_defaults.asr_enc_hidden=256 \
model.model_defaults.lm_dec_hidden=256 \
model.encoder.n_layers=12 \
model.transf_encoder.num_layers=0 \
model.transf_decoder.config_dict.num_layers=12 \
model.train_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_train.json \
++model.train_ds.is_tarred=false \
model.train_ds.batch_duration=60 \
+model.train_ds.text_field="answer" \
+model.train_ds.lang_field="target_lang" \
model.validation_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_val.json \
+model.validation_ds.text_field="answer" \
+model.validation_ds.lang_field="target_lang" \
model.test_ds.manifest_filepath=/home/TestData/asr/manifests/canary/an4_canary_val.json \
+model.test_ds.text_field="answer" \
+model.test_ds.lang_field="target_lang" \
model.tokenizer.langs.spl_tokens.dir=/home/TestData/asr_tokenizers/canary/canary_spl_tokenizer_v32 \
model.tokenizer.langs.spl_tokens.type="bpe" \
model.tokenizer.langs.en.dir=/home/TestData/asr_tokenizers/canary/en/tokenizer_spe_bpe_v1024_max_4 \
model.tokenizer.langs.en.type=bpe \
++model.tokenizer.langs.es.dir=/home/TestData/asr_tokenizers/canary/es/tokenizer_spe_bpe_v1024_max_4 \
++model.tokenizer.langs.es.type=bpe \
trainer.devices=[0] \
trainer.accelerator="gpu" \
+trainer.use_distributed_sampler=false \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_aed_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'
}
}
}
}