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cicd-main.yml
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# Copyright (c) 2020-2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
name: "CICD NeMo"
on:
pull_request:
branches:
- 'main'
- 'r**'
types: [ labeled ]
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
gpu-test:
runs-on: self-hosted-azure
if: ${{ github.event.label.name == 'Run CICD' }}
steps:
- name: Run nvidia-smi test
run: |
whoami
nvidia-smi
cicd-cluster-clean:
runs-on: self-hosted-azure-builder
if: ${{ github.event.label.name == 'Run CICD' }}
steps:
- name: Clean server from old files
run: |
docker container prune --filter "until=24h" --force
docker image prune -a --filter "until=24h" --force
cicd-test-container-setup:
needs: [cicd-cluster-clean]
runs-on: self-hosted-azure-builder
if: ${{ github.event.label.name == 'Run CICD' }}
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
path: ${{ github.run_id }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
# We use `docker` driver as this speeds things up for
# trivial (non-multi-stage) builds.
driver: docker
- name: Build and push
uses: docker/build-push-action@v5
with:
file: Dockerfile.ci
push: true
cache-from: nemoci.azurecr.io/nemo_container:latest
cache-to: type=inline
tags: |
nemoci.azurecr.io/nemo_container_${{ github.run_id }}
nemoci.azurecr.io/nemo_container:latest
- name: Run some checks
run: |
docker run --rm --device=/dev/nvidia0 --gpus all --shm-size=8g --env TRANSFORMERS_OFFLINE=0 --env HYDRA_FULL_ERROR=1 --env PYTHONUNBUFFERED=1 nemoci.azurecr.io/nemo_container_${{ github.run_id }} bash -c '\
# PyTorch Lightning version
python -c "import pytorch_lightning; print(pytorch_lightning.__version__)"
# PyTorch Lightning DDP Checks
CUDA_VISIBLE_DEVICES="0,1" python "tests/core_ptl/check_for_ranks.py"
# Basic Import Checks
python -c "import nemo.collections.asr as nemo_asr"
python -c "import nemo.collections.nlp as nemo_nlp"
python -c "import nemo.collections.tts as nemo_tts"
python setup.py style
python tests/check_copyright_header.py --dir .
# These checks are not crucial
exit 0
'
### \'\'
L0_Unit_Tests_GPU:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
TIMEOUT: 60
SCRIPT: |
NEMO_NUMBA_MINVER=0.53 pytest -m "not pleasefixme" --with_downloads
IS_OPTIONAL: true
L0_Unit_Tests_CPU:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-cpu
TIMEOUT: 60
SCRIPT: |
CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat
L0_Setup_Test_Data_And_Models:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
python -m tests.setup --save_dir /home/TestData/nlp
## - name: L2: Multimodal Imagen Train
# L2: Community LLM Checkpoints tests
L2_Community_LLM_Checkpoints_tests_Llama:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
CUDA_VISIBLE_DEVICES=0 python scripts/checkpoint_converters/convert_llama_hf_to_nemo.py \
--input_name_or_path=/home/TestData/nlp/megatron_llama/llama-ci-hf-tiny \
--output_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
--precision=16
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_llama/model_weights
L2_Community_LLM_Checkpoints_tests_Llama3:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
CUDA_VISIBLE_DEVICES=0 python scripts/checkpoint_converters/convert_llama_hf_to_nemo.py \
--input_name_or_path=/home/TestData/nlp/megatron_llama/llama3-ci-hf \
--output_path=/home/TestData/nlp/megatron_llama/llama3-ci-hf/llama3_ci.nemo \
--precision=16
AFTER_SCRIPT: |
rm -f /home/TestData/nlp/megatron_llama/llama3-ci-hf/llama3_ci.nemo
rm -rf /home/TestData/nlp/megatron_llama/llama3-ci-hf/model_weights
L2_Community_LLM_Checkpoints_tests_StarCoder:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
mkdir -p /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/${{ github.run_id }};
python scripts/checkpoint_converters/convert_starcoder_hf_to_nemo.py \
--input_name_or_path /home/TestData/nlp/megatron_gpt/starcoder-ci-hf \
--output_path /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/${{ github.run_id }}
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/megatron_starcoder_tp1_pp1.nemo;
rm -rf /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/${{ github.run_id }}/
rm -rf /home/TestData/nlp/megatron_gpt/starcoder-ci-hf/model_weights
L2_Community_LLM_Checkpoints_tests_Falcon:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
python scripts/checkpoint_converters/convert_falcon_hf_to_nemo.py \
--input_name_or_path /home/TestData/nlp/megatron_gpt/falcon-ci-hf \
--output_path /home/TestData/nlp/megatron_gpt/falcon-ci-hf/falcon_ci.nemo
rm -f /home/TestData/nlp/megatron_gpt/falcon-ci-hf/falcon_ci.nemo
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_gpt/falcon-ci-hf/model_weights
# L2: Community llava multimodal Checkpoints tests
L2_Community_vita_Checkpoints_tests_Llama3:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
export PYTHONPATH=/home/TestData/multimodal/video_neva/LLaVA:$PYTHONPATH
CUDA_VISIBLE_DEVICES=0 python examples/multimodal/multimodal_llm/neva/convert_llava_to_neva.py \
--in-file /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/llm \
--mm-projector-ckpt-dir /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/mm_projector \
--mm-vision-tower /home/TestData/multimodal/video_neva/Llama-3-VILA1.5-8B/vision_tower \
--tokenizer-model /home/TestData/multimodal/video_neva/vita-tokenizer/ \
--config-file vita_config.yaml \
--out-file=/home/TestData/multimodal/video_neva/llama3-ci-hf/llama3_ci.nemo \
--model-type VITA \
--conv-template llama_3
AFTER_SCRIPT: |
rm -f /home/TestData/multimodal/video_neva/llama3-ci-hf/llama3_ci.nemo
rm -rf /home/TestData/multimodal/video_neva/llama3-ci-hf/model_weights
# this test is using a 7B model which is too large for GitHub CI
# replace the model in this test with a toy model or move the test
# to the nightly CI
# OPTIONAL_L2_Community_LLM_Checkpoints_tests_Baichuan2:
# needs: [cicd-test-container-setup]
# runs-on: self-hosted-azure
# container:
# image: nemoci.azurecr.io/nemo_container_${{ github.run_id }}
# options:
# # --user 0:128
# --device=/dev/nvidia0
# --gpus all
# --shm-size=8g
# --env TRANSFORMERS_OFFLINE=0
# --env HYDRA_FULL_ERROR=1
# --volume /mnt/datadrive/TestData:/home/TestData
# steps:
# - name: Checkout repository
# uses: actions/checkout@v4
# - run: |
# python scripts/checkpoint_converters/convert_baichuan2_hf_to_nemo.py \
# --input_name_or_path=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base \
# --output_path=/home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo
# rm -f /home/TestData/nlp/megatron_gpt/Baichuan2-7B-Base/ci.nemo
# - uses: "NVIDIA/NeMo/.github/actions/cancel-workflow@main"
# if: "failure()"
L2_PTQ_Llama2_Export_Only:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/nlp/language_modeling/megatron_gpt_ptq.py \
model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
quantization.algorithm=null \
export.save_path=/home/TestData/nlp/megatron_llama/ci_baseline
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_llama/ci_baseline
L2_PTQ_Llama2_FP8:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
python examples/nlp/language_modeling/megatron_gpt_ptq.py \
model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
model.tensor_model_parallel_size=2 \
trainer.devices=2 \
quantization.calib_dataset=/home/TestData/nlp/test_quantization/test.json \
quantization.algorithm=fp8 \
quantization.num_calib_size=8 \
inference.batch_size=2 \
export.inference_tensor_parallel=2 \
export.save_path=/home/TestData/nlp/megatron_llama/ci_fp8.qnemo
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_llama/ci_fp8.qnemo
L2_PTQ_Llama2_INT8_SQ:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
TIMEOUT: 15
SCRIPT: |
python examples/nlp/language_modeling/megatron_gpt_ptq.py \
model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
quantization.calib_dataset=/home/TestData/nlp/test_quantization/test.json \
quantization.algorithm=int8_sq \
quantization.num_calib_size=8 \
inference.batch_size=2 \
export.save_path=/home/TestData/nlp/megatron_llama/ci_int8_sq.qnemo
AFTER_SCRIPT: |
rm -rf /home/TestData/nlp/megatron_llama/ci_int8_sq.qnemo
# TODO: investigate int4_awq stuck issues and restore the test
#L2_PTQ_Llama2_INT4_AWQ:
# needs: [cicd-test-container-setup]
# runs-on: self-hosted-azure
# timeout-minutes: 10
# container:
# image: nemoci.azurecr.io/nemo_container_${{ github.run_id }}
# options:
# # --user 0:128
# --device=/dev/nvidia0
# --gpus all
# --shm-size=8g
# --env TRANSFORMERS_OFFLINE=0
# --env HYDRA_FULL_ERROR=1
# --volume /mnt/datadrive/TestData:/home/TestData
# steps:
# - name: Checkout repository
# uses: actions/checkout@v4
# - run: |
# python examples/nlp/language_modeling/megatron_gpt_ptq.py \
# model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
# model.tensor_model_parallel_size=1 \
# trainer.devices=1 \
# quantization.calib_dataset=/home/TestData/nlp/test_quantization/test.json \
# quantization.algorithm=int4_awq \
# quantization.num_calib_size=8 \
# inference.batch_size=2 \
# export.save_path=/home/TestData/nlp/megatron_llama/ci_int4_awq.qnemo
#
# rm -rf /home/TestData/nlp/megatron_llama/ci_int4_awq.qnemo
#- uses: "NVIDIA/NeMo/.github/actions/cancel-workflow@main"
# if: "failure()"
L2_QAT_Llama2_INT4:
needs: [cicd-test-container-setup]
runs-on: self-hosted-azure
timeout-minutes: 10
container:
image: nemoci.azurecr.io/nemo_container_${{ github.run_id }}
options:
# --user 0:128
--device=/dev/nvidia0
--gpus all
--shm-size=8g
--env TRANSFORMERS_OFFLINE=0
--env HYDRA_FULL_ERROR=1
--volume /mnt/datadrive/TestData:/home/TestData
steps:
- name: Checkout repository
uses: actions/checkout@v4
- run: |
python examples/nlp/language_modeling/tuning/megatron_gpt_qat.py \
quantization.algorithm=int4 \
quantization.num_calib_size=8 \
trainer.devices=1 \
trainer.num_nodes=1 \
trainer.max_steps=4 \
trainer.val_check_interval=4 \
+trainer.limit_val_batches=2 \
exp_manager.explicit_log_dir=llama2_qat_results \
model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
model.global_batch_size=2 \
model.data.train_ds.file_names=[/home/TestData/nlp/megatron_sft/quarel.jsonl] \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=[/home/TestData/nlp/megatron_sft/quarel.jsonl]
rm -rf llama2_qat_results
- uses: "NVIDIA/NeMo/.github/actions/cancel-workflow@main"
if: "failure()"
# L2: ASR dev run
ASR_dev_run_Speech_to_Text:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_results
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_results
ASR_dev_run_Speech_to_Text_WPE_-_CitriNet:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_wpe_results
ASR_dev_run_Speech_Pre-training_-_CitriNet:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_pre_training_results
ASR_dev_run_Speech_To_Text_Finetuning:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_finetuning_results
OPTIONAL_ASR_dev_run_Speech_To_Text_HF_Finetuning:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |-
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_finetuning_results
IS_OPTIONAL: true
ASR_dev_run_Speech_to_Text_WPE_-_Conformer:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_wpe_conformer_results
# L2: ASR dev run - part two
ASR_dev_run-part_two_Speech_to_Text_WPE_-_Squeezeformer:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_wpe_squeezeformer_results
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_wpe_squeezeformer_results
L2_Speech_to_Text_EMA:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_results
# L2_Speech_to_Text_AED:
# needs: [cicd-test-container-setup]
# runs-on: self-hosted-azure-gpus-1
# container:
# image: nemoci.azurecr.io/nemo_container_${{ github.run_id }}
# options:
# # --user 0:128
# --device=/dev/nvidia0
# --gpus all
# --shm-size=8g
# --env TRANSFORMERS_OFFLINE=0
# --env HYDRA_FULL_ERROR=1
# --volume /mnt/datadrive/TestData:/home/TestData
# steps:
# - name: Checkout repository
# uses: actions/checkout@v4
# - run: |
# 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=1 \
# trainer.accelerator="gpu" \
# +trainer.use_distributed_sampler=false \
# +trainer.fast_dev_run=True \
# exp_manager.exp_dir=examples/asr/speech_to_text_aed_results
# rm -rf examples/asr/speech_to_text_results
# L2: Speaker dev run
L2_Speaker_dev_run_Speaker_Recognition:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/speaker_tasks/recognition/speaker_recognition_results
L2_Speaker_dev_run_Speaker_Diarization:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/speaker_tasks/diarization/speaker_diarization_results
L2_Speaker_dev_run_Speech_to_Label:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_label_results
L2_Speaker_dev_run_Speaker_Diarization_with_ASR_Inference:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/speaker_tasks/diarization/speaker_diarization_asr_results
L2_Speaker_dev_run_Clustering_Diarizer_Inference:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/speaker_tasks/diarization/clustering_diarizer_results
L2_Speaker_dev_run_Neural_Diarizer_Inference:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/speaker_tasks/diarization/neural_diarizer_results
L2_Speaker_dev_run_Multispeaker_ASR_Data_Simulation:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf ./test_simulator
# L2: ASR Multi-dataloader dev run
L2_ASR_Multi-dataloader_dev_run_Speech_to_Text_multi-dataloader:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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=1 \
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_results
L2_ASR_Multi-dataloader_dev_run_Speech_to_Label_multi-dataloader:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_label_results
# L2: ASR Adapters
L2_ASR_Adapters_Linear_Adapters:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_adapters_results
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_adapters_results
L2_ASR_Adapters_RelPos_MHA_Adapters:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=True \
exp_manager.exp_dir=examples/asr/speech_to_text_adapters_mha_results
AFTER_SCRIPT: |
rm -rf examples/asr/speech_to_text_adapters_mha_results
# L2: Speech Transcription
L2_Speech_Transcription_Speech_to_Text_Transcribe:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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
AFTER_SCRIPT: |
rm -rf stt_test_res.json
# L2: Transducer alignment
L2_Transducer_alignment_Running_pytest:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
pytest tests/collections/asr/decoding/rnnt_alignments_check.py --durations=-1
# L2: Segmentation Tool
L2_Segmentation_Tool_Parallel_ctc_segmentation_test_L2_Eng_CitriNet_with_wav:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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${TIME}/verified_segments/nv_test_segments.txt;
AFTER_SCRIPT: |
rm -rf /home/TestData/ctc_segmentation/eng/output${TIME}
L2_Segmentation_Tool_Parallel_ctc_segmentation_test_L2_Ru_QN_with_mp3:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
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/eng/output${TIME}
# L2: G2P Models
L2_G2P_Models_G2P_Conformer_training_evaluation_and_inference:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
cd examples/tts/g2p && \
TIME=`date +"%Y-%m-%d-%T"` && OUTPUT_DIR_CONFORMER=output_ctc_${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=1 \
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 \
phoneme_field=text
# TODO: pleasefixme @redoctopus
# - name: ByT5G2P training, evaluation and inference
# run: |
# cd examples/tts/g2p && \
# TIME=`date +"%Y-%m-%d-%T"` && OUTPUT_DIR_T5=output_byt5_${TIME} && \
# python g2p_train_and_evaluate.py \
# train_manifest=/home/TestData/g2p/g2p.json \
# validation_manifest=/home/TestData/g2p/g2p.json \
# model.test_ds.manifest_filepath=/home/TestData/g2p/g2p.json \
# trainer.max_epochs=1 \
# model.max_source_len=64 \
# trainer.devices=1 \
# do_training=True \
# do_testing=True \
# exp_manager.exp_dir=${OUTPUT_DIR_T5} \
# +exp_manager.use_datetime_version=False\
# +exp_manager.version=test && \
# python g2p_inference.py \
# pretrained_model=${OUTPUT_DIR_T5}/T5G2P/test/checkpoints/T5G2P.nemo \
# manifest_filepath=/home/TestData/g2p/g2p.json \
# phoneme_field=text
# }
# }
# - uses: "NVIDIA/NeMo/.github/actions/cancel-workflow@main"
# if: "failure()"
L2_G2P_Models_HeteronymClassificationModel_training_evaluation_and_inference:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
cd examples/tts/g2p && \
TIME=`date +"%Y-%m-%d-%T"` && OUTPUT_DIR=output_${TIME} && \
python g2p_heteronym_classification_train_and_evaluate.py \
train_manifest=/home/TestData/g2p/manifest.json \
validation_manifest=/home/TestData/g2p/manifest.json \
test_manifest=/home/TestData/g2p/manifest.json \
model.wordids=/home/TestData/g2p/wordids.tsv \
trainer.max_epochs=1 \
model.max_seq_length=64 \
do_training=True \
do_testing=True \
exp_manager.exp_dir=${OUTPUT_DIR} \
+exp_manager.use_datetime_version=False\
+exp_manager.version=test && \
python g2p_heteronym_classification_inference.py \
manifest=/home/TestData/g2p/manifest.json \
pretrained_model=${OUTPUT_DIR}/HeteronymClassification/test/checkpoints/HeteronymClassification.nemo \
output_manifest=preds.json
# L2: Duplex Text Normalization
L2_Duplex_Text_Normalization_with_Tarred_dataset:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure
SCRIPT: |
cd examples/nlp/duplex_text_normalization && \
python duplex_text_normalization_train.py \
data.validation_ds.data_path=/home/TestData/nlp/duplex_text_norm/small_test.tsv \
mode=tn \
lang=en \
tagger_model.do_training=false \
decoder_model.transformer=t5-small \
data.validation_ds.batch_size=2 \
data.train_ds.use_cache=false \
data.validation_ds.use_cache=false \
data.test_ds.batch_size=2 \
data.train_ds.decoder_data_augmentation=false \
data.train_ds.num_workers=2 \
decoder_trainer.devices=[0,1] \
decoder_trainer.accelerator="gpu" \
data.train_ds.use_tarred_dataset=true \
+decoder_trainer.fast_dev_run=true \
decoder_exp_manager.create_checkpoint_callback=false \
data.train_ds.tar_metadata_file=/home/TestData/nlp/duplex_text_norm/tarred_small/metadata.json \
data.test_ds.use_cache=false \
data.test_ds.data_path=/home/TestData/nlp/duplex_text_norm/small_test.tsv
# L2: Intent and Slot Classification Tasks
L2_Intent_and_Slot_Classification_Tasks_Intent_and_Slot_Classification:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
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.devices=1 \
trainer.accelerator="gpu" \
+trainer.fast_dev_run=true \
exp_manager.exp_dir=checkpoints
AFTER_SCRIPT: |
rm -rf checkpoints
L2_Intent_and_Slot_Classification_Tasks_Multi-Label_Intent_and_Slot_Classification:
needs: [cicd-test-container-setup]
uses: ./.github/workflows/_test_template.yml
with:
RUNNER: self-hosted-azure-gpus-1
SCRIPT: |
cd examples/nlp/intent_slot_classification && \
python multi_label_intent_slot_classification.py \
model.data_dir=/home/TestData/nlp/new_multiatis \
model.validation_ds.prefix=dev \
model.test_ds.prefix=dev \
trainer.devices=1 \
+trainer.fast_dev_run=true \
exp_manager.exp_dir=checkpoints2
AFTER_SCRIPT: |
rm -rf checkpoints2