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**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.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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The following annotations have been identified:\n\n```\n************* Module nemo.collections.multimodal.data.energon.base\nnemo/collections/multimodal/data/energon/base.py:83:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:84:0: C0301: Line too long (204/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:116:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:171:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:179:0: C0301: Line too long (153/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:209:0: C0301: Line too long (144/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:279:0: C0301: Line too long (150/119) (line-too-long)\nnemo/collections/multimodal/data/energon/base.py:291:0: C0301: Line too long (127/119) (line-too-long)\n************* Module nemo.collections.vlm.neva.data.lazy\nnemo/collections/vlm/neva/data/lazy.py:564:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/data/lazy.py:67:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:72:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:115:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:120:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:134:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:161:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:236:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:246:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/data/lazy.py:418:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:491:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/data/lazy.py:557:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:568:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:571:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/lazy.py:574:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.vlm.neva.data.mock\nnemo/collections/vlm/neva/data/mock.py:29:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/data/mock.py:74:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:85:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:90:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:95:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.vlm.neva.model.base\nnemo/collections/vlm/neva/model/base.py:473:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:476:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:484:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:493:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:93:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:134:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:151:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:172:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:227:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:242:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:261:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:282:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:313:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:352:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:365:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:854:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:871:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:875:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:899:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:902:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:905:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:909:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:915:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:922:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.46/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.mllama.data.lazy\nnemo/collections/vlm/mllama/data/lazy.py:249:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/mllama/data/lazy.py:120:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/mllama/data/lazy.py:173:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/mllama/data/lazy.py:242:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/mllama/data/lazy.py:257:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/mllama/data/lazy.py:260:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/mllama/data/lazy.py:263:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.82/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @yaoyu-33 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12268212528) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-25T18:13:40 | 2024-12-11T23:21:33 | 2024-12-11T23:21:32 | COLLABORATOR | {
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} | # What does this PR do ?
Add vlm nemo run scripts
**Collection**: [multimodal]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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 know the reason, it should be \n\n> #diarizer.speaker_embeddings.parameters.window_length_in_sec=[1.5,1.0,0.5] \\\n #diarizer.speaker_embeddings.parameters.shift_length_in_sec=[0.75,0.5,0.25] \\\n #diarizer.speaker_embeddings.parameters.multiscale_weights=[0.33,0.33,0.33] "
] | 2024-11-25T09:00:25 | 2024-11-25T09:45:37 | 2024-11-25T09:45:37 | NONE | {
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} | null | null | null | **Describe the bug**
I am following the tutorial of asr+diariation under examples/speaker_tasks/diarization.
I want to use diar_msdd_telephonic model so I have to add more parameters like this:
```
python clustering_diarizer/offline_diar_with_asr_infer.py \
diarizer.manifest_filepath='./manifest.json' \
diarizer.out_dir='demo_asr_output' \
diarizer.asr.model_path='stt_en_conformer_ctc_large' \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.speaker_embeddings.model_path='diar_msdd_telephonic' \
parameters.window_length_in_sec=[1.5,1.0,0.5] \
parameters.shift_length_in_sec=[0.75,0.5,0.25] \
parameters.multiscale_weights=[0.33,0.33,0.33]
```
But this setting will get error message caught, somthing like the follows:
> raise ConfigCompositionException(
hydra.errors.ConfigCompositionException: Could not override 'parameters.window_length_in_sec'.
To append to your config use +parameters.window_length_in_sec=[1.5,1.0,0.5]
I do not know if I understood it correctly, so I used "+parameters.window_length_in_sec=[1.5,1.0,0.5] \ " instead.
The script can be executed successfully and proper results can be returned. But I am not sure if I have
set the config correctly. I did not find anywhere in the verbose logging message my config had taken effect.
Here is logging output.
> speaker_embeddings:
model_path: diar_msdd_telephonic
parameters:
window_length_in_sec:
- 3.0
- 2.5
- 2.0
- 1.5
- 1.0
- 0.5
shift_length_in_sec:
- 1.5
- 1.25
- 1.0
- 0.75
- 0.5
- 0.25
multiscale_weights:
- 1
- 1
- 1
- 1
- 1
- 1
save_embeddings: false
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.lightning.pytorch.strategies.fsdp_strategy\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:86:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:92:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:117:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:143:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:152:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:162:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:170:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:178:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:199:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.83/10\n```\n\n---\n\nThank you for improving NeMo's documentation!"
] | 2024-11-25T07:27:54 | 2024-11-27T02:37:37 | 2024-11-27T02:37:26 | COLLABORATOR | {
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} | # What does this PR do ?
Example CI failure: https://github.com/NVIDIA/NeMo/actions/runs/11828217381/job/32975003718#step:5:243
The current behavior is that when `ckpt_save_optimizer=True`, only the sharded optimizer states are included in the checkpoint dict. However when `ckpt_save_optimizer=False`, the unsharded optimizer states are still included in the checkpoint dict to be saved. This causes OOMs later on by default because:
1. Unsharded optimizer states are included in the common state dict here: https://github.com/NVIDIA/Megatron-LM/blob/e21ce31db7a1789b2346da0e0906231094fb8cf7/megatron/core/dist_checkpointing/state_dict_transformation.py#L56
2. In the subsequent check to validate the common state dict, an OOM occurs when all gathering optimizer states
This PR fixes the bug such that when `ckpt_save_optimizer=False`, the unsharded optimizer states will not be included in the checkpoint dict at all.
Test command that no longer errors out:
```
TORCHDYNAMO_DISABLE=1 python tests/lightning/test_ddp_parity_checker.py --vocab-path=/home/TestData/nlp/megatron_gpt/data/gpt/vocab.json --merges-path=/home/TestData/nlp/megatron_gpt/data/gpt/merges.txt --data-path=/home/TestData/nlp/megatron_gpt/data/gpt/simple_wiki_gpt_preproc_text_document
```
**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.
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)
- [ ] 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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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.neva.data.mock\nnemo/collections/vlm/neva/data/mock.py:29:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/data/mock.py:79:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:90:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:95:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/data/mock.py:100:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.47/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.neva.data.llava_next_energon\nnemo/collections/vlm/neva/data/llava_next_energon.py:56:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/data/llava_next_energon.py:57:0: C0301: Line too long (130/119) (line-too-long)\nnemo/collections/vlm/neva/data/llava_next_energon.py:65:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/data/llava_next_energon.py:135:0: C0115: Missing class docstring (missing-class-docstring)\n\n-----------------------------------\nYour code has been rated at 9.42/10\n```\n\n---\n\nThank you for improving NeMo's documentation!"
] | 2024-11-25T03:26:44 | 2024-11-25T23:55:16 | 2024-11-25T23:55:16 | COLLABORATOR | {
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} | # What does this PR do ?
LLava next data module changes for energon and mock
**Collection**: [Note which collection this PR will affect]
# Changelog
- Modified NeVA Mock data module to support llava-next
- Modified llava next sample/task encoder to include attention mask and image sizes
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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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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.neva.model.utils\nnemo/collections/vlm/neva/model/utils.py:86:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:87:0: C0301: Line too long (188/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:142:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:157:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:167:0: C0301: Line too long (129/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:255:0: C0301: Line too long (127/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:335:0: C0301: Line too long (124/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:343:0: C0301: Line too long (203/119) (line-too-long)\nnemo/collections/vlm/neva/model/utils.py:350:0: C0301: Line too long (122/119) (line-too-long)\n\n-----------------------------------\nYour code has been rated at 9.42/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.neva.model.base\nnemo/collections/vlm/neva/model/base.py:475:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:478:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:486:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:495:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:99:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:133:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:151:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:172:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:227:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:242:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:261:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:282:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:314:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:352:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:365:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:647:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:664:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:668:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:694:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:697:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:700:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:704:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:710:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:717:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:28:0: W0611: Unused TEDotProductAttention imported from megatron.core.extensions.transformer_engine (unused-import)\n************* Module nemo.collections.vlm.neva.model.llava\nnemo/collections/vlm/neva/model/llava.py:133:0: C0301: Line too long (137/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:134:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:135:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:136:0: C0301: Line too long (144/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:176:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:177:0: C0301: Line too long (161/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:178:0: C0301: Line too long (157/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:179:0: C0301: Line too long (150/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:180:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:181:0: C0301: Line too long (158/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:182:0: C0301: Line too long (154/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:183:0: C0301: Line too long (135/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:184:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:185:0: C0301: Line too long (135/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:186:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:41:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:47:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:60:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:72:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:76:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:80:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:91:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:108:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:130:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:207:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:213:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:253:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:364:0: C0115: Missing class docstring (missing-class-docstring)\n\n-----------------------------------\nYour code has been rated at 8.89/10\n```\n\n---\n\nThank you for improving NeMo's documentation!"
] | 2024-11-25T02:26:11 | 2024-11-25T19:02:53 | 2024-11-25T19:02:53 | COLLABORATOR | {
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Extended neva model to support llava_next
**Collection**:
VLM
# Changelog
- Utility functions for image tiling
- LLaVA Next model class
- LLaVA Next HF checkpoint importer class
-NeVA fwd function changes to handle image tiling
# 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".
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**Pre checks**:
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**PR Type**:
- [Y] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n\n------------------------------------\nYour code has been rated at 10.00/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @huvunvidia 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12056793147) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
Update recipes for 3B and 11B T5 model, to use `run.Config`, so the objects can be serialized when running with nemo_run.
**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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**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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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"I was able to solve this by pulling the Nemo code from the 2.0.0 tag, and using that. I believe a mismatch in source code and the nemo library caused this. Probably because of the recent commit that changed from 'pytorch_lightning' -> 'lightning.pytorch'."
] | 2024-11-23T00:54:48 | 2024-11-27T00:08:01 | 2024-11-27T00:07:59 | NONE | {
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} | null | null | null | **Describe the bug**
I'm getting this error when trying to fine-tune the Parakeet model using the `/examples/asr/speech_to_text_finetune.py` file.
```python
Exception has occurred: TypeError
`model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `EncDecCTCModelBPE`
File "/Users/gerrycervantes/code/NeMo/examples/asr/speech_to_text_finetune.py", line 221, in main
trainer.fit(asr_model)
File "/Users/gerrycervantes/code/NeMo/examples/asr/speech_to_text_finetune.py", line 225, in <module>
main() # noqa pylint: disable=no-value-for-parameter
^^^^^^
TypeError: `model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `EncDecCTCModelBPE`
```
**Steps/Code to reproduce bug**
**Command**
This is the command I use for fine-tuning
```bash
python examples/asr/speech_to_text_finetune.py --config-path=conf/asr_finetune --config-name=asr_custom
```
**Data**
So I've downloaded the Librispeech Datasets and processed it using `/scripts/dataset_processing/get_librispeech_data.py`
**Model**
I've also downloaded the models from HuggingFace. https://huggingface.co/nvidia/parakeet-ctc-0.6b
**Config**
I've done a few changes to the configuration file so that it fits my environment. I'm on a Mac computer, so I've tried running with CPU and MPS, but get the error with both ways.
This is the corresponding config YAML file, resembling the example config file.
```YAML
name: "ASR_Custom_Finetuning"
# use `init_from_nemo_model` or `init_from_pretrained_model` to initialize the model
# We do not currently support `init_from_ptl_ckpt` to create a single script for all types of models.
init_from_nemo_model: models/parakeet-ctc-1.1b.nemo # path to nemo model
# init_from_pretrained_model: nvidia/parakeet-ctc-0.6b # path to nemo model
model:
sample_rate: 16000
train_ds:
manifest_filepath: data/TRAIN_CLEAN_100.json
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 20
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "fully_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: data/dev_clean.json
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
char_labels: # use for char based models
update_labels: false
labels: null # example list config: \[' ', 'a', 'b', 'c'\]
tokenizer: # use for spe/bpe based tokenizer models
update_tokenizer: false
dir: null # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
optim:
name: adamw
lr: 1e-4
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: CosineAnnealing
# scheduler config override
warmup_steps: 5000
warmup_ratio: null
min_lr: 5e-6
trainer:
devices: 4 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 50
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: cpu
strategy:
_target_: lightning.pytorch.strategies.SingleDeviceStrategy
# _target_: lightning.pytorch.strategies.DDPStrategy
gradient_as_bucket_view: true
accumulate_grad_batches: 1
gradient_clip_val: 0.0
precision: 32 # 16, 32, or bf16
log_every_n_steps: 10 # Interval of logging.
enable_progress_bar: True
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files along with PTL checkpoints
resume_if_exists: false
resume_ignore_no_checkpoint: false
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: From source (git commit 34f740822a11802c2f0cea1c8039163b2b8bff92)
```bash
conda init --all
conda create --name nemo python==3.11.1
conda activate nemo
conda install pytorch::pytorch torchvision torchaudio -c pytorch
brew install mecab
conda install -c conda-forge pynini
pip install cython packaging
cd ~/code
git clone https://github.com/NVIDIA/NeMo.git
cd NeMo
pip install 'nemo_toolkit[asr]'
```
**Environment details**
- macOS: Sonoma 14.6.1
- PyTorch: 2.5.1
- Python version: 3.11.8
- lightning 2.4.0 pyhd8ed1ab_0 conda-forge
- lightning-utilities 0.11.9 pyhff2d567_0 conda-forge | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module hf\nexamples/llm/peft/hf.py:25:0: C0301: Line too long (184/119) (line-too-long)\nexamples/llm/peft/hf.py:76:0: C0301: Line too long (162/119) (line-too-long)\nexamples/llm/peft/hf.py:21:0: C0116: Missing function or method docstring (missing-function-docstring)\nexamples/llm/sft/hf.py:74:0: C0301: Line too long (162/119) (line-too-long)\nexamples/llm/sft/hf.py:26:0: C0115: Missing class docstring (missing-class-docstring)\nexamples/llm/sft/hf.py:39:0: C0116: Missing function or method docstring (missing-function-docstring)\nexamples/llm/sft/hf.py:23:0: W0611: Unused ModelCallback imported from nemo.lightning.pytorch.callbacks (unused-import)\n************* Module nemo.collections.llm.gpt.data.api\nnemo/collections/llm/gpt/data/api.py:26:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/data/api.py:32:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/data/api.py:38:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/data/api.py:44:0: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.llm.gpt.data.hf_dataset\nnemo/collections/llm/gpt/data/hf_dataset.py:21:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/llm/gpt/data/hf_dataset.py:51:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/data/hf_dataset.py:77:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/data/hf_dataset.py:87:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:26:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:34:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:58:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:69:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:72:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:89:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:101:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:115:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:126:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.llm.recipes.hf_auto_model_for_causal_lm\nnemo/collections/llm/recipes/hf_auto_model_for_causal_lm.py:103:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/llm/recipes/hf_auto_model_for_causal_lm.py:154:0: C0301: Line too long (126/119) (line-too-long)\nnemo/collections/llm/recipes/hf_auto_model_for_causal_lm.py:193:0: C0301: Line too long (121/119) (line-too-long)\n\n-----------------------------------\nYour code has been rated at 9.61/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @akoumpa 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12030129045) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"@oyilmaz-nvidia FYI"
] | 2024-11-22T21:39:56 | 2024-11-26T15:54:04 | 2024-11-26T14:28:29 | MEMBER | {
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} | # What does this PR do ?
Hf -> HF
Since io.IOMixin, fn.FNMixin exist (three capital letter in a row).
**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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The following annotations have been identified:\n\n```\n************* Module nemo.collections.vlm.neva.model.base\nnemo/collections/vlm/neva/model/base.py:473:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:476:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:484:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:493:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/vlm/neva/model/base.py:93:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:134:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:151:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:172:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:227:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:242:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:261:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:282:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:313:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:352:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:365:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:853:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/base.py:870:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:874:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:898:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:901:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:904:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:908:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:914:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/base.py:921:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module 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(line-too-long)\nnemo/collections/vlm/neva/model/llava.py:156:0: C0301: Line too long (154/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:157:0: C0301: Line too long (135/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:158:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:159:0: C0301: Line too long (135/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:160:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/vlm/neva/model/llava.py:41:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:47:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:60:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:72:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:84:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/vlm/neva/model/llava.py:106:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:181:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:187:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/vlm/neva/model/llava.py:227:0: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.07/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @yaoyu-33 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12243587376) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-22T19:12:12 | 2024-12-10T19:01:52 | 2024-12-10T19:01:51 | COLLABORATOR | {
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} | # What does this PR do ?
Add VLM running scripts and github action CI tests.
**Collection**: [multimodal]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update wfst_text_normalization doc to reflect coverage of Hindi TN/ITN
**Collection**: N/A
# 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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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
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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"
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.asr.models.ctc_models\nnemo/collections/asr/models/ctc_models.py:196:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:197:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:813:0: C0301: Line too long (133/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:819:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:820:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:826:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:827:0: C0301: Line too long (141/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:833:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:834:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:840:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:841:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:847:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:848:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:854:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:855:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:861:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:862:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:868:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:869:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:875:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:876:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:882:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:883:0: C0301: Line too long (141/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:889:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:890:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:897:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:898:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:904:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:905:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:545:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:602:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:620:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:662:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:680:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:916:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.asr.models.rnnt_models\nnemo/collections/asr/models/rnnt_models.py:1084:0: C0301: Line too long (164/119) (line-too-long)\nnemo/collections/asr/models/rnnt_models.py:1085:0: C0301: Line too long (166/119) (line-too-long)\nnemo/collections/asr/models/rnnt_models.py:230:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:705:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:803:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:820:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:888:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:896:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1061:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1092:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.54/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @nithinraok 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11976280310) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
Adds check for timestamps when only override_cff is used.
**Collection**: ASR
# Changelog
- update timestamps check based on override_cfg value
# 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)
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | # What does this PR do ?
Rewrite the error handling strategy when model does not have tokenizer.
Previously the bare try-except block would mask out actual errors such as wrong model path.
**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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"Did you solve this? \n"
] | 2024-11-22T02:15:20 | 2024-12-30T17:09:40 | 2024-12-30T02:00:09 | NONE | {
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} | null | null | null | Hello everyone and Nvidia folks,
We are trying to finetune llama3_1_8b_instruct. (70b as well after 8b)
Docker pull Nvidia NeMo docker image 24.09 and run inside a container.
Using this notebook from Brev (now part of Nvidia) https://github.com/brevdev/notebooks/blob/main/llama31_law.ipynb
The error below happened when running the 1st cell at step 2 in the notebook for finetuning
We are running this on our local machine with 4* A10 GPUs.
Tried both 1 GPU and 4 GPUs but got the same FileNotFoundError for shard
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpxoyazmhf/model_weights/model.decoder.layers.self_attention.core_attention._extra_state/shard_0_32.pt'
Anyone knows why this happened and how to solve?
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.asr.models.ctc_models\nnemo/collections/asr/models/ctc_models.py:195:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:196:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:812:0: C0301: Line too long (133/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:818:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:819:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:825:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:826:0: C0301: Line too long (141/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:832:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:833:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:839:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:840:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:846:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:847:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:853:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:854:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:860:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:861:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:867:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:868:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:874:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:875:0: C0301: Line too long (143/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:881:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:882:0: C0301: Line too long (141/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:888:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:889:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:896:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:897:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:903:0: C0301: Line too long (139/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:904:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/asr/models/ctc_models.py:544:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:601:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:619:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:661:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:679:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/ctc_models.py:915:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.asr.models.rnnt_models\nnemo/collections/asr/models/rnnt_models.py:1084:0: C0301: Line too long (164/119) (line-too-long)\nnemo/collections/asr/models/rnnt_models.py:1085:0: C0301: Line too long (166/119) (line-too-long)\nnemo/collections/asr/models/rnnt_models.py:230:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:705:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:803:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:820:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:888:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:896:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1061:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1092:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.54/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @pablo-garay 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11965100507) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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If you haven't finished some of the above items you can still open "Draft" PR.
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"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n\n------------------------------------\nYour code has been rated at 10.00/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"This is used in exp_manager in 1.0 codepath. Can we update the import in this PR to ensure 1.0 codepath still works as intended?",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.lightning.pytorch.callbacks.preemption\nnemo/lightning/pytorch/callbacks/preemption.py:50:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/callbacks/preemption.py:55:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/callbacks/preemption.py:62:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/callbacks/preemption.py:66:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/callbacks/preemption.py:103:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/callbacks/preemption.py:112:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.utils.callbacks.preemption\nnemo/utils/callbacks/preemption.py:26:0: C0301: Line too long (157/119) (line-too-long)\nnemo/utils/callbacks/preemption.py:31:0: C0301: Line too long (160/119) (line-too-long)\nnemo/utils/callbacks/preemption.py:34:0: C0301: Line too long (137/119) (line-too-long)\nnemo/utils/callbacks/preemption.py:58:0: C0301: Line too long (122/119) (line-too-long)\nnemo/utils/callbacks/preemption.py:68:0: C0301: Line too long (122/119) (line-too-long)\nnemo/utils/callbacks/preemption.py:46:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/callbacks/preemption.py:85:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/callbacks/preemption.py:89:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/callbacks/preemption.py:103:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.utils.exp_manager\nnemo/utils/exp_manager.py:354:0: C0301: Line too long (120/119) (line-too-long)\nnemo/utils/exp_manager.py:379:0: C0301: Line too long (124/119) (line-too-long)\nnemo/utils/exp_manager.py:383:0: C0301: Line too long (122/119) (line-too-long)\nnemo/utils/exp_manager.py:388:0: C0301: Line too long (127/119) (line-too-long)\nnemo/utils/exp_manager.py:402:0: C0301: Line too long (120/119) (line-too-long)\nnemo/utils/exp_manager.py:409:0: C0301: Line too long (120/119) (line-too-long)\nnemo/utils/exp_manager.py:410:0: C0301: Line too long (126/119) (line-too-long)\nnemo/utils/exp_manager.py:412:0: C0301: Line too long (136/119) (line-too-long)\nnemo/utils/exp_manager.py:414:0: C0301: Line too long (139/119) (line-too-long)\nnemo/utils/exp_manager.py:424:0: C0301: Line too long (123/119) (line-too-long)\nnemo/utils/exp_manager.py:425:0: C0301: Line too long (128/119) (line-too-long)\nnemo/utils/exp_manager.py:426:0: C0301: Line too long (132/119) (line-too-long)\nnemo/utils/exp_manager.py:427:0: C0301: Line too long (127/119) (line-too-long)\nnemo/utils/exp_manager.py:515:0: C0301: Line too long (122/119) (line-too-long)\nnemo/utils/exp_manager.py:603:0: C0301: Line too long (208/119) (line-too-long)\nnemo/utils/exp_manager.py:604:0: C0301: Line too long (230/119) (line-too-long)\nnemo/utils/exp_manager.py:606:0: C0301: Line too long (178/119) (line-too-long)\nnemo/utils/exp_manager.py:643:0: C0301: Line too long (136/119) (line-too-long)\nnemo/utils/exp_manager.py:778:0: C0301: Line too long (121/119) (line-too-long)\nnemo/utils/exp_manager.py:786:0: C0301: Line too long (156/119) (line-too-long)\nnemo/utils/exp_manager.py:833:0: C0301: Line too long (141/119) (line-too-long)\nnemo/utils/exp_manager.py:841:0: C0301: Line too long (152/119) (line-too-long)\nnemo/utils/exp_manager.py:1111:0: C0301: Line too long (142/119) (line-too-long)\nnemo/utils/exp_manager.py:1135:0: C0301: Line too long (154/119) (line-too-long)\nnemo/utils/exp_manager.py:1201:0: C0301: Line too long (120/119) (line-too-long)\nnemo/utils/exp_manager.py:1220:0: C0301: Line too long (123/119) (line-too-long)\nnemo/utils/exp_manager.py:1246:0: C0301: Line too long (121/119) (line-too-long)\nnemo/utils/exp_manager.py:92:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:107:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:133:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:142:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:151:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:162:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/utils/exp_manager.py:275:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:278:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:281:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:284:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:287:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:290:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:293:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:296:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:334:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:337:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:340:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:343:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:1127:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:1228:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:1247:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:1250:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/utils/exp_manager.py:1266:0: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.12/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"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."
] | 2024-11-21T21:39:48 | 2025-01-03T01:58:39 | 2025-01-03T01:58:38 | COLLABORATOR | {
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There are 2 nearly equivalent classes for handling preemption. This PR adds a deprecation warning for the older version
Old: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/callbacks/preemption.py
New: https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/pytorch/callbacks/preemption.py
**Collection**: N/A
# Changelog
Deprecate old preemption callback
# 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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"[🤖]: Hi @JRD971000 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11966458383) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-21T21:24:20 | 2024-11-22T05:44:04 | 2024-11-22T05:44:03 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"[🤖]: Hi @hemildesai 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12283700867) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-21T21:17:11 | 2024-12-11T22:32:53 | 2024-12-11T22:32:51 | COLLABORATOR | {
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} | In `llm.api.deploy`, the function is also named `deploy` and the import is also named `deploy` as `from nemo.collections.llm import deploy`. This PR fixes the imports to avoid the name conflicts.
Currently, python gives an attribute error when doing `deploy.` because of the naming conflict. | {
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(line-too-long)\nnemo/collections/asr/models/rnnt_models.py:230:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:705:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:803:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:820:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:888:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:896:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1061:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/asr/models/rnnt_models.py:1092:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.54/10\n```\n\n---\n\nThank you for improving NeMo's documentation!"
] | 2024-11-21T21:03:09 | 2024-11-21T21:03:52 | 2024-11-21T21:03:50 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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?
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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?
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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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"based on current test failure, looks like this requires changes in MCore as well to handle and existing dist ckpt dir. i'd say we should make that change first before merging this",
"> based on current test failure, looks like this requires changes in MCore as well to handle and existing dist ckpt dir. i'd say we should make that change first before merging this\r\n\r\nHow can I check the failing tests? The only failing job I see is this one but it doesn't have any useful info: https://github.com/NVIDIA/NeMo/actions/runs/11958751953/job/33339441430?pr=11362\r\n\r\nRegardless of the failure, I would recommend ensuring that on the NeMo side the checkpointing logic ensures that we don't save twice to the same directory, otherwise that could lead to silent errors.\r\n\r\nWe did it for NeMo 1.0 and currently all such circumstances (saving twice to the same dir) are explicitly treated as an error which I believe leads to a better experiments handling from a user perspective",
"from that link, go to the Summary, and see the 'Failed jobs'. This [one](https://github.com/NVIDIA/NeMo/actions/runs/11958751953/job/33339268893) is failing.\r\n> I would recommend ensuring that on the NeMo side the checkpointing logic ensures that we don't save twice to the same directory, otherwise that could lead to silent errors.\r\n\r\nYou're right, I completely agree. Currently on main, async save will crash if the dir exists. Non-async save will just print warning and skip saving. Looks like this change is exposing that problem in one of the tests. \r\n\r\nWhat I don't understand is why we aren't running the PTL logic that appends a version counter ([1](https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/callbacks/model_checkpoint.py#L661-L665), [2](https://github.com/Lightning-AI/pytorch-lightning/blob/master/src/lightning/pytorch/callbacks/model_checkpoint.py#L685-L689))",
">Currently on main, async save will crash if the dir exists. Non-async save will just print warning and skip saving. Looks like this change is exposing that problem in one of the tests.\r\n\r\nWith this PR, both async and synchronous checkpointing will error if we try to save to an existing directory, which I think is what we want, right? In that case, maybe we can just update the test to save to different directories",
"ran the failing test locally with your changes, I think FSDPStrategy is missing `import os` so removing previous last checkpoint will fail: https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/pytorch/strategies/fsdp_strategy.py#L198-L205",
"> ran the failing test locally with your changes, I think FSDPStrategy is missing `import os` so removing previous last checkpoint will fail: https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/pytorch/strategies/fsdp_strategy.py#L198-L205\r\n\r\nHm yeah, I noticed that and tried to update but looks like there was a problem with pushing my changes. Let me try again",
"[🤖]: Hi @ashors1 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12001688420) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.lightning.io.pl\nnemo/lightning/io/pl.py:82:0: C0301: Line too long (130/119) (line-too-long)\nnemo/lightning/io/pl.py:58:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/lightning/io/pl.py:64:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/io/pl.py:73:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/io/pl.py:282:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.lightning.pytorch.strategies.fsdp_strategy\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:86:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:92:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:117:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:143:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:152:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:162:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:170:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:178:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:199:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.47/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.lightning.io.pl\nnemo/lightning/io/pl.py:82:0: C0301: Line too long (130/119) (line-too-long)\nnemo/lightning/io/pl.py:58:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/lightning/io/pl.py:64:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/io/pl.py:73:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/io/pl.py:282:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.lightning.pytorch.strategies.fsdp_strategy\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:86:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:92:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:117:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:143:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:152:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:162:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:170:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:178:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/lightning/pytorch/strategies/fsdp_strategy.py:199:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.47/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @ashors1 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12014964851) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-21T17:44:44 | 2024-11-26T17:41:45 | 2024-11-26T17:41:43 | COLLABORATOR | {
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} | # What does this PR do ?
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"You can use the following program to modify your vocabulary. Additionally, the reason it is 1030 is that it originally consists of 1024 + 5 (duration tokens) + 1 (padding).\n\n```python\nasr_model=ASRModel.from_pretrained('nvidia/parakeet-tdt_ctc-110m')\nasr_model.change_vocabulary(\n new_tokenizer_dir=\"your_tokenizer\", \n new_tokenizer_type=\"bpe\",\n )\n```",
"> You can use the following program to modify your vocabulary. Additionally, the reason it is 1030 is that it originally consists of 1024 + 5 (duration tokens) + 1 (padding).\n> \n> asr_model=ASRModel.from_pretrained('nvidia/parakeet-tdt_ctc-110m')\n> asr_model.change_vocabulary(\n> new_tokenizer_dir=\"your_tokenizer\", \n> new_tokenizer_type=\"bpe\",\n> )\n\nThank you for your response! Will this affect performance, as my tokenizer doesn't have duration tokens? ( or does it add them automatically?) Also, does this mean it will train the decoder from scratch? ( as I change it with a different size one)",
"1. The program will add it automatically.\n2. Yes, when you want to train a new language or if the token size is different, you need to retrain the decoder from scratch."
] | 2024-11-21T16:50:36 | 2024-11-22T10:42:39 | 2024-11-22T10:42:38 | CONTRIBUTOR | {
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} | null | null | null | Hello!
I am trying to fine-tune a Parakeet 110M TDT-CTC model, but with a different tokenizer with 1024 tokens. I get the following error when i do `asr_model.maybe_init_from_pretrained_checkpoint(cfg)`, from which I understand that the huggingface model's TDT decoder has 1030 tokens? From the model card I understood that it has 1024, the same as my decoder's size:
```
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for EncDecHybridRNNTCTCBPEModel:
size mismatch for joint.joint_net.2.weight: copying a param with shape torch.Size([1030, 640]) from checkpoint, the shape in current model is torch.Size([1025, 640]).
size mismatch for joint.joint_net.2.bias: copying a param with shape torch.Size([1030]) from checkpoint, the shape in current model is torch.Size([1025]).
```
This is my config:
```
# It contains the default values for training a FastConformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with sub-word encoding.
# The model would have two decoders: RNNT (Transducer) and CTC
# You may find more detail:
# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer
# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc
# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml
# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml
name: "FastConformer-Hybrid-Transducer-CTC-BPE"
init_from_nemo_model: "models/parakeet_tdt-ctc_110m.nemo"
init_strict: false
model:
sample_rate: 16000
compute_eval_loss: true # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
skip_nan_grad: false
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: "..."
sample_rate: ${model.sample_rate}
batch_size: 80 # you may increase batch_size if your memory allows
use_semi_sorted_batching: true
randomization_factor: 0.1
drop_last: false
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 20 # you may need to update it for your dataset
min_duration: 0.2
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: ...
sample_rate: ${model.sample_rate}
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: ..
sample_rate: ${model.sample_rate}
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
# We recommend to use vocab size of 1024 with SPE Unigram for most languages
tokenizer:
dir: "tokenizer/" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 5 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 17
d_model: 512
# Sub-sampling parameters
subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 8 # must be power of 2 for striding and vggnet
subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 9
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.2
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 10
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
# The section which would contain the decoder and decoding configs of the auxiliary CTC decoder
aux_ctc:
ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss
use_cer: false
ctc_reduction: 'mean_batch'
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
decoding:
strategy: "greedy"
# config for InterCTC loss: https://arxiv.org/abs/2102.03216
# specify loss weights and which layers to use for InterCTC
# e.g., to reproduce the paper results, set loss_weights: [0.3]
# and apply_at_layers: [8] (assuming 18 layers). Note that final
# layer loss coefficient is automatically adjusted (to 0.7 in above example)
interctc:
loss_weights: []
apply_at_layers: []
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
# It also helps to improve the accuracy of the model in streaming mode
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
optim:
name: adamw
lr: 6e-4
# optimizer arguments
betas: [0.9, 0.98]
# weight decay of 0.0 with lr of 2.0 also works fine
weight_decay: 1e-3
# scheduler setup
sched:
name: CosineAnnealing
# scheduler config override
warmup_steps: 25000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 100
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 4
gradient_clip_val: 1.0
precision: bf16-mixed # 16, 32, or bf16
log_every_n_steps: 100 # Interval of logging.
enable_progress_bar: True
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: "parakeet_tdt_ctc_110m"
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 100
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints.
resume_if_exists: true
resume_ignore_no_checkpoint: true
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
(nemo) gpirlogeanu@vm-gabi-fr:~/git_repos/NeMo/examples/asr/train_dir$ vim config/tdt.yaml
(nemo) gpirlogeanu@vm-gabi-fr:~/git_repos/NeMo/examples/asr/train_dir$ cat config/tdt.yaml
# It contains the default values for training a FastConformer-Hybrid-Transducer-CTC ASR model, large size (~115M) with sub-word encoding.
# The model would have two decoders: RNNT (Transducer) and CTC
# You may find more detail:
# FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer
# Hybrid ASR: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#hybrid-transducer-ctc
# FastConformer-CTC's architecture config: NeMo/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml
# FastConformer-Transducer's architecture config, along with the optimal batch size and precision: NeMo/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml
name: "FastConformer-Hybrid-Transducer-CTC-BPE"
init_from_nemo_model: "models/parakeet_tdt-ctc_110m.nemo"
model:
sample_rate: 16000
compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
skip_nan_grad: false
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: "manifests/rodigits.json" #???
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 20 # you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: "manifests/rodigits.json" #???
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
# We recommend to use vocab size of 1024 with SPE Unigram for most languages
tokenizer:
dir: "tokenizers/5_normal_BAS+news_unigram/tokenizer_spe_unigram_v1024_max_5" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 17
d_model: 512
use_bias: True # whether to apply bias in the feedforward, MHA and convolution modules
# Sub-sampling parameters
subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 8 # must be power of 2 for striding and vggnet
subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 9
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.2
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 10
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
# The section which would contain the decoder and decoding configs of the auxiliary CTC decoder
aux_ctc:
ctc_loss_weight: 0.3 # the weight used to combine the CTC loss with the RNNT loss
use_cer: false
ctc_reduction: 'mean_batch'
decoder:
_target_: nemo.collections.asr.modules.ConvASRDecoder
feat_in: null
num_classes: -1
vocabulary: []
decoding:
strategy: "greedy"
# config for InterCTC loss: https://arxiv.org/abs/2102.03216
# specify loss weights and which layers to use for InterCTC
# e.g., to reproduce the paper results, set loss_weights: [0.3]
# and apply_at_layers: [8] (assuming 18 layers). Note that final
# layer loss coefficient is automatically adjusted (to 0.7 in above example)
interctc:
loss_weights: []
apply_at_layers: []
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
# It also helps to improve the accuracy of the model in streaming mode
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
optim:
name: adamw
lr: 5.0
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 10000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 1000
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
#_target_: lightning.pytorch.strategies.DDPStrategy
#gradient_as_bucket_view: true
accumulate_grad_batches: 1
gradient_clip_val: 1.0
precision: 32 # 16, 32, or bf16
log_every_n_steps: 10 # Interval of logging.
enable_progress_bar: True
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
resume_if_exists: false
resume_ignore_no_checkpoint: false
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
Any help would be greatly appreciated!! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/11360 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/11360/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/11360/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/11360/events | https://github.com/NVIDIA/NeMo/issues/11360 | 2,679,388,999 | I_kwDOC_bI7s6ftDtH | 11,360 | Drastic difference between .nemo and HF checkpoint | {
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"I have compared a bunch of things between the 2 models and looks like there is a large difference between the logits of the 2 models.\n\n```\nnemo_model = MegatronGPTModel.restore_from(\n nemo_path,\n trainer=dummy_trainer,\n override_config_path=model_config,\n map_location=map_location\n)\n\n# Load HuggingFace model\nhf_model = AutoModelForCausalLM.from_pretrained(\n hf_path,\n local_files_only=True,\n torch_dtype=torch.bfloat16 # nemo_model.dtype\n)\n\n# Load tokenizer\ntokenizer = LlamaTokenizer.from_pretrained(tokenizer_path, legacy=False)\ntokenizer.pad_token = tokenizer.eos_token\ntokenizer.pad_token_id = tokenizer.eos_token_id\n\n# Move models to device\nnemo_model = nemo_model.to(device)\nhf_model = hf_model.to(device)\n\n# Set both models to eval mode\nnemo_model.eval()\nhf_model.eval()\n\n# Create random input ids\ninput_ids = torch.randint(\n 100, 1000,\n (test_batch_size, test_seq_length),\n device=device\n)\nattention_mask = torch.ones_like(input_ids)\n\nwith torch.no_grad():\n # NeMo forward pass\n nemo_output = nemo_model(\n tokens=input_ids,\n text_position_ids=torch.arange(test_seq_length, device=device),\n attention_mask=attention_mask,\n labels=None\n )\n \n # HF forward pass\n hf_output = hf_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_hidden_states=True,\n return_dict=True\n ).logits\n\n# Compare logits\nlogits_match = torch.allclose(\n nemo_output,\n hf_output,\n rtol=rtol,\n atol=atol\n)\n\nmetrics['logits_max_diff'] = float(\n torch.max(torch.abs(nemo_output - hf_output)).cpu()\n)\n```\n\nOutput:\n```\nConversion test results:\nLogits match: False (max diff: 4.91e+00)\n Parameters match: True (max diff: 0.00e+00)\n Generation match: 0.0\n Sample generation comparison:\n Input text: '<s>[INST] Hello [/INST]\\n'\n NeMo output: \"<s>[INST] Hello [/INST]\\n Hello. It's nice to meet you. Is there something I can help you with or\"\n HF output: '<s> [INST] Hello [/INST]\\n Hello. ನಿಮ್ಮನ್ನ ಭೇಟಿ ಮಾಡಿ ಸಂತೋಷ ಆಯ್ತು. ನಿಮಗೆ ಏನ'\nNumber of parameters match: 1.0 (Nemo: 2525087744, HF: 2525087744)\n❌ Conversion test failed!\n```\n\nI am not able to pinpoint why this is happening. Any pointers will be helpful.",
"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."
] | 2024-11-21T13:08:59 | 2025-01-01T02:03:36 | 2025-01-01T02:03:36 | NONE | {
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} | null | null | null | **Describe the bug**
I have trained a llama-like model with nemo using the below model config:
```
model:
mcore_gpt: True
micro_batch_size: 1
global_batch_size: 512
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
virtual_pipeline_model_parallel_size: null
context_parallel_size: 1
encoder_seq_length: 8192
max_position_embeddings: ${.encoder_seq_length}
num_layers: 28
hidden_size: 2048
ffn_hidden_size: 11008
num_attention_heads: 16
init_method_std: 0.02
use_scaled_init_method: True
hidden_dropout: 0.0
attention_dropout: 0.0
ffn_dropout: 0.0
kv_channels: null
apply_query_key_layer_scaling: True
normalization: 'rmsnorm'
layernorm_epsilon: 1e-6
do_layer_norm_weight_decay: False
make_vocab_size_divisible_by: 128
pre_process: True
post_process: True
persist_layer_norm: True
bias: False
activation: 'fast-swiglu'
headscale: False
transformer_block_type: 'pre_ln'
openai_gelu: False
normalize_attention_scores: True
position_embedding_type: 'rope'
rotary_percentage: 1.0
attention_type: 'multihead'
share_embeddings_and_output_weights: False
overlap_p2p_comm: False
batch_p2p_comm: True
num_query_groups: 8
rotary_base: 10000.0
```
The model works well when I run inference using the nemo checkpoint ([script](https://github.com/NVIDIA/NeMo/blob/main/scripts/deploy/nlp/query.py)). But the converted checkpoint ([script](https://github.com/NVIDIA/NeMo/blob/main/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py)) drastically drops in performance. Any ideas why this might be happening? My only hunch is that `apply_query_key_layer_scaling=True` in nemo, which might not be the case in HF.
**Environment details**
https://docs.nvidia.com/nemo-framework/user-guide/latest/softwarecomponentversions.html#nemo-framework-24-05
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https://api.github.com/repos/NVIDIA/NeMo/issues/11359 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/11359/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/11359/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/11359/events | https://github.com/NVIDIA/NeMo/issues/11359 | 2,679,336,329 | I_kwDOC_bI7s6fs22J | 11,359 | Doesn't have the shards in expected directory. | {
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-11-21T12:47:29 | 2024-12-29T02:04:09 | 2024-12-29T02:04:08 | NONE | {
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} | null | null | null | **Describe the bug**
I was trying to run the following lines:
```
%%bash
# Set paths to the model, train, validation and test sets.
MODEL="./llama-3_1-8b-instruct-nemo_v1.0/llama3_1_8b_instruct.nemo"
TRAIN_DS="[./curated-data/law-qa-train_preprocessed.jsonl]"
VALID_DS="[./curated-data/law-qa-val_preprocessed.jsonl]"
TEST_DS="[./curated-data/law-qa-test_preprocessed.jsonl]"
TEST_NAMES="[law]"
SCHEME="lora"
TP_SIZE=1
PP_SIZE=1
rm -rf results
OUTPUT_DIR="./results/Meta-llama3.1-8B-Instruct-titlegen"
torchrun --nproc_per_node=1 \
/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \
exp_manager.exp_dir=${OUTPUT_DIR} \
exp_manager.explicit_log_dir=${OUTPUT_DIR} \
trainer.devices=1 \
trainer.num_nodes=1 \
trainer.precision=bf16-mixed \
trainer.val_check_interval=0.2 \
trainer.max_steps=50 \
model.megatron_amp_O2=True \
++model.mcore_gpt=True \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.micro_batch_size=1 \
model.global_batch_size=64 \
model.restore_from_path=${MODEL} \
model.data.train_ds.file_names=${TRAIN_DS} \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=${VALID_DS} \
model.peft.peft_scheme=${SCHEME}
```
but keep getting the following error:
`'FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpelprgyyt/model_weights/model.decoder.layers.self_attention.core_attention._extra_state/shard_0_32.pt''`
Just can't seem to have the weights file under the directory of `"/tmp/tmpelprgyyt/model_weights/model.decoder.layers.self_attention.core_attention._extra_state"`
**Expected behavior**
To successfully load the shards to the model.
**Environment overview (please complete the following information)**
- Environment location: Primehub docker website interface(Jupyter lab)
- Method of NeMo install: pull from nvcr.io/nvidia/nemo:24.09
**Environment details**
I'm using NeMo image 24.09 version on 4 A10 GPU cores.
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.llm.t5.data.squad\nnemo/collections/llm/t5/data/squad.py:33:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/llm/t5/data/squad.py:34:0: C0301: Line too long (123/119) (line-too-long)\nnemo/collections/llm/t5/data/squad.py:38:0: C0301: Line too long (129/119) (line-too-long)\nnemo/collections/llm/t5/data/squad.py:39:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/llm/t5/data/squad.py:103:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/llm/t5/data/squad.py:78:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/t5/data/squad.py:154:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.29/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
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] | 2024-11-21T03:53:10 | 2024-11-22T16:59:52 | 2024-11-22T16:59:49 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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"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."
] | 2024-11-21T01:56:47 | 2025-01-06T02:01:13 | 2025-01-06T02:01:12 | NONE | {
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Fixes the bug with TE linear layer replacement | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"@tfogal NeMo commits need to have --signoff can you amend and push again? In the meantime I've started CI since it takes a few hours to complete. If CI has passed before you push we can ask someone to force merge if needed.",
"The `Isort and ...` issues are apparently coming up because the source branch is in another repo:\r\n\r\n```\r\n Warning: Unable to find merge base between 34f740822a11802c2f0cea1c8039163b2b8bff92 and ecc8a1f3a2d532bf771746ed5a81367acf866da9\r\n Error: Unable to locate the commit sha: 34f740822a11802c2f0cea1c8039163b2b8bff92\r\n Error: Please verify that the commit sha is correct, and increase the 'fetch_depth' input if needed\r\n Warning: fatal: bad object 34f740822a11802c2f0cea1c8039163b2b8bff92\r\n \r\n Warning: If this pull request is from a forked repository, please set the checkout action `repository` input to the same repository as the pull request.\r\n Warning: This can be done by setting actions/checkout `repository` to ${{ github.event.pull_request.head.repo.full_name }}\r\n Error: Unable to determine a difference between 34f740822a11802c2f0cea1c8039163b2b8bff92..ecc8a1f3a2d532bf771746ed5a81367acf866da9\r\n```\r\n\r\nThis is not something I can fix, though, since I can't make branches in NeMo.\r\n\r\nUnrelatedly, marking ready for review. I still question whether this is the *correct* fix but it's definitely an improvement and we could always refine.",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n\n------------------------------------\nYour code has been rated at 10.00/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"@pablo-garay https://github.com/NVIDIA/NeMo/pull/11354#issuecomment-2498721924 any solution?"
] | 2024-11-21T00:18:23 | 2024-11-27T11:07:55 | 2024-11-26T10:24:42 | CONTRIBUTOR | {
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} | The SingleDeviceStrategy never sets the requisite attribute; workaround it in the profiling code.
# What does this PR do ?
Fixes using the nsys callback for single-GPU benchmarking.
**Collection**: core
# Changelog
- Fix for cuda profiling of single-GPU runs
# Usage
```python
nsyscb = nl.pytorch.callbacks.nsys.NsysCallback(
start_step=0,
end_step=40,
ranks=[0],
gen_shape=False,
)
llm.api.finetune(..., callbacks=[nsyscb])
```
is how I am using it.
# 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)
- [ ] Did you write any new necessary tests? *Not sure how to test this. Let's first get some confirmation that this is even the right approach*
- [ ] Did you add or update any necessary documentation? *Let's first get some confirmation that this is even the right approach*
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) N/A
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"[🤖]: Hi @hemildesai 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12262137655) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-20T23:45:57 | 2024-12-17T16:41:03 | 2024-12-17T16:41:01 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.llm.recipes.mamba2_hybrid_8b\nnemo/collections/llm/recipes/mamba2_hybrid_8b.py:258:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/llm/recipes/mamba2_hybrid_8b.py:38:0: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.46/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @JRD971000 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11942650935) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-20T22:14:43 | 2024-11-21T15:10:10 | 2024-11-21T15:10:09 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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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
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] 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.
# Additional Information
* Related to # (issue)
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"Ok docker quietened following error log from pip, this is an apache-beam issue, closing issue.\n\n> ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n> cudf 24.4.0 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n> cugraph 24.4.0 requires dask-cuda==24.4.*, but you have dask-cuda 24.8.2 which is incompatible.\n> cugraph 24.4.0 requires rapids-dask-dependency==24.4.*, but you have rapids-dask-dependency 24.8.0 which is incompatible.\n> cugraph-service-server 24.4.0 requires dask-cuda==24.4.*, but you have dask-cuda 24.8.2 which is incompatible.\n> cugraph-service-server 24.4.0 requires rapids-dask-dependency==24.4.*, but you have rapids-dask-dependency 24.8.0 which is incompatible.\n> cuml 24.4.0 requires dask-cuda==24.4.*, but you have dask-cuda 24.8.2 which is incompatible.\n> cuml 24.4.0 requires rapids-dask-dependency==24.4.*, but you have rapids-dask-dependency 24.8.0 which is incompatible.\n> cuml 24.4.0 requires treelite==4.1.2, but you have treelite 4.3.0 which is incompatible.\n> dask-cudf 24.4.0 requires rapids-dask-dependency==24.4.*, but you have rapids-dask-dependency 24.8.0 which is incompatible.\n> multiprocess 0.70.16 requires dill>=0.3.8, but you have dill 0.3.1.1 which is incompatible.\n> tensorflow-metadata 1.16.1 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.5 which is incompatible.\n> tensorrt-llm 0.12.0 requires nvidia-modelopt~=0.15.0, but you have nvidia-modelopt 0.0.0 which is incompatible.\n> tensorrt-llm 0.12.0 requires pynvml>=11.5.0, but you have pynvml 11.4.1 which is incompatible.\n> tensorrt-llm 0.12.0 requires transformers<=4.42.4,>=4.38.2, but you have transformers 4.46.2 which is incompatible.",
"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."
] | 2024-11-20T20:58:34 | 2024-12-29T02:04:11 | 2024-12-29T02:04:10 | NONE | {
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} | null | null | null | **Describe the bug**
HuggingFace datasets imported by nemo.collections.llm.gpt.data in /opt/NeMo/nemo/collections/llm/gpt/data/__init__.py:15
`from nemo.collections.llm.gpt.data.dolly import DollyDataModule`
/opt/NeMo/nemo/collections/llm/gpt/data/dolly.py:20
`from datasets import load_dataset`
usr/local/lib/python3.10/dist-packages/datasets/__init__.py:17
from .arrow_dataset import Dataset
...
Typo in Python 3.10? should be threading.Condition instead of threading._Condition ? cf [huggingface/datasets:5613](https://github.com/huggingface/datasets/issues/5613) [apache-beam:24458](https://github.com/apache/beam/issues/24458)
Typo in source code ? should be subclassing threading.Condition instead of threading._Condition ?
~/.local/lib/python3.10/site-packages/multiprocess/dummy/__init__.py:87
class Condition(threading._Condition):
88 # XXX
89 if sys.version_info < (3, 0):
90 notify_all = threading._Condition.notify_all.__func__
AttributeError: module 'threading' has no attribute '_Condition'
**Steps/Code to reproduce bug**
`from nemo.collections import llm`
> ---------------------------------------------------------------------------
> AttributeError Traceback (most recent call last)
> Cell In[4], line 1
> ----> 1 from nemo.collections import llm
>
> File /opt/NeMo/nemo/collections/llm/__init__.py:22
> 20 from nemo.collections.llm import peft
> 21 from nemo.collections.llm.api import export_ckpt, finetune, generate, import_ckpt, pretrain, train, validate
> ---> 22 from nemo.collections.llm.gpt.data import (
> 23 DollyDataModule,
> 24 FineTuningDataModule,
> 25 MockDataModule,
> 26 PreTrainingDataModule,
> 27 SquadDataModule,
> 28 )
> 29 from nemo.collections.llm.gpt.data.api import dolly, mock, squad
> 30 from nemo.collections.llm.gpt.model import (
> 31 Baichuan2Config,
> 32 Baichuan2Config7B,
> (...)
> 103 gpt_forward_step,
> 104 )
>
> File /opt/NeMo/nemo/collections/llm/gpt/data/__init__.py:15
> 1 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
> 2 #
> 3 # Licensed under the Apache License, Version 2.0 (the "License");
> (...)
> 12 # See the License for the specific language governing permissions and
> 13 # limitations under the License.
> ---> 15 from nemo.collections.llm.gpt.data.dolly import DollyDataModule
> 16 from nemo.collections.llm.gpt.data.fine_tuning import FineTuningDataModule
> 17 from nemo.collections.llm.gpt.data.mock import MockDataModule
>
> File /opt/NeMo/nemo/collections/llm/gpt/data/dolly.py:20
> 17 from typing import TYPE_CHECKING, List, Optional
> 19 import numpy as np
> ---> 20 from datasets import load_dataset
> 22 from nemo.collections.llm.gpt.data.core import get_dataset_root
> 23 from nemo.collections.llm.gpt.data.fine_tuning import FineTuningDataModule
>
> File /usr/local/lib/python3.10/dist-packages/datasets/__init__.py:17
> 1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
> 2 #
> 3 # Licensed under the Apache License, Version 2.0 (the "License");
> (...)
> 12 # See the License for the specific language governing permissions and
> 13 # limitations under the License.
> 15 __version__ = "3.1.0"
> ---> 17 from .arrow_dataset import Dataset
> 18 from .arrow_reader import ReadInstruction
> 19 from .builder import ArrowBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
>
> File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:77
> 75 from . import config
> 76 from .arrow_reader import ArrowReader
> ---> 77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
> 78 from .data_files import sanitize_patterns
> 79 from .download.streaming_download_manager import xgetsize
>
> File /usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py:27
> 24 from fsspec.core import url_to_fs
> 26 from . import config
> ---> 27 from .features import Audio, Features, Image, Value, Video
> 28 from .features.features import (
> 29 FeatureType,
> 30 _ArrayXDExtensionType,
> (...)
> 37 to_pyarrow_listarray,
> 38 )
> 39 from .filesystems import is_remote_filesystem
>
> File /usr/local/lib/python3.10/dist-packages/datasets/features/__init__.py:17
> 1 __all__ = [
> 2 "Audio",
> 3 "Array2D",
> (...)
> 15 "Video",
> 16 ]
> ---> 17 from .audio import Audio
> 18 from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, LargeList, Sequence, Value
> 19 from .image import Image
>
> File /usr/local/lib/python3.10/dist-packages/datasets/features/audio.py:13
> 11 from ..table import array_cast
> 12 from ..utils.file_utils import xopen, xsplitext
> ---> 13 from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
> 16 if TYPE_CHECKING:
> 17 from .features import FeatureType
>
> File /usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py:37
> 34 from urllib.parse import urlparse
> 36 import multiprocess
> ---> 37 import multiprocess.pool
> 38 import numpy as np
> 39 from tqdm.auto import tqdm
>
> File ~/.local/lib/python3.10/site-packages/multiprocess/pool.py:609
> 603 self._cond.release()
> 605 #
> 606 #
> 607 #
> --> 609 class ThreadPool(Pool):
> 611 from .dummy import Process
> 613 def __init__(self, processes=None, initializer=None, initargs=()):
>
> File ~/.local/lib/python3.10/site-packages/multiprocess/pool.py:611, in ThreadPool()
> 609 class ThreadPool(Pool):
> --> 611 from .dummy import Process
> 613 def __init__(self, processes=None, initializer=None, initargs=()):
> 614 Pool.__init__(self, processes, initializer, initargs)
>
> File ~/.local/lib/python3.10/site-packages/multiprocess/dummy/__init__.py:87
> 81 return None
> 83 #
> 84 #
> 85 #
> ---> 87 class Condition(threading._Condition):
> 88 # XXX
> 89 if sys.version_info < (3, 0):
> 90 notify_all = threading._Condition.notify_all.__func__
>
> AttributeError: module 'threading' has no attribute '_Condition'
**Expected behavior**
Import should occur without triggering an error
**Environment overview (please complete the following information)**
- Environment location: [Docker]
- Method of NeMo install: Docker, docker pull nvcr.io/nvidia/nemo:24.09 docker run docker run --gpus all -it --rm --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/nemo:24.09
**Additional context**
Add any other context about the problem here.
Typo in Python 3.10? should be subclassing threading.Condition instead of threading._Condition ? | {
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"> approving after the 1 comment above is resolved (I don't seem to have permission to actually approve the PR)\r\n\r\nfixed.",
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
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# 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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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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This PR changes the constructor signature to avoid setting an empty list as the default value for these arguments. This change is backwards compatible with existing callers.
**Collection**: Diffusion
# Changelog
- Remove default mutable arguments from AbstractEmbModel constructor
# 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.
To re-run CI remove and add the label again.
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- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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## 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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"[🤖]: Hi @hemildesai 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11940450988) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-20T19:25:48 | 2024-11-20T23:37:52 | 2024-11-20T23:37:50 | COLLABORATOR | {
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.diffusion.encoders.conditioner\nnemo/collections/diffusion/encoders/conditioner.py:22:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:40:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:44:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:48:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:75:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:133:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:138:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:169:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:185:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/diffusion/encoders/conditioner.py:190:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.06/10\n```\n\n---\n\nThank you for improving NeMo's documentation!"
] | 2024-11-20T19:01:40 | 2024-11-20T19:53:58 | 2024-11-20T19:38:41 | COLLABORATOR | {
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} | # What does this PR do ?
This changes the constructor signature. This change is backwards compatible with existing callers.
**Collection**: [Note which collection this PR will affect]
Diffusion for NeMo2
# Changelog
- Remove default mutable arguments from `AbstractEmbModel` constructor
# 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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# Before your PR is "Ready for review"
**Pre checks**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-11-20T18:34:20 | 2024-12-13T02:05:58 | 2024-12-13T02:05:58 | NONE | {
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} | # What does this PR do ?
Improves docs and fixes tool to convert from a NeMo/Megatron DCP checkpoint to a HF checkpoint
**Collection**: NLP
# Changelog
- Added cmd launch to convert from NeMo/Megatron DCP checkpoint to .nemo
- Fixed `scripts/checkpoint_converters/convert_llama_nemo_to_hf.py`
# Usage
Hi!
There has been some discussions regarding NeMo/Megatron checkpoint conversions in both projects [[1]](https://github.com/NVIDIA/NeMo/issues/10831), [[2]](https://github.com/NVIDIA/NeMo/issues/10480), [[3]](https://github.com/NVIDIA/Megatron-LM/issues/1206), [[4]](https://github.com/NVIDIA/NeMo/issues/10884). I would like to share some insights regarding this issue, as I've been able to successfully convert checkpoints to HF.
First, my dependencies:
- NeMo commit `fc7905d`, I know it's a month old but it's the version we are working with
- Megatron commit `bf74129`
- TransformerEngine commit `7d576ed`. It's a bit old, but it's the one used in the NeMo/Dockerfile.ci image
- PyTorch NGC Container `[nvcr.io/nvidia/pytorch:24.05-py3](http://nvcr.io/nvidia/pytorch:24.05-py3)`
Some comments after converting a NeMo/Megatron Llama3.1-8B checkpoint stored with the TORCH distributed backend:
- The [official documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/checkpoints/convert_mlm.html#converting-checkpoints) points out the examples/nlp/language_modeling/megatron_lm_ckpt_to_nemo.py , but it doesn't works. Instead I could use a pretty similar script stored under the same folder examples/nlp/language_modeling/megatron_ckpt_to_nemo.py. If I'm not wrong, the docs state *You can convert your GPT-style model checkpoints trained with Megatron-LM into the NeMo Framework* but now when using NeMo with `model.dist_ckpt_format: torch_dist` we are indeed storing Megatron-LM checkpoints, right? So this step will be essential to convert to `.nemo` before converting to HF.
- With this later script I could successfully convert a NeMo/Megatron TORCH distributed checkpoint to .nemo this way:
```python
python -m torch.distributed.launch --nproc_per_node=4 examples/nlp/language_modeling/megatron_ckpt_to_nemo.py --checkpoint_folder megatron_ckpts/checkpoints/ --checkpoint_name llama_3_1_70b-step=50-consumed_samples=750.0-last --nemo_file_path nemo_converted/converted.nemo --hparams_file megatron_ckpts/hparams.yaml --tensor_model_parallel_size 4 --model_type gpt --gpus_per_node 4 --pipeline_model_parallel_size 1
```
- Then, to convert from .nemo to HF I used this script: scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
- Which I had to edit all the `model.state_dict()` calls with `model.model[0].state_dict()`. Also the key --> `[f'model. ...']`with `[f'...']`
- Hardcode `model_config.sequence_parallel = 0` as it raised a error if the model was trained with TP (We are also overriding TP = 1)
- To run the conversion:
```python
python3 -u scripts/checkpoint_converters/convert_llama_nemo_to_hf.py --input_name_or_path=nemo_converted/converted.nemo --output_path=hf_converted/pytorch_model.bin --cpu-only
```
- I also experienced some issues loading the tokenizer, but I've seen some changes in the recent commits. Also I think it would be a great idea to convert to a HF .safetensors checkpoint automatically without the necessity of specifying a path to a hf model as we can build a model from the `hparams.yaml` file (I can push this changes too)
- This changes might be also necessary for the other model conversion scripts, but I haven't tried
IMO I think it would be nice to clarify how to convert checkpoints to HF as it's the most popular platform to share checkpoints with the community.
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# Before your PR is "Ready for 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.
# Additional Information
* Related to # (issue)
cc @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, @okuchaiev
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"[🤖]: Hi @janekl 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11939301101) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"[🤖]: Hi @janekl 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11956643299) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-20T18:07:49 | 2024-11-21T18:08:07 | 2024-11-21T18:08:06 | COLLABORATOR | {
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} | # What does this PR do ?
Several updates & bugfixes for `nemo.export` module.
* Fixing `UnboundLocalError` when building tokenizer in `nemo/export/trt_llm/nemo_ckpt_loader/nemo_file.py`
* Making vLLM deployment consistent with TRT-LLM as for `output_generation_logits` (disabled in case of vLLM for now)
* Using `BlockManagerV2` by default for vLLM deployment (as `V1` is getting deprecated)
* Listing all (minimal) requirements to run vLLM in `/opt/venv` in the dedicated file `requirements_vllm.txt.`
* Other minor changes
**Collection**: NLP / LLM
# 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
- [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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"[🤖]: Hi @akoumpa 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11939062345) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
left-over from https://github.com/NVIDIA/NeMo/pull/11313 :/
**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.
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**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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"merged_at": "2024-11-21T15:15:54"
} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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```
model = MegatronAutoModel("meta-llama/Llama-3.1-8B")
print(model)
> LlamaModel()
```
**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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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] 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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HfAutoModelForCausalLM's forward used input named `tokens` but the underlying model was using `input_ids` coincidenting with mcore's `input_ids`. -> renamed param to `input_ids` to have a unified interface.
**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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} | # What does this PR do ?
Adds optional sample generate to PTQ and validation of the exported checkpoint
**Collection**: llm
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} | # What does this PR do ?
The goal of this PR is to extend the type of `model` argument of `llm.api` from `LightningModule` to `Union[LightningModule, nn.Module, fdl.Config]`, therefore allowing users to pass either instantiated models (nn.Module) or a config to be instantiated.
This change enables further flexibility on the user side and allows the NeMo framework to avoid duplicating code by not having to wrap users' modules in LightningModule (and instead do so internally).
**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.
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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} | # What does this PR do ?
Fix CLIP transformer layer api for a ci failure
**Collection**: [multimodal]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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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
- [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 ?
This is the second PR that deals with unit tests for
Sortformer Diarizer Model, 4 speaker limit, v1 model.
**Previous PR:**
This is PR 2nd out of 3.
1st PR: https://github.com/NVIDIA/NeMo/pull/11336
3rd PR: <Coming Soon>
**Sortformer Paper:**
[Sortformer Paper Link](https://arxiv.org/pdf/2409.06656)
**Overview:**
- We moved all the speaker recognition related test files from
`NeMo/tests/collections/asr` to `NeMo/tests/collections/speaker_tasks`.
- We have added all Sortformer diarizer related unit-test functions and classes.
**Collection**: ASR
# Changelog
Updated the `test_data.tar.gz` file in https://github.com/NVIDIA/NeMo/releases/tag/v1.0.0rc1
```bash
NeMo/tests/collections/speaker_tasks/.
├── test_diar_datasets.py (moved, modified)
├── test_diar_label_models.py (moved, modified)
├── test_diar_lhotse_datasets.py (created)
├── test_diar_metrics.py (moved, modified)
├── test_diar_neural_inference.py (moved, modified)
├── test_diar_sortformer_models.py (created)
├── test_speaker_label_models.py (moved)
└── utils
├── test_data_simul_utils.py (moved)
├── test_diar_utils.py (moved)
├── test_multispeaker_utils.py (created)
└── test_vad_utils.py (moved)
```
# Usage
```python
pytest <NeMo Root>/tests/collections/speaker_tasks/*
```
# GitHub Actions CI
Added the new CIs for Sortformer Diarizer.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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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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} | # What does this PR do ?
Leave target_module as default in PEFT Recipes. Only specify target_module as linear_qkv in performance recipes to match with published performance numbers. Otherwise leave it as default to match with other models.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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📝 Please remember the following to-do's before merge:
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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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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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(unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:30:0: W0611: Unused OmegaConf imported from omegaconf (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:72:0: W0611: Unused activation_to_func imported from nemo.collections.nlp.parts.utils_funcs (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:81:4: W0611: Unused megatron.core imported as core (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:82:4: W0611: Unused tensor_parallel imported from megatron.core (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:101:4: W0611: Unused init_method_normal imported from megatron.core.utils (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:101:4: W0611: Unused scaled_init_method_normal imported from megatron.core.utils (unused-import)\n\n-----------------------------------\nYour code has been rated at 9.50/10\n```\n\n---\n\nMitigation guide:\n\n* Add sensible and useful docstrings to functions and methods\n* For trivial methods like getter/setters, consider adding `# pylint: disable=C0116` inside the function itself\n* To disable multiple functions/methods at once, put a `# pylint: disable=C0116` before the first and a `# pylint: enable=C0116` after the last.\n\nBy applying these rules, we reduce the occurance of this message in future.\n\nThank you for improving NeMo's documentation!",
"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."
] | 2024-11-19T19:41:05 | 2025-01-17T01:56:02 | 2025-01-17T01:56:02 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add a new option 'use_te_activation_func' to megatron config.
**Collection**: [Note which collection this PR will affect]
# Changelog
- In `megatron_gpt_model.py` add support to parse `use_te_activation_func` option from config file and pass this to megatron. This is to support a new feature in megatron ([!1233](https://github.com/NVIDIA/Megatron-LM/pull/1233)) that uses transformerengine implementation of activation function kernel (swiglu, gelu, etc).
# 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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"[🤖]: Hi @ShriyaPalsamudram 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11918981736) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
] | 2024-11-19T17:55:38 | 2024-11-19T22:37:43 | 2024-11-19T22:37:42 | COLLABORATOR | {
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} | # What does this PR do ?
Add StragglerDetection and FTlauncher to NeMo2.0
**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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If you haven't finished some of the above items you can still open "Draft" PR.
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The output of patched `batch_decode` method differs from the desired output used so far (dropped one level of nesting). This resulted in empty `model_output` after [postprocessing in Lambada accuracy check](https://github.com/NVIDIA/NeMo/blob/425a4dd0f31ca10b83ad42bb540e8007f2f9bf1b/tests/export/nemo_export.py#L134) and yields incorrect [100% relaxed accuracy](https://github.com/NVIDIA/NeMo/blob/425a4dd0f31ca10b83ad42bb540e8007f2f9bf1b/tests/export/nemo_export.py#L143) score.
**Collection**: NLP / LLM
# Changelog
Patching for NeMo tokenizer:
- For HuggingFace: unwrap the original methods
- For SentencePiece: define (quasi) equivalent methods based on https://github.com/NVIDIA/NeMo/blob/main/nemo/export/sentencepiece_tokenizer.py
# 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
- [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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# 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".
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**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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"[🤖]: Hi @ko3n1g 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11919235376) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
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"[🤖]: Hi @ko3n1g 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11951638396) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"[🤖]: Hi @ko3n1g 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11970320432) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
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"[🤖]: Hi @ko3n1g 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12196739377) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"Thanks for your detailed review @titu1994! This PR lost a bit traction b/c of the delayed Mcore bump. But it's now ready for another review!",
"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.",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module setup\nsetup.py:57:0: C0116: Missing function or method docstring (missing-function-docstring)\nsetup.py:72:0: C0116: Missing function or method docstring (missing-function-docstring)\nsetup.py:150:0: C0115: Missing class docstring (missing-class-docstring)\n\n-----------------------------------\nYour code has been rated at 9.64/10\n```\n\n---\n\nMitigation guide:\n\n* Add sensible and useful docstrings to functions and methods\n* For trivial methods like getter/setters, consider adding `# pylint: disable=C0116` inside the function itself\n* To disable multiple functions/methods at once, put a `# pylint: disable=C0116` before the first and a `# pylint: enable=C0116` after the last.\n\nBy applying these rules, we reduce the occurance of this message in future.\n\nThank you for improving NeMo's documentation!"
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This PR pushes dependencies from Dockerfile to down to requirements files. This is done for all dependencies except of Apex, because Apex stricly requires `--no-build-isolation`. This is a bad practice and not required for other dependencies, so I decided to separate it.
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2. Before pushing to pypi, we comment out/delete all git-dependencies
**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.
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)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] 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
```
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] 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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```python
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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**:
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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?
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"[🤖]: Hi @akoumpa 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12319937751) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n\n------------------------------------\nYour code has been rated at 10.00/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module hf\nexamples/llm/peft/hf.py:25:0: C0301: Line too long (184/119) (line-too-long)\nexamples/llm/peft/hf.py:81:0: C0301: Line too long (162/119) (line-too-long)\nexamples/llm/peft/hf.py:21:0: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:26:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:34:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:62:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:73:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:76:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:99:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:102:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:119:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:135:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.43/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @akoumpa 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12377449361) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module hf\nexamples/llm/peft/hf.py:26:0: C0301: Line too long (184/119) (line-too-long)\nexamples/llm/peft/hf.py:22:0: C0116: Missing function or method docstring (missing-function-docstring)\nexamples/llm/peft/hf.py:61:0: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.llm.gpt.model.hf_auto_model_for_causal_lm\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:26:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:34:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:62:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:73:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:76:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:99:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:102:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:125:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/llm/gpt/model/hf_auto_model_for_causal_lm.py:145:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.48/10\n```\n\n---\n\nMitigation guide:\n\n* Add sensible and useful docstrings to functions and methods\n* For trivial methods like getter/setters, consider adding `# pylint: disable=C0116` inside the function itself\n* To disable multiple functions/methods at once, put a `# pylint: disable=C0116` before the first and a `# pylint: enable=C0116` after the last.\n\nBy applying these rules, we reduce the occurance of this message in future.\n\nThank you for improving NeMo's documentation!"
] | 2024-11-19T12:11:23 | 2024-12-17T22:32:48 | 2024-12-17T22:32:47 | MEMBER | {
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} | # What does this PR do ?
Adding AutoModelForImageTextToText and PeFT example for instruct models.
Tested with:
- Llama-3.2-11B-Vision-Instruct
- Qwen/Qwen2-VL-2B-Instruct
**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.
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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] | 2024-11-19T09:22:04 | 2024-12-28T01:57:02 | 2024-12-28T01:57:02 | NONE | {
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} | null | null | null | Hi there,
I gone through the repo, and i ran the inference for speaker diarization on toy sample using system VAD, its giving the output. But for the recorded audio file its giving errors. can you provide the code for custom samples inference for system VAD. | {
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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.
# Additional Information
* Related to # (issue)
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📝 Please remember the following to-do's before merge:
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"[🤖]: Hi @BoxiangW 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/12127372193) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
Add different recipe examples to NeMo 2.0
**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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Fix a previous workaround in llama32 vision to use attention bias in attention calculation.
**Collection**: [multimodal]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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.
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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?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"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."
] | 2024-11-18T16:05:37 | 2024-12-28T01:57:03 | 2024-12-28T01:57:03 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
TensorRT-LLM only accepts a single rank .nemo LoRA checkpoint (in the case of Llama 3.1 8b). Therefore, the only way to use my fine-tuned model with TensorRT-LLM backend is to merge my distributed LoRA checkpoints into the base model using the scripts/nlp_language_modeling/merge_lora_weights/merge.py script. However, that results in a lot of big models, if I want to do that for multiple downstream tasks/fine-tuned models.
More specifically, my checkpoints after training with TP=PP=2 look like (the contents of the megatron_gpt_peft_lora_tuning.nemo LoRA checkpoint file):
```
./
./model_config.yaml
./tp_rank_00_pp_rank_000/
./tp_rank_00_pp_rank_000/model_weights.ckpt
./tp_rank_00_pp_rank_001/
./tp_rank_00_pp_rank_001/model_weights.ckpt
./tp_rank_01_pp_rank_000/
./tp_rank_01_pp_rank_000/model_weights.ckpt
./tp_rank_01_pp_rank_001/
./tp_rank_01_pp_rank_001/model_weights.ckpt
```
**Describe the solution you'd like**
It would be nice if we could merge the distributed LoRA weights to a .nemo LoRA checkpoint file that only contains weights for a single rank. That way, the LoRA would be compatible with TensorRT-LLM even if training on multiple GPUs.
Thanks in advance!
Best regards,
John
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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
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If you haven't finished some of the above items you can still open "Draft" PR.
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] | 2024-11-18T08:19:50 | 2024-11-19T10:02:46 | 2024-11-19T10:02:46 | COLLABORATOR | {
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} | # What does this PR do ?
Add the env var (`NCCL_P2P_NET_CHUNKSIZE`) that adjusts P2P communication chunk size to nemo 2.0, which is critical to unload the buffering time from NCCL kernel execution time.
# Changelog
Added `NCCL_P2P_NET_CHUNKSIZE` nemo 2.0 env var plugin
# Usage
Set `nccl_pp_comm_chunksize` in `PerfEnvPlugin`.
# GitHub Actions CI
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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?
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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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📝 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 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."
] | 2024-11-17T20:02:11 | 2024-12-12T02:05:09 | 2024-12-12T02:05:08 | NONE | {
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} | # What does this PR do ?
There was a crash when nvidia_torch_version came back as None because it was still being used before the None check.
**Collection**: [NLP]
# Changelog
Fixed crash when NVIDIA_PYTORCH_VERSION is not set
# Usage
* You can potentially add a usage example below
Run a anything that exercises this code without NVIDIA_PYTORCH_VERSION set
# 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**:
- [ ] New Feature
- [X ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@MaximumEntropy @thomasdhc (Not sure if I'm supposed to ping the whole NLP reviewer list)
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)
This is a minor fix. No associated tests or documentation. I tried to keep it simple. Devs may prefer different logic (e.g. an extra none check before the official release check, move the is_official_release_version def inside the None check block, etc) | {
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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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"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.tts.models.speechllm.megatron_base_speechllm_prompt_model\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:92:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:186:0: C0301: Line too long (183/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:82:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:405:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:408:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:411:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:414:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_base_speechllm_prompt_model.py:421:4: C0116: Missing function or method docstring (missing-function-docstring)\n************* Module nemo.collections.tts.models.speechllm.megatron_t5_speechllm_model\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:107:0: C0301: Line too long (180/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:109:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:281:0: C0301: Line too long (137/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:283:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:289:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:302:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:303:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:313:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:407:0: C0301: Line too long (150/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:408:0: C0301: Line too long (127/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:472:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:975:0: C0301: Line too long (123/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:977:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:985:0: C0301: Line too long (130/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1741:0: C0301: Line too long (120/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1752:0: C0301: Line too long (130/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1792:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1829:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1831:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1834:0: C0301: Line too long (130/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1869:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1921:0: C0301: Line too long (151/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1969:0: C0301: Line too long (159/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:2240:0: C0301: Line too long (123/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:2639:0: C0301: Line too long (150/119) (line-too-long)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:86:0: C0115: Missing class docstring (missing-class-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:326:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:589:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:608:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:944:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:950:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:956:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1069:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1089:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1107:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1113:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1519:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1551:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1630:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:1707:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/tts/models/speechllm/megatron_t5_speechllm_model.py:2641:4: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.68/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @maanug-nv 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11884716451) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
Use `lightning` instead of `pytorch_lightning`.
Depends on #11252 .
**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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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days."
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.nlp.models.language_modeling.megatron_gpt_model\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:327:0: C0301: Line too long (149/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:367:0: C0301: Line too long (136/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:413:0: C0301: Line too long (126/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:460:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:791:0: C0301: Line too long (131/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1105:0: C0301: Line too long (146/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1126:0: C0301: Line too long (168/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1334:0: C0301: Line too long (122/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1391:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1572:0: C0301: Line too long (132/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1573:0: C0301: Line too long (136/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1575:0: C0301: Line too long (159/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1746:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1766:0: C0301: Line too long (140/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1774:0: C0301: Line too long (155/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1795:0: C0301: Line too long (141/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1865:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1892:0: C0301: Line too long (134/119) (line-too-long)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:140:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:153:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:179:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:198:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:245:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:283:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:287:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:299:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:307:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:469:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:472:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:701:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:705:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:787:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1103:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1176:12: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1225:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1253:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1404:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1539:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1547:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1556:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1840:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:1993:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:2000:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:2006:4: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:20:0: W0611: Unused fields imported from dataclasses (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:27:0: W0611: Unused OmegaConf imported from omegaconf (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:30:0: W0611: Unused _DataFetcherWrapper imported from pytorch_lightning.loops.fetchers (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:72:0: W0611: Unused activation_to_func imported from nemo.collections.nlp.parts.utils_funcs (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:81:4: W0611: Unused megatron.core imported as core (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:82:4: W0611: Unused tensor_parallel imported from megatron.core (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:101:4: W0611: Unused init_method_normal imported from megatron.core.utils (unused-import)\nnemo/collections/nlp/models/language_modeling/megatron_gpt_model.py:101:4: W0611: Unused scaled_init_method_normal imported from megatron.core.utils (unused-import)\n\n-----------------------------------\nYour code has been rated at 9.50/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"Jenkins\r\n\r\nOn Fri, Nov 29, 2024, 18:01 github-actions[bot] ***@***.***>\r\nwrote:\r\n\r\n> This PR is stale because it has been open for 14 days with no activity.\r\n> Remove stale label or comment or update 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/pull/11304#issuecomment-2508784312>, or\r\n> unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AGFLGNFCDUJ2JTUJBNITONL2DEL7DAVCNFSM6AAAAABR4EYZ7WVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBYG44DIMZRGI>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"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."
] | 2024-11-15T23:28:22 | 2024-12-24T01:58:46 | 2024-12-24T01:58:45 | COLLABORATOR | {
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} | # What does this PR do ?
Fixes a situation where the SFT loss for MegatronGPTModel for a minibatch will be NaN if loss_mask for at least one sample has all zeros
**Collection**: NLP
# Changelog
- nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py: adds a clamp to the denominator of the SFT loss func
# 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)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"@pzelasko fyi",
"Is this related to https://github.com/lhotse-speech/lhotse/issues/518? @pzelasko ",
"I changed it to 1 worker and it is much more stable",
"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."
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} | null | null | null | **Describe the bug**
When training a model consuming more memory, I noticed that my training would stop after a constant number of epochs. Upon further investigation, I found that during training / validation, the memory usage of the CPU memory (RAM) continues to rise and never get released, which results in OOM after a number of epochs. This happens while I was using the Lhotse dataloader, while I was able to train a small fast-conformer CTC model for hundreds of epochs, the same version of NeMo only is able to run the training for an XL fast-conformer CTC model for ~20 epochs. ~110 epochs if I use 1/4 of the works for the dataloader. So somehow the dataloader is not releasing memory for the data loaded.
**Steps/Code to reproduce bug**
- shows up in XL fast-conformer CTC and medium fast-conformer CTC RNNT hybrid
- standard configs for these models in terms of hyperparameters
- other lhotse related configs:
```
trainer:
devices: -1
num_nodes: 1
max_epochs: 150
max_steps: 150000
val_check_interval: 1000
accelerator: auto
strategy: ddp
accumulate_grad_batches: 1
gradient_clip_val: 1.0
precision: bf16-mixed
log_every_n_steps: 200
enable_progress_bar: true
num_sanity_val_steps: 1
check_val_every_n_epoch: 1
sync_batchnorm: true
enable_checkpointing: false
logger: false
benchmark: false
use_distributed_sampler: false
limit_train_batches: 1000
train_ds:
manifest_filepath: null
sample_rate: 16000
batch_size: null
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 45
min_duration: 1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
shar_path:
- xxxxx
use_lhotse: true
bucket_duration_bins:
- xxx
batch_duration: 600
quadratic_duration: 30
num_buckets: 30
bucket_buffer_size: 10000
shuffle_buffer_size: 10000
num_cuts_for_bins_estimate: 10000
use_bucketing: true
```
**Expected behavior**
Training should continue until specified stop.
**Environment overview (please complete the following information)**
- GCP
- Method of NeMo install: poetry: nemo-toolkit = {version = "2.0.0rc1", extras = ["asr"]}
**Environment details**
- nemo-toolkit 2.0.0rc1 / 2.0.0
- PyTorch-lightning 2.4.0
- PyTorch 2.2.2+cu121
- Python 3.11.8
**Additional context**
GPU: A100 40G.
@nithinraok ~~has told me to try using limit_validation_batches, use smaller duration audios, use fully_randomized which I haven't fully tested. I'll report back when these are tested but the issue persists so far~~
Edit: tried these suggestions, still OOM.
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"merged_at": "2024-11-15T23:38:21"
} | # What does this PR do ?
Remove redundant documentation.
**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 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?
- [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
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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This PR broke other models and was unnecessarily fixing gptnext when I should have just used llama | {
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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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} | # 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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-11-15T14:37:18 | 2024-12-24T01:58:47 | 2024-12-24T01:58:47 | NONE | {
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} | null | null | null | **Describe the bug**
In the current implementation of HF AutoTokenizer, the tokenizer does not behave in the same way as the HF version. I noticed the problem with the BOS token (which is extremely important for the Gemma 2 model). Specifically, the problem occurs with `text_to_ids` function, https://github.com/NVIDIA/NeMo/blob/ed244d943948b105982f5cb17b51991e8acebfb7/nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py#L222-L225 which is relevant for `preprocess_data_for_megatron.py`. The problem is that this function first transforms text to tokens and then tokens to IDs and consequently does not append the BOS token at the beginning as the HF encode method does (I assume the problem applies to other special tokens as well).
**Steps/Code to reproduce bug**
```
from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer as ATNemo
from transformers import AutoTokenizer as ATHF
tokenizer_path = "google/gemma-2-9b"
hf_tokenizer = ATHF.from_pretrained(tokenizer_path)
nemo_tokenizer = ATNemo(tokenizer_path)
text = "Text to tokenize"
# Common HF tokenization
ids = hf_tokenizer.encode(text)
print("HF tokenization:", ids)
# NeMo tokenization
ids = nemo_tokenizer.text_to_ids(text)
print("NeMo tokenization:", ids)
# HF tokenization using NeMo steps
tokens = hf_tokenizer.tokenize(text)
ids = hf_tokenizer.convert_tokens_to_ids(tokens)
print("HF tokenization using NeMo steps:", ids)
```
The outputs for provided code snippet are:
```
HF tokenization: [2, 1637, 577, 223491]
NeMo tokenization: [1637, 577, 223491]
HF tokenization using NeMo steps: [1637, 577, 223491]
```
Notice how HF tokenization have token ID 2 at the beginning, which corresponds to the BOS token.
**Expected behavior**
I expect NeMo HF wrapper to behave the same way as the HF tokenizer. The solution for this case would be to change the `text_to_ids` function to:
```
def text_to_ids(self, text):
ids = self.tokenizer.encode(text)
return ids
```
Is there any reason why it isn't implemented this way?
**Environment overview (please complete the following information)**
I was using official NeMo container (24.09). However, I see that code for HF wrapper is still the same on main branch, so the issue should still apply.
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"<!-- pylint-output-strict-mode-true -->\n\nbeep boop 🤖: 🚨 The following files must be fixed before merge!\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module /home/runner/work/NeMo/NeMo/.pylintrc\n.pylintrc:1:0: W0012: Unknown option value for '--enable', expected a valid pylint message and got 'C9001' (unknown-option-value)\n************* Module Command line or configuration file\nCommand line or configuration file:1:0: E0013: Plugin 'tests.no_import_experimental' is impossible to load, is it installed ? ('No module named 'tests'') (bad-plugin-value)\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.core.config.base_config\nnemo/core/config/base_config.py:18:0: W0611: Unused experimental imported from megatron.core (unused-import)\n\n-----------------------------------\nYour code has been rated at 8.57/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
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] | 2024-11-15T14:29:47 | 2024-12-07T02:03:48 | 2024-12-07T02:03:47 | COLLABORATOR | {
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} | # What does this PR do ?
This adds a new directory `nemo.experimental` where experimental code can be added. This follows the ask to allow for Megatron experimental code.
A custom pylint checker was added to prevent leakage of experimental code into production code. This is done by prohibiting importing `nemo.experimental` or `megatron.core.experimental` anywhere else than inside of `nemo.experimental` or `tests.experimental`.
**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 re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-11-15T07:54:59 | 2024-12-07T02:03:49 | 2024-12-07T02:03:49 | COLLABORATOR | {
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Interface for asymmetric pipeline schedule, where the first and the last Transformer layers use a dummy layer so they have only embedding and logit+loss functions.
# Changelog
- Add `standalone_embedding_stage` and `standalone_loss_stage` to enable dummy Transformer layer in the first and last Transformer layers.
- Support both nemo 1.0 and 2.0
# Usage
Set `standalone_embedding_stage=True` and `standalone_loss_stage=Trrue`.
# GitHub Actions CI
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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)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] 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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"LGTM"
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} | # What does this PR do ?
Fix broken links
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.export.trt_llm.converter.model_to_trt_llm_ckpt\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:223:0: C0301: Line too long (121/119) (line-too-long)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:241:0: C0301: Line too long (125/119) (line-too-long)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:325:0: C0301: Line too long (128/119) (line-too-long)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:41:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:47:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:55:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:72:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:87:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:97:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:101:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:116:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:278:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:285:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:292:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:492:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:17:0: W0611: Unused import math (unused-import)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:22:0: W0611: Unused numpy imported as np (unused-import)\nnemo/export/trt_llm/converter/model_to_trt_llm_ckpt.py:24:0: W0611: Unused torch_to_numpy imported from tensorrt_llm._utils (unused-import)\n\n-----------------------------------\nYour code has been rated at 9.37/10\n```\n\n---\n\nThank you for improving NeMo's documentation!",
"[🤖]: Hi @meatybobby 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11848453879) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g"
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} | # What does this PR do ?
Add openai-gelu in gated activation for TRTLLM export
**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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