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} | # What does this PR do ?
**Collection**: [ASR, NLP, TTS]
# Changelog
Fixes bugs and typos in
- https://nvbugspro.nvidia.com/bug/4413845
- https://nvbugspro.nvidia.com/bug/4411322
- https://nvbugspro.nvidia.com/bug/4410625
- https://nvbugspro.nvidia.com/bug/4407526
- https://nvbugspro.nvidia.com/bug/4405781
- https://nvbugspro.nvidia.com/bug/4426768
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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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
* Related to # (issue)
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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?
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- [ ] 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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* Related to # (issue)
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"Thanks for letting us know. @athitten could you take a look if we can safely unpin Transformers ? Fyi @ericharper ",
"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.",
"Any news related to this issue?",
"This issue is ... | 2023-12-20T11:51:44 | 2024-03-01T01:45:25 | 2024-03-01T01:45:25 | NONE | {
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} | null | null | null | The `transformers` requirement is bounded here https://github.com/NVIDIA/NeMo/blob/39d883f16ce57f06c40be19a99c881324b93360d/requirements/requirements_lightning.txt up to version `4.33.3`
There is a Critical CVE affecting versions < `4.36.0`, related information
- https://www.cve.org/CVERecord?id=CVE-2023-6730
- https://github.com/huggingface/transformers/commit/1d63b0ec361e7a38f1339385e8a5a855085532ce
- https://huntr.com/bounties/423611ee-7a2a-442a-babb-3ed2f8385c16/
This also would solve https://github.com/NVIDIA/NeMo/issues/7765 and https://github.com/NVIDIA/NeMo/pull/7620#issuecomment-1745874618
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"I managed to reproduce🙃"
] | 2023-12-20T10:06:32 | 2023-12-25T03:30:51 | 2023-12-25T03:30:51 | NONE | {
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} | null | null | null | **Describe the bug**
Refer to this blog, training LLaMA 13B with NeMo 23.08 could achieve 2357 Tokens/s/GPU
Yet, I can only achieve under 1900 Tokens/s/GPU
**Steps/Code to reproduce bug**
I'm running on nvcr.io/nvidia/nemo:23.08 with 8xA100
Here is my launching script
```
INDEX_MAPPING_DIR=""
DATA_PREFIX=""
TOKENIZER_DIR=""
TP_SIZE=4
PP_SIZE=1
torchrun --nproc_per_node=8 \
/workspace/nemo/examples/nlp/language_modeling/megatron_llama_pretraining.py \
trainer.precision=bf16 \
trainer.devices=8 \
trainer.num_nodes=1 \
model.num_layers=40 \
model.hidden_size=5120 \
model.ffn_hidden_size=13824 \
model.num_attention_heads=40 \
model.num_query_groups=40 \
model.micro_batch_size=1 \
model.global_batch_size=32 \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.megatron_amp_O2=False \
model.sequence_parallel=True \
model.activations_checkpoint_granularity=selective \
model.activations_checkpoint_method=uniform \
model.optim.name=distributed_fused_adam \
model.optim.lr=2e-4 \
model.tokenizer.model=${TOKENIZER_DIR} \
model.data.index_mapping_dir=${INDEX_MAPPING_DIR} \
++model.data.data_prefix=${DATA_PREFIX} \
exp_manager.create_wandb_logger=False \
exp_manager.explicit_log_dir=/results \
exp_manager.resume_if_exists=False \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=validation_loss \
++cluster_type=BCP
```
The `megatron_llama_pretraining.py`
```
import torch.multiprocessing as mp
from omegaconf.omegaconf import OmegaConf, open_dict
from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
mp.set_start_method("spawn", force=True)
@hydra_runner(config_path="conf", config_name="megatron_llama_config")
def main(cfg) -> None:
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'\n{OmegaConf.to_yaml(cfg)}')
trainer = MegatronTrainerBuilder(cfg).create_trainer()
exp_manager(trainer, cfg.exp_manager)
model = MegatronGPTModel(cfg.model, trainer)
trainer.fit(model)
if __name__ == '__main__':
main()
```
**Expected behavior**
Do I have to modify the configuration to get better performance? If so, could you show the config🥺?
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mirror changes in https://github.com/NVIDIA/NeMo/pull/8056 | {
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} | # What does this PR do ?
1) Move wrapper to base class
2) Need to pass in args[module] = self.model to bert model
**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**:
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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?
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**: [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?
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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
- [ ] 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 ?
Fixes a bug where inference on CPU was segfaulting.
**Collection**: Tutorials
# Changelog
- Moved tensor to GPU when possible
# 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**:
- [ ] 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.
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} | # What does this PR do ?
Adds conversion script for mistral-7b to NeMo.
**Collection**: [Note which collection this PR will affect]
nlp/language_modeling
# 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**:
- [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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* Related to # (issue)
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"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"Re-opening, can someone please help review this minor chan... | 2023-12-19T14:16:06 | 2024-01-22T16:40:03 | 2024-01-22T16:40:02 | COLLABORATOR | {
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} | # What does this PR do ?
This prevents NeMo from crashing when trying to delete a checkpoint that has already been deleted.
This situation may happen for instance after manually fixing corrupted checkpoints.
Note that I believe PTL also has a similar safety guard: https://github.com/Lightning-AI/lightning/blob/119039b932c79f1e5daef67345628e675ab3c0bf/src/lightning/fabric/plugins/io/torch_io.py#L94
**Collection**: nlp
# Changelog
- Fix crash when attempting to delete an old checkpoint that does not actually exist anymore
# 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**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
## 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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https://api.github.com/repos/NVIDIA/NeMo/issues/8050 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8050/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8050/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8050/events | https://github.com/NVIDIA/NeMo/issues/8050 | 2,048,602,468 | I_kwDOC_bI7s56GzFk | 8,050 | Broken links in main README (leading to NVIDIA docs) | {
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"The docs should be up and running again, I'll inform the team about the broken links. ",
"Links are updated, pls let us know if you find any issues. "
] | 2023-12-19T12:53:20 | 2023-12-23T17:54:40 | 2023-12-23T17:54:40 | NONE | {
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NLP
[NeMo Megatron pre-training of Large Language Models](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html)
[Neural Machine Translation (NMT)](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/machine_translation/machine_translation.html)
and the links send me to a page with this error:
An error occurred while processing your request.
Reference #30.4cf01202.1702990306.31b50b8c
Worth fixing the links | {
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... | closed | false | null | [] | null | [
"I found that enabling ub_tp_comm_overlap will create a new mpi process group, which will cause problems. I don't know if it has something to do with the mpi version.",
"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.",
"... | 2023-12-19T08:26:51 | 2024-01-27T01:43:31 | 2024-01-27T01:43:31 | NONE | {
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} | null | null | null | **Describe the bug**
crash when run gpt with ub_tp_comm_overlap
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
PyTorch Docker: nvcr.io/nvidia/pytorch 23.10-py3
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
Fixes a bug where inference on CPU was segfaulting.
**Collection**: Tutorials
# Changelog
- Moved tensor to GPU when possible
# 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**:
- [ ] 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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"@titu1994 <- `wer` metric changes as discussed in thread. (gonna just do the refactoring first before deciding on what to do with bleu)\r\n\r\n@AlexGrinch <- fly by change for `transformer_bpe`",
"> Wait what exactly is going on with the removal of decoding logic?? Revert that\r\n\r\nWhich, moving the decoding i... | 2023-12-19T01:37:04 | 2024-01-05T23:45:52 | 2024-01-05T23:45:52 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
- Reformats `collections.asr.metrics` wer classes into a single class for model modularity.
**Collection**: [Note which collection this PR will affect]
ASR TTS Multimodal
# Changelog
- Refactored all `wer` metrics into a single metric
- Moved `ctc_decoding` and `rnnt_decoding` (plus configs) into `parts.submodules`
- Updated ASR tests to reflect new metric classes
- Updated ASR tutorials to reflect new metric classes and decoder locations
- Updated tts/g2p to reflect new metric classes.
- Updates speech_cv to reflect new metric classes and decoder locations.
- Added `wer` getter and setters to all ASR, Speech_cv, and G2P models to allow access to WER metric
- Flyby update of `transformer_bpe` to address translation issue.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
jenkins
# 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)
- [Y] 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
- [Y ] Bugfix
- [ ] Documentation
## 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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"jenkins",
"I think that this is not needed in main, since lightning-utilities has released fixed version https://github.com/Lightning-AI/utilities/releases/tag/v0.10.1\r\nI'm closing this PR\r\ncc: @titu1994, @ericharper "
] | 2023-12-18T21:03:44 | 2023-12-29T07:37:01 | 2023-12-29T07:37:01 | CONTRIBUTOR | {
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} | # What does this PR do ?
Pin Lightning-Utitilies package version due to broken NamedTuple in `0.10.0`
https://github.com/Lightning-AI/utilities/pull/160 introduced the issue,
https://github.com/Lightning-AI/utilities/blob/a00e1144dd300e3bc2fc8746a8e85510ffe35aa7/src/lightning_utilities/core/apply_func.py#L67 here NamedTuple force converted to tuple.
This fixes the bug https://nvbugspro.nvidia.com/bug/4405798 (broken `ASR_TTS_Tutorial`)
**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?
- [ ] 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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} | # What does this PR do ?
Fix Common Voice data download.
**PR Type**:
- [ ] New Feature
- [V] Bugfix
- [ ] Documentation
# Additional Information
* Related to bug 4405781
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"Thanks for letting us know, we're looking into it. ",
"This should be fixee now. Thanks for letting us know !"
] | 2023-12-18T12:32:08 | 2023-12-19T17:53:06 | 2023-12-19T17:53:05 | NONE | {
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} | null | null | null | **Describe the bug**
Nemo Documentation User Guide doesn't work
Link:
https://docs.nvidia.com/deeplearning/nemo/user-guide/index.html
Output Message:
An error occurred while processing your request.
Reference #30.3ff01202.1702902642.32d3d058
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"jenkins",
"merging this as this is critical to unblock any llama2 workflows"
] | 2023-12-18T12:19:13 | 2024-01-04T16:35:43 | 2024-01-04T16:35:19 | CONTRIBUTOR | {
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} | # What does this PR do ?
Fix the issue when running Llama2 eval or LLama2 SFT.
```
megatron.core.dist_checkpointing.core.CheckpointingException: Global shape mismatch for loaded ((32, 22016, 4096)) and expected ((32, 11008, 4096)) tensor for key model.decoder.layers.mlp.linear_fc1.weight
```
**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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# Additional Information
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"We are aware of this, hence the Todo to remove it. But our condition for removal is that we update all the PC models we release such that they do not have spaces between special tokens. So the removal won't have an effect at that point since all the models will anyway emit no space between special tokens. \r\n\r\n... | 2023-12-18T09:47:05 | 2024-01-25T01:47:44 | 2024-01-25T01:47:44 | NONE | {
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} | null | null | null | Hello,
I've come across an issue in the `nemo/collections/asr/metrics/wer.py` file, specifically within the `decode_hypothesis` function. There's a particular segment of code, marked for future removal (noted with a TODO comment) at line 546, which I believe should be eliminated.
This issue becomes apparent during the training of models that include punctuation marks as separate tokens, mirroring the ground-truth data which incorporates spaces preceding these punctuation marks. The problematic code collapses these spaces during decoding, resulting in significantly higher WER and loss calculations due to mismatches in space formatting. This code segment appears redundant when training models without punctuation marks, as it has no effect in such scenarios. It only impacts models trained with punctuation in the specific context I've outlined.
For instance, consider the ground-truth data: `This is a test , with some extra spaces .` and its prediction: `This is a test, with some extra spaces.` This alters the prediction by removing spaces before the punctuation marks.
Here's the code snippet:
`# TODO: remove`
` # collapse leading spaces before . , ? for PC models`
` hypothesis = re.sub(r'(\s+)([\.\,\?])', r'\2', hypothesis)`
`
Could you please confirm if you're aware of this and if there are plans to remove this section of code?
Thank you | {
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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."
] | 2023-12-16T09:18:31 | 2024-01-31T01:44:09 | 2024-01-31T01:44:09 | CONTRIBUTOR | {
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What can be done about this?
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https://api.github.com/repos/NVIDIA/NeMo/issues/8038 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8038/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8038/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8038/events | https://github.com/NVIDIA/NeMo/pull/8038 | 2,044,543,681 | PR_kwDOC_bI7s5iJ47q | 8,038 | Adding method back that was removed accidentally | {
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"jenkins",
"@ericharper should we move this to the base model instead ?",
"jenkins",
"> @ericharper should we move this to the base model instead ?\r\n\r\nYes, let's do it in another pr."
] | 2023-12-16T01:11:43 | 2023-12-16T08:58:20 | 2023-12-16T08:58:19 | COLLABORATOR | {
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"merged_at": "2023-12-16T08:58:19"
} | # 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
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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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
- [ ] 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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cc @vsl9, @VahidooX
# What does this PR do ?
Adding the notebook that allows us to do real-time streaming using a cache-aware fastconformer model (supports single-lookahead and multi-lookahead models).
**Collection**: `tutorials/asr`
# Changelog
- Add cache-aware streaming notebook in `tutorials/asr`
- Add call to `self.setup_streaming_params` inside `set_default_att_context_size` method of `ConformerEncoder`.
# Usage
# Jenkins CI
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**Pre checks**:
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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**:
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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.
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# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/8035 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8035/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8035/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8035/events | https://github.com/NVIDIA/NeMo/pull/8035 | 2,044,482,233 | PR_kwDOC_bI7s5iJrrF | 8,035 | Restore O2 FP16/BF16 wrapper for GPT | {
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"Closed by https://github.com/NVIDIA/NeMo/pull/8038."
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} | # What does this PR do ?
When running GPT with O2 enabled, I am seeing errors at the following line since `_wrap_model_for_O2` is not defined:
https://github.com/NVIDIA/NeMo/blob/852338459c78c257a2b5f6688c0b1a5d2b048366/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L266
This function was removed in https://github.com/NVIDIA/NeMo/pull/7814. I see that this PR added the function to BERT, so could this be a bug?
**Collection**: NLP
# Changelog
- Restore `_wrap_model_for_O2` function in GPT
# Usage
Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml, and set `model.megatron_amp_O2=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**:
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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.
Pinging @shanmugamr1992
# Additional Information
* Bug from https://github.com/NVIDIA/NeMo/pull/7814 | {
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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.",
"Touch.",
"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 ... | 2023-12-15T22:57:16 | 2024-03-02T01:43:20 | 2024-03-02T01:43:20 | NONE | {
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} | null | null | null | Can someone from nemo fix this performance comparison and have A100 also on the same nemo version as the H200? The same blog is posted in this repo readme as indication of nemo performance numbers.
https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/
A100: Nemo 23.08
H200: Nemo 24.01-alpha
I do not understand why Nvidia would publish this chart using different versions of nemo. Is A100 not supported in the latest nemo alpha/git release? Please help the community by doing apples to apples comparison, or provide the code/data to reproduce the data.
Moreover, most of us do not have access to 8xA100 or 8XH100 to verify the results so it is even more important we, as potential end users, see true apples to apples comparisons. Thanks.
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"@athitten Can you write unit tests for this? It would be nice to have at leas a docsting (it could be in the test itself) describing what is the expected behaviour of this function and what purpose does it serve.\r\n\r\nIf this code is here to fix a bug in PTL or cover some case not covered by PTL please describe ... | 2023-12-15T13:40:55 | 2024-01-19T01:46:51 | 2024-01-19T01:46:51 | COLLABORATOR | {
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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
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# Before your PR is "Ready for review"
**Pre checks**:
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/8030",
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"merged_at": "2024-03-08T19:26:28"
} | # What does this PR do ?
Upgrades NeMo to use PTL 2.1 and 2.2. NeMo now uses PTL 2.2.1 (the current latest release).
Major changes that are seen across a lot of files in the PR:
1. The function signature of `train/validation/test_step` is changed while using the `dataloader_iter` API. The `batch_idx` arg is removed from the function signature and `next(dataloader_iter)` now returns `batch, batch_idx, dataloader_idx`.
With this change the support for multiple dataloaders which was removed with PTL 2.0 is added back. [More details here](https://lightning.ai/docs/pytorch/stable/upgrade/from_2_0.html#:~:text=predict_step(self%2C%20dataloader_iter%2C%20batch_idx)%20in%20LightningModule-,remove%20batch_idx%20from%20the%20signature%20and%20expect%20dataloader_iter%20to%20return%20a%20triplet%20(batch%2C%20batch_idx%2C%20dataloader_idx),-PR18390)
2. PTL 2.1 and above does not support having both precision and precision plugin in the trainer. It can be observed in the changes that if precision plugin is created, then precision is made None.
3. Some of the other simple changes are explained with comments in the PR.
**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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"@HeyyyyyyG is this WIP?",
"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 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 ... | 2023-12-15T01:04:20 | 2024-02-23T04:17:34 | 2024-02-23T04:17:34 | COLLABORATOR | {
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"merged_at": "2024-02-23T04:17:34"
} | # What does this PR do ?
Add a condition for all adapters modules: run through the adapter only when it is enabled
Add merging script for all lora layers.
# Usage
To enable or disable an adapter, we can use
```python
AdapterModuleMixin.set_enabled_adapters(name, enabled = True)
AdapterModuleMixin.set_enabled_adapters(name, enabled = False)
```
If ```name``` is not provided, then all adapters will be enabled/disabled. For example, one can disable all adapters using the following codes, which is equivalent to use only the base model.
```python
from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
for _, module in model.named_modules():
if isinstance(module, AdapterModuleMixin) and module.is_adapter_available():
module.set_enabled_adapters(enabled=False)
```
**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.",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins"... | 2023-12-14T18:15:38 | 2024-01-16T22:49:57 | 2024-01-16T22:49:56 | COLLABORATOR | {
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"merged_at": "2024-01-16T22:49:56"
} | # What does this PR do ?
Add Docs and Tests to NeMo Multimodal
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/8027 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8027/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8027/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8027/events | https://github.com/NVIDIA/NeMo/pull/8027 | 2,041,779,833 | PR_kwDOC_bI7s5iAbDB | 8,027 | Support torch jit script | {
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"jenkins",
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"jenkins",
"> Tests seem to have fa... | 2023-12-14T14:09:06 | 2024-01-12T19:27:49 | 2024-01-12T19:27:48 | COLLABORATOR | {
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} | # What does this PR do ?
Fix compatibility of the core classes (`NeuralModule`, `Exportable`, etc.) with `torch.jit.script`.
Currently inheriting from `NeuralModule` instead of `nn.Module` breaks `torch.jit.script` compiler.
**Collection**: [Core]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
class SimpleLinear(NeuralModule):
def __init__(self):
super().__init__()
self.linear = nn.Linear(10, 20, bias=False)
def forward(self, x):
return self.linear(x)
module = torch.jit.script(SimpleLinear())
x = torch.zeros(2, 10)
result = module(x)
```
# 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."
] | 2023-12-14T05:45:03 | 2024-01-18T01:46:03 | 2024-01-18T01:46:03 | NONE | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/8026",
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} | # What does this PR do ?
After #7455 , `ModelPT.val_dataloader()` and `ModelPT.test_dataloader()` return empty lists instead of None.
This modification causes `ModelPT.setup()` failed to pass the 'if' statements at line 835 and 844, and the dataloaders are therefore remaining empty lists without construction:
https://github.com/NVIDIA/NeMo/blob/8216178cfaf89228884bd1e4205776a2667c6e51/nemo/core/classes/modelPT.py#L829-L845
This PR changes the `self.xxx_dataloader() is None` statements into `not self.xxx_dataloader()`, such that both "None" and empty lists could fit the condition.
**Collection**: All
# Changelog
- change `self.xxx_dataloader() is None` into `not self.xxx_dataloader()`, so as to handle both None and empty lists
```
def setup(self, stage: Optional[str] = None):
...
if stage in ('fit', 'validate'):
val_deferred_setup = ...
if not self.val_dataloader() and val_deferred_setup:
self.setup_multiple_validation_data(val_data_config=self._cfg.validation_ds)
if stage == 'test':
test_deferred_setup = ...
if not self.test_dataloader() and test_deferred_setup:
self.setup_multiple_test_data(test_data_config=self._cfg.test_ds)
```
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #5569 , #7455
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https://api.github.com/repos/NVIDIA/NeMo/issues/8025 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8025/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8025/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8025/events | https://github.com/NVIDIA/NeMo/issues/8025 | 2,040,280,206 | I_kwDOC_bI7s55nDSO | 8,025 | GPTSFTChatDataset loss_mask becomes all False when prompt length > max_seq_length | {
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"I ran into the same issue => hacked the code to zero out the loss on problematic micro-batches: https://github.com/odelalleau/NeMo/commit/b383e6a1e55dd315fa194c0a8fafd7ee3fef3437\r\n\r\n(obviously not a proper fix but can be useful to get unblocked)",
"Yeah, this will also fix the problem. ",
"This issue is st... | 2023-12-13T18:46:32 | 2024-02-24T01:43:03 | 2024-02-24T01:43:03 | NONE | {
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} | null | null | null | **Describe the bug**
In the `GPTSFTChatDataset`, if the first prompt length exceeds `max_seq_length`, all following turns are truncated out. Then the `loss_mask` becomes all `False` for the example.
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py#L359
This is problematic because, when the `loss_mask` is all False, the `loss` of the MegatronGPTModel will be `nan`.
https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1015
**Steps/Code to reproduce bug**
This can be reproduced by passing into `GPTSFTChatDataset` an example whose first turn prompt has > 2048 tokens when the `max_seq_length=2048`. Then use the `GPTSFTChatDataset` in a supervised fine-tuning job (e.g., `train_gpt_sft.py` in [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_gpt_sft.py))
**Expected behavior**
In the `collate_fn` function, check if the loss_masks of all examples are not empty. If not, raise an error. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8024 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8024/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8024/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8024/events | https://github.com/NVIDIA/NeMo/issues/8024 | 2,040,024,102 | I_kwDOC_bI7s55mEwm | 8,024 | Can I use a Sentencepiece tokenizer model from my ASR model in order to train a NeuralRescorer? | {
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"Closing this issue as it was already answered here:\r\n\r\nhttps://github.com/NVIDIA/NeMo/issues/4390"
] | 2023-12-13T16:03:29 | 2023-12-19T15:09:15 | 2023-12-19T15:09:15 | CONTRIBUTOR | {
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I would like to train a Neural Rescorer model for ASR using the transformer_lm.py script. I have one small question:
In the documentation it says: "Our LMs support all tokenizers available in NeMo, but require special beginning-of-string <bos> and end-of-string <eos> tokens."
But I imagine that the tokenizer of the TransformerLM must be the same as the tokenizer of the ASR model, right? Can I pass the tokenizer model of the ASR model directly to TransformerLM? Should I add a <bos> and <eos> token and change the size of the tokenizer? ( but I imagine the ASR and Transformer should have the same tokenizer size)
Could you help me on this matter?
Thank you in advance!
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https://api.github.com/repos/NVIDIA/NeMo/issues/8023 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8023/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8023/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8023/events | https://github.com/NVIDIA/NeMo/issues/8023 | 2,038,523,001 | I_kwDOC_bI7s55gWR5 | 8,023 | Finetuning | {
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"Hi, apologies for late response. \r\n\r\nHave you looked at these sections on finetuning: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/configs.html#fine-tuning-configurations and https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/examples/kinyarwanda_asr.html#trainin... | 2023-12-12T20:44:19 | 2024-01-31T01:44:11 | 2024-01-31T01:44:10 | NONE | {
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} | null | null | null | How is it possible to finetune the citrinet model? On nvidias documentation I saw something similar to TAO toolkit but never saw any example on it. On the other hand I saw the japanese finetuning process. And for me I’m spotting for a wer of less 5%. I got a deadline in less than 3 weeks , can someone help ?! | {
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"jenkins",
"@titu1994 It seem to be a know issue, hope will be fixed soon https://github.com/Lightning-AI/utilities/issues/196",
"Merging for now - fyi @ericharper @athitten ",
"Don't merge this to main for now (unless Eric or @athitten gives the go ahead for main)",
"@titu1994 The issue is fixed in Lightni... | 2023-12-12T17:35:49 | 2023-12-20T18:57:17 | 2023-12-18T21:02:59 | COLLABORATOR | {
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} | # What does this PR do ?
Pin Lightning-Utitilies package version due to broken NamedTuple in `0.10.0`
https://github.com/Lightning-AI/utilities/pull/160 introduced the issue,
https://github.com/Lightning-AI/utilities/blob/a00e1144dd300e3bc2fc8746a8e85510ffe35aa7/src/lightning_utilities/core/apply_func.py#L67 here NamedTuple force converted to tuple.
This fixes the bug https://nvbugspro.nvidia.com/bug/4405798 (broken `ASR_TTS_Tutorial`)
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} | # What does this PR do ?
Check `torchaudio` dependencies. Exit with the appropriate message if some dependencies are not installed.
Fixes bug: https://nvbugspro.nvidia.com/bug/4403803
**Collection**: [CI]
# Changelog
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"should be possible. Are you facing any issues while finetuning? \r\n@VahidooX to confirm. ",
"Not for offline model to streaming. You can do this if you keep the encoder architecture the same, with the flags for loading a checkpoint inside the maybe init from checkpoint function. You will have to exclude the dec... | 2023-12-12T15:19:04 | 2024-08-08T03:44:21 | 2024-02-15T01:44:10 | NONE | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8019 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8019/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8019/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8019/events | https://github.com/NVIDIA/NeMo/pull/8019 | 2,037,658,673 | PR_kwDOC_bI7s5hybU1 | 8,019 | Check `torchaudio` dependencies in installation script | {
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"> Ooh so the issue was missing dependencies and not the torchaudio code itself ? Won't we need to install the dependencies anyway inside container?\r\n\r\n@titu1994 We install the dependencies when building the container https://github.com/NVIDIA/NeMo/blob/main/Dockerfile#L34"
] | 2023-12-12T12:32:49 | 2023-12-12T16:21:52 | 2023-12-12T15:46:40 | COLLABORATOR | {
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} | # What does this PR do ?
Check `torchaudio` dependencies. Exit with the appropriate message if some dependencies are not installed.
Fixes bug: https://nvbugspro.nvidia.com/bug/4403803
**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**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Check `torchaudio` dependencies. Exit if not installed with the appropriate message.
Fixes bug: https://nvbugspro.nvidia.com/bug/4403803
**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**:
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- [ ] 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.
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} | # What does this PR do ?
Fix Common Voice data download.
**PR Type**:
- [ ] New Feature
- [ V] Bugfix
- [ ] Documentation
# Additional Information
* Related to # (issue)
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"jenkins",
"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."
] | 2023-12-12T09:35:00 | 2024-01-03T01:45:42 | 2024-01-03T01:45:41 | NONE | {
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} | # What does this PR do ?
Fix for en to any training / finetuning of model for different language pairs,
With this fix , the en to any NMT model can be finetuned for a different subset of language pairs than the model was originally pretrained on. During pretraining , we need to set the pretrained_language_list to - null.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Adds a new field pretrained_language_list in the aayn_base_megatron.yaml config file, which specifies the list of languages the model was pretrained on
- Changes to the lang id processing so that the model can finetuned on a small subset of languages of the initial pretrained list
# 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.
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"This PR was closed because it has been inactive for 7 days since being marked as... | 2023-12-12T09:18:14 | 2024-01-09T16:55:36 | 2024-01-09T16:55:36 | COLLABORATOR | {
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} | # What does this PR do ?
This PR enhances the flexibility of CustomProgressBar by modifying the create_trainer method to accept callbacks as a method argument, replacing its previous fixed implementation.
**Collection**: NLP
# Changelog
Moved callbacks to method arguement.
# 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)
- [ ] 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] Refactor
- [ ] 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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} | changed link from `../../examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml` to `/content/configs/contextnet_rnnt.yaml` because the latter did not exist and threw and error when trying to load the yaml config file.
# 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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} | # What does this PR do ?
Fix missing argument in script argparse
**Collection**: [ASR, NLP]
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} | # What does this PR do ?
Fix missing argument in script argparse
**Collection**: [ASR, NLP]
# Jenkins CI
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# Before your PR is "Ready for review"
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"jenkins",
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"jenkins"
] | 2023-12-11T23:50:37 | 2024-01-20T02:08:54 | 2024-01-20T02:08:54 | COLLABORATOR | {
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} | # What does this PR do ?
This PR adds an abstract base class for vector quantizers.
**Collection**: TTS
# Changelog
- Added `VectorQuantizerBase`
# Usage
n/a
# Jenkins CI
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"This PR was c... | 2023-12-11T21:32:54 | 2024-02-07T00:36:05 | 2024-02-07T00:35:43 | CONTRIBUTOR | {
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} | # What does this PR do ?
Enables use of megatron timers in NeMo GPT pretraining and megatron_base based models.
**Collection**: collections/nlp/models/language_modeling
# Usage
```
model.enable_megatron_timers=True
model.megatron_timer_kwargs.log_every_n_steps= 10
model.megatron_timer_kwargs.log_mode=minmax
model.megatron_timer_kwargs.barrier= False
```
```python
torchrun --nnodes=1 --nproc-per-node=8 \
/gsw/code/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/gsw/code/NeMo/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.devices=8 \
trainer.num_nodes=1 \
model.enable_megatron_timers=True \
++model.megatron_timer_kwargs.log_option=max \
```
# Jenkins CI
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} | # What does this PR do ?
Add a Chinese open-source LLM Baichuan2 (7B/13B) support.
**Collection**: NLP
# Changelog
- Add Baichuan2 model configuration.
- Add Baichuan2 hf-to-nemo conversion.
# Usage
```python
# Model conversion from huggingface to nemo
python ./scripts/nlp_language_modeling/convert_hf_baichuan2_to_nemo.py --in-file '/mount/data/Baichuan2-7B-Base' --out-file '/mount/data/Baichuan2-7B-Base/baichuan2.nemo'
```
```python
# Inference
python ./examples/nlp/language_modeling/megatron_gpt_eval.py \
--config-name=megatron_baichuan2_inference \
gpt_model_file=${BAICHUAN2_NEMO_MODEL} \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 pipeline_model_parallel_size=1
```
```python
# Pretrain
python3 -u examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-name=megatron_baichuan2_config \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.val_check_interval=100 \
trainer.precision=bf16 \
trainer.max_steps=1000 \
trainer.max_epochs=1 \
model.megatron_amp_O2=True \
model.sequence_parallel=True \
model.tensor_model_parallel_size=2 \
model.pipeline_model_parallel_size=1 \
model.global_batch_size=16 \
model.micro_batch_size=2
```
```python
# Parameter efficient fine-tuning
python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
trainer.max_steps=200 \
trainer.devices=2 \
trainer.val_check_interval=200 \
exp_manager.exp_dir=exp \
exp_manager.name=test \
model.tensor_model_parallel_size=1 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.global_batch_size=32 \
model.micro_batch_size=4 \
model.data.train_ds.file_names=${TRAIN} \
model.data.validation_ds.file_names=${VALID} \
model.answer_only_loss=True \
model.peft.peft_scheme="lora" \
model.data.train_ds.concat_sampling_probabilities=[1.0] 2>&1|tee /mount/data/test.log
```
```python
# SFT
python ./examples/nlp/language_modeling/tuning/megatron_gpt_sft.py \
trainer.devices=8 \
trainer.val_check_interval=200 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.answer_only_loss=True \
model.global_batch_size=16 \
model.micro_batch_size=2 \
model.data.train_ds.file_names=${TRAIN} \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=${VALID} \
model.data.validation_ds.global_batch_size=64 \
model.data.validation_ds.micro_batch_size=8 \
model.megatron_amp_O2=True \
exp_manager.exp_dir=exp \
exp_manager.name=test \
trainer.precision=bf16-mixed 2>&1|tee /mount/data/log/test.log
```
```python
# peft model evaluation
CUDA_VISIBLE_DEVICES='0,1' python ./examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \
trainer.devices=2 \
trainer.num_nodes=1 \
trainer.precision=32 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.peft.restore_from_path=${LORA_MODEL} \
model.data.test_ds.file_names=["path/to/your/dataset.jsonl"] \
model.data.test_ds.global_batch_size=32 \
model.data.test_ds.micro_batch_size=16 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
inference.greedy=True \
inference.outfile_path=..
```
# Before your PR is "Ready for review"
**Pre checks**:
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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.",
"@Davood-M I am having the same error as input is not long enough in my ... | 2023-12-10T23:49:38 | 2024-05-24T17:03:06 | 2024-05-23T03:22:49 | COLLABORATOR | {
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} | # What does this PR do ?
When using small prompts, the GPT_SFT_DATASET generated input prompts because of this logic:
```
if len(ids) < truncation_length:
logging.warning(f'{key} is not long enough to truncate.')
truncation_length = len(ids)
if self.truncation_method == 'left':
window_offset = truncation_length
elif self.truncation_method == 'right':
window_offset = 0
else:
raise ValueError(f'{self.truncation_method} is not supported')
window_length = len(ids) - truncation_length
template_ids[i] = ids[window_offset : window_offset + window_length]
```
In this part of code, if the truncation method is right and the prompt len is less than ```truncation_length```, then the window_length would become 0 and the template_ids would become empty! Tested this on tuned llama2 models and it seems to solved the issue.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Updated the mentioned part for both left and right truncations
# 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)
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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
- [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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"Thanks !"
] | 2023-12-10T19:21:42 | 2023-12-12T15:48:47 | 2023-12-12T15:48:36 | CONTRIBUTOR | {
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"merged_at": "2023-12-12T15:48:36"
} | # What does this PR do ?
Simply add a missing word in the doc `Update asr_language_modeling.rst`.
# Changelog
NA
# Usage
NA
# 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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If you haven't finished some of the above items you can still open "Draft" PR.
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"jenkins",
"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.",
"jenkins",
"jenkins"
] | 2023-12-09T05:53:40 | 2024-01-10T13:25:10 | 2024-01-10T13:25:10 | COLLABORATOR | {
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} | # What does this PR do ?
Enable the use of pipelined tensor-parallel communication overlap with mcore path.
Removed the condition that enables the use of `ub_tp_comm_overlap` only with `transformer_engine=True`.
# 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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- [ ] 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**:
- [ ] 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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An attempt to fix https://github.com/NVIDIA/NeMo/issues/7445 so that the data simulator .ctm are compliant with RT-09 specification (see https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf):
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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
* Related to # (issue)
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"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2023-12-08T13:59:57 | 2023-12-30T01:44:49 | 2023-12-30T01:44:49 | COLLABORATOR | {
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} | # What does this PR do ?
Merge Toxicity classifier branch into main
**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**:
- [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.... | 2023-12-08T09:36:31 | 2024-03-21T18:26:59 | 2024-02-29T17:49:35 | CONTRIBUTOR | {
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} | # What does this PR do ?
Fix convert_nemo_llama_to_hf.py:
- Correctly account for `megatron_amp_O2` flag
- Save HF model with correct precision
- Transfer over the NeMo tokenizer, instead of using the possibly-incompatible default tokenizer
- Make sure to save fast version of tokenizer
- Resize the model's embedding tensor to match the new tokenizer's vocab
- Correct a typo in how-to-use example
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
## Who can review?
@ericharper
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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] | 2023-12-08T00:19:15 | 2023-12-08T23:59:11 | 2023-12-08T23:59:11 | CONTRIBUTOR | {
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I have put <NA> for fields unknown e.g. <TOKEN>.
This makes it also easy to use the generated sessions with https://github.com/lhotse-speech/lhotse | {
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} | # What does this PR do ?
Fix crash when converting to mcore a model using rotary embeddings.
The problem is that mcore models don't register `rotary_pos_emb.inv_freq` as a buffer anymore.
I made it a command-line argument so that if the same issue occurs with other parameters / buffers, users can still run the conversion script by providing them on the command-line (instead of having to wait for a new version of NeMo).
I tested this PR on a model where the script failed previously with container `nemofw-training:23.11`.
**Collection**: nlp
# Changelog
- Fix crash when converting to mcore a model using rotary embeddings
# Before your PR is "Ready for review"
**Pre checks**:
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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**:
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- [X] Bugfix
- [ ] Documentation
## 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 rotary base paramter to conversion script for llama hf to nemo
**Collection**: NLP
```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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"It has fixed, please re open if you face additional issues. \r\nhttps://github.com/NVIDIA/NeMo/blob/c67243521b048e7ce1446a0d45a0b6e262528e73/scripts/tokenizers/process_asr_text_tokenizer.py#L162 "
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} | null | null | null | **Describe the bug**
There is no `spe_byte_fallback` argument in process_asr_text_tokenizer.py
**Steps/Code to reproduce bug**
Just run the process_asr_text_tokenizer with any args, e.g.:
`python ./scripts/tokenizers/process_asr_text_tokenizer.py --manifest=/path/to/manifest --vocab_size=1024 --tokenizer=spe --data_root=/path/to/dataroot --spe_type=unigram
**Expected behavior**
The expected behavior is that this works, but the error thrown is
`AttributeError: 'Namespace' object has no attribute 'spe_byte_fallback'`.
This makes sense because this commit - https://github.com/NVIDIA/NeMo/commit/1d4d3975e339df819b97d29ceafdd98f37b34906 - added three of the four required args for the argparse, excluding the `spe_byte_fallback` one.
@titu1994
```[tasklist]
### Tasks
```
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} | # What does this PR do ?
Notebook fixes
**Collection**: [ASR]
# Changelog
- Update Librosa signature to use only kwargs
# 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**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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Notebook fixes
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# Changelog
- Update Librosa signature to use only kwargs
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Fixes issue with librosa display issue with 0.10.1 https://github.com/librosa/librosa/issues/1763
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Fixes issue with librosa display issue with 0.10.1 https://github.com/librosa/librosa/issues/1763
# 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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# 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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## 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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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2023-12-07T08:48:41 | 2024-01-14T01:49:22 | 2024-01-14T01:49:22 | NONE | {
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} | null | null | null | Mistral has been shown to be a competitive open sourced model. Many recently competitive models on the open-llm-leaderboard are derived from mistral. However, I can't seem to find the supported training pipeline of mistral as that for llama.
Is there a plan for incorporating mistral into the training pipeline? Or has it been incorporated and I am just dumb not being able to find it?
Kind thanks
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"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"jenkins\r\n",
"jenkins\r\n",
"jenkins\r\n... | 2023-12-07T00:31:50 | 2024-02-21T06:29:08 | 2024-02-21T06:29:07 | COLLABORATOR | {
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} | # What does this PR do ?
Add LoRA support to all linear transformations in Attention and MLP modules in NeMo and MCore GPT models.
**Collection**: [Note which collection this PR will affect]
nlp
# Usage
To add LoRa to specific layer, just add the **target_modules** parameter to lora_tuning. The available configuration are below:
```yaml
lora_tuning:
target_modules: ['attention_qkv'] # default, adds LoRA to QKV projection layer
target_modules: ['attention_qkv','attention_dense'] # adds LoRA to QKV projection and Dense layers attention
target_modules: ['mlp_fc1','mlp_fc2']# adds LoRA to linear layers in the MLP module
target_modules: ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] # adds LoRA to every linear layer in attention and MLP modules
```
There are also shortcut options defined as below:
```yaml
lora_tuning:
target_modules: ['attention'] # same as ['attention_qkv','attention_dense']
target_modules: ['mlp'] # same as ['mlp_fc1','mlp_fc2']
target_modules: ['attention','mlp'] # same as ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2']
target_modules: ['all'] # same as ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2']
```
If the target_modules parameter is not provided by the user, the default value is
```yaml
target_modules: ['attention_qkv']
```
**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?
@arendu
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, @Davood-M Could you help to review? I wrote code for avoiding repetition during inference.",
"jenkins",
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"This PR was closed because it has been inactive for... | 2023-12-06T23:02:40 | 2023-12-30T01:44:55 | 2023-12-30T01:44:54 | NONE | {
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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**:
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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
- [ ] 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 ?
Rename a test that was shadowing another test.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Renamed test
# 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**:
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- [ ] 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 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."
] | 2023-12-06T20:04:38 | 2024-02-02T02:04:00 | 2023-12-29T01:43:12 | COLLABORATOR | {
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} | # What does this PR do ?
Pre-create cumulative sequence argmins and the max sequence length per micro-batch on the CPU tensor. This avoids copying the GPU tensor to calculate this information on the CPU.
# 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 news section to README.
Edit:
News section will temporarily be at the top of the page. We'll move the news lower in the README as the news ages.
**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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https://api.github.com/repos/NVIDIA/NeMo/issues/7983 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7983/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7983/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7983/events | https://github.com/NVIDIA/NeMo/issues/7983 | 2,028,995,749 | I_kwDOC_bI7s548ASl | 7,983 | Export ThutmoseTagger ITN Torchscript module | {
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"hi @MarceloSancinetti , this was my model, but I do not know about torchscript so can't help with that. Basically, ThutmoseTagger is just a token classification bert-based model. If you have questions about meaning of its elements, you can ask",
"Hello @bene-ges, thank you for your response. I've been using your... | 2023-12-06T16:58:29 | 2024-04-26T14:57:04 | 2024-01-19T01:46:54 | NONE | {
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} | null | null | null | Hello, I'm trying to export a ThutmoseTaggerModel using TorchScript. I was able to export the forward method, but not other methods. I was able to trace the forward function and run it in C++ by running this code:
```
from helpers import ITN_MODEL, instantiate_model_and_trainer
from omegaconf import DictConfig, OmegaConf
import torch
import omegaconf
from nemo.collections.nlp.data.text_normalization_as_tagging import bert_example
cfg_path = "/home/msancinetti/NeMo/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml"
cfg = omegaconf.OmegaConf.load(cfg_path)
cfg["pretrained_model"] = "itn_en_thutmose_bert"
_, model = instantiate_model_and_trainer(cfg, ITN_MODEL, False)
model.eval()
model.to('cpu')
input_example = [torch.zeros([64,128], dtype=torch.int).to("cpu")] * 3
model.set_input_names(["input_1"])
model.export('itn_model.ts', verbose=True, input_example=input_example)
```
For this to work I had to:
- Add `~/NeMo/examples/nlp/text_normalization_as_tagging/` to my PYTHONPATH for the first import
- Add the following code to the ThutmoseTaggerModel class:
```
@property
def input_names(self):
return self._input_names
def set_input_names(self, new_value):
self._input_names = new_value
```
(because the input_names property is necessary to run the export method)
However, the model.export method only traces the forward function. I would like to export other methods (that may not be traceable, so I think I have to use torch.jit.script). The problem is that if I try to script any method that uses "self", I get an error.
As a minimal example, I added this method to the ThutmoseTaggerModel class:
```
def dummy(self):
return self.num_labels
```
When I try to script that function, I get this error:
```
In [1]: torch.jit.script(model.dummy)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[1], line 1
----> 1 torch.jit.script(model.dummy)
File ~/miniconda3/envs/nemo_debug/lib/python3.8/site-packages/torch/jit/_script.py:1381, in script(obj, optimize, _frames_up, _rcb, example_inputs)
1379 if _rcb is None:
1380 _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
-> 1381 fn = torch._C._jit_script_compile(
1382 qualified_name, ast, _rcb, get_default_args(obj)
1383 )
1384 # Forward docstrings
1385 fn.__doc__ = obj.__doc__
RuntimeError:
'Tensor (inferred)' object has no attribute or method 'num_labels'.:
File "/home/msancinetti/miniconda3/envs/nemo_debug/lib/python3.8/site-packages/nemo/collections/nlp/models/text_normalization_as_tagging/thutmose_tagger.py", line 503
def dummy(self):
return self.num_labels
~~~~~~~~~~~~~~~ <--- HERE
```
I also get an error if I try to script the whole model with `torch.jit.script(model)`
_TL;DR:_
How do I export methods from ThutmoseTagger ITN model other than forward to run them in C++?
Any help will be appreciated!
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"> I don't know why we need this. It breaks a bunch of things at this point. Is it worth it to change it?\r\n\r\nThat's quite surprising.. Let me retry the Jenkins pipeline.\r\n\r\nI'm not insisting on that t... | 2023-12-06T10:26:39 | 2024-01-28T01:45:03 | 2024-01-28T01:45:03 | COLLABORATOR | {
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} | # What does this PR do ?
Removes explicit code to call superclass method (cleanup).
**Collection**: [NLP]
# 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] Improvement
## 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 https://github.com/NVIDIA/NeMo/pull/7935
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} | # What does this PR do ?
Removes deprecated trainer option in MPT conversion script.
**Collection**: [NLP]
# 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)
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**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.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/7935
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"jenkins",
"> Thanks LGTM\r\n\r\nThanks! I've just rebased it, I'll merge once pipeline completes."
] | 2023-12-06T10:22:48 | 2023-12-18T11:21:17 | 2023-12-18T11:21:16 | COLLABORATOR | {
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} | # What does this PR do ?
When debugging artifact items I struggled with `ArtifactItem` as it is defined with `init=False` which results in issues when printing an instance of this class.
The following minimal example illustrates the problem - this code
```
from dataclasses import dataclass
@dataclass(init=False)
class Point:
x: int
y: int
p = Point()
print(p)
```
results in **AttributeError: 'Point' object has no attribute 'x'**. So it doesn't make it easier to check what's going on.
**Collection**: [NLP]
# 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] Improvement
## 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 https://github.com/NVIDIA/NeMo/pull/7935
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} | # What does this PR do ?
An improvement to use `torch_dtype_from_precision` helper to resolve precision.
**Collection**: [NLP]
# 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] Improvement
## 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 https://github.com/NVIDIA/NeMo/pull/7935
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} | # What does this PR do ?
Minor extra fixes to Llama conversion script.
**Collection**: [NLP]
# 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] Documentation
- [x] Improvement
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 https://github.com/NVIDIA/NeMo/pull/7935
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"jenkins",
"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."
] | 2023-12-06T10:11:09 | 2024-01-19T16:18:18 | 2024-01-19T16:18:17 | COLLABORATOR | {
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} | # What does this PR do ?
- Provide a common way for loading state_dict for HF models among different conversion scripts
- Deduplicate tokenizers in the Nemo file produced: the model now comes with a single copy instead of an extra redundant tokenizer file (which is an undesired side effect of [`load_model` function](https://github.com/NVIDIA/NeMo/blob/52d50e9e09a3e636d60535fd9882f3b3f32f92ad/scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py#L58))
**Collection**: [NLP]
# Jenkins CI
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**Pre checks**:
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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**:
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# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/7935
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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 is now at parity with the original blank_as_pad greedy RNN-T decoding implementation. Currently, I can decode librispeech test clean in 1 min, 39 seconds, whil... | 2023-12-06T05:33:15 | 2024-02-09T01:43:49 | 2024-02-09T01:43:49 | COLLABORATOR | {
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} | # What does this PR do ?
Greedy RNNT inference currently sees the GPU idle 90% of the time because of CPU overheads. This experiments with using cuda graphs to do greedy inference, so that we can decoder at much higher throughputs. Very WIP. I began to work on this because I discovered that using TensorRT was not helpful at all, because it speeds up such a small portion of the code right now.
**Collection**: ASR
# Changelog
- Will have to do after cleaning up
Not ready for serious review. | {
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"Hi, @benhoff. \r\n`__call__()` function should be preceded by \r\n`diarize = NeuralDiarizer.from_pretrained(model_name='diar_msdd_telephonic')`\r\nTo run diarization as the simplest form, e.g., `diarize('audio.wav')`\r\n\r\nThis call function is designed for hugging face API, to minimize the preparation steps.\r\n... | 2023-12-05T18:38:48 | 2024-01-08T23:36:10 | 2024-01-08T23:36:10 | NONE | {
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} | null | null | null | **Describe the bug**
If you use the `__call__` method from NeuralDiarizer instead of the `diarize` method, you get the below stack trace.
```
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:1514, in NeuralDiarizer.__call__(self, audio_filepath, batch_size, num_workers, max_speakers, num_speakers, out_dir, verbose)
1503 self._initialize_configs(
1504 manifest_path=manifest_path,
1505 max_speakers=max_speakers,
(...)
1510 verbose=verbose,
1511 )
1513 self.msdd_model.cfg.test_ds.manifest_filepath = manifest_path
-> 1514 self.diarize()
1516 pred_labels_clus = rttm_to_labels(f'{tmpdir}/pred_rttms/{Path(audio_filepath).stem}.rttm')
1517 return labels_to_pyannote_object(pred_labels_clus)
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:1180, in NeuralDiarizer.diarize(self)
1173 @torch.no_grad()
1174 def diarize(self) -> Optional[List[Optional[List[Tuple[DiarizationErrorRate, Dict]]]]]:
1175 """
1176 Launch diarization pipeline which starts from VAD (or a oracle VAD stamp generation), initialization clustering and multiscale diarization decoder (MSDD).
1177 Note that the result of MSDD can include multiple speakers at the same time. Therefore, RTTM output of MSDD needs to be based on `make_rttm_with_overlap()`
1178 function that can generate overlapping timestamps. `self.run_overlap_aware_eval()` function performs DER evaluation.
1179 """
-> 1180 self.clustering_embedding.prepare_cluster_embs_infer()
1181 self.msdd_model.pairwise_infer = True
1182 self.get_emb_clus_infer(self.clustering_embedding)
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:699, in ClusterEmbedding.prepare_cluster_embs_infer(self)
695 """
696 Launch clustering diarizer to prepare embedding vectors and clustering results.
697 """
698 self.max_num_speakers = self.cfg_diar_infer.diarizer.clustering.parameters.max_num_speakers
--> 699 self.emb_sess_test_dict, self.emb_seq_test, self.clus_test_label_dict, _ = self.run_clustering_diarizer(
700 self._cfg_msdd.test_ds.manifest_filepath, self._cfg_msdd.test_ds.emb_dir
701 )
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:879, in ClusterEmbedding.run_clustering_diarizer(self, manifest_filepath, emb_dir)
875 self._embs_and_timestamps = get_embs_and_timestamps(
876 self.clus_diar_model.multiscale_embeddings_and_timestamps, self.clus_diar_model.multiscale_args_dict
877 )
878 session_scale_mapping_dict = self.get_scale_map(self._embs_and_timestamps)
--> 879 emb_scale_seq_dict = self.load_emb_scale_seq_dict(emb_dir)
880 clus_labels = self.load_clustering_labels(emb_dir)
881 emb_sess_avg_dict, base_clus_label_dict = self.get_cluster_avg_embs(
882 emb_scale_seq_dict, clus_labels, speaker_mapping_dict, session_scale_mapping_dict
883 )
File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:961, in ClusterEmbedding.load_emb_scale_seq_dict(self, out_dir)
957 pickle_path = os.path.join(
958 out_dir, 'speaker_outputs', 'embeddings', f'subsegments_scale{scale_index}_embeddings.pkl'
959 )
960 logging.info(f"Loading embedding pickle file of scale:{scale_index} at {pickle_path}")
--> 961 with open(pickle_path, "rb") as input_file:
962 emb_dict = pkl.load(input_file)
963 for key, val in emb_dict.items():
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpil5i2a_2/speaker_outputs/embeddings/subsegments_scale0_embeddings.pkl'
```
**Steps/Code to reproduce bug**
If you go to the `Speaker_Diarization_Inference.ipynb` file, and run everything, you can change the code to `system_vad_msdd_model('data/an4_diarize_test.wav')`, and the error will get thrown.
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"@vysarge \r\nAre you going to open this PR?",
"We need this update, right? \r\nLet's merge this PR ASAP as the change looks to be straightforward. \r\n\r\n@vysarge \r\nPlease change the merge target to the main branch."
] | 2023-12-05T18:08:11 | 2024-03-12T22:36:45 | 2023-12-16T01:26:01 | COLLABORATOR | {
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"merged_at": null
} | # What does this PR do ?
Adds progress bar logging for sequence length (including padding tokens) and real token count for each train step with MegatronGPTSFTModel to enable accurate calculation of tokens/sec and related metrics. Depends upon #7945.
**Collection**: nlp/language_modeling
# Changelog
- Alters GPTSFTDataset and GPTSFTPackedDataset to pass out a list of token counts as part of the batch
- Alters MegatronGPTSFTModel to expect, average, and log that list of token counts at the beginning of the step
# 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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- [ ] 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
* Depends upon #7945.
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Support O2 training (`model.megatron_amp_O2=True`) of PEFT and SFT using the refactored PEFT APIs.
This was listed as a known issue since the PEFT Refactor (#7308). This PR fixes that issue.
Code was tested on GPT and T5, PEFT and SFT.
**Collection**: NLP
# 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)
- [ ] 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 #7308
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} | # What does this PR do ?
Add multimodal support, second PR, includes text to image, x to nerf source code
**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?
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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**:
- [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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* Related to # (issue)
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Add starcoder NeMo -> HF conversion script
**Collection**: NLP
# Usage
See script header
# 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**:
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- [ ] Bugfix
- [ ] 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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[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 ?
Enables running MegatronGPTSFTModel with virtual pipeline parallel when peft_scheme=null.
**Collection**: NLP (language_modeling)
# Changelog
- Alters word embedding initialization and passing of several parameters in MegatronGPTSFTModel to accommodate virtual pipeline parallelism when self.use_peft is false
- Adds a guard in `MegatronGPTSFTModel.__init__` to raise a ValueError if self.use_peft is true
- Corrects the TP group initialization check in MegatronGPTSFTModel to account for both TE and MCore flags
# Usage
Run a GPT model with SFT and set `virtual_pipeline_model_parallel_size`, such as:
```
python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
++model.virtual_pipeline_model_parallel_size=10 \
...
```
# Jenkins CI
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- [ ] Bugfix
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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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"jenkins",
"@hsiehjackson \r\nI don't know the background of why this is set as False for inference.\r\nWhen we remove this condition, we use sequence parallelism for both training and inference but I didn't see any issue with this.",
"@erhoo82 for inference do you mean generation mode instead of calculating va... | 2023-12-02T02:13:35 | 2023-12-18T20:18:27 | 2023-12-18T20:18:26 | COLLABORATOR | {
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} | # What does this PR do ?
SFT patch: (1) enable sequence parallelism and (2) enable profile
# 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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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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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
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# Before your PR is "Ready for review"
**Pre checks**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | null | null | null | **Describe the bug**
Encounter this error when execute scripts/nlp_language_modeling/merge_lora_weights/merge.py with lora and base gpt 126m model.
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
execute this on A100 dgxa100.80g.2.norm
```bash
python3 /opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py \
trainer.devices=1 \
trainer.num_nodes=1 \
trainer.precision=bf16 \
tensor_model_parallel_size=1 \
pipeline_model_parallel_size=1 \
gpt_model_file=/mount_workspace/results/gpt3_126m/convert_nemo/results/megatron_gpt.nemo \
lora_model_path=/mount_workspace/results/lora-tp1/checkpoints/megatron_gpt_peft_tuning-lora.nemo \
merged_model_path=/mount_workspace/results/lora-merge/checkpoints/lora-gpt126m-merged.nemo"
```
failed with error message
```
Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.precision=bf16', 'tensor_model_parallel_size=1', 'pipeline_model_parallel_size=1', 'gpt_model_file=/mount_workspace/results/gpt3_126m/convert_nemo/results/megatron_gpt.nemo', 'lora_model_path=/mount_workspace/results/lora-tp1/checkpoints/megatron_gpt_peft_tuning-lora.nemo', 'merged_model_path=/mount_workspace/results/lora-merge/checkpoints/lora-gpt126m-merged.nemo']
Traceback (most recent call last):
File "/opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py", line 238, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/opt/NeMo/nemo/core/config/hydra_runner.py", line 126, in wrapper
_run_hydra(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py", line 198, in main
lora_model_cfg = MegatronGPTLoRAModel.restore_from(
TypeError: ModelPT.restore_from() got an unexpected keyword argument 'mcore'
```
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
**Expected behavior**
no error
**Environment overview (please complete the following information)**
- Environment location: Nvidia DGX cloud
- Method of NeMo install: container "nemofw-training:23.08.03-ucx-downgrade-no-redirect"
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
failed at this [line](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/merge_lora_weights/merge.py#L199)
I tried manually remove the `mcore` param in script it seems passed the point.
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"The `compute_logprob` argument seems to have different meaning in different functions.\r\n\r\nWhen starting a model server, the `compute_logprob` argument will return the logprobs of the generated tokens as well. See the following example. I started a model server ([script](https://gitlab-master.nvidia.com/shengya... | 2023-11-30T16:03:29 | 2024-01-31T01:44:12 | 2024-01-31T01:44:12 | COLLABORATOR | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
It's very confusing to have a `compute_logprob` flag in `SamplingParam` that has nothing to do with sampling from the model, and actually completely changes the behavior of the `generate()` function so that instead of generating text, it only computes the logprobs of the input text. I've always thought this option could be used to obtain the logprobs of the *generated* data (and I was unable to find a function to compute the logprob of some text when I needed it).
Having code like this:
```python
compute_prob_response = get_computeprob_response(self.tokenizer, response, batch)
return compute_prob_response
```
is also quite confusing since actually `get_computeprob_response()` doesn't compute the log prob of the response at all, only of the input.
**Describe the solution you'd like**
Something like this:
* Rename `SamplingParam` into `InferenceParam`
* Rename the `generate()` function(s) and variants (e.g. `megatron_gpt_generate()`) to `infer()`
* Replace `compute_logprob` with a `mode` that can either be `"generate"` or `"compute_logprob"`
* Move the generation-specific settings into a `generate` config node, so that the config looks like
```
inference_params:
mode: generate
generate:
temperature: 1.0
top_k: 0
...
```
(and could be extended with new modes and associated options if needed in the future)
* Deprecate the old API while keeping it functional | {
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"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"Hi!\r\n\r\nAny update on this issue? I'm interested at least on logging `to... | 2023-11-30T15:31:05 | 2024-08-10T12:19:54 | 2024-01-07T01:48:53 | NONE | {
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} | null | null | null | Made some updates to the Docker image to support Pytorch NGC 23.10 so that i can consume the performance updates in NCCL 2.19.3. I am running some benchmarks with a prepared NemoMegatron 7B w/ Wikipedia and was wondering how i get a more representative performance callback data.
For example
```
Epoch 0: : 3%|▎ | 9000/301500 [1:42:42<55:37:46, v_num=0-56, reduced_train_loss=nan.0, global_step=9e+3, consumed_samples=6.3e+5, train_step_timing in s=0.615, val_loss=7.040]
Epoch 0, global step 9000: 'val_loss' reached 7.04238 (best 7.04238), saving model to '/data/nemo_megatron/nemo_experiments/megatron_gpt/2023-11-29_23-30-56/checkpoints/megatron_gpt--val_loss=7.04-step=9000-consumed_samples=630000.0.ckpt' as top 3
```
Wondering how i can get MFU or tokens/sec from this. In the legacy Megatron-LM implementation it had forward-compute/backward-compute and collective timings reported per step. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7956 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7956/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7956/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7956/events | https://github.com/NVIDIA/NeMo/pull/7956 | 2,018,279,971 | PR_kwDOC_bI7s5gwmyl | 7,956 | multimodal/speech_cv collection update (Audio Visual Speech Recognition) | {
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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 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 P... | 2023-11-30T09:58:13 | 2024-01-09T01:46:17 | 2024-01-09T01:46:16 | CONTRIBUTOR | {
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} | # What does this PR do ?
Update the multimodal/speech_cv branch to support audio-visual speech recognition (CTC, RNN-T and Hybrid)
**Collection**: [multimodal/speech_cv]
# Changelog
- Update speech_cv branch
- bug fix
- Add audio-visual datasets
- Add audio-visual models: (AudioVisualEncDecCTCModelBPE, AudioVisualEncDecCTCModel, AudioVisualEncDecHybridRNNTCTCBPEModel, AudioVisualEncDecHybridRNNTCTCModel, AudioVisualEncDecRNNTBPEModel, AudioVisualEncDecRNNTModel)
- Add audio-visual modules (FusionModule, InterCTCMixin)
- Optional ConformerEncoder module 'pre_encode' by setting 'feat_in' to None
# Usage
* You can potentially add a usage example below
```python
import nemo.collections.multimodal.speech_cv as nemo_cv
from omegaconf import OmegaConf
config_path = "model_config.yaml"
params = OmegaConf.load(config_path)
# hybrid CTC/RNN-T BPE audio-visual speech recognition model
model = nemo_cv.models.AudioVisualEncDecHybridRNNTCTCBPEModel(cfg=params.model)
```
# 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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"looks like there are no existing checkpoints for it to restore from. Hence you also need to add `ignore_no_checkpoints` option as well to ignore when there are no checkpoints. Note that checkpoints need to be in `ckpt` format. ",
"Hi, @nithinraok \r\n\r\nI trained for one epoch using [speaker_reco.py](https://gi... | 2023-11-30T08:05:07 | 2024-03-04T01:46:52 | 2024-03-04T01:46:52 | NONE | {
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} | null | null | null | Hi,
How can I resume the training of a SpeakerNet?
I already set `resume_if_exists: True `.
But get error:
`nemo.utils.exp_manager.NotFoundError: There was no checkpoint folder at checkpoint_dir :exp/SpeakerNet/SpeakerNet/checkpoints. Cannot resume.`
I also set `create_checkpoint_callback: True`, I thought this would generate a checkpoint, but in reality, it did not.
This is my exp folder architecture:
- SpeakerNet
- 2023-11-29_19-21-04
- cmd-args.log
- events.out.tfevents.1701256897.biic188.12314.0
- git-info.log
- hparams.yaml
- labels.txt
- lightning_logs.txt
- nemo_error_log.txt
- nemo_log_globalrank-0_localrank-0.txt
- spkr.nemo
Tool version
python 3.10.13
NeMo 1.20.0
pytorch-lightning 1.9.4
torch 2.1.0 | {
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"I don't believe we've implemented beam search for transducer plus greedy word timestamp extraction.",
"Would this be possible using CTC decoding, if I used a Conformer-CTC model instead?",
"@titu1994 does this PR not give the results that I am looking for? Or does the 'preserve_alignments' part refer to someth... | 2023-11-29T16:51:41 | 2023-12-04T20:59:45 | 2023-12-04T20:59:44 | NONE | {
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} | null | null | null | I am using a Conformer-Transducer model and using the `transcribe_speech.py` script for transcript generation.
I am looking to get a time-aligned word-level transcript output, but instead of just the best hypothesis, I want to get N best tokens for each token location, i.e. for each word location, I want the N best predicted words and the time for that location (beam size = N).
For example, with a beam size of 2:
Spoken text: "I can see clearly"
What I have now:
"timestamps_word": [{"word": "I", "start_offset": 16, "end_offset": 20}, {"word": "can", "start_offset": 20, "end_offset": 159}, {"word": "see", "start_offset": 159, "end_offset": 164}, {"word": "clearly", "start_offset": 164, "end_offset": 326}]
Theoretically what I am looking for: "timestamps_word":
[{"words": ["I", "eye"], "start_offset": 16, "end_offset": 20}, {"words": ["can", "cam"], "start_offset": 20, "end_offset": 159}, {"words": ["see", "sea"], "start_offset": 159, "end_offset": 164}, {"words": ["clearly", "freely"], "start_offset": 164, "end_offset": 326}]
My current decoder-specific arguments to the script are as follows:
```decoder_type='rnnt' compute_timestamps=True rnnt_decoding.strategy='beam' rnnt_decoding.beam.beam_size=3 rnnt_decoding.preserve_alignments=True rnnt_decoding.beam.return_best_hypothesis=False```
Is what I am looking for possible? | {
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"@titu1994 have you worked something similar before? ",
"Is your model on Cuda ? \r\n\r\nCuda does not work with multi threading, only with multi processing. It simply does not support instantiating two separate cuda contexts (that's why all data loaders are multi process) on the same process. That can be one rea... | 2023-11-29T16:39:55 | 2024-01-31T01:44:13 | 2024-01-31T01:44:13 | CONTRIBUTOR | {
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} | null | null | null | Hi everybody,
I have an application that communicates through rabbitmq queues. I am using a class that inherits multiprocessing.Process:
`class ThreadedConsumer(multiprocessing.Process)`
In this class, I would like to instantiate 3 processes, where each process transcribes an input file.
Now, in order to use as little memory as possible, I would like to only create the 'asr_model' once, in the constructor of the ThreadedConsumer class, and each process to share the same model.
I have the following problem: when I create the self.asr_model inside the __init__ class, when i do asr.transcribe() inside the callback (when I receive a message) everything freezes. (probably a thread gets latched or something) When I create the self.asr_model each time I receive a new message in the queue, everything works fine. However, in this way I would have 3 models loaded into the memory.. I do not understand why declaring the model inside the constructor results in the pipeline getting stuck.
Could you please help me with an idea for model sharing, is it possible for ASR models? I know that it would be a solution to use a model with batch_size=3, however once new message come into the queue, I would like another queue that's free to transcribe them and not continously wait for 3 messages.
If anybody has any insights or ideas on how to share the model/ use transcriber with multiprocessing.Process class, I would greatly appreciate any help!
Thank you in advance,
Gabi | {
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