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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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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?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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The existing doc on S3CheckpointIO is pretty sparse. Can there be a more complete example of using it within a script like https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_pretraining.py?
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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
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# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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Cherry-pick megatron export fix from main
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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} | …variety of tensors coming out of the dataloader
Add more flexibility to the default data step inside megatron parallel.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Megatron parallel's default data step can operate on wrapped tuples and unwrapped items coming directly out of the dataloader
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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 ?
We enable the default data step to operate on a wider variety of outputs from the dataloader.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Enables the dataloader to pass a wrapped tuple to the default data step in megatron parallel
- Enables the dataloader to pass any unwrapped item to the default data step
# Usage
* You can potentially add a usage example below
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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**Collection**: [Note which collection this PR will affect]
# Changelog
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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
```
# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
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If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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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# 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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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📝 Please remember the following to-do's before merge:
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🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-07-07T15:45:18 | 2024-08-22T01:52:56 | 2024-08-22T01:52:55 | NONE | {
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} | null | null | null | Thank you for your contribution for the Nemo framework, it is an awesome work.
I use the Diarization pipeline, which is vad -> spk_emb -> clustering(nmesc+kmeans). When I use myself speaker model, I found the diarization performance is even worse than the open-source titanet-large model on my own test dataset.
Note that in the aspect of the speaker recognition performance, my speaker model is better than the provided titanet-large on my own test set. However, the diarization equipped with my speaker model cannot get good performance (better eer can not get better der?).
Is there any hyper-parameters in clustering need to be tuned for different speaker model and test set (such as the self.nme_mat_size, self.max_rp_thershold, self.sparse_search_volume, etc.)
Hope for your valuable suggestion, thanks ! | {
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} | # What does this PR do ?
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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."
] | 2024-07-05T23:17:50 | 2024-08-15T16:39:24 | 2024-08-15T16:39:24 | CONTRIBUTOR | {
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} | These two sets of weight and bias have been renamed according to the `module_name_rewrite_list` given in https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/inference/gpt/state_dict_hooks.py#L116-L119
Also change the underscores in the two command-line options to hyphens to follow the convention of using hyphens in command-line options. Note that `argparse` automatically converts hyphens in argument names to underscores in the attributes of the namespace returned by `parse_args()`.
# What does this PR do ?
Update the names of the two sets of weight and bias according to latest Megatron-LM.
**Collection**: NLP
# Changelog
- Update the names of the two sets of weight and bias according to latest Megatron-LM.
- Change the underscores in the two command-line options to hyphens to follow the convention of using hyphens in command-line options.
# Usage
https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/dpo.html
```
python convert_gpt_nemo_to_mcore.py --input-name-or-path ./model_checkpoint --output-path ./mcore_gpt.nemo
```
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**Pre checks**:
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} | # What does this PR do ?
Fix NeMo 2 pretraining with different data split sizes and dataset weights
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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"Hi, try to follow up here: I think we have few different version of NeMo checkpoints at the moment. The current script works for more recent nemo checkpoints but maybe not 2B.",
"The error is not caused by the input NeMo checkpoint. It is caused by this change in the output mcore checkpoint, i.e., these two sets of weight and bias have been renamed according to the module_name_rewrite_list given in https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/inference/gpt/state_dict_hooks.py#L116-L119",
"@yaoyu-33 Please let me know if you need more info about this issue or the pull request. Thanks!",
"Hi sorry for the delay. Yes, it makes sense now. added a comment in your PR.\r\nCan you sign your PR when you commit by `commit -sm \"commit msg\"`",
"@yaoyu-33 No problem. Thank you for the response! I addressed your comment in my PR. I signed my PR and rebased it onto the latest main.",
"@yaoyu-33 Gentle ping. Please let me know if you have any other comment. Thanks!",
"hi @renweizhukov we should probably change others then. We try to keep the core args the same (using _) across the checkpoint_converters folder. \r\n\r\nAfter this change, I think it's good to go",
"@yaoyu-33 Make sense. I have changed the hyphens to underscores for the two other command-line options.",
"@yaoyu-33 I have made the change per your suggestion. Could you please take a look? Thanks!",
"> @yaoyu-33 I have made the change per your suggestion. Could you please take a look? Thanks!\n\nwill get it merged today",
"@yaoyu-33 Just wonder if we have merged the pull request.",
"there were some ci issues last week, finally merged.",
"Great. Thank you!"
] | 2024-07-05T22:46:36 | 2024-08-17T05:46:30 | 2024-08-15T16:39:53 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
RuntimeError "Unexpected key" when running checkpoint_converters script convert_got_nemo_to_mcore.py
**Steps/Code to reproduce bug**
Follow the instructions given in https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/dpo.html to convert a GPT-2B checkpoint to Megatron-Core checkpoint.
1. Download the 2B checkpoint.
```
wget https://huggingface.co/nvidia/GPT-2B-001/resolve/main/GPT-2B-001_bf16_tp1.nemo
```
2. Extract the NeMo file to a folder.
```
mkdir model_checkpoint && tar -xvf GPT-2B-001_bf16_tp1.nemo -C model_checkpoint
```
3. Run the script to convert from old NeMo checkpoint to Megatron-Core checkpoint. The original linked script [convert_nemo_gpt_to_mcore.py](https://github.com/NVIDIA/NeMo/blob/86b198ff93438d454f9c7f3550bcfb7d4e59feab/scripts/nlp_language_modeling/convert_nemo_gpt_to_mcore.py) has been replaced with [convert_gpt_nemo_to_mcore.py](https://github.com/renweizhukov/NeMo/blob/main/scripts/checkpoint_converters/convert_gpt_nemo_to_mcore.py).
```
python convert_gpt_nemo_to_mcore.py --input_name_or_path ./model_checkpoint --output_path ./mcore_gpt.nemo
```
4. Hit the following `RuntimeError`.
```
[NeMo W 2024-06-29 08:37:13 megatron_gpt_model:327] megatron_amp_O2 is enabled but transformer-engine is not.
Traceback (most recent call last):
File "/workspace/src/3rdparty/NeMo/scripts/checkpoint_converters/convert_gpt_nemo_to_mcore.py", line 323, in <module>
convert(
File "/workspace/src/3rdparty/NeMo/scripts/checkpoint_converters/convert_gpt_nemo_to_mcore.py", line 244, in convert
mcore_model = load_model(mcore_model, mcore_state_dict, ignore_if_missing=ignore_if_missing)
File "/workspace/src/3rdparty/NeMo/scripts/checkpoint_converters/convert_gpt_nemo_to_mcore.py", line 172, in load_model
raise RuntimeError(f"Unexpected key: {name} not in state_dict but in model.")
RuntimeError: Unexpected key: model.module.decoder.layers.0.input_layernorm.weight not in state_dict but in model.
```
**Expected behavior**
The script should write the converted checkpoint under the given `output_path`.
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: from source
```
# Checked out at commit e33c8f7
uv pip install --system --no-build-isolation --no-deps -e /workspace/src/3rdparty/Megatron-LM
# Checked out at commit f1062b7
uv pip install --system --no-build-isolation --no-deps -e "/workspace/src/3rdparty/NeMo[nlp]" && cd /workspace/src/3rdparty/NeMo/nemo/collections/nlp/data/language_modeling/megatron && make
```
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 22.04.4 LTS
- PyTorch version: 2.3.0a0+6ddf5cf85e.nv24.04
- Python version: Python 3.10.12
**Additional context**
Add any other context about the problem here.
GPU model: NVIDIA A100-SXM4-80GB
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} | # What does this PR do ?
Updates Apex module replacements we do inside export() with Megatron Core modules replacement.
**Collection**: [Note which collection this PR will affect]
NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
python3 examples/nlp/language_modeling/megatron_export.py model_type=gpt gpt_model_file=Nemotron-3-8B-Base-4k.nemo onnx_model=Nemotron-3-8B-Base-4k.nemo
```
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} | # What does this PR do ?
The refactoring PR [#9147](https://github.com/NVIDIA/NeMo/pull/9147) in missed a line that should have been refactored too.
Adding the necessary change to prevent errors in this PR
**Collection**: [Note which collection this PR will affect]
ASR/speaker_tasks
# Changelog
```python3
logits, embs_a = self.msdd._speaker_model.forward_for_export(
processed_signal=audio_signal[detach_ids[0]], processed_signal_len=audio_signal_len[detach_ids[0]]
)
```
to
```python3
logits, embs_a = self.msdd._speaker_model.forward_for_export(
audio_signal=audio_signal[detach_ids[0]], length=audio_signal_len[detach_ids[0]]
)
```
# Usage
```python
python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \
trainer.devices=1 \
model.base.diarizer.speaker_embeddings.model_path="titanet_large" \
model.train_ds.manifest_filepath="<train_manifest_path>" \
model.validation_ds.manifest_filepath="<dev_manifest_path>" \
model.train_ds.emb_dir="<train_temp_dir>" \
model.validation_ds.emb_dir="<dev_temp_dir>" \
exp_manager.name='sample_train' \
exp_manager.exp_dir='./msdd_exp'
"""
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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 NeMo ASR
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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
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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# Before your PR is "Ready for review"
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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
* Related to # (issue)
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Fix model_opt path with async save, where `strategy.checkpoint_io` is a wrapper around the base strategy.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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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
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```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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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)
- [ ] 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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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"An offline model is not supposed to be used with cache-aware scripts, specially with small chunk sizes. You need to use cache-aware models like:\r\nhttps://huggingface.co/nvidia/stt_en_fastconformer_hybrid_large_streaming_multi\r\n\r\nRiva does not support cache-aware models publicly yet as far as I know. You may use nemo for cache-aware until they are fully supported in Riva in the meantime.\r\n\r\nIf for some reasons, you need to use offline models for streaming in nemo, you need to use this script for buffered streaming. Riva is supporting buffered streaming which is a streaming approach for offline-trained models:\r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py\r\n\r\nJust keep that in mind that in buffered streaming (the one Riva uses), chunk size is not comparable to cache-aware models. They add some eight and left contexts to the chunk which increases the latency, usually buffer size is 4s and the 800ms chunk is the middle of the buffer.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-07-04T12:33:39 | 2024-09-09T01:57:04 | 2024-09-09T01:57:03 | NONE | {
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} | null | null | null | Hello. I am testing the [STT En Conformer-CTC Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large/files) model in streaming mode. To do this, I use the [speech_to_text_cache_aware_streaming_infer.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py) script and the [librispeech-test-other](https://www.openslr.org/12) dataset. With the default `chunk_size` value of 4000 ms, everything works quite well. The WER is 8.67%. However, when I reduce the `chunk_size` to 800 ms, I get very poor results. The WER increases to 35.6%.
I understand that this model was trained with full context in offline mode. And the accuracy would not be very good with small chunks. But I also tested this model in streaming mode (`chunk_size` = 800ms) with RIVA. I converted the nemo model to riva with [nemo2riva](https://github.com/nvidia-riva/nemo2riva) and built a pipeline. In this case, the WER was 7.9%. I understand that the RIVA service contains certain techniques and optimizations for working with the streaming mode. But I wonder why there is such a difference in WER when using the same model and the same chunk size.
Can anyone explain this behavior? Maybe RIVA reconfigures the model in some way or something else that I might have missed? | {
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"Also, did you use nemo text normalization for text preprocessing? If yes, did you use only it?",
"> Hi. I'm interested what datasets did you use for this model? https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu\r\n\r\nWe used a combination of the train sets used to train English, Spanish, French and German monolingual models. ",
"> Also, did you use nemo text normalization for text preprocessing? If yes, did you use only it?\r\n\r\ni believe so. ",
"thank you for answering\r\n\r\nIts interesting, because we tried to resume the training (with NeMo) on the same dataset and load from pretrained nemo, and the training improves on validation set, but deteriorates on fleurs test set. Thats why we thought maybe you used other cleaning or processing of the text label.",
"Hello, I see this is essentially a duplicate of https://github.com/NVIDIA/NeMo/issues/10122, which I have answered. I hope we have been able to help you. Can I mark both these issues closed?",
"Yes, thank you for answering\r\n\r\n\r\nOn Tue, Aug 20, 2024 at 3:06 AM Elena Rastorgueva ***@***.***>\r\nwrote:\r\n\r\n> Hello, I see this is essentially a duplicate of #10122\r\n> <https://github.com/NVIDIA/NeMo/issues/10122>, which I have answered. I\r\n> hope we have been able to help you. Can I mark both these issues closed?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/NVIDIA/NeMo/issues/9614#issuecomment-2297667496>, or\r\n> unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AFC5EFNJGG6EDXJRSPZF3I3ZSJ27PAVCNFSM6AAAAABKLLISJ6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEOJXGY3DONBZGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n"
] | 2024-07-04T11:26:58 | 2024-08-20T16:46:11 | 2024-08-20T16:46:11 | NONE | {
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} | # What does this PR do ?
With distributed checkpoints, the mismatches between the runtime model and checkpoint model manifest during `dist_checkpoint.load` (not during `model.load_state_dict` as with regular checkpoints).
This PR adds a flag that allows to adjust load strictness (e.g. ignore unexpected keys).
This PR relies on MCore feature that is not merged yet (merge ETA 5th July): https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/1628
**Collection**: NLP
# Changelog
- Add `model.dist_ckpt_load_strictness` flag to control dist ckpt load strictness. The most useful value is `log_all` which warns about all mismatches but performs the checkpoint load for a matching state dict subset.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
Adding Mamba but commented to resolve mcore hash issue
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
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} | # What does this PR do ?
Support for preprocessing jsonl files in a folder.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```
python preprocess_data_for_megatron.py \
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--preproc-folder \
--json-keys=text \
--tokenizer-library=huggingface \
--tokenizer-type=/path/to/hftokenizer/ \
--output-prefix=/path/to/output/prefix \
--workers=224 \
--log-interval=100000 \
--append-eod
```
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# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
I am changing the dict format returned by `scheduler` method in the new `lightning/optim` module.
I followed the example of `LRSchedulerModule` to create a custom cosine annealing scheduler. My scheduler was returning the dict format:
```
return {
"optimizer": optimizers,
# REQUIRED: The scheduler instance
# `interval` is the unit of the scheduler's step size, could also be 'step'.
# 'epoch' updates the scheduler on epoch end whereas 'step'
# updates it after a optimizer update.
"scheduler": lr_scheduler,
"interval": self.interval,
"frequency": self.frequency,
# Metric to monitor for schedulers like `ReduceLROnPlateau`
"monitor": self.monitor,
}
```
With this format, I was getting a warning:
```
[NeMo W 2024-07-03 16:27:38 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/optimizer.py:376: Found unsupported keys in the optimizer configuration: {'scheduler', 'frequency', 'interval'}
```
and the learning rate was not decreasing (which is wrong)
changing it to the following format fixed the issue:
```
return {
"optimizer": optimizers,
# REQUIRED: The scheduler instance
# `interval` is the unit of the scheduler's step size, could also be 'step'.
# 'epoch' updates the scheduler on epoch end whereas 'step'
# updates it after a optimizer update.
"lr_scheduler": {
"scheduler": lr_scheduler,
"interval": self.interval,
"frequency": self.frequency,
},
# Metric to monitor for schedulers like `ReduceLROnPlateau`
"monitor": self.monitor,
}
```
**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 ?
Fixed an issue for PP > 1 resume.
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"merged_at": "2024-07-10T16:37:37"
} | # What does this PR do ?
1. Unpins transformers package version.
2. Guards deprecated imports in NeMo which were deleted in transformers >= 4.40
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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} | # What does this PR do ?
Fix the script to convert Llama 2/3 from .nemo format to hf format. This will resolve #9496
**Collection**: [Note which collection this PR will affect]
# Changelog
- In Llama3, basically `AutoTokenizer` (fast tokenizer) is used, but in Llama2 and other derivatives, both (`LlamaTokenizer` and `AutoTokenizer`) are used. This PR works both patterns.
# Usage
No changes in API
```python
# llama3
export INPUT="/workspace/models/Meta-Llama-3-8B.nemo"
export OUTPUT="/workspace/models/llama3-8b/pytorch_model.bin"
python /opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py \
--input_name_or_path=${INPUT} \
--output_path=${OUTPUT} \
--hf_input_path /workspace/models/Meta-Llama-3-8B \
--hf_output_path /workspace/models/llama3-8b \
--input_tokenizer /workspace/models/Meta-Llama-3-8B \
--hf_output_tokenizer /workspace/models/llama3-8b
# llama2
export INPUT="/workspace/models/Llama-2-7b-hf.nemo"
export OUTPUT="/workspace/models/llama2-7b/pytorch_model.bin"
python /opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py \
--input_name_or_path=${INPUT} \
--output_path=${OUTPUT} \
--hf_input_path /workspace/models/Llama-2-7b-hf \
--hf_output_path /workspace/models/llama2-7b \
--input_tokenizer /workspace/models/Llama-2-7b-hf \
--hf_output_tokenizer /workspace/models/llama2-7b
```
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Support enabling inference server only in megatron_gpt_eval.py, thus overriding the first and second examples if `cfg.prompts` is empty.
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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
```
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- [ ] Did you write any new necessary tests?
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Add a code snippet demonstrating how to use this
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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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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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"Could you please address all of these CodeQL reviews?"
] | 2024-07-03T07:21:11 | 2024-07-09T17:30:18 | 2024-07-09T17:30:18 | CONTRIBUTOR | {
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"merged_at": "2024-07-09T17:30:18"
} | # What does this PR do ?
Adding better support for triton deployment of in-framework models using `MegatronLLMDeployable`.
**Collection**: [Note which collection this PR will affect]
scripts/deploy
tests/deploy
# Changelog
- Add `NemoQueryLLMPyTorch` class for easy query of `MegatronLLMDeployable`
- Changes to `nemo_export.py` to accurately test `MegatronLLMDeployable` models
- A separate `deploy_inframework_triton.py` deployment script to easily deploy `MegatronLLMDeployable` on a Triton server
# Usage
```
python scripts/deploy/nlp/deploy_inframework_triton.py -nc <path to .nemo checkpoint> -tmn TritonModelName
python tests/export/nemo_export.py --checkpoint_dir <path to .nemo checkpoint> --model_type gpt --min_tps 1 --max_output_len 24 --test_deployment False --in_framework True --model_name TritonModelName
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [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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"@KunalDhawan pls have a look at this issue. ",
"Hi @MarcisTU, Thank you for the detailed description! Could you please help me reproduce the issue? \r\n\r\nI tried to replicate the issue on my end but I am able to run the code snippet you shared above without any errors. Let me describe my replication setup in detail:\r\n\r\n**Environment:**\r\nI built a fresh conda env using [NeMo main](https://github.com/NVIDIA/NeMo.git)\r\n\r\n**Reproducing the code:**\r\n```\r\n>>> import copy\r\n>>> import torch\r\n>>> import librosa\r\n>>> from omegaconf import OmegaConf, open_dict\r\n>>> from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel\r\n\r\n>>> asr_model_path = \"/models/stt_de_fastconformer_hybrid_large_pc.nemo\"\r\n>>> asr_model = EncDecHybridRNNTCTCBPEModel.restore_from(restore_path=asr_model_path)\r\n.....\r\n[NeMo I 2024-08-01 17:36:37 save_restore_connector:275] Model EncDecHybridRNNTCTCBPEModel was successfully restored from /models/stt_de_fastconformer_hybrid_large_pc.nemo.\r\n\r\n>>> device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n>>> device\r\ndevice(type='cuda')\r\n\r\n>>> audio_path = \"/data/ASR/en/librispeech/wav/test-clean/61-70968-0000.wav\"\r\n>>> input_wav, sr = librosa.load(audio_path, sr=16000)\r\n\r\n>>> with open_dict(decoding_cfg):\r\n... decoding_cfg.preserve_alignments = True\r\n... decoding_cfg.compute_timestamps = True\r\n... asr_model.change_decoding_strategy(decoding_cfg)\r\n\r\n[NeMo I 2024-08-01 17:38:17 rnnt_models:224] Using RNNT Loss : warprnnt_numba\r\n Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}\r\n[NeMo I 2024-08-01 17:38:17 hybrid_rnnt_ctc_bpe_models:457] Changed decoding strategy of the RNNT decoder to \r\n model_type: rnnt\r\n strategy: greedy_batch\r\n compute_hypothesis_token_set: false\r\n preserve_alignments: true\r\n confidence_cfg:\r\n preserve_frame_confidence: false\r\n preserve_token_confidence: false\r\n preserve_word_confidence: false\r\n exclude_blank: true\r\n aggregation: min\r\n tdt_include_duration: false\r\n method_cfg:\r\n name: entropy\r\n entropy_type: tsallis\r\n alpha: 0.33\r\n entropy_norm: exp\r\n temperature: DEPRECATED\r\n fused_batch_size: null\r\n compute_timestamps: true\r\n compute_langs: false\r\n word_seperator: ' '\r\n rnnt_timestamp_type: all\r\n greedy:\r\n max_symbols_per_step: 10\r\n preserve_alignments: false\r\n preserve_frame_confidence: false\r\n tdt_include_duration_confidence: false\r\n confidence_method_cfg:\r\n name: entropy\r\n entropy_type: tsallis\r\n alpha: 0.33\r\n entropy_norm: exp\r\n temperature: DEPRECATED\r\n loop_labels: true\r\n use_cuda_graph_decoder: true\r\n max_symbols: 10\r\n beam:\r\n beam_size: 2\r\n search_type: default\r\n score_norm: true\r\n return_best_hypothesis: false\r\n tsd_max_sym_exp_per_step: 50\r\n alsd_max_target_len: 2.0\r\n nsc_max_timesteps_expansion: 1\r\n nsc_prefix_alpha: 1\r\n maes_num_steps: 2\r\n maes_prefix_alpha: 1\r\n maes_expansion_gamma: 2.3\r\n maes_expansion_beta: 2\r\n language_model: null\r\n softmax_temperature: 1.0\r\n preserve_alignments: false\r\n ngram_lm_model: null\r\n ngram_lm_alpha: 0.0\r\n hat_subtract_ilm: false\r\n hat_ilm_weight: 0.0\r\n tsd_max_sym_exp: 50\r\n temperature: 1.0\r\n durations: []\r\n big_blank_durations: []\r\n\r\n>>> hypotheses = asr_model.transcribe(input_wav, return_hypotheses=True)\r\nTranscribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.45s/it]\r\n\r\n>>> hypotheses\r\n([Hypothesis(score=-26.476362228393555, y_sequence=tensor([ 5, 1004, 354, 6, 5, 91, 255, 255, 38, 21, 166, 8,\r\n 5, 255, 22, 15, 90, 28, 621, 5, 91, 354, 38, 16,\r\n 5, 8, 121, 5, 153, 43, 43, 9, 8, 50, 22, 5,\r\n 482, 3, 8, 236, 199, 22, 114, 4, 551, 95, 8, 38,\r\n 10, 5, 8, 121, 5, 247, 291, 2], device='cuda:0'), text='Ygan accinfust complant against the wizzertwo identisch boye Curtin und the loft .', dec_out=None, dec_state=(tensor([[[ 1.1207e-03, 2.4281e-03, 3.4973e-08, -7.2756e-01, -3.1125e-05,\r\n -3.0149e-04, 1.0904e-05, -9.7909e-06, -1.3529e-04, 5.1211e-05,\r\n 2.7526e-04, -7.5015e-01, -7.4380e-01, 4.6821e-02, 3.1424e-10,\r\n -1.6009e-07, 7.6155e-01, -9.2109e-07, 6.8400e-05, -7.5959e-01,\r\n 7.5943e-01, -7.5767e-01, 2.4640e-06, 2.4134e-05, -2.8190e-03,\r\n ........\r\n```\r\n\r\nI was able to transcribe with a FastConformer-Hybrid-Transducer-CTC-BPE model without any issues. Could you please share some more details and kindly help me identify where there could be a possible mismatch between my replication and your setup? ",
"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.",
"I have same problem, model.transcribe works fine when compute_timestamps=true not passed in decoder config, but when pass compute_timestamps=true in decoder config, gain \"TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.\"\n\nHas anyone encountered this? Did you manage to solve it?",
"can you instead try with just `timestamps=True` option to .transcribe() see: https://github.com/NVIDIA/NeMo/pull/10950 "
] | 2024-07-03T03:43:25 | 2024-12-06T14:19:37 | 2024-09-09T01:57:07 | NONE | {
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} | null | null | null | **Describe the bug**
After following installation instructions for Linux there is a weird bug about torch tensor not being on CPU when converting to numpy.
```
Transcribing: 0%| | 0/1 [00:02<?, ?it/s]
Traceback (most recent call last):
File "/root/inference_fastconformer_stt_lv.py", line 29, in <module>
hypotheses = asr_model.transcribe(input_wav, return_hypotheses=True)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py", line 138, in transcribe
return super().transcribe(
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py", line 279, in transcribe
return super().transcribe(
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/parts/mixins/transcription.py", line 274, in transcribe
for processed_outputs in generator:
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/parts/mixins/transcription.py", line 385, in transcribe_generator
processed_outputs = self._transcribe_output_processing(model_outputs, transcribe_cfg)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py", line 179, in _transcribe_output_processing
return super()._transcribe_output_processing(outputs, trcfg)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py", line 908, in _transcribe_output_processing
best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor(
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py", line 510, in rnnt_decoder_predictions_tensor
hypotheses[hyp_idx] = self.compute_rnnt_timestamps(hypotheses[hyp_idx], timestamp_type)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py", line 741, in compute_rnnt_timestamps
char_offsets = self._compute_offsets(hypothesis, token_repetitions, self.blank_id)
File "/opt/conda/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py", line 851, in _compute_offsets
start_indices = np.concatenate(([start_index], end_indices[:-1]))
File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 1062, in __array__
return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
```
**Steps/Code to reproduce bug**
Code to reproduce:
```
import copy
import torch
import librosa
from omegaconf import OmegaConf, open_dict
from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel
if __name__ == "__main__":
asr_model_path = "./nemo_experiments/FastConformer-Hybrid-Transducer-CTC-BPE/2024-07-02_09-14-19/checkpoints/FastConformer-Hybrid-Transducer-CTC-BPE.nemo"
asr_model = EncDecHybridRNNTCTCBPEModel.restore_from(restore_path=asr_model_path)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
audio_path = "./test_data/2321187.wav"
input_wav, sr = librosa.load(audio_path, sr=16000)
decoding_cfg = copy.deepcopy(asr_model.cfg.decoding)
with open_dict(decoding_cfg):
decoding_cfg.preserve_alignments = True
decoding_cfg.compute_timestamps = True
asr_model.change_decoding_strategy(decoding_cfg)
# specify flag `return_hypotheses=True``
hypotheses = asr_model.transcribe(input_wav, return_hypotheses=True)
# if hypotheses form a tuple (from RNNT), extract just "best" hypotheses
if type(hypotheses) == tuple and len(hypotheses) == 2:
hypotheses = hypotheses[0]
timestamp_dict = hypotheses[0].timestep # extract timesteps from hypothesis of first (and only) audio file
print("Hypothesis contains following timestep information :", list(timestamp_dict.keys()))
# For a FastConformer model, you can display the word timestamps as follows:
# 80ms is duration of a timestep at output of the Conformer
time_stride = 8 * asr_model.cfg.preprocessor.window_stride
word_timestamps = timestamp_dict['word']
for stamp in word_timestamps:
start = stamp['start_offset'] * time_stride
end = stamp['end_offset'] * time_stride
word = stamp['char'] if 'char' in stamp else stamp['word']
print(f"Time : {start:0.2f} - {end:0.2f} - {word}")
```
**Expected behavior**
Inference code runs and it is possible to get the result.
**Environment overview (please complete the following information)**
- Environment location: Vast.ai instance.
- Method of NeMo install: pip install from source.
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 22
- PyTorch version 2.2.1
- Python version 3.11
**Additional context**
Also tried to install in WSL2 windows, but got the same bug.
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"@titu1994 why this was closed? ",
"@nithinraok why is this closed? shouldn't we fix the paper link ?",
"Please add changes from https://github.com/NVIDIA/NeMo/pull/10711 to here and rebase with main. ",
"@nithinraok is this ok now?"
] | 2024-07-02T22:38:18 | 2024-10-04T11:42:04 | 2024-10-04T11:42:04 | CONTRIBUTOR | {
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Fix semi_sorted paper link
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Adds support for LLAMA3 and fixes a few other issues with vLLM.
**Collection**: NLP
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- Adds support for passing multiple paths to `PreTrainingDataModule`
- Provides support for resuming dataloader when restoring from a checkpoint
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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* Related to # (issue)
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} | # What does this PR do ?
Update NeMo Clip to Use MCore Modules. Add SigLIP supports.
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"I also attempted to run the tests in the repository but encountered an issue. NaNs appear when a mask with a fully False row is passed to MHA. Because of such mask, filling the `matrix_bd` with `-inf` values using `matrix_bd.masked_fill_(mask.logical_not(), float(\"-inf\"))` results in a row of only `-inf`, and after the softmax, this entire row becomes NaNs. I am unsure how to resolve this since the softmax and multiplication by value occur within `torch.nn.functional.scaled_dot_product_attention`, and I cannot intervene. In your implementation, this is handled by manually filling with zeros `attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)`.\r\n\r\nWhat can be done about this? Should we write a separate test that doesn't use such masks as input?",
"@SeanNaren @titu1994 An option of using SDPA is a good thing also because a Triton-based version of FAv2 with custom `attn_bias` support (FlexAttention) is being added into PyTorch core: https://github.com/pytorch/pytorch/pull/130250#issuecomment-2216318222, so Conformer attention can benefit in the future from the speed-ups and proper compilation of SDPA in core PyTorch developments ",
"@SeanNaren @titu1994 haha, and now that FAv3 is out, probably PyTorch would integrate it as well in some near term - for maximum brr on H100 :) so having Nemo's Conformer auto-benefitting from this new work would be awesome ",
"cc @redoctopus @jbalam-nv @okuchaiev",
"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.",
"stale bump",
"Thanks for the contribution!\r\n@titu1994 please take a look at this PR as it looks like an interesting addition to speedup conformer models.\r\n\r\nJust some notes:\r\n\r\n+ Please use -10000 instead of -inf if it is possible as -inf may cause NAN with some data types.\r\n\r\n+ Please add it as a config to the config files somewhere like here to be able to control it from configs:\r\nhttps://github.com/NVIDIA/NeMo/blob/07d536babf25f34d2866e7fa76c05eeef3b0086b/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml#L147\r\nName is \"use_pytorch_sdpa\"?\r\n\r\n+ I suggest to make it True as default if we can make sure it works in all cases? @titu1994 what do you think?\r\n\r\n+ Please evaluate one of the pretrained models on NGC on a test-other LS to make sure that it produces the same exact output and accuracy.\r\n\r\n+ You need to set the dropout to zero manually in non-training model as sdpa does not respect that and it always uses the dropout.\r\n\r\n+ Have you used matrix_ac in your code/calculations?",
"@VahidooX Big thanks for review!\r\n* I replaced -inf with -10000\r\n* Added use_pytorch_sdpa to config\r\n* Fixed dropout for torch sdpa\r\n* I dont calculate `matrix_ac` manually but it is calculated under the hood of [torch.nn.functional.scaled_dot_product_attention](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) (look on implementation example).\r\n ```python\r\n attn_weight = q_with_bias_u @ key.transpose(-2, -1) * scale_factor\r\n # so matrix_ac would be equivalent to attn_weight\r\n ```\r\n\r\nDo you have any script for calculating metrics on LS? And reference metrics?",
"Thanks @titu1994 \r\nOkey, I'll try to add tests.",
"I'm working with tests and faced a problem with `tests/collections/asr/test_conformer_encoder.py:test_stochastic_depth_forward`.\r\nBut I think that in this test, the data in `random_length` is incorrect, because `random_length` must one dim with shape `batch`, not two dims. Am I right?\r\nhttps://github.com/NVIDIA/NeMo/blob/6774fdcf23a915421135b223404b2213bf2c1b7a/tests/collections/asr/test_conformer_encoder.py#L84",
"> I'm working with tests and faced a problem with `tests/collections/asr/test_conformer_encoder.py:test_stochastic_depth_forward`. But I think that in this test, the data in `random_length` is incorrect, because `random_length` must one dim with shape `batch`, not two dims. Am I right?\r\n> \r\n> https://github.com/NVIDIA/NeMo/blob/6774fdcf23a915421135b223404b2213bf2c1b7a/tests/collections/asr/test_conformer_encoder.py#L84\r\n\r\nYes, that looks like to be incorrect.",
"As I mentioned above [here](https://github.com/NVIDIA/NeMo/pull/9590#issuecomment-2205160174), I encountered an error caused by the mask for padding tokens being all False, which led to NaNs appearing after the softmax operation.\r\n\r\nI resolved this issue by adding dummy columns and rows to the query, key, value, and mask https://github.com/NVIDIA/NeMo/blob/8d292d6ef218d5dc5f40878cde2052448e08aabb/nemo/collections/asr/parts/submodules/multi_head_attention.py#L160-L162\r\nHowever, this may not be the most appropriate solution since it could affect the divisibility of dimensions by powers of two.\r\n\r\nDo you think this approach is acceptable?\r\n\r\nRecently, a similar issue was resolved for SDPA https://github.com/pytorch/pytorch/pull/131863. I guess when this fix is included in the global version of PyTorch, we can remove this workaround involving the additional columns.\r\n",
"Maybe we should run a training test with mem efficient backend before setting use_pytorch_sdpa=True by default. Does anyone have a ready script for this?\r\n",
"> As I mentioned above [here](https://github.com/NVIDIA/NeMo/pull/9590#issuecomment-2205160174), I encountered an error caused by the mask for padding tokens being all False, which led to NaNs appearing after the softmax operation.\r\n> \r\n> I resolved this issue by adding dummy columns and rows to the query, key, value, and mask\r\n> \r\n> https://github.com/NVIDIA/NeMo/blob/8d292d6ef218d5dc5f40878cde2052448e08aabb/nemo/collections/asr/parts/submodules/multi_head_attention.py#L160-L162\r\n> \r\n> \r\n> However, this may not be the most appropriate solution since it could affect the divisibility of dimensions by powers of two.\r\n> Do you think this approach is acceptable?\r\n> \r\n> Recently, a similar issue was resolved for SDPA [pytorch/pytorch#131863](https://github.com/pytorch/pytorch/pull/131863). I guess when this fix is included in the global version of PyTorch, we can remove this workaround involving the additional columns.\r\n\r\nHmm, did you try to use -10000 instead of -inf and then slice off the excess as suggested in the above pr as option 2? \r\n\r\nIf that doesn't work, I'm wondering if we should merge this without the WAR and guard this code path for only after pytorch 2.4 is released ? We can do a version check and run the correct code for relposmha. \r\n\r\nWe can fall back to older codebase for lower pytorch versions. @VahidooX what do you think ?\r\n\r\n",
"Oh god, sorry for all the mess around with git :(",
"So, @titu1994 , yes, it was possible to get rid of these NaNs after softmax by simply zeroing them out using a mask, which is what I just did.\r\n```python\r\nout = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=dropout_rate)\r\n\r\n# this IF block can be deleted when https://github.com/pytorch/pytorch/pull/131863 is in the stable version\r\nif mask is not None:\r\n all_masked_rows = torch.all(~mask, dim=-1)\r\n all_masked_rows.unsqueeze_(-1)\r\n out = out.masked_fill(all_masked_rows, 0.0)\r\n```\r\n\r\nIn the end, it seems that before merging with the flag set to True by default, it would be good to run a training test to ensure everything works fine. Does anyone have the code ready for this?",
"I'm on it - launched Canary-1B training with this, will keep you posted.",
"Unfortunately the Canary-1B training on 32 GPUs with bf16 AMP is diverging after ~10 hours with this SDPA implementation. I launched a second run with a different seed to confirm and it replicates. I have a ~1 month old baseline run that works fine, and I launched another from today's main that also runs fine. Any ideas what may have caused this?\r\n\r\n\r\n",
"@pzelasko I think the first step should be running SDPA with the most basic `math` algo selected:\r\n```python\r\nwith torch.backends.cuda.sdpa_kernel(backends=[SDPBackend.MATH]): # needs to be checked if this breaks torch.compile(fullgraph=True) or not, see https://github.com/pytorch/pytorch/issues/130098\r\n # by default running with SDPBackend.EFFICIENT_ATTENTION as it's supposedly the only backend besides MATH which supports passed custom attn_bias arg\r\n x = F.sdpa(...)\r\n```\r\n\r\nThis would validate/benchmark that the basic SDPA impl works fine (should be more similar to the original Nemo's impl). I heard somewhere on PyTorch forum/issues that numerics of different SDPA methods [are slightly different](https://github.com/pytorch/pytorch/issues/96099#issuecomment-1675029272) and this [can lead](https://github.com/Dao-AILab/flash-attention/issues/16) to blow-ups during internal accumulation :( This is why I think `use_pytorch_sdpa` should not be default just yet and that the SDPA backend selection should be exposed in config too. In future, if @WoodieDudy takes on https://github.com/Dao-AILab/flash-attention/pull/617, `FLASH_ATTENTION` backend would also become available. And not sure if modern `CUDNN_ATTENTION` can handle custom attention bias - maybe it's also available now in `CUDNN_ATTENTION`\r\n\r\nFor more collaborative debugging, it would be great if some subset of mutlilingual ASRSET-1 was published as filelists. Then relatively large (larger than LibriSpeech) train runs can be done by the users for validation (e.g. to try again using RoPE or AliBI positional encoding + FAv2) and more reproducible research\r\n\r\nmaybe need to look at loss spikes, activation range, weight range... maybe can be fixed by simply clipping some qkv projection weights... or skipping batches with large loss (after initial training) - this is not very trivial to do with DDP as replicas must be in sync, but still possible...",
"@pzelasko what pytorch version did you use? We found that the latest versions where more stable.\r\nDid you try with bf16_mixed?",
"So, let's make `use_pytorch_sdpa` False by default and merge this PR? And then may be continue work in different PR",
"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.",
"That sounds fine with me. Can you make it False by default. @titu1994 wdyt? ",
"Sounds ok to me ",
"I set the flag everywhere to false by default. And also added an argument `use_pytorch_sdpa_backends` in which you can set the list of backends for sdpa",
"Let's merge? @titu1994 ",
"It looks ok for inference at least, lets avoid if for training for now ",
"@VahidooX @pzelasko 👀 ",
"Uh, sorry for the lack of responsiveness lately. I used bf16-mixed and PyTorch 2.4.0 with CUDA 12.5. I agree we should merge it disabled by default. Let's take a look again later with the newer backends. \r\n\r\nWe should also enable it for inference by default if possible.",
"Just initiated the CI run: looks like some of them are failing like this: https://github.com/NVIDIA/NeMo/actions/runs/11128061705/job/30922390803?pr=9590 \r\n\r\naddress them and once CI passes this is good to go. Thanks! "
] | 2024-07-02T12:37:17 | 2024-10-16T02:17:19 | 2024-10-16T02:17:19 | CONTRIBUTOR | {
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} | Hola. I changed the mha implementation for the ASR modules so that it uses `torch.nn.functional.scaled_dot_product_attention`.
This accelerated forward in the mha by 27% and backward by 17% on the A100.
Pytorch sdpa is continuously being optimized, ensuring that we benefit from the latest performance improvements.
My code uses [memory efficient backend](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) in sdpa because flash attention doesn't support custom attention bias. There is ongoing work to contribute custom bias support in the flash-attention repository. [PR](https://github.com/Dao-AILab/flash-attention/pull/617).
What else do I need to do to merge this pr?
## Usage
There is also my benchmark:
```python
import torch
import torch.nn as nn
import torch.utils.benchmark as benchmark
from nemo.collections.asr.parts.submodules.multi_head_attention import RelPositionMultiHeadAttention
torch.manual_seed(123)
device = "cuda"
batch_size = 32
seq_len = 1024
d_model = 512
n_head = 8
query = torch.rand(batch_size, seq_len, d_model, device=device, requires_grad=True)
key = torch.rand(batch_size, seq_len, d_model, device=device, requires_grad=True)
value = torch.rand(batch_size, seq_len, d_model, device=device, requires_grad=True)
mask = torch.ones(batch_size, seq_len, seq_len, device=device, requires_grad=False)
mask = torch.triu(mask, diagonal=1).bool() # mask: True - make zero, False - leave unchanged
mask = None
pos_emb = torch.rand(batch_size, seq_len, d_model, device=device, requires_grad=True)
attention_sdpa = RelPositionMultiHeadAttention(n_head, d_model, 0.0, None, None, use_pytorch_sdpa=True).to(device)
attention_original = RelPositionMultiHeadAttention(n_head, d_model, 0.0, None, None, use_pytorch_sdpa=False).to(device)
for original_param, sdpa_param in zip(attention_original.parameters(), attention_sdpa.parameters()):
original_param.data.copy_(sdpa_param.data)
# attention_sdpa = torch.compile(attention_sdpa)
# attention_original = torch.compile(attention_original)
def measure_time(attention, query, key, value, mask, pos_emb):
timer = benchmark.Timer(
stmt='attention(query, key, value, mask, pos_emb);torch.cuda.synchronize()',
setup='torch.cuda.synchronize()',
globals={'attention': attention, 'query': query, 'key': key, 'value': value, 'mask': mask, 'pos_emb': pos_emb}
)
with torch.no_grad():
torch.cuda.synchronize()
results = timer.blocked_autorange(min_run_time=10)
forward_time = results.mean
output = attention(query, key, value, mask, pos_emb)
return forward_time, output
def measure_fwd_bwd_time(attention, query, key, value, mask, pos_emb):
timer = benchmark.Timer(
stmt='loss=attention(query, key, value, mask, pos_emb).sum();torch.cuda.synchronize();loss.backward();torch.cuda.synchronize()',
globals={'attention': attention, 'query': query, 'key': key, 'value': value, 'mask': mask, 'pos_emb': pos_emb}
)
torch.cuda.synchronize()
results = timer.blocked_autorange(min_run_time=10)
fwd_bwd_time = results.mean
return fwd_bwd_time
time_fwd_original, output_original = measure_time(attention_original, query, key, value, mask, pos_emb)
time_fwd_sdpa, output_sdpa = measure_time(attention_sdpa, query, key, value, mask, pos_emb)
print(f"Original implementation time: {time_fwd_original:.6f} seconds")
print(f"SDPA implementation time: {time_fwd_sdpa:.6f} seconds")
print(f"SDPA boost {(time_fwd_original - time_fwd_sdpa) / time_fwd_original * 100:.2f}%")
time_fwd_bwd_original = measure_fwd_bwd_time(attention_original, query, key, value, mask, pos_emb)
time_fwd_bwd_sdpa = measure_fwd_bwd_time(attention_sdpa, query, key, value, mask, pos_emb)
time_bwd_original = time_fwd_bwd_original - time_fwd_original
time_bwd_sdpa = time_fwd_bwd_sdpa - time_fwd_sdpa
print(f"Original implementation backward time: {time_bwd_original:.6f} seconds")
print(f"SDPA implementation backward time: {time_bwd_sdpa:.6f} seconds")
print(f"SDPA backward boost {(time_bwd_original - time_bwd_sdpa) / time_bwd_original * 100:.2f}%")
print(f"Outputs are {'the same' if torch.allclose(output_original, output_sdpa, atol=1e-5) else 'different'}")
# Original implementation time: 0.049075 seconds
# SDPA implementation time: 0.035598 seconds
# SDPA boost 27.46%
# Original implementation backward time: 0.127004 seconds
# SDPA implementation backward time: 0.104986 seconds
# SDPA backward boost 17.34%
# Outputs are the same
```
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
cc @titu1994 @SeanNaren
## Additional Information
* Related to [(issue)](https://github.com/NVIDIA/NeMo/issues/6257)
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**Collection**: [Note which collection this PR will affect]
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**Collection**: [Note which collection this PR will affect]
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```python
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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.
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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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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"TO DO LIST:\r\n1. to move `convert_llava_hf_to_nemo.py`\r\n2. to add tutorial"
] | 2024-07-01T15:43:37 | 2024-07-11T01:24:24 | 2024-07-11T01:24:24 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR aims to add [LITA](https://github.com/NVlabs/LITA) from checkpoint conversion, dataset preprocessing for DVC datasets to finetuning.
It also adds vila to the lita model.
**Collection**: [Note which collection this PR will affect]
* multimodal
* nlp
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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} | # What does this PR do ?
Adds the Fabric-API to nemo 2
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
from nemo import lightning as nl
from nemo.collections.llm import MistralModel, MockDataModule
trainer = nl.Trainer(
devices=2,
strategy=nl.MegatronStrategy(tensor_model_parallel_size=2),
plugins=nl.MegatronMixedPrecision(precision='16-mixed')
)
fabric = trainer.to_fabric()
# Import model
model = fabric.import_model("hf://mistralai/Mistral-7B-v0.1", MistralModel)
# Load model
# model = fabric.load_model("/workspaces/llm-workspace/nemo_experiments/default/2024-07-02_01-14-20/checkpoints/default--reduced_train_loss=nan-epoch=0")
fabric.print(type(model))
# The model here will be the MistralModel wrapped by the MegatronStrategy
# Some dummy data
dataset = MockDataModule(tokenizer=model.module.tokenizer).train_dataloader()
batch = next(iter(dataset))
fabric.print(batch)
# Manual model forward
output = model(
batch,
forward_step=model.config.forward_step_fn,
loss_reduction=model.module.validation_loss_reduction(),
)
fabric.print(output)
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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 is the reasoning behind overloading num_workers for the num_dataset_builder_threads, instead of passing num_dataset_builder_threads in as a separate config parameter? \r\n\r\nI could imagine a case where I set num_dataset_builder_threads to a very large number (64 or 128) but wouldn't want that many workers during the typical data loading. That would allow many workloads to specify a high num_dataset_builder_threads without needing to explicitly use the data_cache_generation_only offline step.\r\n\r\nInstead I would suggest num_dataset_builder_threads as an explicit new parameter in megatron_gpt_config.yaml, and default it to 1 to mirror https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/datasets/blended_megatron_dataset_config.py#L48",
"> What is the reasoning behind overloading num_workers for the num_dataset_builder_threads, instead of passing num_dataset_builder_threads in as a separate config parameter?\r\n> \r\n> I could imagine a case where I set num_dataset_builder_threads to a very large number (64 or 128) but wouldn't want that many workers during the typical data loading. That would allow many workloads to specify a high num_dataset_builder_threads without needing to explicitly use the data_cache_generation_only offline step.\r\n> \r\n> Instead I would suggest num_dataset_builder_threads as an explicit new parameter in megatron_gpt_config.yaml, and default it to 1 to mirror https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/datasets/blended_megatron_dataset_config.py#L48\r\n\r\nthank you for the review, fixed."
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} | # What does this PR do ?
1. Adds "offline" data cache generation support. The training script will be stopped once data cache is generated.
2. Enable mcore's `num_dataset_builder_threads` param usage.
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Refinements in PTQ workflow (calibration + export) including:
* script & config rename for consistency with https://github.com/NVIDIA/NeMo/pull/9326
* `model_type` as optional parameter for "qnemo" export
* other minor updates
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# Changelog
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```python
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Makes `Dockerfile` consistent with [`Dockerfile.ci`](https://github.com/NVIDIA/NeMo/blob/main/Dockerfile.ci) and fixes build errors:
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Build log: https://gitlab-master.nvidia.com/vbataev/nemo_containers/-/jobs/101973040
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# Changelog
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# Usage
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If you haven't finished some of the above items you can still open "Draft" PR.
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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
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```
# GitHub Actions CI
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"Tested that it works. The output of the old code and the new code are exactlly the same. The old code for tp2pp2 gpt3 43b, takes 15 seconds for generating 100 tokens . The new code takes 8.5 seconds all same settings. The text outputs are exactly the same: \r\nOLD CODE :\r\nTOTAL TIME TAKEN 14.98s\r\n***************************\r\n[{'sentences': [\"Q: How are you?\\n\\nA: I'm doing well. How are you?\", 'Q: How big is the universe?\\n\\nA: The observable universe is 93 billion light years in diameter.']\r\n['Q: Explain the concept of linear algebra to a five year old?\\n\\nA: Linear algebra is a way to solve problems by using numbers and shapes. It helps us understand how things move and change, like how a ball rolls down a hill or how a car turns a corner. We can use linear algebra to make things move in the right direction, without having to try lots of different ways first.', 'Q: Write a poem about the sun and moon.\\n\\nThe Sun and Moon']\r\n\r\nNEW CODE : \r\nTOTAL TIME TAKEN 8.68s\r\n------------- RESULT FOR PROMPT 0 ---------------\r\n{'id': '0', 'input_prompt': 'Q: How are you?', 'generated_text': \"\\n\\nA: I'm doing well. How are you?\",\r\n\r\n------------- RESULT FOR PROMPT 1 ---------------\r\n{'id': '1', 'input_prompt': 'Q: How big is the universe?', 'generated_text': '\\n\\nA: The observable universe is 93 billion light years in diameter.',\r\n\r\n------------- RESULT FOR PROMPT 2 ---------------\r\n{'id': '2', 'input_prompt': 'Q: Explain the concept of linear algebra to a five year old?', 'generated_text': '\\n\\nA: Linear algebra is a way to solve problems by using numbers and shapes. It helps us understand how things move and change, like how a ball rolls down a hill or how a car turns a corner. We can use linear algebra to make things move in the right direction, without having to try lots of different ways first.', \r\n\r\n------------- RESULT FOR PROMPT 3 ---------------\r\n{'id': '3', 'input_prompt': 'Q: Write a poem about the sun and moon', 'generated_text': '.\\n\\nThe Sun and Moon',"
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} | # What does this PR do ?
Supports generating though megatron core
**Collection**: NLP
# Changelog
- Added deprecation notice to old generate.
- Added support for generation using mcore.
# 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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- [ ] 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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"can you please also add docs under docs checkpoint part?",
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"I have the same issue. Have you solved it? How can we avoid it？",
"First, to triage whether it's the model or data store as the problem, run with a subset of data, maybe 50 hours of so. What is the max duration of the data ? Reduce it to at most 40 seconds, preferably 30 sec. We have some tools to segment data automatically. \r\n\r\nNext, nccl timeout is hard to debug because NeMo code mostly uses pytorch, we don't do much at nccl level so it can be due to many different reasons. See if model fine-tuning on single gpu with small bs is working first then try two gpus. \r\n\r\nLR and optimizer State is preserved in the ckpt files saved by Lightning during training. If you use exp manager, resuming a job is quite easy, see the docs for exp manager and tutorials showcasing training with it (just run the same script again with same output dir if you have set the two resume flags in exp manager). \r\n\r\nWe don't have much information about hardware effects on certain operation in our team, we rely on pytorch and pytorch lightning to provide stable training engine ",
"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.",
"It might be a memory leak, it happened sometime when the system RAM is\r\nfull.\r\n\r\nOn Sat, Aug 10, 2024 at 9:56 PM github-actions[bot] <\r\n***@***.***> wrote:\r\n\r\n> This issue is stale because it has been open for 30 days with no activity.\r\n> Remove stale label or comment or this will be closed in 7 days.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/NVIDIA/NeMo/issues/9563#issuecomment-2282347886>, or\r\n> unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ACERJIKBOGY46SDYCTO5Z3TZQ3ADTAVCNFSM6AAAAABKCCCZV2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEOBSGM2DOOBYGY>\r\n> .\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: ***@***.***>\r\n>\r\n",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-06-28T15:02:00 | 2024-09-18T01:55:49 | 2024-09-18T01:55:48 | NONE | {
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} | null | null | null | Hi,
We're trying to finetune the stt_en fastconformer_ctc large model on around 20k hours of data on 2 h100s. We're using 128 batch size and 8 num_workers and we trained the tokenizer with 1024 vocab_size. The training is taking very long, more than 30 hours per epoch and after around 70-80% of the first epoch it's crashing with the following error:
```
Epoch 0: 78%|█████████████████████████████████████████████████████████████▎ | 64809/83556 [25:06:58<7:15:54, 1.40s/it, v_num=18][rank1]:[E ProcessGroupNCCL.cpp:563] [Rank
1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1363596, OpType=BROADCAST, NumelIn=5158880, NumelOut=5158880, Timeout(ms)=1800000) ran for 1800047 milliseconds before timin
g out.
[rank1]:[E ProcessGroupNCCL.cpp:1316] [PG 0 Rank 1] Heartbeat monitor timed out! Process will be terminated after dumping debug info. workMetaList_.size()=2
[rank1]:[E ProcessGroupNCCL.cpp:1153] [PG 0 Rank 1] ProcessGroupNCCL preparing to dump debug info.
[rank1]:[F ProcessGroupNCCL.cpp:1169] [PG 0 Rank 1] [PG 0 Rank 1] ProcessGroupNCCL's watchdog got stuck for 600 seconds without making progress in monitoring enqueued collectives. This typi
cally indicates a NCCL/CUDA API hang blocking the watchdog, and could be triggered by another thread holding the GIL inside a CUDA api, or other deadlock-prone behaviors.If you suspect the
watchdog is not actually stuck and a longer timeout would help, you can either increase the timeout (TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC) to a larger value or disable the heartbeat monitor (TO
RCH_NCCL_ENABLE_MONITORING=0).If either of aforementioned helps, feel free to file an issue to PyTorch about the short timeout or false positive abort; otherwise, please attempt to debug th
e hang. workMetaList_.size() = 2
[rank0]:[E ProcessGroupNCCL.cpp:563] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1363598, OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 18
00095 milliseconds before timing out.
[rank0]:[E ProcessGroupNCCL.cpp:1537] [PG 0 Rank 0] Timeout at NCCL work: 1363598, last enqueued NCCL work: 1363598, last completed NCCL work: 1363597.
[rank0]:[E ProcessGroupNCCL.cpp:577] [Rank 0] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted
/incomplete data.
[rank0]:[E ProcessGroupNCCL.cpp:583] [Rank 0] To avoid data inconsistency, we are taking the entire process down.
[rank0]:[E ProcessGroupNCCL.cpp:1414] [PG 0 Rank 0] Process group watchdog thread terminated with exception: [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1363598,
OpType=ALLREDUCE, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800095 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:565 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f47fd81e897 in /anaconda/envs/nemo_hindi/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x1d2 (0x7f47feaf7c62 in /anaconda/envs/nemo_hindi/lib/python
3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1a0 (0x7f47feafca80 in /anaconda/envs/nemo_hindi/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f47feafddcc in /anaconda/envs/nemo_hindi/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0xdbbf4 (0x7f484a5a6bf4 in /anaconda/envs/nemo_hindi/bin/../lib/libstdc++.so.6)
frame #5: <unknown function> + 0x8609 (0x7f484ba9b609 in /lib/x86_64-linux-gnu/libpthread.so.0)
frame #6: clone + 0x43 (0x7f484b866353 in /lib/x86_64-linux-gnu/libc.so.6)
Aborted (core dumped)
```
How do we avoid this issue? Should we consider reducing the finetuning data size? If we save intermediate checkpoints, is there a way to also save the lr scheduler state to effectively resume the training if it crashes? Any guidance regarding this would be of great help.
Also, unrelated to the issue, we noticed we didn't get much boost by using h100 instead of a100, and sometimes using bf16-mixed was slower on h100 than using fp16 on h100, on the other hand, bf16-mixed is almost always faster on a100 than fp16, is this expected?
Thank you! | {
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} | # What does this PR do ?
Fixes
- optimizer state restore
- model state dict loading
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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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"@fayejf maybe you have an idea? Thanks in advance",
"Updating from Cuda 12.2 to Cuda 12.3 seems to have solved the issue.",
"Closing"
] | 2024-06-28T10:36:48 | 2024-07-01T08:56:01 | 2024-07-01T08:56:01 | NONE | {
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} | null | null | null | **Describe the bug**
I'm trying to fine-tune the Ambernet model on Cantonese and Chinese.
But I'm getting this Segmentation fault;
```
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]
Segmentation fault
```
[full_output.txt](https://github.com/user-attachments/files/16029811/full_output.txt)
**Steps/Code to reproduce bug**
The config I'm using; (converted to json to be able to upload it here)
[ambernet_config.json](https://github.com/user-attachments/files/16029835/ambernet_config.json)
My manifests look like this;
```
{"audio_filepath": "/home/<masked>/finetune-lid/data/cv-corpus-18.0-2024-06-14/yue/clips/common_voice_yue_32329425.mp3", "duration": 3.78, "label": "yue"}
{"audio_filepath": "/home/<masked>/finetune-lid/data/cv-corpus-18.0-2024-06-14/yue/clips/common_voice_yue_38973572.mp3", "duration": 3.276, "label": "yue"}
{"audio_filepath": "/home/<masked>/finetune-lid/data/cv-corpus-18.0-2024-06-14/yue/clips/common_voice_yue_38956269.mp3", "duration": 3.168, "label": "yue"}
{"audio_filepath": "/home/<masked>/finetune-lid/data/cv-corpus-18.0-2024-06-14/zh-CN/clips/common_voice_zh-CN_32666586.mp3", "duration": 2.88, "label": "zh-CN"}
{"audio_filepath": "/home/<masked>/finetune-lid/data/cv-corpus-18.0-2024-06-14/zh-CN/clips/common_voice_zh-CN_33111477.mp3", "duration": 5.796, "label": "zh-CN"}
```
I run this script;
```
!python speech_to_label.py --config-path="configs/" --config-name="ambernet_config" \
model.train_ds.manifest_filepath="manifests/train_manifest.json" \
model.validation_ds.manifest_filepath="manifests/val_manifest.json" \
model.test_ds.manifest_filepath="manifests/test_manifest.json" \
model.decoder.num_classes=2 \
trainer.devices=1 \
trainer.max_epochs=40 \
trainer.accelerator="gpu" \
exp_manager.create_wandb_logger=False \
exp_manager.wandb_logger_kwargs.name="titanet" \
exp_manager.wandb_logger_kwargs.project="langid" \
+exp_manager.checkpoint_callback_params.monitor="val_acc_macro" \
+exp_manager.checkpoint_callback_params.mode="max" \
+trainer.precision=16
```
**Environment overview**
- Environment location: GCE VM
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
```
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
pip install nemo_toolkit['asr']
```
**Environment details**
- OS version
```
PRETTY_NAME="Debian GNU/Linux 11 (bullseye)"
NAME="Debian GNU/Linux"
VERSION_ID="11"
VERSION="11 (bullseye)"
VERSION_CODENAME=bullseye
ID=debian
HOME_URL="https://www.debian.org/"
SUPPORT_URL="https://www.debian.org/support"
BUG_REPORT_URL="https://bugs.debian.org/"
```
- PyTorch version: 2.3.1+cu121
- Nemo Version: 2.0.0rc0 (also on 1.23.0)
- Python version: 3.10.14
**Additional context**
Add any other context about the problem here.
Example: GPU model
```
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Tesla T4 On | 00000000:00:04.0 Off | 0 |
| N/A 36C P8 9W / 70W | 1MiB / 15360MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Jul_11_02:20:44_PDT_2023
Cuda compilation tools, release 12.2, V12.2.128
Build cuda_12.2.r12.2/compiler.33053471_0
```
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"CI passed (test that failed flaky & is now fixed): https://github.com/NVIDIA/NeMo/actions/runs/9702936067"
] | 2024-06-27T18:13:20 | 2024-06-28T17:07:55 | 2024-06-28T17:07:55 | COLLABORATOR | {
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Update tps and pps and depricate n_gpus param in the scripts. | {
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# Changelog
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```python
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
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# GitHub Actions CI
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- [ ] Did you write any new necessary tests?
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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If you haven't finished some of the above items you can still open "Draft" PR.
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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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"yes, it is due to additional `<blank>` token that gets added to the provided tokenizer. ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-06-27T10:51:35 | 2024-09-09T01:57:09 | 2024-09-09T01:57:08 | NONE | {
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} | null | null | null | I trained a Citrinet and intended to use the CTC Decoder to retrieve a corrected output.
Using the CTC Beam Search Decoder of DeepSpeech I get the following error:
[ctc_beam_search_decoder.cpp:279] FATAL: "(alphabet.GetSize()+1) == (class_dim)" check failed. Number of output classes in acoustic model does not match number of labels in the alphabet file. Alphabet file must be the same one that was used to train the acoustic model.
I have controlled the alphabet and it has the size of 1023, even though I built it with 1024 characters. I built the tokenizer and alphabet with the nemo script setting the vocab size to 1024, spe and unigram. The output shape of the model is 1025. I believe the mismatch is 1 character. I thought of blank or unk token, but I aint sure if that is the cause of the error. Any idea why this happens or how to solve it? I appreciate your help!!
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] | 2024-06-27T02:34:08 | 2024-10-18T01:59:14 | 2024-10-18T01:59:13 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds a `MegatronNevaDeployable` class implementing `ITritonDeployable` enabling deployment of multimodal models like LLaVA/Neva to Triton inference server directly from .nemo checkpoints.
Currently this has only been tested with a LLaVA 1.5 model created with the instructions [here](https://docs.nvidia.com/nemo-framework/user-guide/latest/multimodalmodels/multimodallanguagemodel/neva/dataprep.html).
**Collection**: [Note which collection this PR will affect]
deploy/
# Changelog
- Add `MegatronNevaDeployable` class
- Add `NemoQueryMultimodalPyTorch` class
- Add `test_neva_deploy.py` script for testing deployment
- Add `query_inframework.py` script for easy use of `NemoQueryMultimodalPyTorch`
# Usage
See `test_neva_deploy.py` for full example on how to use `MegatronNevaDeployable`.
```python
megatron_deployable = MegatronNevaDeployable(args.nemo_checkpoint, args.num_gpus)
...
nm = DeployPyTriton(
model=megatron_deployable,
triton_model_name=model_name,
triton_model_version=1,
max_batch_size=8,
port=8000,
address="0.0.0.0",
streaming=False,
)
nm.deploy()
```
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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} | # What does this PR do ?
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"answered here: https://github.com/NVIDIA/NeMo/issues/9695 "
] | 2024-06-26T21:11:40 | 2024-08-01T13:19:22 | 2024-08-01T13:19:22 | NONE | {
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} | null | null | null | **Describe the bug**
When I tried to generate timestamp with model: nvidia/parakeet-tdt-1.1b, I got following error,
```
ValueError: `char_offsets`: [{'char': [tensor(524)], 'start_offset': 2, 'end_offset': 3}, {'char': [tensor(106)], 'start_offset': 5, 'end_offset': 6}, {'char': [tensor(40)], 'start_offset': 6, 'end_offset': 7}, {'char': [tensor(556)], 'start_offset': 7, 'end_offset': 8}, {'char': [tensor(988)], 'start_offset': 9, 'end_offset': 10}, {'char': [tensor(8)], 'start_offset': 10, 'end_offset': 11}, {'char': [tensor(88)], 'start_offset': 11, 'end_offset': 12}, {'char': [tensor(1002)], 'start_offset': 12, 'end_offset': 13}] and `processed_tokens`: [524, 106, 40, 556, 988, 8, 88, 1002] have to be of the same length, but are: `len(offsets)`: 8 and `len(processed_tokens)`: 8
```
call stack,
```
----> 1 hypotheses = asr_model.transcribe(['test.wav'], return_hypotheses=True)
requirements/lib/python3.10/site-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
116
117 return decorate_context
requirements/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py in transcribe(self, audio, batch_size, return_hypotheses, partial_hypothesis, num_workers, channel_selector, augmentor, verbose, override_config)
136 )
137
--> 138 return super().transcribe(
139 audio=audio,
140 batch_size=batch_size,
requirements/lib/python3.10/site-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
116
117 return decorate_context
requirements/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py in transcribe(self, audio, batch_size, return_hypotheses, partial_hypothesis, num_workers, channel_selector, augmentor, verbose, override_config)
277 * An optional list of beam search transcript texts / Hypothesis / NBestHypothesis.
278 """
--> 279 return super().transcribe(
280 audio=audio,
281 batch_size=batch_size,
requirements/lib/python3.10/site-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
116
117 return decorate_context
requirements/lib/python3.10/site-packages/nemo/collections/asr/parts/mixins/transcription.py in transcribe(self, audio, batch_size, return_hypotheses, num_workers, channel_selector, augmentor, verbose, override_config, **config_kwargs)
275 generator = self.transcribe_generator(audio, override_config=transcribe_cfg)
276
--> 277 for processed_outputs in generator:
278 # Store results
279 if isinstance(processed_outputs, list):
requirements/lib/python3.10/site-packages/nemo/collections/asr/parts/mixins/transcription.py in transcribe_generator(self, audio, override_config)
387 # Run forward pass
388 model_outputs = self._transcribe_forward(test_batch, transcribe_cfg)
--> 389 processed_outputs = self._transcribe_output_processing(model_outputs, transcribe_cfg)
390
391 # clear up memory
requirements/lib/python3.10/site-packages/nemo/collections/asr/models/hybrid_rnnt_ctc_models.py in _transcribe_output_processing(self, outputs, trcfg)
177 ) -> Tuple[List['Hypothesis'], List['Hypothesis']]:
178 if self.cur_decoder == "rnnt":
--> 179 return super()._transcribe_output_processing(outputs, trcfg)
180
181 # CTC Path
requirements/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py in _transcribe_output_processing(self, outputs, trcfg)
906 encoded_len = outputs.pop('encoded_len')
907
--> 908 best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor(
909 encoded,
910 encoded_len,
requirements/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py in rnnt_decoder_predictions_tensor(self, encoder_output, encoded_lengths, return_hypotheses, partial_hypotheses)
508 timestamp_type = self.cfg.get('rnnt_timestamp_type', 'all')
509 for hyp_idx in range(len(hypotheses)):
--> 510 hypotheses[hyp_idx] = self.compute_rnnt_timestamps(hypotheses[hyp_idx], timestamp_type)
511
512 if return_hypotheses:
requirements/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py in compute_rnnt_timestamps(self, hypothesis, timestamp_type)
751
752 if num_flattened_tokens != len(hypothesis.text):
--> 753 raise ValueError(
754 f"`char_offsets`: {char_offsets} and `processed_tokens`: {hypothesis.text}"
755 " have to be of the same length, but are: "
```
**Steps/Code to reproduce bug**
```
asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-1.1b")
decoding_cfg = asr_model.cfg.decoding
with open_dict(decoding_cfg):
decoding_cfg.preserve_alignments = True
decoding_cfg.compute_timestamps = True
asr_model.change_decoding_strategy(decoding_cfg)
asr_model.change_decoding_strategy(decoding_cfg)
hypothesis = asr_model.transcribe([audio], return_hypotheses=True)[0][0]
timestamp_dict = hypothesis.timestep
word_timestamps = timestamp_dict['word']
print(word_timestamps)
```
**Expected behavior**
It should output word timestamps instead of exception.
**Environment overview (please complete the following information)**
- Environment location: run in local redhat 7.9 machine.
- Method of NeMo install: pip install nemo_toolkit['asr']==2.0.0rc0
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: redhat 7.9
- PyTorch version: 2.0.1+cu117
- Python version: 3.10.13
**Additional context**
This is a replica of the issue: https://github.com/NVIDIA/NeMo/issues/8451 which got closed due to inactivity.
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Fixes nemo export test. | {
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} | # What does this PR do ?
These tests were failing on main when torchaudio was present in the container. The changes are caused by the dynamic bucketing sampler optimization in the new lhotse version - the sampler simply draws different buckets in the test setup. I removed the conditional trigger on torchaudio since newer lhotse versions don't require it for the functionalities under test anymore.
Edit: also fixing a new regression in RIR test (the random seed was not fixed).
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} | # What does this PR do ?
- Fix OOM issues that may happen for large models when both Python and C++ runtime are tested at once. The latter test is only launched when explicitly required by specifying `--test_cpp_runtime True`.
- Also, the MR enables C++ runtime test by passing `use_python_runtime=False` to `TensorRTLLM` exporter.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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**PR Type**:
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- [x] Bugfix
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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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