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"merged_at": "2024-02-26T15:37:19"
} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
Add FP8 support for SD training.
**Collection**: [multimodal/text_to_image/statble_diffusion]
# Changelog
- Add config argument - use_te_fp8 to switch between fp16 and fp8 training
- Add transformer engine fp8 layers to Stable diffusion Unet
- Update a few legacy paths in notebook tutorial
# Usage
```yaml
unet_config:
use_te_fp8=True
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
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"@prathwishshetty \r\nI have raised a PR for this at https://github.com/NVIDIA/NeMo/pull/8498\r\nLet's wait to get it merged soon. Thanks for raising PR",
"This can be closed as PR gets merged",
"Thank you for the quick PR!"
] | 2024-02-23T00:31:48 | 2024-02-23T23:01:11 | 2024-02-23T23:01:10 | NONE | {
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"Same issue in FSDP+SFT.\r\nThe loss is 3-4 times higher than TP, PP, and DP cases. I doubted fused_adam at first (currently, FSDP cannot apply distributed_fused_adam) but, fused_adam worked well except for FSDP.\r\n\r\n### Environment details\r\n\r\nDocker Container: `nvcr.io/nvidia/nemo:24.01.01.framework`\r\n\r\... | 2024-02-22T23:55:22 | 2024-05-29T08:39:16 | 2024-03-25T21:34:15 | COLLABORATOR | {
"total": 0,
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} | null | null | null | **Describe the bug**
We'd like to use FSDP when doing LoRA fine-tuning with large LLMs. We noticed in our experiments that the train loss when using FSDP vs. no FSDP is very different. With FSDP, the train loss is extremely high, but after turning off FSDP and doing a regular LoRA tuning, we see normal train loss convergence. We also noted that the outputs of the forward pass when using FSDP are different than those when not using FSDP. Specifically, we observed that the forward pass using FSDP + LoRA seems to have lower magnitude outputs than the forward pass with just LoRA.
Train Loss Comparison:
<img width="431" alt="Screenshot 2024-02-22 at 3 41 45 PM" src="https://github.com/NVIDIA/NeMo/assets/51731412/da7478a8-0517-4f7d-b0b6-809afcba63ee">
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
1. Mount the changes in this [PR](https://github.com/NVIDIA/NeMo/pull/8414) to include the FSDP + LoRA integration.
2. Use the following script to run an example fine-tuning job:
```
#!/bin/bash
set -x
HOME=/lustre/fsw/coreai_dlalgo_llm/zeeshanp
NEMO=$HOME/NeMo
MEGATRON=$HOME/Megatron-LM
PEFT_SCHEME='lora'
MODEL_SIZE=7b
MBS=1
TP=1
PP=1
NUM_DEVICES=2
GBS=8
SEQ_LEN=4096
DATASET=squad # dolly, squad
if [ "${PEFT_SCHEME}" == "sft" ]; then
PEFT_SCHEME_=none
EXTRA_ARGS="model.optim.name=distributed_fused_adam \ # don't work
model.optim.lr=5e-6
"
else
PEFT_SCHEME_=${PEFT_SCHEME}
EXTRA_ARGS=""
fi
EXTRA_ARGS+="
+model.fp8=False \
+model.fp8_e4m3=False \
+model.fp8_hybrid=True \
+model.fp8_margin=0 \
+model.fp8_interval=1 \
+model.fp8_amax_history_len=1024 \
+model.fp8_amax_compute_algo=max
"
MODEL=$HOME/pretrained_models/Llama-2-${MODEL_SIZE}-bf16-mcore
EXP_DIR=$HOME/exp/fsdp_test/interactive-llama2-${MODEL_SIZE}-${PEFT_SCHEME}_${DATASET}
if [ "${DATASET}" == "dolly" ]; then
TRAIN_DS=[$HOME/datasets/dolly_packed/first_fit_shuffle_with_loss_mask/dolly_packed_${SEQ_LEN}_seed0.npy]
VALID_DS=[$HOME/datasets/dolly/validation.jsonl]
elif [ "${DATASET}" == "squad" ]; then
TRAIN_DS=[$HOME/datasets/squad_packed/first_fit_shuffle/packed_${SEQ_LEN}_seed0.npy]
VALID_DS=[$HOME/datasets/squad/1_squad_validation.jsonl]
else
echo "Unknown dataset ${DATASET}"
exit
fi
WANDB_PROJECT="fsdp_lora"
wandb login $WANDB_API_KEY
export PYTHONPATH="${NEMO}/.:${MEGATRON}/.:${PYTHONPATH}"
# export PYTHONPATH="${NEMO}/.:${PYTHONPATH}"
torchrun --nproc_per_node=${NUM_DEVICES} ${NEMO}/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \
trainer.devices=${NUM_DEVICES} \
trainer.num_nodes=1 \
trainer.val_check_interval=5 \
trainer.max_steps=100 \
+trainer.num_sanity_val_steps=0 \
+trainer.limit_val_batches=10 \
model.megatron_amp_O2=False \
exp_manager.create_wandb_logger=False \
exp_manager.wandb_logger_kwargs.project=llama_packed_${DATASET} \
exp_manager.wandb_logger_kwargs.name=fsdp_test \
exp_manager.resume_if_exists=False \
exp_manager.exp_dir="${EXP_DIR}" \
model.tensor_model_parallel_size=${TP} \
model.pipeline_model_parallel_size=${PP} \
model.micro_batch_size=${MBS} \
model.global_batch_size=${GBS} \
model.restore_from_path=${MODEL} \
model.data.train_ds.num_workers=0 \
model.data.validation_ds.num_workers=0 \
+model.data.train_ds.packed_sequence=True \
+model.log_token_counts=True \
model.data.train_ds.file_names=${TRAIN_DS} \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=${VALID_DS} \
model.peft.peft_scheme=${PEFT_SCHEME_} \
model.data.train_ds.max_seq_length=${SEQ_LEN} \
model.data.validation_ds.max_seq_length=${SEQ_LEN} \
model.sequence_parallel=False \
+model.apply_rope_fusion=True \
${EXTRA_ARGS} \
trainer.precision=bf16 \
model.answer_only_loss=True \
model.fsdp=True \
model.fsdp_sharded_checkpoint=True \
model.fsdp_use_orig_params=True
```
**Expected behavior**
We expect a normal train loss curve matching that seen when not using FSDP.
**Environment overview (please complete the following information)**
Used enroot to set up NeMo docker container.
**Environment details**
Docker Container: `nvcr.io/nvidia/nemo:24.01.framework`
**Additional context**
We added new code to wrap the parameters used in LoRA with the FSDP wrapper to ensure they are sharded correctly. From our train logs it seems that the sharding does occur correctly, so we're not sure where the forward pass is having a bug.
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clean up for specchllm | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8485 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8485/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8485/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8485/events | https://github.com/NVIDIA/NeMo/issues/8485 | 2,149,651,042 | I_kwDOC_bI7s6AIRJi | 8,485 | fastconformer collapse after some improvment, seems to be irrelevant on the amount of data. | {
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... | closed | false | null | [] | null | [
"What batch size are you using for training ? Can you try grad accumulation to make your global batch size at least 128 ?",
"Thanks @titu1994 , I am using:\r\naccumulate_grad_batches=18\r\nbatch_size=15\r\n2 GPUs.\r\n So if I understand correctly, the global batch size = 18 * 15 * 2 = 540, right?",
"Yes that m... | 2024-02-22T18:12:25 | 2024-04-06T01:44:00 | 2024-04-06T01:44:00 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I'm trying to train a fastconformer model from scratch, I did manage to overfit for a small dataset, but it diverges for the full dataset, when I try a smaller dataset (30%) of the large it also diverges in what seems to be the exact same time:
<img width="614" alt="image" src="https://github.com/NVIDIA/NeMo/assets/8983713/1f955fd1-95f8-4505-b3db-ef3e4870dc8b">
<img width="614" alt="image" src="https://github.com/NVIDIA/NeMo/assets/8983713/7c447a33-5a72-4c5f-9b4e-af502e071520">
I've tried to lower the learning rate, but then the model just does not improve at all, the amount of data is about 1k-2k hours. I've also tried with 32bit, it did not help.
Happy to hear if you have any intuition of what could help.
**Steps/Code to reproduce bug**
Here is the config file:
```
model:
sample_rate: 16000
log_prediction: true
ctc_reduction: mean_volume
skip_nan_grad: false
train_ds:
manifest_filepath: PATH
sample_rate: ${model.sample_rate}
batch_size: 15
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 16.7
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: fully_randomized
bucketing_batch_size: null
normalize_overtime: false
mean_overtime: false
validation_ds:
ds_item:
- name: SILENT_GIP_inset
manifest_filepath: PATH
sample_rate: ${model.sample_rate}
batch_size: ${model.train_ds.batch_size}
use_start_end_token: false
num_workers: 8
pin_memory: true
max_duration: 16.7
min_duration: 0.1
tokenizer:
dir: /PATH/tokenizer_spe_bpe_v128
type: bpe
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
log: true
frame_splicing: 1
dither: 0.00001
pad_to: 0
pad_value: 0.0
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.embedding_dim}
feat_out: -1
n_layers: 18
d_model: 512
subsampling: dw_striding
subsampling_factor: 4
subsampling_conv_channels: 256
causal_downsampling: false
subsampling_input_channels: 1
ff_expansion_factor: 4
self_attention_model: rel_pos
n_heads: 8
att_context_size:
- -1
- -1
att_context_style: regular
xscaling: true
untie_biases: true
pos_emb_max_len: 5000
conv_kernel_size: 9
conv_norm_type: batch_norm
conv_context_size: null
dropout: 0.1
dropout_pre_encoder: 0.1
dropout_emb: 0.0
dropout_att: 0.1
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
interctc:
loss_weights: []
apply_at_layers: []
optim:
name: adamw
lr: 0.0003
betas:
- 0.9
- 0.999
weight_decay: 0
sched:
name: CosineAnnealing
warmup_steps: 15000
warmup_ratio: null
min_lr: 2.9999999999999997e-05
trainer:
devices:
- 0
- 1
num_nodes: 1
max_epochs: 40
max_steps: -1
val_check_interval: 1.0
accelerator: gpu
strategy: ddp
accumulate_grad_batches: 18
gradient_clip_val: 0.0
precision: bf16
log_every_n_steps: 10
enable_progress_bar: true
num_sanity_val_steps: 0
check_val_every_n_epoch: 1
sync_batchnorm: true
enable_checkpointing: false
logger: false
benchmark: false
exp_manager:
exp_dir: /.../nemo_experiments
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: val_wer
mode: min
save_top_k: 5
always_save_nemo: true
resume_from_checkpoint: null
resume_if_exists: false
resume_ignore_no_checkpoint: false
create_wandb_logger: true
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: `git clone & pip install requirements`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version:
Linux
- PyTorch version
```
In [3]: torch.__version__
Out[3]: '2.2.0'```
- Python version
`Python 3.10.12`
2x4090
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"jenkins",
"jenkins",
"jenkins",
"jenkins"
] | 2024-02-22T15:04:51 | 2024-02-29T17:47:06 | 2024-02-29T17:47:05 | COLLABORATOR | {
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} | null | false | {
"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/8484",
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"merged_at": "2024-02-29T17:47:05"
} | # What does this PR do ?
Add NeMo Audio Codec Models to the collection
**Collection**: [TTS]
# Changelog
- Add audio codec 16kHz config
- Add NGC model to pretrained list
# Usage
### Automatically load the model from NGC
```python
from nemo.collections.tts.models import AudioCodecModel
nemo_codec_model = AudioCodecModel.from_pretrained('audio_codec_16khz_small')
```
### Getting discrete tokens from Audio
```python
import librosa
import torch
audio,sr = librosa.load("<path_to_audio>", sr=16000)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
audio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)
audio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)
nemo_codec_model.freeze()
encoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)
```
### Reconstructing audio from discrete tokens
```python
reconstructed_audio = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)
```
### Listen to audio
```python
import soundfile as sf
output_audio = reconstructed_audio.cpu().numpy()[0]
sf.write(<path_to_output_audio>, output_audio, samplerate=16000)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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# Changelog
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# Usage
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# Jenkins CI
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If you haven't finished some of the above items you can still open "Draft" PR.
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"@stevehuang52 this problem basically happens when we try to resume 2nd time right ? ",
"> @stevehuang52 this problem basically happens when we try to resume 2nd t... | 2024-02-21T22:51:14 | 2024-03-23T03:14:17 | 2024-03-23T03:14:16 | COLLABORATOR | {
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} | # What does this PR do ?
Fix PTL2.2 saving multiple `*-last.ckpt` checkpoints in resumed training.
In current PTL2.2 [model_checkpoint.py](https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/callbacks/model_checkpoint.py#L783), it doesn't delete the previous `*-last.ckpt` checkpoint that the model was resumed on when saving a new `*-last.ckpt` checkpoint, which results in multiple checkpoints. This will cause errors of "multiple *-last.ckpt checkpoints" when trying to resume from a previous job.
The fix is to check whether the checkpoint to be saved is also a `-last.ckpt` checkpoint. If it is, delete the previous `-last.ckpt` if they are under the same directory.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"merged_at": "2024-03-02T01:19:07"
} | # What does this PR do ?
PEFT better error message and doc
**Collection**: NLP
# Changelog
- Better error message for setting `packed_sequence` to True but using an unpacked dataset
- Better error message for using a different TP value for PEFT evaluation from training
- Documentation for using `megatron_gpt_generate.py` on a pretrained base model
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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] | 2024-02-21T21:07:04 | 2024-02-23T08:07:00 | 2024-02-23T08:06:59 | COLLABORATOR | {
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"merged_at": "2024-02-23T08:06:59"
} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"~Note: requires some fixes in lhotse, will bump the requirements as soon as I release it.~ EDIT: done",
"jenkins",
"jenkins",
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} | # What does this PR do ?
We're adding several features to enable audio-to-audio dataloading:
* Conversion script from NeMo audio-to-audio manifests to Lhotse format
* Audio-to-audio Lhotse dataset (covers a2a, a2a with reference and a2a with embedding cases)
* Auto-supports Lhotse Shar (tarred) format for audio-to-audio
* Options to cut/truncate examples into smaller chunks on the fly
**Collection**: All speech collections
# Changelog
* Conversion script from NeMo audio-to-audio manifests to Lhotse format
* Audio-to-audio Lhotse dataset (covers a2a, a2a with reference and a2a with embedding cases)
* Auto-supports Lhotse Shar (tarred) format for audio-to-audio
* Options to cut/truncate examples into smaller chunks on the fly
# Usage
Generally the workflow to use this is as follows:
```bash
# First convert NeMo manifest to Lhotse:
python scripts/audio_to_audio/convert_nemo_to_lhotse.py \
nemo_manifest.json \
lhotse_manifest.jsonl.gz \
-i input_filepath \
-t target_filepath
# Then, convert to Lhotse Shar (tarred) format
# Meaning of args:
# -j4 is 4 jobs
# -s 100 is the shard size
# -c specifies custom fields we want to export and their format, see lhotse shar export --help
lhotse shar export \
-j4 -v -s 100 \
-a flac \
-c target_recording:flac \
lhotse.jsonl.gz \
data_shar
```
Then you can instantiate the dataloader like the following:
```python
from omegaconf import OmegaConf
from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
from nemo.collections.asr.data.audio_to_audio_lhotse import LhotseAudioToTargetDataset
config = {
'shar_path': 'data_shar',
'sample_rate': 16000,
'batch_duration': 100,
'use_bucketing': True,
'num_buckets': 2,
}
dl = get_lhotse_dataloader_from_config(
OmegaConf.create(config), global_rank=0, world_size=1, dataset=LhotseAudioToTargetDataset()
)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"@titu1994, @ericharper To make this finally work we need a separate tok... | 2024-02-21T19:04:49 | 2024-05-10T04:05:50 | 2024-05-10T04:05:49 | COLLABORATOR | {
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} | # What does this PR do?
Update black using incremental changes only for files changed in PRs.
- the `black` package is updated to the latest version
- all collections are excluded, so `black .` will reformat nothing
- it is possible also to exclude collection from the list, once it is reformatted though PRs
- there will be 2 approaches to reformat the files manually:
- pass the file(s) explicitly (e.g., `black my_file.py`)
- use a pre-commit hook
- the automatic reformatting is done by GitHub Actions only for PRs and only for changed files
Tests (target branch is `update_black`):
- #9017 - Test from fork (code reformatted, pushed back to the branch from fork)
- #8996 - Test from fork, with updates
- #9019 - Test from branch in the main repo
Approach:
- create a custom GitHub Action responsible for reformatting the code. For black use only changed files
- custom token is used to push changes, since only custom token can trigger workflows after reformatting
# Q&A About the Approach
> Why do we need a custom GitHub Action if we have pre-commit.ci?
The pre-commit hook is still active and works, but pre-commit.ci reformats all the code (the pre-commit.ci team explicitly denied changing this behavior, see https://github.com/pre-commit-ci/issues/issues/90), thus it is replaced with a custom GitHub Action, which does the same, but only for the changed files (in the PR)
> Why do we need `isort` GitHub action?
Since both `isort` and `black` can change files, this should be done in one action to avoid concurrent changes. So, the GitHub Action will execute both formatters and commit changes if needed (same behavior as for pre-commit.ci).
> Why do we need a custom GitHub token?
When GitHub action pushes the commit with the default secret token, it cannot trigger other workflows.
See the discussion: https://github.com/orgs/community/discussions/25702
This can only be done with custom tokens.
**Collection**: [CI]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-02-21T15:54:07 | 2024-03-31T01:45:25 | 2024-03-31T01:45:24 | NONE | {
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} | null | null | null | I trained a Nemo ASR model (nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel) a few weeks ago using Nemo v1.21.0 and was able to decode with the .nemo model without any issues. Since then I updated my codebase and python environment to use v1.22.0. After that I tried to go back to export some models that I trained using v1.21.0 to onnx but get an error. I have the models trained with v1.21.0 on disk and here's what I'm doing to convert them to Onnx with my v1.22.0 environment:
```
import nemo.collections.asr as nemo_asr
from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel
from omegaconf import OmegaConf
asr_model = nemo_asr.models.ASRModel.restore_from(restore_path=args.nemo_model)
asr_model.change_decoding_strategy(decoder_type='ctc')
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
asr_model.eval()
asr_model.encoder.freeze()
asr_model.decoder.freeze()
asr_model.export(args.onnx_model)
```
and then I get an error
```
Traceback (most recent call last):
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/core/classes/common.py", line 485, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py", line 89, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py", line 43, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py", line 92, in __init__
self.decoding = RNNTDecoding(
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/metrics/rnnt_wer.py", line 1115, in __init__
super(RNNTDecoding, self).__init__(
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/metrics/rnnt_wer.py", line 257, in __init__
self._init_confidence(self.cfg.get('confidence_cfg', None))
File "~/.conda/envs/nemo2/lib/python3.10/site-packages/nemo/collections/asr/parts/utils/asr_confidence_utils.py", line 339, in _init_confidence
ConfidenceConfig() if confidence_cfg is None else ConfidenceConfig(**confidence_cfg)
TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'measure_cfg'
```
Ideally, an export to onnx would be agnostic to the version of Nemo used. Is there a way to address this?
**Environment overview (please complete the following information)**
- I'm running this on an on-prem Slurm cluster
- OS = Ubuntu 20.04
- Python = 3.10.12
- Pytorch = 2.1.0
- Nemo was installed with `pip install nemo-toolkit['all']==1.22.0` | {
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} | null | null | null | hi,
I have export the language identification model to onnx and I want to use it for inference.
how should I use this model for inference on an audio file?
how should be the format of my audio input?
is there any example or tutorial?
after using the following code:
```
import nemo
import nemo.collections.asr as nemo_asr
vad_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="langid_ambernet")
vad_model.export('qn.onnx')
ort_session = onnxruntime.InferenceSession('qn.onnx')
ttt = torch.rand(1,80,8000)
outputs = ort_session.run(None, {ort_session.get_inputs()[0].name:to_numpy(ttt)})
```
i get this error:
```
ValueError Traceback (most recent call last)
[<ipython-input-64-4dddcd66c01b>](https://localhost:8080/#) in <cell line: 1>()
----> 1 onnx_outputs = ort_session.run(None, {ort_session.get_inputs()[0].name:to_numpy(ttt)})
[usr/local/lib/python3.10/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py](https://localhost:8080/#) in _validate_input(self, feed_input_names)
196 missing_input_names.append(input.name)
197 if missing_input_names:
--> 198 raise ValueError(
199 f"Required inputs ({missing_input_names}) are missing from input feed ({feed_input_names})."
200 )
ValueError: Required inputs (['length']) are missing from input feed (['audio_signal']).
```
and how map the output embedding to labels?
any help is really appreciated.
thanks | {
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"I'm in the same situation. I get : \r\n\r\n```\r\n transcript = asr_model.transcribe(batch, batch_size=1)\r\n File \"/home/ubuntu/miniconda3/envs/nemo202403/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/home/ubuntu/min... | 2024-02-21T10:23:43 | 2024-04-22T01:45:30 | 2024-04-22T01:45:29 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to use the NeMo Canary ASR model for transcribing audio in memory.
However when i try to use the audio argument of the class like it is documented here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html#transcribing-inference
i get the error `Error while processing audio: EncDecMultiTaskModel.transcribe() got an unexpected keyword argument 'audio'`
and looking at the code, it seems it expects a list of files or input manifest and nothing else.
**Steps/Code to reproduce bug**
```py
import numpy as np
from nemo.collections.asr.models import EncDecMultiTaskModel
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
sample_rate = 16000
duration = 1
audio = np.random.randint(-32768, 32768, size=(sample_rate * duration), dtype=np.int16)
predicted_text = canary_model.transcribe(
audio=audio,
batch_size=16, # batch size to run the inference with
)
```
**Expected behavior**
Transcribing correctly without error or having to write files
**Environment overview (please complete the following information)**
- Environment location: Local windows PC
- Method of NeMo install: pip install git+https://github.com/NVIDIA/NeMo.git@r1.23.0#egg=nemo_toolkit[asr]
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Windows 11
- PyTorch version: 2.3.0
- Python version: 3.11
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} | # What does this PR do ?
Add a description of the CTC-WS context-biasing method to the documentation.
**Collection**: [ASR]
# Changelog
- Rename docs/source/asr/asr_language_modeling.rst -> docs/source/asr/asr_language_modeling_and_customization.rst
- Add context-biasing description to docs/source/asr/asr_language_modeling_and_customization.rst
- Update docs/source/asr/intro.rst
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
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# Additional Information
* Must wait https://github.com/NVIDIA/NeMo/pull/8462
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"@dkurt \r\nThank you for preparing this PR.\r\nLet me test this and review it.\r\nNeed to verify on multiple samples. ",
"@tango4j, any updates?",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"@tango4j , f... | 2024-02-21T08:19:59 | 2024-08-19T01:52:51 | 2024-08-19T01:52:50 | NONE | {
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} | # What does this PR do ?
Reduce memory footprint by replacing torch.argsort. With my local sample, diarization pipeline works on 12GB. Without the patch, it failed with an error `torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.60 GiB. GPU 0 has a total capacty of 23.64 GiB of which 3.21 GiB is free. Including non-PyTorch memory, this process has 20.35 GiB memory in use. Of the allocated memory 16.07 GiB is allocated by PyTorch, and 665.59 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF`
**Collection**: [asr]
# Changelog
Replace torch.argsort to reduce memory usage
# Usage
Running MSDD on long audio sample
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add Gemma Support and Conversion Scripts
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add Gemma Model Support and Conversion Scripts
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"I need to get back to this.\r\n\r\nOn Wed, Mar 20, 2024 at 6:44 PM github-actions[bot] <\r\n***@***.***> wrote:\... | 2024-02-21T00:15:30 | 2024-04-13T01:39:16 | 2024-04-13T01:39:16 | COLLABORATOR | {
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} | # What does this PR do ?
Hiddens modules such as `a_mim` and `vae` output a number of keys, including `{"z", "z_mean"}` for example. The current implementation supports specifying a single key to be used as the encoder output, and generally in configs this is set to `z` for those variational losses. This is not what you expect at inference time though. In that case you want the latent space to be a point estimate (the mean) rather than a noisy sampling. This PR accomplishes this by adding an optional second encoder output key to be used at inference time, and if it is not set then the current key is used for both. Inference/training is determined by checking the pytorch module property `self.training`.
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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"jenkins",
"Closing this in favor of https://github.com/NVIDIA/NeMo/pull/8449"
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} | # What does this PR do ?
Support relative audio paths in Lhotse `LazyNeMoIterator`
**Collection**: ASR
# Changelog
- Support relative audio paths in Lhotse `LazyNeMoIterator`
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"Solved the issue, by providing the ckpt path, could I ask why it doesn't work using \".nemo\" files?",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days sin... | 2024-02-20T13:34:51 | 2024-04-26T14:56:11 | 2024-03-29T01:44:02 | CONTRIBUTOR | {
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} | null | null | null | Hi, I have trained an msdd model together with it's speaker embeddings extractor unfrozen. When I try to run:
```
python3 neural_diarizer/multiscale_diar_decoder_infer.py diarizer.manifest_filepath=manifests/dev.json diarizer.oracle_vad=True diarizer.out_dir=test diarizer.msdd_model.model_path=train_msdd/msdd_experiments/exp1/checkpoints/exp1.nemo
```
I get the following error:
```
[NeMo E 2024-02-20 13:28:37 common:505] Model instantiation failed!
Target class: nemo.collections.asr.models.msdd_models.EncDecDiarLabelModel
Error(s): Key 'speaker_model_cfg' is not in struct
full_key: speaker_model_cfg
object_type=dict
Traceback (most recent call last):
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/classes/common.py", line 484, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/collections/asr/models/msdd_models.py", line 136, in __init__
self.msdd._speaker_model = EncDecSpeakerLabelModel.from_config_dict(cfg.speaker_model_cfg)
File "/usr/local/lib/python3.10/site-packages/omegaconf/dictconfig.py", line 359, in __getattr__
self._format_and_raise(key=key, value=None, cause=e)
File "/usr/local/lib/python3.10/site-packages/omegaconf/base.py", line 231, in _format_and_raise
format_and_raise(
File "/usr/local/lib/python3.10/site-packages/omegaconf/_utils.py", line 819, in format_and_raise
_raise(ex, cause)
File "/usr/local/lib/python3.10/site-packages/omegaconf/_utils.py", line 797, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set env var OC_CAUSE=1 for full trace
File "/usr/local/lib/python3.10/site-packages/omegaconf/dictconfig.py", line 351, in __getattr__
return self._get_impl(
File "/usr/local/lib/python3.10/site-packages/omegaconf/dictconfig.py", line 442, in _get_impl
node = self._get_child(
File "/usr/local/lib/python3.10/site-packages/omegaconf/basecontainer.py", line 73, in _get_child
child = self._get_node(
File "/usr/local/lib/python3.10/site-packages/omegaconf/dictconfig.py", line 475, in _get_node
self._validate_get(key)
File "/usr/local/lib/python3.10/site-packages/omegaconf/dictconfig.py", line 164, in _validate_get
self._format_and_raise(
File "/usr/local/lib/python3.10/site-packages/omegaconf/base.py", line 231, in _format_and_raise
format_and_raise(
File "/usr/local/lib/python3.10/site-packages/omegaconf/_utils.py", line 899, in format_and_raise
_raise(ex, cause)
File "/usr/local/lib/python3.10/site-packages/omegaconf/_utils.py", line 797, in _raise
raise ex.with_traceback(sys.exc_info()[2]) # set env var OC_CAUSE=1 for full trace
omegaconf.errors.ConfigAttributeError: Key 'speaker_model_cfg' is not in struct
full_key: speaker_model_cfg
object_type=dict
Error executing job with overrides: ['diarizer.manifest_filepath=manifests/dev_displace24.json', 'diarizer.oracle_vad=True', 'diarizer.out_dir=test', 'diarizer.msdd_model.model_path=train_msdd/msdd_experiments/exp1/checkpoints/exp1.nemo']
Traceback (most recent call last):
File "/home/gpirlogeanu/git_repos/diarization_nemo/diarization/neural_diarizer/multiscale_diar_decoder_infer.py", line 32, in main
diarizer_model = NeuralDiarizer(cfg=cfg)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/collections/asr/models/msdd_models.py", line 982, in __init__
self._init_msdd_model(cfg)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/collections/asr/models/msdd_models.py", line 1074, in _init_msdd_model
self.msdd_model = EncDecDiarLabelModel.restore_from(restore_path=model_path)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/classes/modelPT.py", line 316, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/connectors/save_restore_connector.py", line 235, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/connectors/save_restore_connector.py", line 158, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/classes/common.py", line 506, in from_config_dict
raise e
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/core/classes/common.py", line 498, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/home/gpirlogeanu/git_repos/diarization_nemo/nemo/collections/asr/models/msdd_models.py", line 136, in __init__
self.msdd._speaker_model = EncDecSpeakerLabelModel.from_config_dict(cfg.speaker_model_cfg)
omegaconf.errors.ConfigAttributeError: Key 'speaker_model_cfg' is not in struct
full_key: speaker_model_cfg
object_type=dict
```
When I run inside the EncDecDiarLabel class:
`print(msdd_model.msdd._speaker_model)`
It returns None.
Could you please help? | {
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"@artbataev thank you so much for the review!\r\n\r\nAbout your suggestions/comments:\r\n\r\n1. Done\r\n2. Done\r\n3. I leaved installing graphviz optional. The inst... | 2024-02-20T13:19:53 | 2024-03-14T15:32:49 | 2024-03-14T15:32:49 | COLLABORATOR | {
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} | # What does this PR do ?
Add a context-biasing tutorial (based on CTC-WS method) for CTC and RNN-T models.
**Collection**: [ASR]
# Changelog
- Add tutorials/asr/ASR_Context_Biasing.ipynb tutorial
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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} | # What does this PR do ?
Code to enable video pretraining for NeVa.
[W&B Project](https://wandb.ai/joc/video_neva)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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"@dimapihtar @ericharper \r\nSame issue occurs when trying to load the distributed checkpoint for continued training / sft.\r\n\r\nLoading distributed checkpoint with a single A100 works fine, with gbs=1,tp=1,pp=1,mbs=1. \r\nWhen scaling to 8 gpus, and changing gbs to 8, loading the checkpoint fails.\r\n\r\nHas the... | 2024-02-20T02:09:45 | 2024-05-28T01:46:53 | 2024-05-28T01:46:52 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
I have an mcore distributed checkpoint trained with PP=1, TP=1. When running inference with this distributed checkpoint, when I set the TP to higher than 1, it results in exceptions and inconsistent hangs.
When running inference with mcore distributed checkpoint with a tp > 1, there is an exception raised for:
```
Traceback (most recent call last):
File "/workspace/src/3rdparty/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py", line 271, in main
model = MegatronGPTModel.load_from_checkpoint(checkpoint_path, hparams_file=cfg.hparams_file, trainer=trainer, **kwargs)
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/models/nlp_model.py", line 397, in load_from_checkpoint
checkpoint = dist_checkpointing.load(sharded_state_dict=checkpoint, checkpoint_dir=checkpoint_dir)
File "/workspace/src/3rdparty/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 92, in load
validate_sharding_integrity(nested_values(sharded_state_dict))
File "/workspace/src/3rdparty/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 306, in validate_sharding_integrity
_validate_sharding_for_key(shardings)
File "/workspace/src/3rdparty/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 344, in _validate_sharding_for_key
raise CheckpointingException(f'Invalid access pattern for {rank_sharding[0][1]}')
megatron.core.dist_checkpointing.core.CheckpointingException: Invalid access pattern for ShardedTensor(key='model.embedding.word_embeddings.weight')
Error executing job with overrides: ['inference.greedy=True', 'inference.add_BOS=True', 'trainer.devices=8', 'trainer.num_nodes=1', 'tensor_model_parallel_size=8', 'pipeline_model_parallel_size=1']
Traceback (most recent call last):
File "/workspace/src/3rdparty/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py", line 308, in main
response = model.generate(
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 1414, in generate
return megatron_gpt_generate(
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py", line 127, in megatron_gpt_generate
output = generate(
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py", line 645, in generate
output = synced_generate(
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py", line 510, in synced_generate
for tokens, lengths, output_logits, full_logits in batch_token_iterator:
File "/workspace/src/3rdparty/NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py", line 888, in sample_sequence_batch
torch.distributed.broadcast(done, src, group)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 1947, in broadcast
work = group.broadcast([tensor], opts)
torch.distributed.DistNetworkError: Broken pipe
```
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
1. Have an existing distributed mcore gpt checkpoint saved in a directory trained with TP=1, PP=1
2. Pass checkpoint_dir, checkpoint_name into megatron_gpt_eval.py
3. [Optional] In my case, I enabled activation check pointing so I also had to pass kwargs["activations_checkpoint_granularity"] = None and kwargs["activations_checkpoint_method"] = None into `MegatronGPTModel.load_from_checkpoint`
4. Run inference with TP > 1 on a device with multiple gpus
This results in an exception everytime, and a percentage of runs are able to complete, but most of the time the process ends up hanging.
**Expected behavior**
A clear and concise description of what you expected to happen.
With mcore distributed checkpointing, I expect to be able to load an mcore model with different model parallel configs without any error using the example scripts for inference.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] AWS p4de.24xlarge (A100)
- Method of NeMo install: [pip install or from source]. From source, using NeMo r1.23.0 branch and dependencies
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version - Ubuntu 22.04.3 LTS (pytorch 23.12 container)
- PyTorch version - 2.2.0a0+81ea7a4
- Python version - Python 3.10.12
**Additional context**
Attaching full logs in files.
Add any other context about the problem here.
Example: GPU model
[tp8_error.log](https://github.com/NVIDIA/NeMo/files/14338551/tp8_error.log)
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"Probably related: https://github.com/pytorch/pytorch/issues/86566",
"Yikes. Fyi @tango4j @nithinraok @artbataev ",
"@dkurt What version of PyTorch do you use? What is the environment (OS, conda/pip, etc)?",
"@artbataev, Extremely apologize. Segmentation fault reproduced with Python `3.11.0rc1` and PyTorch 2.... | 2024-02-19T16:14:28 | 2024-02-20T11:02:23 | 2024-02-20T11:02:23 | NONE | {
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Segmentation fault when import asr:
```
$ python -c "from nemo.collections.asr.models.msdd_models import NeuralDiarizer"
Segmentation fault (core dumped)
```
`faulthandler` reports a problem with `@torch.jit.script` compilation: https://github.com/NVIDIA/NeMo/blob/df5a39583e31fd99bda371759c7954f928195594/nemo/collections/asr/parts/utils/online_clustering.py#L119-L120
```
Fatal Python error: Segmentation fault
Current thread 0x00007f0df18b7000 (most recent call first):
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_script.py", line 1399 in script
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_recursive.py", line 1003 in try_compile_fn
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_script.py", line 1395 in script
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_recursive.py", line 1003 in try_compile_fn
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_script.py", line 1395 in script
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_recursive.py", line 1003 in try_compile_fn
File "/home/dkurtaev/NeMo/.venv/lib/python3.11/site-packages/torch/jit/_script.py", line 1395 in script
File "/home/dkurtaev/NeMo/nemo/collections/asr/parts/utils/online_clustering.py", line 119 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "/home/dkurtaev/NeMo/nemo/collections/asr/parts/utils/longform_clustering.py", line 24 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "/home/dkurtaev/NeMo/nemo/collections/asr/parts/utils/speaker_utils.py", line 31 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "/home/dkurtaev/NeMo/nemo/collections/asr/parts/utils/manifest_utils.py", line 25 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1234 in _handle_fromlist
File "/home/dkurtaev/NeMo/nemo/collections/asr/models/aed_multitask_models.py", line 44 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "/home/dkurtaev/NeMo/nemo/collections/asr/models/__init__.py", line 15 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1234 in _handle_fromlist
File "/home/dkurtaev/NeMo/nemo/collections/asr/__init__.py", line 15 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 940 in exec_module
File "<frozen importlib._bootstrap>", line 690 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1149 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1128 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1128 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1178 in _find_and_load
File "/home/dkurtaev/audio_analytics/run.py", line 54 in <module>
Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, torch._C, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, yaml._yaml, matplotlib._c_internal_utils, PIL._imaging, matplotlib._path, kiwisolver._cext, matplotlib._image, regex._regex, charset_normalizer.md, sentencepiece._sentencepiece, google._upb._message, psutil._psutil_linux, psutil._psutil_posix, _cffi_backend, scipy._lib._ccallback_c, scipy.signal._sigtools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._flinalg, scipy.linalg._decomp_lu_cython, 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scipy.stats.beta_ufunc, scipy.stats._boost.beta_ufunc, scipy.stats.binom_ufunc, scipy.stats._boost.binom_ufunc, scipy.stats.nbinom_ufunc, scipy.stats._boost.nbinom_ufunc, scipy.stats.hypergeom_ufunc, scipy.stats._boost.hypergeom_ufunc, scipy.stats.ncf_ufunc, scipy.stats._boost.ncf_ufunc, scipy.stats.ncx2_ufunc, scipy.stats._boost.ncx2_ufunc, scipy.stats.nct_ufunc, scipy.stats._boost.nct_ufunc, scipy.stats.skewnorm_ufunc, scipy.stats._boost.skewnorm_ufunc, scipy.stats.invgauss_ufunc, scipy.stats._boost.invgauss_ufunc, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy.stats._ansari_swilk_statistics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.signal._peak_finding_utils, pyarrow.lib, pyarrow._hdfsio, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, 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numba.core.runtime._nrt_python, numba.np.ufunc._internal, numba.experimental.jitclass._box, numba.mviewbuf, numba.types.itertools, cytoolz.utils, cytoolz.itertoolz, cytoolz.functoolz, cytoolz.dicttoolz, cytoolz.recipes (total: 197)
Segmentation fault (core dumped)
```
When use Python 3.10 or Python 3.11 with `PYTORCH_JIT=0` import passes.
**Steps/Code to reproduce bug**
```
python3.11 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements/requirements_lightning.txt
pip install -r requirements/requirements_asr.txt
pip install -r requirements/requirements.txt
pip install -r requirements/requirements_common.txt
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"jenkins",
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} | # What does this PR do ?
1) FSDP bug fix.
2) FSDP support for GPT fine-tuning.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"Hi, can you add me to the org/team ea-bignlp/ga-participants. I can't get any info about private repository, so i can't pull that container. Thanks you so much!",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This ... | 2024-02-19T07:50:05 | 2024-04-08T01:44:40 | 2024-04-08T01:44:40 | NONE | {
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training container image update to `nvcr.io/nvidia/nemo:24.01.framework`
but nemo inference container image is still in private repository `nvcr.io/ea-bignlp/ga-participants/nemofw-inference`
Is there a plan to migrate the nemo inference container image to nvcr.io/nvidia as well? | {
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] | 2024-02-18T22:17:39 | 2024-03-27T01:44:10 | 2024-03-27T01:44:09 | NONE | {
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Thank you | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I tried to use forced aligner to generate timestamps for my audio files, I got ctm results in tokens like this:
100001 1 0.00 0.44 \<b>
100001 1 0.44 0.04 WORD1
100001 1 0.48 0.20 \<b>
100001 1 0.68 0.04 WORD2
100001 1 0.72 0.20 \<b>
100001 1 0.92 0.04 WORD3
100001 1 0.96 0.16 \<b>
100001 1 1.12 0.04 WORD4
Every valid token(none blank ones) has the same duration 0.04, while \<b>'s duration changes. when I tried to use another model , the fixed value changed from 0.04 to 0.08, but still keeps the same for valid phones.
I believe the real duration for a valid phone varies, and some parts of the vowels were wrongly moved to \<b>, which makes it hard to discriminate valid phones and real silence part. It should be quite common that there are no noteworthy silence between words in some sentences.
**Describe the solution you'd like**
Is it possible to make the timestamp around \<b> more accurate, or add some other symbols for noteworthy silence like "sp"/"sil"?
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"Also seeing this error.\r\n\r\nMy temporary workaround is to catch ValueErrors and just add a second or two of blank audio to the end of the file before re-processing which seems to work as a temporary stop-gap until this can be fixed.",
"This issue is stale because it has been open for 30 days with no activity.... | 2024-02-17T10:08:31 | 2024-06-19T01:54:08 | 2024-05-18T01:45:29 | NONE | {
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} | null | null | null | **Describe the bug**
When I tried to generate timestamp with model: nvidia/parakeet-tdt-1.1b, I got following error,
ValueError: `char_offsets`: [{'char': [tensor(607, dtype=torch.int32)], 'start_offset': 28, 'end_offset': 29}....
call stack,
```
Traceback (most recent call last):
File "/tmp/inference/nvidia_asr.py", line 103, in <module>
main()
File "/tmp/inference/nvidia_asr.py", line 94, in main
tt = parakeet_rnnt( audio, 'tdt' )
File "/tmp/inference/nvidia_asr.py", line 45, in parakeet_rnnt
hypothesis = asr_model.transcribe([audio], return_hypotheses=True)[0][0]
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/rnnt_models.py", line 298, in transcribe
best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/metrics/rnnt_wer.py", line 497, in rnnt_decoder_predictions_tensor
hypotheses[hyp_idx] = self.compute_rnnt_timestamps(hypotheses[hyp_idx], timestamp_type)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/metrics/rnnt_wer.py", line 699, in compute_rnnt_timestamps
raise ValueError(
```
**Steps/Code to reproduce bug**
The code to reproduce above the bug,
(The code below can be used to get timestamp if use parakeet rnnt-1.1b model )
```
asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-1.1b")
decoding_cfg = asr_model.cfg.decoding
with open_dict(decoding_cfg):
decoding_cfg.preserve_alignments = True
decoding_cfg.compute_timestamps = True
decoding_cfg.rnnt_timestamp_type = 'word'
asr_model.change_decoding_strategy(decoding_cfg)
hypothesis = asr_model.transcribe([audio], return_hypotheses=True)[0][0]
timestamp_dict = hypothesis.timestep
word_timestamps = timestamp_dict['word']
print(word_timestamps)
```
**Expected behavior**
It should output word timestamps instead of exception.
**Environment overview (please complete the following information)**
- Environment location: run in local ubuntu22.04 machine.
- Method of NeMo install: pip install nemo_toolkit['all']
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: ubuntu 22.04
- PyTorch version: 2.2.0+cu118
- Python version: 3.10.12
**Additional context**
Add any other context about the problem here.
GPU model: GTX 1080T
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"For Parakeet, we have several tutorials in the Nemo tutorials directory - here is one to get started - https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb\r\n\r\nMost of the concepts will apply to Parakeet CTC / RNNT/ TDT. \r\n\r\nFor Canary, it is a very new model, we are stil... | 2024-02-17T05:56:31 | 2024-12-04T08:27:19 | 2024-02-19T18:21:13 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
- Can you explain, how to fine-tune Nvidia latest ASR models like Canary 1B and NeMo Parakeet ASR Models.
https://nvidia.github.io/NeMo/blogs/2024/2024-02-canary/
https://nvidia.github.io/NeMo/blogs/2024/2024-01-parakeet/
**Describe the solution you'd like**
- A notebook/code block explaining how to fine-tune Nvidia Canary models for low-resource Indic languages like Malayalam.
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} | # What does this PR do ?
- fix canary chunk infer rare bug, where the last segment might have mismatched shape with pre-allocated buffer.
- update audio_segment.py to allow duration is None
- add lhotse support for relative audio paths
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} | # What does this PR do ?
This PR adds landing pages for LLM, CV, and multimodal collections. Also, some suggestions on how to split the introduction into a few sections for better readability.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Support S3 data loading for the GPTDataset
This PR introduces the S3IndexedDataset, which supports loading a dataset stored in S3 in the same format as the MMapIndexedDataset. In particular, the .idx file is downloaded to a local directory at initialization so that we can memory map it and the .bin file is streamed into memory block-by-block.
In order for S3 data loading to be performant, we introduce block shuffling to GPTDataset, where we divide samples into blocks, shuffle the blocks and then shuffle within the blocks. In this way, we introduce a minimal amount of correlation into the shuffle, which can be exploited to download larger, contiguous chunks of data from S3. We set the default block size for shuffling to 1, which recovers the shuffling behavior before this commit.
## Example
I now describe the S3 data loading strategy in more detail by walking through a small example. In NeMo, a sample consists of `seq_length` tokens. For simplicity, suppose each token is 1 byte and `seq_length` is 100.
Each sample then takes 100 bytes.
Suppose we have a dataset with 12 samples.
Sample index 0 is stored in bytes [0, 100), sample index 1 is stored in bytes [100, 200), ..., and sample index 11 is stored in bytes [1100, 1200).
Currently, NeMo takes the list of sample indices:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
And produces a `shuffle_idx`, which is just a permutation of those sample indices like:
[11, 3, 0, 6, 1, 9, 10, 2, 5, 7, 4, 8]
The `shuffle_idx` determines the order in which NeMo processes samples.
We could have the `S3IndexedDataset` just grab the bytes for a sample at a time. The first request would be for the bytes [1100, 1200), the second request would be for the bytes [300, 400), the third request would be for the bytes [0, 100) and so on in the order determined by `shuffle_idx`. That works, but it's slow, because you're making one request for each sample.
Let's try to introduce an in-memory cache. In particular, suppose the S3IndexedDataset does this:
* If the requested bytes range [`start`, `end`) is in the cache, then extract the requested bytes range from the cache.
* Otherwise, first refresh the cache by downloading the bytes range [`start`, `start` + `cache_nbytes`) and then extract the requested bytes range from the cache.
Suppose the `cache_nbytes` is 400. The first request would be for the bytes [1100, 1200). The cache is initially empty, so we refresh the cache by downloading the bytes [1100, 1500) and then extract the requested bytes range from the cache. The second request would be for the bytes [300, 400). Those bytes are not in the cache, so we refresh the cache by downloading the bytes [300, 700) and then extract the requested bytes range from that cache. And so on.
We actually made the problem worse. For most samples, we have to refresh the cache, so we have not reduced the number of requests much. We've just made the requests have to download a larger number of bytes. The issue is that the bytes needed for a sample index are probably not next to the bytes needed for the previous sample index.
To use the cache effectively, we have to introduce some correlation in the shuffle. In particular, we divide the original list of sample indices into blocks like:
* [0, 1, 2, 3]
* [4, 5, 6, 7]
* [8, 9, 10, 11]
We then shuffle within the blocks like:
* [3, 0, 2, 1]
* [4, 6, 5, 7]
* [11, 10, 8, 9]
We then shuffle the order of the blocks like:
* [11, 10, 8, 9]
* [4, 6, 5, 7]
* [3, 0, 2, 1]
And we construct the block-shuffled `shuffle_idx` like:
[11, 10, 8, 9, 4, 6, 5, 7, 3, 0, 2, 1]
We also have to change which bytes we download on a cache miss. In particular, we download the bytes [`cache_start`, `cache_start` + `cache_nbytes`), where cache_start is (`start`//`cache_nbytes`) * `cache_nbytes`.
The first request would be for the bytes [1100, 1200). The cache is initially empty, so we refresh the cache by downloading the bytes [800, 1200) and then extract the requested bytes range from that cache. The second request would be for the bytes [1000, 1100). We extract those bytes from the cache. The third request would be for the bytes [800, 1200). We extract those bytes from the cache. And so on. In this way, we only have to refresh cache at the start of each new block.
**Collection**: nlp
## Known issues
Each data worker will download the same cache (though still prepare different batches). It does not look like there's a straightforward, performant way to avoid that.
## Alternatives
We could remove block shuffling and add an option to disable shuffling under the assumption that the dataset is preshuffled.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Here's an example data config:
```
data:
data_impl: mmap
seq_length: 2048
# 128 * 1024 * 1024, i.e., 128 MiB
data_cache_nbytes: 134217728
# data_cache_nbytes / (bytes per token * seq_length)
# = (128 * 1024 * 1024) / (2 * 2048) = 32768
shuffle_block_size: 32768
shuffle_documents: false
no_seqlen_plus_one_input_tokens: true
data_prefix:
- 0.1
- s3://path/to/shard0
- 0.1
- s3://path/to/shard1
...
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"jenkins"
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} | # What does this PR do ?
Integrating AMMO library to the project and providing utilities for quantizing models with Llama2 PTQ example.
Different quantization algorithms are available including **INT8 SmoothQuant**, **INT4 AWQ**, and **FP8**.
Main class `Quantizer` from the `nemo.export.quantize` submodule produces directory or **.qnemo** tarball to be consumed by TensorRT-LLM toolbox for efficient inference. This will be a part of NeMo Framework Inference Container.
**Collection**: [NLP]
# Changelog
- Adding **nvidia-ammo** package to requirements
- Adding `nemo.export.quantize` submodule for quantizing models
- Adding `tests.setup` module to facilitate Jenkins setup
- Adding PTQ test to Jenkins
# Usage
Example for INT8 SmoothQuant method:
```
python examples/nlp/language_modeling/megatron_llama_quantization.py \
model_file=llama2-7b-fp16.nemo \
model_save=llama2-7b-int8_sq.qnemo \
quantization.algorithm=int8_sq \
export.decoder_type=llama \
export.inference_tensor_parallel=1
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
For more transparent and easier review process some components were isolated into individual MRs:
* https://github.com/NVIDIA/NeMo/pull/8281
* https://github.com/NVIDIA/NeMo/pull/8429 | {
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} | # What does this PR do ?
Adds bug fixes to support TP, PP =and SP
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
Fixes conversion script for NeMo to HF for mistral-7B.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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"Running into a similar issue training the conformer large model \r\nin a docker container with the latest `nvcr.io/nvidia/nemo:23.10` image, on p2.16xlarge (V100 instances). What is your training environment like?",
"This is my docker container\r\n\r\n```\r\nnvcr.io/nvidia/clara/bionemo-framework:latest \"/wor... | 2024-02-16T15:40:24 | 2024-05-25T14:37:01 | 2024-03-26T01:44:06 | NONE | {
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I am using the default configs, code and data to train a model within BioNeMo framework. The timeout occurs at the middle of the training.
```
Epoch 0: 6%|██ | 32040/500150 [6:28:43<94:39:17, 1.37it/s, loss=2.6, v_num=95nc, reduced_train_loss=2.590, global_step=3.2e+4, consumed_samples=2.56e+7][E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96120, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25624887 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96125, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625593 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96125, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625593 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 7] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64088, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625251 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64088, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625251 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96120, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25624887 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64088, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625251 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96120, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25624886 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96120, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25624887 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:472] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:478] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:830] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
terminate called after throwing an instance of 'std::runtime_error'
what(): [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:472] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:478] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:830] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96125, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625593 milliseconds before timing out.
terminate called after throwing an instance of 'std::runtime_error'
what(): [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96125, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625593 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:472] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[E ProcessGroupNCCL.cpp:478] To avoid data inconsistency, we are taking the entire process down.
[E ProcessGroupNCCL.cpp:830] [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
terminate called after throwing an instance of 'std::runtime_error'
what(): [Rank 0] NCCL watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=96117, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 25625613 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 4] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64090, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 1800741 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 5] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64090, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 1800733 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64090, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 1800769 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 6] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64090, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 1800781 milliseconds before timing out.
[E ProcessGroupNCCL.cpp:458] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=64090, OpType=ALLREDUCE, Timeout(ms)=1800000) ran for 1800847 milliseconds before timing out.
^C
```
The configs are:
```
name: esm2nv
do_training: True # set to false if data preprocessing steps must be completed
do_testing: False # set to true to run evaluation on test data after training, requires test_dataset section
restore_from_path: null # used when starting from a .nemo file
trainer:
devices: 8 # number of GPUs or CPUs
num_nodes: 1
accelerator: gpu #gpu or cpu
precision: 16 #16 or 32
logger: False # logger is provided by NeMo exp_manager
enable_checkpointing: False # checkpointing is done by NeMo exp_manager
replace_sampler_ddp: False # use NeMo Megatron samplers
max_epochs: null # # use max_steps instead with NeMo Megatron model
log_every_n_steps: 10 # number of interations between logging
val_check_interval: 15e4
limit_val_batches: 50 # number of batches in validation step, use fraction for fraction of data, 0 to disable
limit_test_batches: 500 # number of batches in test step, use fraction for fraction of data, 0 to disable
accumulate_grad_batches: 1
gradient_clip_val: 1.0
benchmark: False
max_steps: 500000
exp_manager:
name: ${name}
exp_dir: ${oc.env:BIONEMO_HOME}/results/nemo_experiments/${.name}/${.wandb_logger_kwargs.name}
explicit_log_dir: ${.exp_dir}
create_wandb_logger: True
create_tensorboard_logger: True
wandb_logger_kwargs:
project: ${name}_pretraining
name: ${name}_pretraining
group: ${name}
job_type: Localhost_nodes_${trainer.num_nodes}_gpus_${trainer.devices}
notes: "date: ${now:%y%m%d-%H%M%S}"
tags:
- ${name}
offline: False # set to True if there are issues uploading to WandB during training
resume_if_exists: True # automatically resume if checkpoint exists
resume_ignore_no_checkpoint: True # leave as True, will start new training if resume_if_exists is True but no checkpoint exists
create_checkpoint_callback: True # leave as True, use exp_manger for for checkpoints
checkpoint_callback_params:
monitor: val_loss
save_top_k: 10 # number of checkpoints to save
mode: min # use min or max of monitored metric to select best checkpoints
always_save_nemo: False # saves nemo file during validation, not implemented for model parallel
filename: 'megatron_bert--{val_loss:.2f}-{step}-{consumed_samples}'
model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
model:
precision: 16
# ESM2-specific parameters
micro_batch_size: 100
seq_length: 1024
num_layers: 6
hidden_size: 320
ffn_hidden_size: ${multiply:${model.hidden_size}, 4} # Transformer FFN hidden size. Usually 4 * hidden_size.
num_attention_heads: 20
megatron_legacy: false
position_embedding_type: rope # ESM2 uses relative positional encoding 'ROPE' to extrapolate to longer sequences unseen during training
hidden_dropout: 0 # ESM2 removes dropout from hidden layers and attention
embedding_use_attention_mask: True # ESM2 uses attention masking on the embeddings
embedding_token_dropout: True # ESM2 rescales embeddings based on masked tokens
mask_token_id: ${.tokenizer.mask_id} # Needed for token dropout rescaling
attention_dropout: 0.0 # ESM2 does not use attention dropout
normalize_attention_scores: False # ESM2 does not use normalized attention scores
tensor_model_parallel_size: 1 # model parallelism
pipeline_model_parallel_size: 1 # model parallelism. If enabled, you need to set data.dynamic_padding to False as pipeline parallelism requires fixed-length padding.
bias_gelu_fusion: False
# NOTE: these are compatability features
use_esm_attention: True # Use specific attention modifications for ESM2
esm_gelu: True # ESM2 uses custom gelu in the ML layer
use_pt_layernorm: False # Use pytorch implementation of layernorm instead of fused nemo layernorm. Important for equivalency of results with ESM2.
use_pt_mlp_out: False # Use pytorch implementation of attention output mlp instead of the nemo version. Important for equivalency of results with ESM2.
# Not specified in ESM2 models:
# model architecture
max_position_embeddings: ${.seq_length}
encoder_seq_length: ${.seq_length}
optim:
name: fused_adam # fused optimizers used by Megatron model
lr: 4e-4
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
warmup_steps: 2000
constant_steps: 50000
min_lr: 4e-5
init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.')
kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number.
layernorm_epsilon: 1e-5
make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency.
pre_process: True # add embedding
post_process: True # add pooler
bert_binary_head: False # BERT binary head
resume_from_checkpoint: null # manually set the checkpoint file to load from
# NOTE: is this one of the new fields?
masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask.
tokenizer:
# Use ESM2 tokenizers from HF
library: 'huggingface'
type: 'BertWordPieceLowerCase'
model_name: "???"
mask_id: 32
model: null
vocab_file: null
merge_file: null
# precision
native_amp_init_scale: 4294967296 # 2 ** 32
native_amp_growth_interval: 1000
fp32_residual_connection: False # Move residual connections to fp32
fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16
# miscellaneous
seed: 1234
use_cpu_initialization: False # Init weights on the CPU (slow for large model)
onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter.
# not implemented in NeMo yet
activations_checkpoint_method: null # 'uniform', 'block'
activations_checkpoint_num_layers: 1
data:
ngc_registry_target: uniref50_2022_05
ngc_registry_version: v23.06
data_prefix: "" # must be null or ""
num_workers: 8
dataloader_type: single # cyclic
reset_position_ids: False # Reset position ids after end-of-document token
reset_attention_mask: False # Reset attention mask after end-of-document token
eod_mask_loss: False # Mask loss for the end of document tokens
masked_lm_prob: 0.15 # Probability of replacing a token with mask.
short_seq_prob: 0.1 # Probability of producing a short sequence.
skip_lines: 0
drop_last: False
pin_memory: False
dynamic_padding: False # If True, each batch is padded to the maximum sequence length within that batch.
# Set it to False when model.pipeline_model_parallel_size > 1, as pipeline parallelism requires fixed-length padding.
# Below is the configuration for UF90 resampling.
force_regen_sample_mapping: false # When true, will always generate a new uf90 resampling. otherwise, change the seed.
data_impl: "csv_mmap"
# Supported kwargs (with default values):
# text_mmap (newline_int=10, header_lines=0, workers=None, sort_dataset_paths=True)
# csv_mmap (newline_int=10, header_lines=0,workers=None, sort_dataset_paths=True, data_col=1, data_sep=",")
dataset: # inclusive range of data files to load x[000..049] or can a single file, e.g. x000
train: x[000..049]
test: x[000..049]
val: x[000..049]
data_impl_kwargs:
csv_mmap:
data_col: 1 # 0-based
header_lines: 1
# Rest of these are inherited
uf50_datapath: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/uniref50_train_filt.fasta
uf90_datapath: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/uniref90membersandreps_ur50trainfiltreps.fasta
cluster_mapping_tsv: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/mapping.tsv
# TODO These need to be updated to values we actually like
dataset_path: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/uf50 # parent directory for data, contains train / val / test folders. Needs to be writeable for index creation.
# NOTE test_size and val_size must be smaller than the total dataset size.
# you can check this with grep -c '>' <uf50_datapath.fasta>
val_size: 5000
test_size: 1000000
sort_fastas: false # If true, assumes the input files are not sorted, and sorts them before creating the cluster mapping. Unsorted fastas will break the cluster map.
uf90:
uniref90_path: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/uf90/ # created and populated my preprocessing
dataset:
uf90_csvs: x[000..049] # created and populated by preprocessing, this key is a directory inside uniref90_path
data_impl: 'csv_fields_mmap'
data_impl_kwargs:
csv_fields_mmap:
header_lines: 1
newline_int: 10 # byte-value of newline
workers: ${model.data.num_workers} # number of workers when creating missing index files (null defaults to cpu_num / 2)
sort_dataset_paths: True # if True datasets will be sorted by name
data_sep: ',' # string to split text into columns
data_fields:
sequence: 3
sequence_id: 1
index_mapping_dir: ${model.data.index_mapping_dir}
use_upsampling: True # if the data should be upsampled to max number of steps in the training
seed: ${model.seed} # Random seed
max_seq_length: ${model.seq_length} # Maximum input sequence length. Longer sequences are truncated
modify_percent: 0.1 # Percentage of characters in a protein sequence to modify. (Modification means replacing with another amino acid or with a mask token)
perturb_percent: 0.5 # Of the modify_percent, what percentage of characters are to be replaced with another amino acid.
index_mapping_dir: ${oc.env:BIONEMO_HOME}/data/uniref50_90_202104_esm2nv_v1.0/
dwnstr_task_validation:
enabled: False
dataset:
class: bionemo.model.core.dwnstr_task_callbacks.PerTokenPredictionCallback
task_type: token-level-classification
infer_target: bionemo.model.protein.esm1nv.infer.ESM1nvInference
max_seq_length: ${model.seq_length}
emb_batch_size: 128
batch_size: 128
num_epochs: 10
shuffle: True
num_workers: 8
task_name: secondary_structure
dataset_path: ${oc.env:BIONEMO_HOME}/data/FLIP/${model.dwnstr_task_validation.dataset.task_name}
dataset:
train: x000
test: x000
sequence_column: "sequence" # name of column with protein sequence in csv file
target_column: [ "3state", "resolved" ] # names of label columns in csv file
target_sizes: [ 3, 2 ] # number of classes in each label
mask_column: [ "resolved", null ] # names of mask columns in csv file, masks must be 0 or 1
random_seed: 1234
optim:
name: adam
lr: 0.0001
betas:
- 0.9
- 0.999
eps: 1e-8
weight_decay: 0.01
sched:
name: WarmupAnnealing
min_lr: 0.00001
last_epoch: -1
warmup_ratio: 0.01
max_steps: 1000
``` | {
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} | # What does this PR do ?
Updates the NeMo readme to include new generative ai focus and move domain centric info to domain readmes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"@biscayan \r\n\r\nDo you remember how you come up with the model_stride_sec = 0.08? [Link to the line](https://github.com/biscayan/NeMo/blob/9f50dbdabed105d2649384e... | 2024-02-16T02:04:16 | 2024-05-21T23:57:18 | 2024-05-21T21:48:22 | NONE | {
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} | # What does this PR do ?
I closed a pull request #8341 because of lots of conflicts in the branch.
I tried to solve, but finally I deleted the branch, made new one, and add sign-off to the commit.
Sorry for my mistakes.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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} | # What does this PR do ?
Address mcore support transformer models instead of just decoder/GPT models.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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"There's a reason those decoding classes aren't modules. When rnnt was being developed it turned out there was a memory leak due to pt graph tracking and Autoregressive calls to the decoder joint. That's why .freeze(), .as_frozen() was developed in NeMos core to deal with that. \r\n\r\nI dunno if that reason is sti... | 2024-02-15T23:05:26 | 2024-02-28T23:40:35 | 2024-02-28T23:40:35 | COLLABORATOR | {
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@artbataev pointed out to me an issue with calling this when the model is using the cuda graph rnn-t decoder:
```
from nemo.collections.asr.models import ASRModel
import torch
asr_model = ASRModel.from_pretrained(model_name, map_location=torch.device("cuda:0"))
asr_model = asr_model.to(torch.device("cuda:1"))
```
Basically, if I initialize the cuda decoder with instance variable buffers (or parameters) that are torch.Tensors on device cuda:0, the "to()" method won't move them over to device cuda:1, because to() recurses only into members that are also torch.nn.Modules: https://github.com/pytorch/pytorch/blob/c3b4d78e175920141de210f44d292971d7c52ff0/torch/nn/modules/module.py#L572
In order to make that behavior work, I would need to every class that transitively uses an instance of RNNTGreedyDecodeCudaGraph to inherit from torch.nn.Module, so that to() will act properly. This seems like a lot of work, but would allow this API call to work as expected. Otherwise, you get an error as Vladimir shows here: https://github.com/NVIDIA/NeMo/pull/8191#discussion_r1491227311
.to() is being called here transitively https://github.com/NVIDIA/NeMo/blob/21990e4e2182cfa1f33d88c21ec4797f79e3ef24/nemo/core/connectors/save_restore_connector.py#L179 by setup_model() here: https://github.com/NVIDIA/NeMo/blob/21990e4e2182cfa1f33d88c21ec4797f79e3ef24/examples/asr/transcribe_speech.py#L217-L241
in his transcribe_speech.py command line.
Basically any code in NeMo that allocates a torch.tensor that isn't tracked by pytorch's tracking of torch.Tensors (via wrapping it in either a parameter or a buffer and putting it inside a torch.nn.Module) will fail to be converted properly by to(). This could also cause subtle bugs in converting a model from float32 to bfloat16 as well. @erastorgueva-nv I don't think this is what you might be seeing in your Canary debugging, but FYI.
I ultimately don't think this is a good idea since it seems like a lot of work. I think a better way to fix this is to avoid calling to() at all, and instead have users set CUDA_VISIBLE_DEVICES environment variable appropriately before starting a process (and remove the cuda=1 config option in transcribe_speech.py). Note that using CUDA_VISIBLE_DEVICES to specify a single GPU and simply using torch.device("cuda") instead of specifying a device index in code via torch.device("cuda", my_index) is what's recommend: https://pytorch.org/docs/stable/generated/torch.cuda.set_device.html#torch.cuda.set_device
Do we have any use for multiple cuda devices in a single process in NeMo? | {
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} | # What does this PR do ?
Unify HF converter scripts and arguments. Moved from https://github.com/NVIDIA/NeMo/pull/8307. Change target to main.
# Jenkins CI
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**PR Type**:
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- carry over custom manifest keys when creating tarred manifests
- selectable text field in ASR eval
- get rid of prompt slicing, create proper inference prompts
# Usage
* You can potentially add a usage example below
```python
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# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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GPU acceleration added,
Data loading optimization with adding multiprocessing,
Code refactoring started
+ Added support for running inside the docker container
@vsl9
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Hotfix
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/8429 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8429/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8429/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8429/events | https://github.com/NVIDIA/NeMo/pull/8429 | 2,136,125,325 | PR_kwDOC_bI7s5m9HF7 | 8,429 | Account for mpirun use case in get_rank | {
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"> Nice ! Thanks for the improvement !\r\n\r\nThanks, I'm merging.."
] | 2024-02-15T10:11:01 | 2024-02-22T10:23:52 | 2024-02-22T10:23:52 | COLLABORATOR | {
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} | # What does this PR do ?
Some distributed workloads can possibly be launched "manually" with `mpirun -n N python ...`. I'm adapting `nemo.utils.get_rank.py` module to take it into account and produce correct rank information in such cases.
Currently the below **rank_example.py** script (with [the project pattern for setting up distributed environment](https://github.com/NVIDIA/NeMo/blob/21990e4e2182cfa1f33d88c21ec4797f79e3ef24/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1378-L1385)) when launched with, for example, `mpirun -n 2 python rank_example.py` outputs
```
RANKS (NeMo, PyT): (0, 0)
RANKS (NeMo, PyT): (0, 1)
```
while the expected output is
```
RANKS (NeMo, PyT): (0, 0)
RANKS (NeMo, PyT): (1, 1)
```
The MR fixes this.
**rank_example.py**:
```
import torch.distributed as dist
from pytorch_lightning.trainer.trainer import Trainer
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
from nemo.utils.get_rank import get_rank
trainer = Trainer(
strategy=NLPDDPStrategy(),
devices=-1,
num_nodes=1,
accelerator="gpu",
logger=False,
precision="bf16",
enable_checkpointing=False,
)
def dummy():
return
if trainer.strategy.launcher is not None:
trainer.strategy.launcher.launch(dummy, trainer=trainer)
trainer.strategy.setup_environment()
print("RANKS (NeMo, PyT):", (get_rank(), dist.get_rank()))
```
**Collection**: [ALL]
# Changelog
- Fixing `is_global_rank_zero` and `get_rank` helper functions in case of using `mpirun` by considering `OMPI_COMM_WORLD_RANK` env var.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] Bugfix
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
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"@karpnv is this being worked on ?",
"@karpnv i'll provide a review la... | 2024-02-15T07:16:06 | 2025-01-18T01:54:20 | 2025-01-18T01:54:18 | COLLABORATOR | {
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} | # Preserve Flashlight and Pyctcdecode beamsearch with Ngram LM
Support Flashlight and Pyctcdecode decoding with pure KenLM and NeMo KenLM
Standardize API of CLI inference scripts
**Collection**: ASR
# Changelog
- Fix install script install_beamsearch_decoders.sh
- Create flashlight_lexicon file during scripts/asr_language_modeling/ngram_lm/train_kenlm.py and tar it with kenlm.bin
- Unify parameters for eval_beamsearch_ngram_ctc.py, speech_to_text_eval.py and training
-- Get logprobs from Hypothesis
-- Use "pyctcdecode" strategy as default beamsearch algorithm denoted as "beam"
-- Remove default seq2seq strategy
-- Check decoding_type and search_type combinations
-- Support empty string in nemo_kenlm_path and word_kenlm_path for beamsearch without LM (ZeroLM)
- Fix bug with EncDecHybridRNNTCTCModel in examples/asr/transcribe_speech.py
- Support AggregateTokenizer in scripts/asr_language_modeling/ngram_lm/create_lexicon_from_arpa.py
```python
python3 scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_ctc.py \
model_path=am_model.nemo \
dataset_manifest=manifest.json \
preds_output_folder=/tmp \
ctc_decoding.strategy=flashlight \
ctc_decoding.beam.kenlm_path=am_model.kenlm \
ctc_decoding.beam.beam_size=[4] \
ctc_decoding.beam.beam_alpha=[0.5] \
ctc_decoding.beam.beam_beta=[0.5] \
batch_size=32 \
beam_batch_size=1 \
cuda=1
python3 examples/asr/speech_to_text_eval.py \
model_path=am_model.nemo \
dataset_manifest=manifest.json \
decoder_type=ctc
ctc_decoding.strategy=flashlight \
ctc_decoding.beam.nemo_kenlm_path=kenlm_model.bin \
ctc_decoding.beam.beam_size=4 \
ctc_decoding.beam.beam_alpha=0.5 \
ctc_decoding.beam.beam_beta=0.5 \
ctc_decoding.beam.flashlight_cfg.lexicon_path=am_model.flashlight_lexicon \ # DEFAULT_TOKEN_OFFSET
ctc_decoding.beam.return_best_hypothesis=true \
batch_size=32 \
output_filename=/tmp/manifest_out.json
cuda=1
```
**PR Type**:
- [ V] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
# Additional Information
* Related to #9067
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} | # What does this PR do ?
Add fix for ensemble model decoding.
**Collection**: ASR
# Changelog
- During addition of MultitaskAED decoding, condition to handle ConfidenseEnsembleModel was missed.
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"merged_at": "2024-02-23T16:37:29"
} | # What does this PR do ?
1) When a float `limit_val_batches` is passed by the user, the PR ensures that the `limit_val_batches` is cast to an equivalent int value and also makes sure the cast `limit_val_batches` is a multiple of num of microbatches as required by PTL >= 2.0.
2) Calls `self._reconfigure_val_batches()` after the setup of datasets, so that the original value of limit_val_batches is used to build the dataset and not the reconfigured value.
3) Returns the` len(dataloader)` in terms of number micro batches instead of num of global batches. This is required for 2 reasons:
- a) Since `limit_val_batches` is reconfigured to be in terms of number of micro batches, if the `len(dataloader)` is in num of global batches then we can run into situations where `len(dataloader) < num_micro_batches` and this can lead to one of the ranks hitting `StopIteration` in the midst of completing a global batch, leading to a hang if a different rank is waiting for the output of the first rank in case of PP.
- b) Another reason being ideally, the `len(dataloader)` should be returned in terms of the granularity of the batch size in which we fetch the data from the `dataloader_iter`. Since in megatron models a micro batch is fetched each time `next(dataloader_iter)` is called, the len(dataloader) should be returned in the same metric. Also, PTL's progress bar is such that it increments the epoch number after the num of batches extracted hit `len(dataloader)`. So if `len(dataloader)` is x in terms of global batches, then we incorrectly increment the epoch after x micro batches are extracted even though, the dataloader still has microbatches and is not empty. This can be very misleading to the end users.
Add a one line overview of what this PR aims to accomplish.
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# Changelog
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"@Davood-M I made some changes to enable any2any training. Mostly, the changes are in the way of creating lang ids. Could you please help to review and merge changes to a proper branch?",
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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} | # What does this PR do ?
Bug fix in diarization scripts
# Changelog
- Call of `get_ctm_line function` in `create_new_ctm_entry` with `conf=0` raises error due to later type check, should be `conf=0.0`
- In function `create_manifest_with_alignments` the call of `get_seg_info_from_ctm_line` should return tuple `start, end, speaker_id`
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"\r\nunrelated changes after rebasing with main\r\n\r\n"
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} | # What does this PR do ?
Adding a few methods to support SD fine-tuning using DRaFT algorithm
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [*] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
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} | # What does this PR do ?
Address mcore support transformer models instead of just decoder/GPT models.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
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https://api.github.com/repos/NVIDIA/NeMo/issues/8420 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8420/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8420/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8420/events | https://github.com/NVIDIA/NeMo/pull/8420 | 2,134,858,925 | PR_kwDOC_bI7s5m4yWo | 8,420 | Fix a bug in CTM line processing function for multi-speaker data simulations | {
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"jenkins"
] | 2024-02-14T17:46:03 | 2024-02-14T23:15:42 | 2024-02-14T23:15:42 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR fixes the bug in `scripts/speaker_tasks/create_alignment_manifest.py`.
The `get_seg_info_from_ctm_line` function was outputing values in a wrong order.
Plus, confidence value could be set to `None` to prevent any type error issues.
**Collection**:
ASR (Multi-speaker Data Simulation, CTM annotation generation part)
# Changelog
- the output variable orders in `get_seg_info_from_ctm_line` function.
- `conf=0` to `conf=None`
# Usage
* You can potentially add a usage example below
```python
python scripts/speaker_tasks/create_alignment_manifest.py \
--input_manifest_filepath LibriSpeech/dev_clean.json \
--base_alignment_path LibriSpeech_Alignments \
--output_manifest_filepath ./dev-clean-align.json \
--ctm_output_directory ./ctm_out \
--libri_dataset_split dev-clean
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR can review this.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/8419 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8419/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8419/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8419/events | https://github.com/NVIDIA/NeMo/pull/8419 | 2,134,805,792 | PR_kwDOC_bI7s5m4myI | 8,419 | Flashlight and Pyctcdecode decoders to branch 1.23 | {
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} | # Preserve CPU Flashlight and Pyctcdecode beamsearch with Ngram LM
Support Flashlight and Pyctcdecode decoding with pure KenLM and NeMo KenLM
Standardize API of CLI inference scripts
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
```python
python3 scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_ctc.py \
nemo_model_file=am_model.nemo \
input_manifest=manifest.json \
preds_output_folder=/tmp \
ctc_decoding.strategy=flashlight \
ctc_decoding.beam.kenlm_path=am_model.kenlm \
ctc_decoding.beam.beam_size=[4] \
ctc_decoding.beam.beam_alpha=[0.5] \
ctc_decoding.beam.beam_beta=[0.5] \
ctc_decoding.beam.flashlight_cfg.lexicon_path=am_model.flashlight_lexicon \ # DEFAULT_TOKEN_OFFSET
batch_size=32 \
beam_batch_size=1 \
device=cuda:1
python3 examples/asr/speech_to_text_eval.py \
nemo_model_file=am_model.nemo \
input_manifest=manifest.json \
decoder_type=ctc
ctc_decoding.strategy=flashlight \
ctc_decoding.beam.kenlm_path=am_model.bin \
ctc_decoding.beam.beam_size=4 \
ctc_decoding.beam.beam_alpha=0.5 \
ctc_decoding.beam.beam_beta=0.5 \
ctc_decoding.beam.flashlight_cfg.lexicon_path=am_model.flashlight_lexicon \ # DEFAULT_TOKEN_OFFSET
ctc_decoding.beam.return_best_hypothesis=true \
batch_size=32 \
output_manifest=/tmp/manifest_out.json
```
**PR Type**:
- [ V] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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https://api.github.com/repos/NVIDIA/NeMo/issues/8418 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8418/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8418/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8418/events | https://github.com/NVIDIA/NeMo/issues/8418 | 2,134,019,503 | I_kwDOC_bI7s5_Mo2v | 8,418 | ZeroDivisionError when diarizing a short file | {
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"Hi @tango4j, would it be possible for you to have a look at this error? Thanks! ",
"This error seems like happening since the audio is containing 0 second of RTTM target evaluation duration. \r\nWe cannot reproduce the error unless we have access to the audio input.\r\n@RafalCer Could you check the rttm file you... | 2024-02-14T10:31:00 | 2024-05-08T18:26:39 | 2024-05-08T18:26:39 | NONE | {
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} | null | null | null | **Describe the bug**
I am getting a `ZeroDivisionError: float division by zero` error when diarizing a very short short file with prior voice activity detection:
`File "nemo/collections/asr/models/clustering_diarizer.py", line 467, in diarize
return score_labels(
File "nemo/collections/asr/metrics/der.py", line 171, in score_labels
CER = metric['confusion'] / metric['total']
ZeroDivisionError: float division by zero`
I've diarized lots of files of various length and this error seems to occur only on really short files that have identified speech them. I understand that diarization of such short files is fundamentally questionable, but the type of error suggests that this might be an edge case bug worth reporting.
**Steps/Code to reproduce bug**
Please refer to this [google colab notebook ](https://colab.research.google.com/drive/15Hudly8dW98mjVG7p0GbL3RYueSzlhSG?usp=sharing) to reproduce the bug.
The files to reproduce the bug can be found [here](https://drive.google.com/drive/folders/1e2b14isJ8CQ0NR3mGqHcmkLlo4D2T38E?usp=sharing).
```
!apt-get update && apt-get install -y libsndfile1 ffmpeg
!pip install nemo_toolkit['asr']
import os
import torch
import yaml
import json
from omegaconf import OmegaConf
from nemo.collections.asr.models import ClusteringDiarizer
def diarize(workdir: str, rttm_filepath):
manifest_path = os.path.join(workdir, "manifest.json")
output_dir = os.path.join(workdir, "output")
manifest = {
'audio_filepath': '/content/diarization_test_file.wav',
'offset': 0,
'duration': None,
'label': 'infer',
'text': '-',
'num_speakers': None,
'rttm_filepath': rttm_filepath,
'uem_filepath': None,
}
with open('/content/config.yaml', "r") as config_file:
config_dict = yaml.load(config_file, Loader=yaml.FullLoader)
config = OmegaConf.create(config_dict['diarizer'])
config.device = "cuda:0" if torch.cuda.is_available() else "cpu"
config.diarizer.manifest_filepath = manifest_path
config.diarizer.oracle_vad = True
config.diarizer.speaker_embeddings.model_path = 'titanet_large'
config.diarizer.out_dir = output_dir
with open(manifest_path, "w") as manifest_file:
json.dump(manifest, manifest_file)
model = ClusteringDiarizer(cfg=config)
model.diarize()
diarize('/content/', '/content/speech_timestamps.rttm')
```
```
[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/metrics/der.py](https://localhost:8080/#) in score_labels(AUDIO_RTTM_MAP, all_reference, all_hypothesis, collar, ignore_overlap, verbose)
169
170 DER = abs(metric)
--> 171 CER = metric['confusion'] / metric['total']
172 FA = metric['false alarm'] / metric['total']
173 MISS = metric['missed detection'] / metric['total']
ZeroDivisionError: float division by zero
```
**Expected behavior**
Diarization should not fail on files of short length.
**Environment overview (please complete the following information)**
- Environment location: Google Colab
- Method of NeMo install:
```
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install nemo_toolkit['asr']
```
**Environment details**
```PRETTY_NAME="Ubuntu 22.04.3 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.3 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=jammy
2.1.0+cu121
Python 3.10.12
``` | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
This PR fixes the bug in `scripts/speaker_tasks/create_alignment_manifest.py`.
The `get_seg_info_from_ctm_line` function was outputing values in a wrong order.
Plus, confidence value could be set to `None` to prevent any type error issues.
**Collection**:
ASR (Multi-speaker Data Simulation, CTM annotation generation part)
# Changelog
- the output variable orders in `get_seg_info_from_ctm_line` function.
- `conf=0` to `conf=None`
# Usage
* You can potentially add a usage example below
```python
python scripts/speaker_tasks/create_alignment_manifest.py \
--input_manifest_filepath LibriSpeech/dev_clean.json \
--base_alignment_path LibriSpeech_Alignments \
--output_manifest_filepath ./dev-clean-align.json \
--ctm_output_directory ./ctm_out \
--libri_dataset_split dev-clean
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR can review this.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR fixes the bug in `scripts/speaker_tasks/create_alignment_manifest.py`.
The `get_seg_info_from_ctm_line` function was outputing values in a wrong order.
Plus, confidence value could be set to `None` to prevent any type error issues.
**Collection**: [Note which collection this PR will affect]
# Changelog
- the output variable orders in `get_seg_info_from_ctm_line` function.
- `conf=0` to `conf=None`
# Usage
* You can potentially add a usage example below
```python
python scripts/speaker_tasks/create_alignment_manifest.py \
--input_manifest_filepath LibriSpeech/dev_clean.json \
--base_alignment_path LibriSpeech_Alignments \
--output_manifest_filepath ./dev-clean-align.json \
--ctm_output_directory ./ctm_out \
--libri_dataset_split dev-clean
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in ASR can review this.
# Additional Information
* Related to # (issue)
| {
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"closing since fsdp + peft is deprioritized"
] | 2024-02-13T18:40:18 | 2024-03-14T18:04:25 | 2024-03-14T18:04:24 | COLLABORATOR | {
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Enables FSDP when using PEFT during training/fine-tuning.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N] Did you write any new necessary tests?
- [N] Did you add or update any necessary documentation?
- [N] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [Y] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fix dreambooth data sampler issue
**Collection**: Multimodal
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
replaces logging.error (actually not an error) with logging.info
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
tools
# Changelog
- Add specific line by line info of high level changes in this PR.
replaces logging.error (actually not an error) with logging.info
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Update Multimodal data preparation tutorial.
**Collection**: Multimodal
# Changelog
- Delete duplicate section
- Populate "TODO" links with actual links from toolkit doc and launcher.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update Multimodal data preparation tutorial.
**Collection**: Multimodal
# Changelog
- Delete duplicate section
- Populate "TODO" links with actual links from toolkit doc and launcher.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
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} | # What does this PR do ?
Update the link used to download the video we use in the NFA tutorial.
**Collection**: tutorials
# Changelog
- Update link in the 2 places it appears in the notebook.
# Usage
N/A
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
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- [ N/A ] Did you add or update any necessary documentation?
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fixes link with `pubmed23n0009`
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Update the link used to download the video we use in the NFA tutorial.
**Collection**: tutorials
# Changelog
- Update link in the 2 places it appears in the notebook.
# Usage
N/A
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [ N/A ] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Mcore customization doc
**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Fixes WER bug in ASR_with_Subwords tutorial.
**Collection**: [ASR]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [N] Did you write any new necessary tests?
- [N] Did you add or update any necessary documentation?
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Fixes breaking tutorial issue
**Collection**: [ASR]
# Changelog
- Minor tutorial fix to reflect WER changes
# Before your PR is "Ready for review"
**Pre checks**:
- [Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N] Did you write any new necessary tests?
- [N] Did you add or update any necessary documentation?
- [N] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] Documentation
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-02-12T08:03:37 | 2024-03-22T01:43:59 | 2024-03-22T01:43:59 | NONE | {
"total": 0,
"completed": 0,
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} | null | null | null | **Describe the bug**
I'm using Nemotron-3 deployed in Triton Inference server, I'm using this connector code with langchain:
(https://github.com/langchain-ai/langchain/blob/master/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py)
which has a bug also and doesn't work by the way but I fixed it locally, and when I want to stream the response it return the whole response at once and doesn't stream it, but the codes are all working fine, I was wondering where is the problem from?
**Steps/Code to reproduce bug**
```python
from llms import TritonTensorRTLLM
pload = {
'tokens':300,
'server_url': "0.0.0.0:8001",
'model_name': "Nemotron-rlhf",
'temperature':1.0,
'top_k':1,
'top_p':0,
}
model = TritonTensorRTLLM(**pload)
for token in model.stream("what is the color of sky?"):
print(token, end="|", flush=True)
```
**Expected behavior**
I expect the model to stream the response.
**Environment overview (please complete the following information)**
- Environment location: Nemofw-Inference:23.10 docker container
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} | # What does this PR do ?
Fix dreambooth data sampler issue
**Collection**: Multimodal
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
Fix dreambooth data sampler issue
**Collection**: Multimodal
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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"jenkins",
"Hi, how can I pass `continuous-integration/jenkins/pr-head` stage? And also how can I select reviewers? I am new to this PR system, so it would be appreciated much if you let me know :)",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update... | 2024-02-12T02:22:36 | 2024-03-08T01:43:54 | 2024-03-08T01:43:54 | NONE | {
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} | # What does this PR do ?
This PR fixes typo in documentation for MLFlow exp manager configuration.
My assumption is that the original author didn't intend the python typing snippets are included in config yaml.
**Collection**: docs/source/core
# Changelog
- Remove unnecessary typing snippets of `run_id` argument in `exp_manager`
# Usage
n/a
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} | # What does this PR do ?
Adds properties required by megatron-core to NeMo tokenizers via the base TokenizerSpec class (see #8283).
**Collection**: common (tokenizers)
# Changelog
- Adds a `unique_identifiers` OrderedDict which is needed when using alongside megatron-core datasets (following [megatron_tokenizer.py](https://github.com/NVIDIA/Megatron-LM/blob/0052bf0de70b266d8648e2655da16f7eb2c9ca56/megatron/core/datasets/megatron_tokenizer.py))
- Adds several property-decorated methods as aliases for equivalent properties already present in tokenizers that inherit from TokenizerSpec
- Removes now-redundant individual fix from AutoTokenizer
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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* Related to #8233
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} | This PR adds an env variable to be set when you want to log peak memory usage at the end of each training step.
This env variable "NEMO_LOG_MEMORY_USAGE" should be set to 1 for the logging to be enabled, this prints the memory usage of rank 0 only.
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} | # What does this PR do ?
1) Adds support for dict data input type.
2) Adds changes for mock dataset.
3) Adds tests for 1 & 2.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add Finetuning of Parakeet model with HF Datasets
**Collection**: ASR
# Changelog
- Add tutorial
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
This PR implements single video inference with a video dataloader fed through NeVA Image Encoder
**Collection**: [Note which collection this PR will affect]
# Changelog
- Modified `TarOrFolderVideoLoader` to include:
- `load_video()`: which loads images as OpenCV object
- `flatten_frames()`: which flattens OpenCV video frames to tensor of image frames (B*T, H, W, C)
- Add config file for single-video inference (adapted from `neva_inference.yaml`)
- Create `video_neva_model.py` derived from `neva_model.py`
- Create `single_video_inference.py` to execute inference pipeline with Video DataLoader + Image Encoder on single video sample
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
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# Jenkins CI
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**Pre checks**:
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Adds properties required by megatron-core to NeMo tokenizers via the base TokenizerSpec class (see #8283).
**Collection**: common (tokenizers)
# Changelog
- Adds a `unique_identifiers` OrderedDict which is needed when using alongside megatron-core datasets (following [megatron_tokenizer.py](https://github.com/NVIDIA/Megatron-LM/blob/0052bf0de70b266d8648e2655da16f7eb2c9ca56/megatron/core/datasets/megatron_tokenizer.py))
- Adds several property-decorated methods as aliases for equivalent properties already present in tokenizers that inherit from TokenizerSpec
- Removes now-redundant individual fix from AutoTokenizer
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | Hello, I encountered this issue while trying to run the Online ASR tutorials on a PC, using python 3.10.13 and nemo-toolkit==1.23.0rc0.
SystemError: PY_SSIZE_T_CLEAN macro must be defined for '#' formats
From web results, this errors seems to happen when old packages don't support newer versions of Python, which is strange since NeMo requires Python >=3.10.
Could you help me with this, please? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8387 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8387/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8387/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8387/events | https://github.com/NVIDIA/NeMo/pull/8387 | 2,127,623,884 | PR_kwDOC_bI7s5mgeX5 | 8,387 | Add fp8 support for SD/Update notebook paths | {
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"jenkins",
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} | # What does this PR do ?
Add FP8 support for SD training.
**Collection**: [multimodal/text_to_image/statble_diffusion]
# Changelog
- Add config argument - use_te_fp8 to switch between fp16 and fp8 training
- Add transformer engine fp8 layers to Stable diffusion Unet
- Update a few legacy paths in notebook tutorial
# Usage
```yaml
unet_config:
use_te_fp8=True
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"The training script seems to be correct. We don't do any network op during validation, but maybe if you're using wandb somehow that crashed and caused a disconnect ?",
"I didn't install and use wandb.",
"> The training script seems to be correct. We don't do any network op during validation, but maybe if you'r... | 2024-02-09T15:02:55 | 2024-03-24T01:45:26 | 2024-03-24T01:45:25 | NONE | {
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} | null | null | null | **Describe the bug**
I start training with a ready ckpt 'QuartzNet15x5_ru--val_wer=0.3912-epoch=116.ckpt'
I start the training with the command 'nohup python command file_name.py > info_about_train.txt 2>&1 &'
After some time (from 1 to 10 epochs), the training is simply interrupted, regardless of the stage (train or validation).
In 4 cases, the following error was recorded in the info_about_train.txt file:
`RuntimeError: DataLoader worker (pid some_pid) is killed by signal: Segmentation fault.`
In 5 cases, was for no stated reason:
`^MEpoch 5: 7%|▋ | 13081/175623 [57:20<11:52:33, 3.80it/s, v_num=4]`
`^MEpoch 6: 70%|██████▉ | 122281/175623 [9:05:50<3:58:`
`^MEpoch 9: 65%|██████▌ | 114210/175623 [9:13:05<4:57:24, 3.44it/s, v_num=10]`
In 2 cases:
`^MValidation DataLoader 0: 54%|█████▍ | 46838/86866 [2:45:06<2:21:06, 4.73it/s]^[[A^@^@^@^@^@^@^@^@^@^@`
`^MValidation DataLoader 0: 7%|▋ | 6260/86866 [07:05<1:31:23, 14.70it/s]^[[A^@^@^@^@^@^@^@^@^@^@^@`
In 1 cases:
`ConnectionResetError: [Errno 104] Connection reset by peer.`
**Please tell me what it could be?**
**Environment**
- Ubuntu: 20.04.6 LTS
- Conda: 23.3.1
- PyTorch: 2.1.2
- Python: 3.11
- NeMo: 1.21.0
GPU:
Training start code: https://github.com/Project-fil/YamlForNeMo/blob/main/resume_from_ckpt.py | {
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