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# What does this PR do ? **Collection**: [ASR, NLP, TTS] # Changelog Fixes bugs and typos in - https://nvbugspro.nvidia.com/bug/4413845 - https://nvbugspro.nvidia.com/bug/4411322 - https://nvbugspro.nvidia.com/bug/4410625 - https://nvbugspro.nvidia.com/bug/4407526 - https://nvbugspro.nvidia.com/bug/4405781 - https://nvbugspro.nvidia.com/bug/4426768 # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Use NLPDDPStrategyNotebook in Multitask_Prompt_and_PTuning.ipynb
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Critical CVE in `transformers` in versions < `4.36.0`
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[ "Thanks for letting us know. @athitten could you take a look if we can safely unpin Transformers ? Fyi @ericharper ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "Any news related to this issue?", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-20T11:51:44
2024-03-01T01:45:25
2024-03-01T01:45:25
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The `transformers` requirement is bounded here https://github.com/NVIDIA/NeMo/blob/39d883f16ce57f06c40be19a99c881324b93360d/requirements/requirements_lightning.txt up to version `4.33.3` There is a Critical CVE affecting versions < `4.36.0`, related information - https://www.cve.org/CVERecord?id=CVE-2023-6730 - https://github.com/huggingface/transformers/commit/1d63b0ec361e7a38f1339385e8a5a855085532ce - https://huntr.com/bounties/423611ee-7a2a-442a-babb-3ed2f8385c16/ This also would solve https://github.com/NVIDIA/NeMo/issues/7765 and https://github.com/NVIDIA/NeMo/pull/7620#issuecomment-1745874618
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[ "I managed to reproduce🙃" ]
2023-12-20T10:06:32
2023-12-25T03:30:51
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**Describe the bug** Refer to this blog, training LLaMA 13B with NeMo 23.08 could achieve 2357 Tokens/s/GPU ![image](https://github.com/NVIDIA/NeMo/assets/73142299/da55a7eb-0992-440c-9c32-4aba21150d99) Yet, I can only achieve under 1900 Tokens/s/GPU ![image](https://github.com/NVIDIA/NeMo/assets/73142299/4f5a5626-2e8b-42aa-a4f3-f14ba4973d20) **Steps/Code to reproduce bug** I'm running on nvcr.io/nvidia/nemo:23.08 with 8xA100 Here is my launching script ``` INDEX_MAPPING_DIR="" DATA_PREFIX="" TOKENIZER_DIR="" TP_SIZE=4 PP_SIZE=1 torchrun --nproc_per_node=8 \ /workspace/nemo/examples/nlp/language_modeling/megatron_llama_pretraining.py \ trainer.precision=bf16 \ trainer.devices=8 \ trainer.num_nodes=1 \ model.num_layers=40 \ model.hidden_size=5120 \ model.ffn_hidden_size=13824 \ model.num_attention_heads=40 \ model.num_query_groups=40 \ model.micro_batch_size=1 \ model.global_batch_size=32 \ model.tensor_model_parallel_size=${TP_SIZE} \ model.pipeline_model_parallel_size=${PP_SIZE} \ model.megatron_amp_O2=False \ model.sequence_parallel=True \ model.activations_checkpoint_granularity=selective \ model.activations_checkpoint_method=uniform \ model.optim.name=distributed_fused_adam \ model.optim.lr=2e-4 \ model.tokenizer.model=${TOKENIZER_DIR} \ model.data.index_mapping_dir=${INDEX_MAPPING_DIR} \ ++model.data.data_prefix=${DATA_PREFIX} \ exp_manager.create_wandb_logger=False \ exp_manager.explicit_log_dir=/results \ exp_manager.resume_if_exists=False \ exp_manager.resume_ignore_no_checkpoint=True \ exp_manager.create_checkpoint_callback=True \ exp_manager.checkpoint_callback_params.monitor=validation_loss \ ++cluster_type=BCP ``` The `megatron_llama_pretraining.py` ``` import torch.multiprocessing as mp from omegaconf.omegaconf import OmegaConf, open_dict from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder from nemo.core.config import hydra_runner from nemo.utils import logging from nemo.utils.exp_manager import exp_manager mp.set_start_method("spawn", force=True) @hydra_runner(config_path="conf", config_name="megatron_llama_config") def main(cfg) -> None: logging.info("\n\n************** Experiment configuration ***********") logging.info(f'\n{OmegaConf.to_yaml(cfg)}') trainer = MegatronTrainerBuilder(cfg).create_trainer() exp_manager(trainer, cfg.exp_manager) model = MegatronGPTModel(cfg.model, trainer) trainer.fit(model) if __name__ == '__main__': main() ``` **Expected behavior** Do I have to modify the configuration to get better performance? If so, could you show the config🥺?
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Fix https://nvbugspro.nvidia.com/bug/4407666
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# What does this PR do ? mirror changes in https://github.com/NVIDIA/NeMo/pull/8056
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Fix https://nvbugspro.nvidia.com/bug/4407666
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Fixing wrapper and moving it to base class
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# What does this PR do ? 1) Move wrapper to base class 2) Need to pass in args[module] = self.model to bert model **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] 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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2023-12-19T21:56:59
2024-07-15T01:52:40
2024-07-15T01:52:39
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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2023-12-19T19:14:13
2023-12-19T19:55:08
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# What does this PR do ? Fixes a bug where inference on CPU was segfaulting. **Collection**: Tutorials # Changelog - Moved tensor to GPU when possible # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Mistral 7b conversion script
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[ "Could you please keep the conversion script consistent with other ones? I specifically mean:\r\n* https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/convert_mpt_7b_hf_to_nemo.py\r\n* https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/convert_starcoder_hf_to_nemo.py\r\n* https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/convert_hf_falcon_to_nemo.py\r\n\r\nIn particular, at best we keep these aligned across scripts:\r\n* defining a new `convert_state_dict` function\r\n* config handling (`override_model_dict` etc.)\r\n* script parameters.\r\n\r\n[Llama2 conversion script](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py) has a bit different flavour, but I believe we should follow the \"majority\" style for new scripts (and align Llama2 at some point).", "As for architecture, Mistral seems to heavily borrow from Llama2.\r\n\r\nWould it be possible to just extend Llama2 conversion script to cover HF Mistral checkpoint?", "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.", "Hi @janekl thanks for your comments! I'm leaning towards merging this as is and once we settle on how we want to refactor the import scripts do the actual refactor. I think you are right it shares a lot with the Llama script, and once the PRs you mention have been approved & merged we can follow the appropriate guidelines here as well.", "jenkins", "jenkins", "I think that adding a Jenkins conversion test for a small Mistral model would be really desirable. \r\n\r\nExamples are here https://github.com/NVIDIA/NeMo/blob/f10d6941d7c18a0154d41c943deaae4d65a4b752/Jenkinsfile#L391-L419\r\n\r\ncc @ericharper ", "> Hi @janekl thanks for your comments! I'm leaning towards merging this as is and once we settle on how we want to refactor the import scripts do the actual refactor. I think you are right it shares a lot with the Llama script, and once the PRs you mention have been approved & merged we can follow the appropriate guidelines here as well.\r\n\r\nSorry for replying late. That's fine, we can polish it later, there is some consistency PR open here https://github.com/NVIDIA/NeMo/pull/8192.\r\n\r\nIn any case, please see my comments in two other threads. We really need to test conversion in analogy to other scripts. Otherwise we won't have any confidence in refactoring work later. I think @ericharper should support this extra effort." ]
2023-12-19T17:32:25
2024-01-25T18:45:57
2024-01-25T18:45:56
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# What does this PR do ? Adds conversion script for mistral-7b to NeMo. **Collection**: [Note which collection this PR will affect] nlp/language_modeling # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fix crash when old checkpoint does not exist anymore
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[ "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale.", "Re-opening, can someone please help review this minor change?", "+1 on this issue", "Closing as this issue has been addressed in #8130" ]
2023-12-19T14:16:06
2024-01-22T16:40:03
2024-01-22T16:40:02
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# What does this PR do ? This prevents NeMo from crashing when trying to delete a checkpoint that has already been deleted. This situation may happen for instance after manually fixing corrupted checkpoints. Note that I believe PTL also has a similar safety guard: https://github.com/Lightning-AI/lightning/blob/119039b932c79f1e5daef67345628e675ab3c0bf/src/lightning/fabric/plugins/io/torch_io.py#L94 **Collection**: nlp # Changelog - Fix crash when attempting to delete an old checkpoint that does not actually exist anymore # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [X] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Broken links in main README (leading to NVIDIA docs)
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[ "The docs should be up and running again, I'll inform the team about the broken links. ", "Links are updated, pls let us know if you find any issues. " ]
2023-12-19T12:53:20
2023-12-23T17:54:40
2023-12-23T17:54:40
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Links in README lead to docs on NVIDIA website, but at least some of them seem to be broken. I checked the NLP section, specifically these two: NLP [NeMo Megatron pre-training of Large Language Models](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html) [Neural Machine Translation (NMT)](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/machine_translation/machine_translation.html) and the links send me to a page with this error: An error occurred while processing your request. Reference #30.4cf01202.1702990306.31b50b8c Worth fixing the links
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crash when run gpt with ub_tp_comm_overlap
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[ "I found that enabling ub_tp_comm_overlap will create a new mpi process group, which will cause problems. I don't know if it has something to do with the mpi version.", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-19T08:26:51
2024-01-27T01:43:31
2024-01-27T01:43:31
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**Describe the bug** crash when run gpt with ub_tp_comm_overlap ![image](https://github.com/NVIDIA/NeMo/assets/30709861/d15b8c2c-f266-4990-a71f-ebe1e1b9a6b0) **Environment overview (please complete the following information)** - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] - Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install. - If method of install is [Docker], provide `docker pull` & `docker run` commands used **Environment details** PyTorch Docker: nvcr.io/nvidia/pytorch 23.10-py3 **Additional context** Add any other context about the problem here. Example: GPU model
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[SE Tutorial] USe GPU for inference, when available
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2023-12-19T01:42:32
2023-12-19T19:13:51
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# What does this PR do ? Fixes a bug where inference on CPU was segfaulting. **Collection**: Tutorials # Changelog - Moved tensor to GPU when possible # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Wer fix
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[ "@titu1994 <- `wer` metric changes as discussed in thread. (gonna just do the refactoring first before deciding on what to do with bleu)\r\n\r\n@AlexGrinch <- fly by change for `transformer_bpe`", "> Wait what exactly is going on with the removal of decoding logic?? Revert that\r\n\r\nWhich, moving the decoding into `parts` or aliasing the `prediction_tensors`?\r\n\r\nIf the former: there's no reason to keep the decoding in metrics since they're not reflecting respective models anymore. We also don't put any other decoding in metrics, only WER has that, and it's really confusing to have to look in a metrics folder for decoding classes. Given we keep the other decoding stuff in `parts.submodules`, it seems like they're better off over there. \r\n\r\nIf the latter: yeah I don't like it, but it's either there or we perform a check in the forward loop. Better solution would be renaming `{rnnt/ctc}_predictions_tensor` to just `predictions_tensor`, but I think people are just accustomed to that name scheme at this point.", "@sam1373 can you review just the metrics files (wer.py, wer_bpe.py) to make sure stuff seems correct for SSL ?", "jenkins ", "Aight, added back in the `fold_consecutive` flag. Still trying to keep the `update` pass model agnostic so had to be a bit hacky with the `__init__`. If that's not gonna work, I may have to either add a conditional in the `update` pass or edit the `ctc_decoding_predictions_tensor` to allow setting the `fold_consecutive` flag. ", "jenkins", "CI seems to have failed ?", "> CI seems to have failed ?\r\n\r\nYeah someone must have added another test over the break. Lemme check", "> CI seems to have failed ?\r\n\r\nAight, seems like I messed up a variable assignment in the lambda call. All my pytests passing on local now (exceping that weird tokenization bug in `test_asr_rnnt_encodec_model`", "jenkins", "jenkins", "jenkins", "@titu1994 I'm getting through unittests but Jenkins seems to be breaking down around when running Speaker Diarization. Don't really have access to the backend but none of it seems related to my PR.", "You can click the link in Jenkins. I'll discuss offline ", "jenkins ", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "@titu1994 Jenkins is happy. Could I get a final review one last time? (Before this extends into yet more modules.)", "jenkins" ]
2023-12-19T01:37:04
2024-01-05T23:45:52
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. - Reformats `collections.asr.metrics` wer classes into a single class for model modularity. **Collection**: [Note which collection this PR will affect] ASR TTS Multimodal # Changelog - Refactored all `wer` metrics into a single metric - Moved `ctc_decoding` and `rnnt_decoding` (plus configs) into `parts.submodules` - Updated ASR tests to reflect new metric classes - Updated ASR tutorials to reflect new metric classes and decoder locations - Updated tts/g2p to reflect new metric classes. - Updates speech_cv to reflect new metric classes and decoder locations. - Added `wer` getter and setters to all ASR, Speech_cv, and G2P models to allow access to WER metric - Flyby update of `transformer_bpe` to address translation issue. # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. jenkins # Before your PR is "Ready for review" **Pre checks**: - [Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [Y] Did you write any new necessary tests? - [N] Did you add or update any necessary documentation? - [N] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [Y] New Feature - [Y ] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Temporary pin Lightning-Utilities version due to broken NamedTuple
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2023-12-18T21:03:44
2023-12-29T07:37:01
2023-12-29T07:37:01
CONTRIBUTOR
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# What does this PR do ? Pin Lightning-Utitilies package version due to broken NamedTuple in `0.10.0` https://github.com/Lightning-AI/utilities/pull/160 introduced the issue, https://github.com/Lightning-AI/utilities/blob/a00e1144dd300e3bc2fc8746a8e85510ffe35aa7/src/lightning_utilities/core/apply_func.py#L67 here NamedTuple force converted to tuple. This fixes the bug https://nvbugspro.nvidia.com/bug/4405798 (broken `ASR_TTS_Tutorial`) **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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update asr eval
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2023-12-18T18:34:31
2023-12-18T22:24:33
2023-12-18T22:24:32
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Update ASR evaluator to support ignoring punctuations and/or capitalizations in both labels and predictions.
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cherry pick bug 4405781
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2023-12-18T18:06:48
2023-12-18T19:22:06
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# What does this PR do ? Fix Common Voice data download. **PR Type**: - [ ] New Feature - [V] Bugfix - [ ] Documentation # Additional Information * Related to bug 4405781
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Nemo Documentation User Guide doesn't work
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2023-12-18T12:32:08
2023-12-19T17:53:06
2023-12-19T17:53:05
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**Describe the bug** Nemo Documentation User Guide doesn't work Link: https://docs.nvidia.com/deeplearning/nemo/user-guide/index.html Output Message: An error occurred while processing your request. Reference #30.3ff01202.1702902642.32d3d058
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fix gated_linear_unit bug
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2023-12-18T12:19:13
2024-01-04T16:35:43
2024-01-04T16:35:19
CONTRIBUTOR
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# What does this PR do ? Fix the issue when running Llama2 eval or LLama2 SFT. ``` megatron.core.dist_checkpointing.core.CheckpointingException: Global shape mismatch for loaded ((32, 22016, 4096)) and expected ((32, 11008, 4096)) tensor for key model.decoder.layers.mlp.linear_fc1.weight ``` **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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8,041
Unexpected Space Removal Before Punctuation in ASR Model Decoding
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[ "We are aware of this, hence the Todo to remove it. But our condition for removal is that we update all the PC models we release such that they do not have spaces between special tokens. So the removal won't have an effect at that point since all the models will anyway emit no space between special tokens. \r\n\r\nThe simplest solution to this problem is to collapse the space between special tokens in both the ground truth and the predicted models, rather than keep the spaces and anyway get incorrect metrics during inference. ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-18T09:47:05
2024-01-25T01:47:44
2024-01-25T01:47:44
NONE
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Hello, I've come across an issue in the `nemo/collections/asr/metrics/wer.py` file, specifically within the `decode_hypothesis` function. There's a particular segment of code, marked for future removal (noted with a TODO comment) at line 546, which I believe should be eliminated. This issue becomes apparent during the training of models that include punctuation marks as separate tokens, mirroring the ground-truth data which incorporates spaces preceding these punctuation marks. The problematic code collapses these spaces during decoding, resulting in significantly higher WER and loss calculations due to mismatches in space formatting. This code segment appears redundant when training models without punctuation marks, as it has no effect in such scenarios. It only impacts models trained with punctuation in the specific context I've outlined. For instance, consider the ground-truth data: `This is a test , with some extra spaces .` and its prediction: `This is a test, with some extra spaces.` This alters the prediction by removing spaces before the punctuation marks. Here's the code snippet: `# TODO: remove` ` # collapse leading spaces before . , ? for PC models` ` hypothesis = re.sub(r'(\s+)([\.\,\?])', r'\2', hypothesis)` ` Could you please confirm if you're aware of this and if there are plans to remove this section of code? Thank you
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The 'get_commonvoice_data.py' script is unable to download dataset from common voice
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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-16T09:18:31
2024-01-31T01:44:09
2024-01-31T01:44:09
CONTRIBUTOR
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The script is being used in the tutorial `Fine-tuning CTC Models on New Languages (https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb#scrollTo=Inwx4OE97guu)` to download the Japanese dataset from Mozilla's Common Voice. However, the download link to the script is no longer valid (I opened it on my browser) and it throws the error `ERROR 404: Not Found.` in colab. I also discovered that the download links generated for the common Voice datasets are not permanent links. What can be done about this? ![common_voice_error](https://github.com/NVIDIA/NeMo/assets/52848633/2539911b-aeca-4908-9327-d7a839b09379)
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Adding method back that was removed accidentally
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[ "jenkins", "@ericharper should we move this to the base model instead ?", "jenkins", "> @ericharper should we move this to the base model instead ?\r\n\r\nYes, let's do it in another pr." ]
2023-12-16T01:11:43
2023-12-16T08:58:20
2023-12-16T08:58:19
COLLABORATOR
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Cache Aware Streaming tutorial notebook [old PR, closed]
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-15T23:16:09
2024-02-01T01:42:15
2024-01-31T01:16:46
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Update: this PR is closed, replaced with https://github.com/NVIDIA/NeMo/pull/8296 cc @vsl9, @VahidooX # What does this PR do ? Adding the notebook that allows us to do real-time streaming using a cache-aware fastconformer model (supports single-lookahead and multi-lookahead models). **Collection**: `tutorials/asr` # Changelog - Add cache-aware streaming notebook in `tutorials/asr` - Add call to `self.setup_streaming_params` inside `set_default_att_context_size` method of `ConformerEncoder`. # Usage # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Restore O2 FP16/BF16 wrapper for GPT
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2023-12-15T23:14:24
2023-12-18T20:14:55
2023-12-18T20:14:44
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# What does this PR do ? When running GPT with O2 enabled, I am seeing errors at the following line since `_wrap_model_for_O2` is not defined: https://github.com/NVIDIA/NeMo/blob/852338459c78c257a2b5f6688c0b1a5d2b048366/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L266 This function was removed in https://github.com/NVIDIA/NeMo/pull/7814. I see that this PR added the function to BERT, so could this be a bug? **Collection**: NLP # Changelog - Restore `_wrap_model_for_O2` function in GPT # Usage Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml, and set `model.megatron_amp_O2=True` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] 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. Pinging @shanmugamr1992 # Additional Information * Bug from https://github.com/NVIDIA/NeMo/pull/7814
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Nvidia nemo blog and Apples to Oranges comparison of H200 vs A100
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2023-12-15T22:57:16
2024-03-02T01:43:20
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Can someone from nemo fix this performance comparison and have A100 also on the same nemo version as the H200? The same blog is posted in this repo readme as indication of nemo performance numbers. https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/ A100: Nemo 23.08 H200: Nemo 24.01-alpha I do not understand why Nvidia would publish this chart using different versions of nemo. Is A100 not supported in the latest nemo alpha/git release? Please help the community by doing apples to apples comparison, or provide the code/data to reproduce the data. Moreover, most of us do not have access to 8xA100 or 8XH100 to verify the results so it is even more important we, as potential end users, see true apples to apples comparisons. Thanks. ![image](https://github.com/NVIDIA/NeMo/assets/417764/209d4151-b0f0-4222-831f-4aa0536b7a39)
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Handle float limit_val_batches
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[ "@athitten Can you write unit tests for this? It would be nice to have at leas a docsting (it could be in the test itself) describing what is the expected behaviour of this function and what purpose does it serve.\r\n\r\nIf this code is here to fix a bug in PTL or cover some case not covered by PTL please describe what it is and write a comment in the code. This looks like a hack to me and should be properly commented to help future developers understand what's going on in this function and when potentially can it be removed.", "jenkins", "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-15T13:40:55
2024-01-19T01:46:51
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Upgrade to PTL 2.1 and 2.2
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2023-12-15T03:47:43
2024-03-08T19:26:29
2024-03-08T19:26:28
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# What does this PR do ? Upgrades NeMo to use PTL 2.1 and 2.2. NeMo now uses PTL 2.2.1 (the current latest release). Major changes that are seen across a lot of files in the PR: 1. The function signature of `train/validation/test_step` is changed while using the `dataloader_iter` API. The `batch_idx` arg is removed from the function signature and `next(dataloader_iter)` now returns `batch, batch_idx, dataloader_idx`. With this change the support for multiple dataloaders which was removed with PTL 2.0 is added back. [More details here](https://lightning.ai/docs/pytorch/stable/upgrade/from_2_0.html#:~:text=predict_step(self%2C%20dataloader_iter%2C%20batch_idx)%20in%20LightningModule-,remove%20batch_idx%20from%20the%20signature%20and%20expect%20dataloader_iter%20to%20return%20a%20triplet%20(batch%2C%20batch_idx%2C%20dataloader_idx),-PR18390) 2. PTL 2.1 and above does not support having both precision and precision plugin in the trainer. It can be observed in the changes that if precision plugin is created, then precision is made None. 3. Some of the other simple changes are explained with comments in the PR. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Jiaqiz/option to disable adapters & merge all lora layers
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2023-12-15T01:04:20
2024-02-23T04:17:34
2024-02-23T04:17:34
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# What does this PR do ? Add a condition for all adapters modules: run through the adapter only when it is enabled Add merging script for all lora layers. # Usage To enable or disable an adapter, we can use ```python AdapterModuleMixin.set_enabled_adapters(name, enabled = True) AdapterModuleMixin.set_enabled_adapters(name, enabled = False) ``` If ```name``` is not provided, then all adapters will be enabled/disabled. For example, one can disable all adapters using the following codes, which is equivalent to use only the base model. ```python from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin for _, module in model.named_modules(): if isinstance(module, AdapterModuleMixin) and module.is_adapter_available(): module.set_enabled_adapters(enabled=False) ``` **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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NeMo Multimodal Docs and Tests Initial PR
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2023-12-14T18:15:38
2024-01-16T22:49:57
2024-01-16T22:49:56
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# What does this PR do ? Add Docs and Tests to NeMo Multimodal **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Support torch jit script
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2023-12-14T14:09:06
2024-01-12T19:27:49
2024-01-12T19:27:48
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# What does this PR do ? Fix compatibility of the core classes (`NeuralModule`, `Exportable`, etc.) with `torch.jit.script`. Currently inheriting from `NeuralModule` instead of `nn.Module` breaks `torch.jit.script` compiler. **Collection**: [Core] # Changelog - Add specific line by line info of high level changes in this PR. # Usage ```python class SimpleLinear(NeuralModule): def __init__(self): super().__init__() self.linear = nn.Linear(10, 20, bias=False) def forward(self, x): return self.linear(x) module = torch.jit.script(SimpleLinear()) x = torch.zeros(2, 10) result = module(x) ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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8,026
Fix defer_setup issue of failing to create validation and test dataloaders
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-14T05:45:03
2024-01-18T01:46:03
2024-01-18T01:46:03
NONE
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# What does this PR do ? After #7455 , `ModelPT.val_dataloader()` and `ModelPT.test_dataloader()` return empty lists instead of None. This modification causes `ModelPT.setup()` failed to pass the 'if' statements at line 835 and 844, and the dataloaders are therefore remaining empty lists without construction: https://github.com/NVIDIA/NeMo/blob/8216178cfaf89228884bd1e4205776a2667c6e51/nemo/core/classes/modelPT.py#L829-L845 This PR changes the `self.xxx_dataloader() is None` statements into `not self.xxx_dataloader()`, such that both "None" and empty lists could fit the condition. **Collection**: All # Changelog - change `self.xxx_dataloader() is None` into `not self.xxx_dataloader()`, so as to handle both None and empty lists ``` def setup(self, stage: Optional[str] = None): ... if stage in ('fit', 'validate'): val_deferred_setup = ... if not self.val_dataloader() and val_deferred_setup: self.setup_multiple_validation_data(val_data_config=self._cfg.validation_ds) if stage == 'test': test_deferred_setup = ... if not self.test_dataloader() and test_deferred_setup: self.setup_multiple_test_data(test_data_config=self._cfg.test_ds) ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation # Additional Information * Related to #5569 , #7455
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I_kwDOC_bI7s55nDSO
8,025
GPTSFTChatDataset loss_mask becomes all False when prompt length > max_seq_length
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[ "I ran into the same issue => hacked the code to zero out the loss on problematic micro-batches: https://github.com/odelalleau/NeMo/commit/b383e6a1e55dd315fa194c0a8fafd7ee3fef3437\r\n\r\n(obviously not a proper fix but can be useful to get unblocked)", "Yeah, this will also fix the problem. ", "This issue is 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 should keep active since the same problem has been encountered by others as well.", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-13T18:46:32
2024-02-24T01:43:03
2024-02-24T01:43:03
NONE
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**Describe the bug** In the `GPTSFTChatDataset`, if the first prompt length exceeds `max_seq_length`, all following turns are truncated out. Then the `loss_mask` becomes all `False` for the example. https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py#L359 This is problematic because, when the `loss_mask` is all False, the `loss` of the MegatronGPTModel will be `nan`. https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1015 **Steps/Code to reproduce bug** This can be reproduced by passing into `GPTSFTChatDataset` an example whose first turn prompt has > 2048 tokens when the `max_seq_length=2048`. Then use the `GPTSFTChatDataset` in a supervised fine-tuning job (e.g., `train_gpt_sft.py` in [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_gpt_sft.py)) **Expected behavior** In the `collate_fn` function, check if the loss_masks of all examples are not empty. If not, raise an error.
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Can I use a Sentencepiece tokenizer model from my ASR model in order to train a NeuralRescorer?
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[ "Closing this issue as it was already answered here:\r\n\r\nhttps://github.com/NVIDIA/NeMo/issues/4390" ]
2023-12-13T16:03:29
2023-12-19T15:09:15
2023-12-19T15:09:15
CONTRIBUTOR
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Hi, I would like to train a Neural Rescorer model for ASR using the transformer_lm.py script. I have one small question: In the documentation it says: "Our LMs support all tokenizers available in NeMo, but require special beginning-of-string <bos> and end-of-string <eos> tokens." But I imagine that the tokenizer of the TransformerLM must be the same as the tokenizer of the ASR model, right? Can I pass the tokenizer model of the ASR model directly to TransformerLM? Should I add a <bos> and <eos> token and change the size of the tokenizer? ( but I imagine the ASR and Transformer should have the same tokenizer size) Could you help me on this matter? Thank you in advance!
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Finetuning
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[ "Hi, apologies for late response. \r\n\r\nHave you looked at these sections on finetuning: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/configs.html#fine-tuning-configurations and https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/examples/kinyarwanda_asr.html#training-from-scratch-and-finetuning \r\n\r\nLet us know if you have issues using the scripts. ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-12T20:44:19
2024-01-31T01:44:11
2024-01-31T01:44:10
NONE
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How is it possible to finetune the citrinet model? On nvidias documentation I saw something similar to TAO toolkit but never saw any example on it. On the other hand I saw the japanese finetuning process. And for me I’m spotting for a wer of less 5%. I got a deadline in less than 3 weeks , can someone help ?!
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Temporary pin Lightning-Utilities version due to broken NamedTuple
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[ "jenkins", "@titu1994 It seem to be a know issue, hope will be fixed soon https://github.com/Lightning-AI/utilities/issues/196", "Merging for now - fyi @ericharper @athitten ", "Don't merge this to main for now (unless Eric or @athitten gives the go ahead for main)", "@titu1994 The issue is fixed in Lightning-Utilities in `main`, waiting for the next release: https://github.com/Lightning-AI/utilities/pull/210\r\n" ]
2023-12-12T17:35:49
2023-12-20T18:57:17
2023-12-18T21:02:59
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# What does this PR do ? Pin Lightning-Utitilies package version due to broken NamedTuple in `0.10.0` https://github.com/Lightning-AI/utilities/pull/160 introduced the issue, https://github.com/Lightning-AI/utilities/blob/a00e1144dd300e3bc2fc8746a8e85510ffe35aa7/src/lightning_utilities/core/apply_func.py#L67 here NamedTuple force converted to tuple. This fixes the bug https://nvbugspro.nvidia.com/bug/4405798 (broken `ASR_TTS_Tutorial`) **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Check `torchaudio` dependencies in installation script
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# What does this PR do ? Check `torchaudio` dependencies. Exit with the appropriate message if some dependencies are not installed. Fixes bug: https://nvbugspro.nvidia.com/bug/4403803 **Collection**: [CI] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fine-tuning Conformer CTC with Conformer-CTC streaming model
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[ "should be possible. Are you facing any issues while finetuning? \r\n@VahidooX to confirm. ", "Not for offline model to streaming. You can do this if you keep the encoder architecture the same, with the flags for loading a checkpoint inside the maybe init from checkpoint function. You will have to exclude the decoder and joint weights when you specify the RNNT model checkpoint inside your ctc train script. \r\n\r\nhttps://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/core/api.html#nemo.core.ModelPT.maybe_init_from_pretrained_checkpoint", "The convolutions in streaming models are different from the offline models, so not sure if it is possible. Names can be different. Have not tried it, but it is better to start from a streaming model instead. We have some pretrained streaming checkpoints already for English. You may keep the decoder if load from a ctc to another ctc.", "Oh I just realized user wants to train offline first and use it for streaming, sadly this may not be possible as Vahid pointed out. I will update my comment above ", "The conformer CTC model is an encoder only architecture. So I tried to initialize and offline Conformer-CTC for the conformer-CTC streaming architecture and then ran the finetune on just 100hrs of train data with the streaming configs. It still worked and gave much lower WER in the zeroth epoch itself. Which means that it is indeed possible!!", "That's pretty amazing ! Wasn't there shape mismatch when loading the checkpoint ?", "Sorry my mistake. There was no error as such and the online Conf-CTC checkpoint loaded correctly. On fine-tuning with conf-CTC streaming, it ran without an error but to my surprise the .nemo model generated still contains model.config which is non-streaming. If that is the case then how did some part of the streaming configs enabled training to proceed as usual in the first place? Please comment if this sounds like an error - \r\n\r\n'''\r\n[NeMo W 2024-01-04 04:28:17 exp_manager:900] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.\r\n[NeMo W 2024-01-04 04:28:19 modelPT:142] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\r\n Train config :\r\n manifest_filepath: ../../data/hindi_normalized/filtered_hindi_v1_manifest.json\r\n sample_rate: 16000\r\n batch_size: 24\r\n shuffle: true\r\n num_workers: 16\r\n pin_memory: true\r\n use_start_end_token: false\r\n trim_silence: false\r\n max_duration: 30\r\n min_duration: 0.1\r\n is_tarred: false\r\n tarred_audio_filepaths: null\r\n shuffle_n: 2048\r\n bucketing_strategy: synced_randomized\r\n bucketing_batch_size: 8\r\n \r\n [NeMo W 2024-01-04 04:30:11 modelPT:486] Trainer wasn't specified in model constructor. Make sure that you really wanted it.\r\n[NeMo W 2024-01-04 04:30:11 lr_scheduler:890] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !\r\n Scheduler will not be instantiated !\r\nInitializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1\r\n----------------------------------------------------------------------------------------------------\r\ndistributed_backend=nccl\r\nAll distributed processes registered. Starting with 1 processes\r\n----------------------------------------------------------------------------------------------------\r\n\r\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\r\n\r\n | Name | Type | Params\r\n------------------------------------------------------------------------\r\n0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0\r\n1 | encoder | ConformerEncoder | 115 M\r\n2 | decoder | ConvASRDecoder | 66.2 K\r\n3 | loss | CTCLoss | 0\r\n4 | spec_augmentation | SpectrogramAugmentation | 0\r\n5 | _wer | WERBPE | 0\r\n6 | spec_augment | SpectrogramAugmentation | 0\r\n------------------------------------------------------------------------\r\n115 M Trainable params\r\n0 Non-trainable params\r\n115 M Total params\r\n460.710 Total estimated model params size (MB)\r\n2024-01-04 04:30:13.561892: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\nSLURM auto-requeueing enabled. Setting signal handlers.\r\n[W reducer.cpp:1256] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())\r\n'''\r\n", "what is the script you used to finetune streaming model? and model config. \r\nCC: @VahidooX ", "I used the script \"examples/asr/speech_to_text_finetune.py\" and large topology of the config \"examples/asr/conf/conformer/cache_aware_streaming/conformer_ctc_bpe_streaming.yaml\" with 17 conformer layers. Please note that the initial pre-trained model is conformer ctc model with 17 layers (non-streaming).", "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.", "> Sorry my mistake. There was no error as such and the online Conf-CTC checkpoint loaded correctly. On fine-tuning with conf-CTC streaming, it ran without an error but to my surprise the .nemo model generated still contains model.config which is non-streaming. If that is the case then how did some part of the streaming configs enabled training to proceed as usual in the first place? Please comment if this sounds like an error -\r\n> \r\n> ''' [NeMo W 2024-01-04 04:28:17 exp_manager:900] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out. [NeMo W 2024-01-04 04:28:19 modelPT:142] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader. Train config : manifest_filepath: ../../data/hindi_normalized/filtered_hindi_v1_manifest.json sample_rate: 16000 batch_size: 24 shuffle: true num_workers: 16 pin_memory: true use_start_end_token: false trim_silence: false max_duration: 30 min_duration: 0.1 is_tarred: false tarred_audio_filepaths: null shuffle_n: 2048 bucketing_strategy: synced_randomized bucketing_batch_size: 8\r\n> \r\n> ```\r\n> [NeMo W 2024-01-04 04:30:11 modelPT:486] Trainer wasn't specified in model constructor. Make sure that you really wanted it.\r\n> ```\r\n> \r\n> ## [NeMo W 2024-01-04 04:30:11 lr_scheduler:890] Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !\r\n> Scheduler will not be instantiated !\r\n> Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1\r\n> ## distributed_backend=nccl\r\n> All distributed processes registered. Starting with 1 processes\r\n> LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\r\n> \r\n> ## | Name | Type | Params\r\n> ## 0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0\r\n> 1 | encoder | ConformerEncoder | 115 M\r\n> 2 | decoder | ConvASRDecoder | 66.2 K\r\n> 3 | loss | CTCLoss | 0\r\n> 4 | spec_augmentation | SpectrogramAugmentation | 0\r\n> 5 | _wer | WERBPE | 0\r\n> 6 | spec_augment | SpectrogramAugmentation | 0\r\n> 115 M Trainable params 0 Non-trainable params 115 M Total params 460.710 Total estimated model params size (MB) 2024-01-04 04:30:13.561892: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. SLURM auto-requeueing enabled. Setting signal handlers. [W reducer.cpp:1256] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator()) '''\r\n\r\nIs there any upadate on this? " ]
2023-12-12T15:19:04
2024-08-08T03:44:21
2024-02-15T01:44:10
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Is it possible to pre-train a model with conformer-ctc bpe type model and then finetune it with the conformer-ctc streaming model yaml?
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Check `torchaudio` dependencies in installation script
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[ "> Ooh so the issue was missing dependencies and not the torchaudio code itself ? Won't we need to install the dependencies anyway inside container?\r\n\r\n@titu1994 We install the dependencies when building the container https://github.com/NVIDIA/NeMo/blob/main/Dockerfile#L34" ]
2023-12-12T12:32:49
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# What does this PR do ? Check `torchaudio` dependencies. Exit with the appropriate message if some dependencies are not installed. Fixes bug: https://nvbugspro.nvidia.com/bug/4403803 **Collection**: [CI] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Check `torchaudio` dependencies in installation script
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# What does this PR do ? Check `torchaudio` dependencies. Exit if not installed with the appropriate message. Fixes bug: https://nvbugspro.nvidia.com/bug/4403803 **Collection**: [CI] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Use convert_hf_dataset_to_nemo
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2023-12-12T10:03:09
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# What does this PR do ? Fix Common Voice data download. **PR Type**: - [ ] New Feature - [ V] Bugfix - [ ] Documentation # Additional Information * Related to # (issue)
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NMT lang code fix en to any training
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2023-12-12T09:35:00
2024-01-03T01:45:42
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# What does this PR do ? Fix for en to any training / finetuning of model for different language pairs, With this fix , the en to any NMT model can be finetuned for a different subset of language pairs than the model was originally pretrained on. During pretraining , we need to set the pretrained_language_list to - null. **Collection**: [Note which collection this PR will affect] # Changelog - Adds a new field pretrained_language_list in the aayn_base_megatron.yaml config file, which specifies the list of languages the model was pretrained on - Changes to the lang id processing so that the model can finetuned on a small subset of languages of the initial pretrained list # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Enhance flexibility by passing callbacks as method argument
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2023-12-12T09:18:14
2024-01-09T16:55:36
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# What does this PR do ? This PR enhances the flexibility of CustomProgressBar by modifying the create_trainer method to accept callbacks as a method argument, replacing its previous fixed implementation. **Collection**: NLP # Changelog Moved callbacks to method arguement. # Usage N/A # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Refactor - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update link to yaml file in ASR_with_Transducers.ipynb
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2023-12-12T05:34:25
2023-12-12T07:52:49
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changed link from `../../examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml` to `/content/configs/contextnet_rnnt.yaml` because the latter did not exist and threw and error when trying to load the yaml config file. # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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8,013
Fix tokenizer argparse in scripts
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# What does this PR do ? Fix missing argument in script argparse **Collection**: [ASR, NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Fix tokenizer argparse in scripts
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2023-12-12T01:40:04
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# What does this PR do ? Fix missing argument in script argparse **Collection**: [ASR, NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Added VectorQuantizer base class
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2023-12-11T23:50:37
2024-01-20T02:08:54
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# What does this PR do ? This PR adds an abstract base class for vector quantizers. **Collection**: TTS # Changelog - Added `VectorQuantizerBase` # Usage n/a # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Refactor - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Logging changes tested for gpt_pretraining
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# What does this PR do ? Enables use of megatron timers in NeMo GPT pretraining and megatron_base based models. **Collection**: collections/nlp/models/language_modeling # Usage ``` model.enable_megatron_timers=True model.megatron_timer_kwargs.log_every_n_steps= 10 model.megatron_timer_kwargs.log_mode=minmax model.megatron_timer_kwargs.barrier= False ``` ```python torchrun --nnodes=1 --nproc-per-node=8 \ /gsw/code/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \ --config-path=/gsw/code/NeMo/examples/nlp/language_modeling/conf \ --config-name=megatron_gpt_config \ trainer.devices=8 \ trainer.num_nodes=1 \ model.enable_megatron_timers=True \ ++model.megatron_timer_kwargs.log_option=max \ ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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2023-12-11T15:38:18
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# What does this PR do ? Add a Chinese open-source LLM Baichuan2 (7B/13B) support. **Collection**: NLP # Changelog - Add Baichuan2 model configuration. - Add Baichuan2 hf-to-nemo conversion. # Usage ```python # Model conversion from huggingface to nemo python ./scripts/nlp_language_modeling/convert_hf_baichuan2_to_nemo.py --in-file '/mount/data/Baichuan2-7B-Base' --out-file '/mount/data/Baichuan2-7B-Base/baichuan2.nemo' ``` ```python # Inference python ./examples/nlp/language_modeling/megatron_gpt_eval.py \ --config-name=megatron_baichuan2_inference \ gpt_model_file=${BAICHUAN2_NEMO_MODEL} \ trainer.devices=1 \ trainer.num_nodes=1 \ tensor_model_parallel_size=1 pipeline_model_parallel_size=1 ``` ```python # Pretrain python3 -u examples/nlp/language_modeling/megatron_gpt_pretraining.py \ --config-name=megatron_baichuan2_config \ trainer.devices=8 \ trainer.num_nodes=1 \ trainer.val_check_interval=100 \ trainer.precision=bf16 \ trainer.max_steps=1000 \ trainer.max_epochs=1 \ model.megatron_amp_O2=True \ model.sequence_parallel=True \ model.tensor_model_parallel_size=2 \ model.pipeline_model_parallel_size=1 \ model.global_batch_size=16 \ model.micro_batch_size=2 ``` ```python # Parameter efficient fine-tuning python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \ trainer.max_steps=200 \ trainer.devices=2 \ trainer.val_check_interval=200 \ exp_manager.exp_dir=exp \ exp_manager.name=test \ model.tensor_model_parallel_size=1 \ model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \ model.global_batch_size=32 \ model.micro_batch_size=4 \ model.data.train_ds.file_names=${TRAIN} \ model.data.validation_ds.file_names=${VALID} \ model.answer_only_loss=True \ model.peft.peft_scheme="lora" \ model.data.train_ds.concat_sampling_probabilities=[1.0] 2>&1|tee /mount/data/test.log ``` ```python # SFT python ./examples/nlp/language_modeling/tuning/megatron_gpt_sft.py \ trainer.devices=8 \ trainer.val_check_interval=200 \ model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \ model.tensor_model_parallel_size=${TP_SIZE} \ model.pipeline_model_parallel_size=${PP_SIZE} \ model.answer_only_loss=True \ model.global_batch_size=16 \ model.micro_batch_size=2 \ model.data.train_ds.file_names=${TRAIN} \ model.data.train_ds.concat_sampling_probabilities=[1.0] \ model.data.validation_ds.file_names=${VALID} \ model.data.validation_ds.global_batch_size=64 \ model.data.validation_ds.micro_batch_size=8 \ model.megatron_amp_O2=True \ exp_manager.exp_dir=exp \ exp_manager.name=test \ trainer.precision=bf16-mixed 2>&1|tee /mount/data/log/test.log ``` ```python # peft model evaluation CUDA_VISIBLE_DEVICES='0,1' python ./examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \ trainer.devices=2 \ trainer.num_nodes=1 \ trainer.precision=32 \ model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \ model.peft.restore_from_path=${LORA_MODEL} \ model.data.test_ds.file_names=["path/to/your/dataset.jsonl"] \ model.data.test_ds.global_batch_size=32 \ model.data.test_ds.micro_batch_size=16 \ model.tensor_model_parallel_size=1 \ model.pipeline_model_parallel_size=1 \ inference.greedy=True \ inference.outfile_path=.. ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] 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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Davidm/gpt sft dataset fix
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale.", "@Davood-M I am having the same error as input is not long enough in my SFT experiment. Do you know if its fixed in latest version?", "jenkins", "Hi @Davood-M, if your entire input length (template + placeholder strings) > sequence length, then we'll need to do the truncation. We truncate only the `truncation_field` part in your placeholder strings. If the `truncation_field` is not long enough to truncate, then we simply make this field to be empty. This situation may happen if the length of your template > the length of your placeholder strings. I think the solutions are either change your template or increase sequence length.", "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.", "HI,\r\nWhat is the latest non-dev container that does not have this issue? I'm guessing this is a recent issue", "This error can be resolved by either 1. setting the truncation_field=\"output\" or 2. setting truncation_field=None/null. ", "Does that resolve the `write_predictions_to_file` bug?", "Hi @arendu\r\nI tried the solution you suggested but get a new error\r\n\r\n```\r\n11: Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.\r\n 9: Traceback (most recent call last):\r\n 9: File \"/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py\", line 164, in main\r\n 9: trainer.test(model)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 754, in test\r\n 9: return call._call_and_handle_interrupt(\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py\", line 43, in _call_and_handle_interrupt\r\n 9: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py\", line 105, in launch\r\n 9: return function(*args, **kwargs)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 794, in _test_impl\r\n 9: results = self._run(model, ckpt_path=ckpt_path)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 987, in _run\r\n 9: results = self._run_stage()\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 1026, in _run_stage\r\n 9: return self._evaluation_loop.run()\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py\", line 182, in _decorator\r\n 9: return loop_run(self, *args, **kwargs)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py\", line 135, in run\r\n 9: self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py\", line 396, in _evaluation_step\r\n 9: output = call._call_strategy_hook(trainer, hook_name, *step_args)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py\", line 309, in _call_strategy_hook\r\n 9: output = fn(*args, **kwargs)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/strategy.py\", line 425, in test_step\r\n 9: return self.lightning_module.test_step(*args, **kwargs)\r\n 9: File \"/opt/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py\", line 434, in test_step\r\n 9: return self.inference_step(dataloader_iter, 'test')\r\n 9: File \"/opt/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py\", line 437, in inference_step\r\n 9: batch, batch_idx, dataloader_idx = next(dataloader_iter)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fetchers.py\", line 207, in __next__\r\n 9: batch, batch_idx, dataloader_idx = super(_DataLoaderIterDataFetcher, fetcher).__next__()\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fetchers.py\", line 60, in __next__\r\n 9: batch = next(self.iterator)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/combined_loader.py\", line 341, in __next__\r\n 9: out = next(self._iterator)\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/combined_loader.py\", line 142, in __next__\r\n 9: out = next(self.iterators[0])\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 631, in __next__\r\n 9: data = self._next_data()\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 675, in _next_data\r\n 9: data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 9: File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\", line 54, in fetch\r\n 9: return self.collate_fn(data)\r\n 9: File \"/opt/NeMo/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py\", line 479, in collate_fn\r\n 9: contexts = torch.LongTensor(self._collate_item(contexts, max_length=max_length, pad_id=self.tokenizer.eos_id))\r\n 9: ValueError: expected sequence of length 4096 at dim 1 (got 4512)\r\n 9: \r\n```", "Hi @aditya-malte, in your setup what values are your `truncation_field` and `prompt_template`?" ]
2023-12-10T23:49:38
2024-05-24T17:03:06
2024-05-23T03:22:49
COLLABORATOR
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# What does this PR do ? When using small prompts, the GPT_SFT_DATASET generated input prompts because of this logic: ``` if len(ids) < truncation_length: logging.warning(f'{key} is not long enough to truncate.') truncation_length = len(ids) if self.truncation_method == 'left': window_offset = truncation_length elif self.truncation_method == 'right': window_offset = 0 else: raise ValueError(f'{self.truncation_method} is not supported') window_length = len(ids) - truncation_length template_ids[i] = ids[window_offset : window_offset + window_length] ``` In this part of code, if the truncation method is right and the prompt len is less than ```truncation_length```, then the window_length would become 0 and the template_ids would become empty! Tested this on tuned llama2 models and it seems to solved the issue. **Collection**: [Note which collection this PR will affect] # Changelog - Updated the mentioned part for both left and right truncations # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] 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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Update asr_language_modeling.rst: Add a missing word
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2023-12-10T19:21:42
2023-12-12T15:48:47
2023-12-12T15:48:36
CONTRIBUTOR
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# What does this PR do ? Simply add a missing word in the doc `Update asr_language_modeling.rst`. # Changelog NA # Usage NA # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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8,005
Make pipelined TP comm overlap available with mcore
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[ "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "jenkins", "jenkins" ]
2023-12-09T05:53:40
2024-01-10T13:25:10
2024-01-10T13:25:10
COLLABORATOR
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# What does this PR do ? Enable the use of pipelined tensor-parallel communication overlap with mcore path. Removed the condition that enables the use of `ub_tp_comm_overlap` only with `transformer_engine=True`. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] 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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8,004
.ctm in data simulator annotator compliant with RT-09 specification
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[ "NeMo contributors, please do not merge this PR until I make sure all the CTM in NeMo is following the official CTM format.", "Seems that also this is not compliant (speaker id instead of channel): \r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/fa8d416793d850f4ce56bea65e1fe28cc0d092c0/scripts/speaker_tasks/create_alignment_manifest.py#L74C79-L74C79\r\n\r\n\r\nThis is instead kinda compliant (but lacks <NA> for missing fields):\r\nhttps://github.com/NVIDIA/NeMo/blob/fa8d416793d850f4ce56bea65e1fe28cc0d092c0/tools/nemo_forced_aligner/utils/make_ctm_files.py#L108", "@erastorgueva-nv Elena, I have found a line that renders CTM and I replaced with the `get_ctm_line` to remove the duplication when users create CTM files with NeMo. Please check if there is anything wrong happening and this does not cause any errors. (There is no unit tests for the make_ctm_files.py)\r\n\r\n@stevehuang52 We are also making slight changes to data simulator. Please review and approve.", "the data simulation parts look good to me", "jenkins", "@tango4j / reviewers, merge when ready. \r\n\r\nAlso reminder, NeMo devs need to explicitly write \"jenkins\" in order to execute the CI ", "> @tango4j / reviewers, merge when ready.\r\n> \r\n> Also reminder, NeMo devs need to explicitly write \"jenkins\" in order to execute the CI\r\n\r\nOh, when did it change the protocol? \r\nThanks for the heads up. From now on, I will do \"jenkins\".\r\n", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]
2023-12-09T00:20:06
2024-01-08T19:20:20
2024-01-08T19:20:20
CONTRIBUTOR
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Redid this PR from https://github.com/NVIDIA/NeMo/pull/7999 An attempt to fix https://github.com/NVIDIA/NeMo/issues/7445 so that the data simulator .ctm are compliant with RT-09 specification (see https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf): ``` <SOURCE><SP><CHANNEL><SP> <BEG-TIME><SP><DURATION><SP><TOKEN><SP> <CONF><SP><TYPE><SP><SPEAKER><NEWLINE> ``` I have put <NA> for fields unknown e.g. <TOKEN>. This makes it also easy to use the generated sessions with https://github.com/lhotse-speech/lhotse
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Sync with main
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2023-12-08T21:10:29
2023-12-08T21:29:24
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MEMBER
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Toxicity classifier
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2023-12-08T13:59:57
2023-12-30T01:44:49
2023-12-30T01:44:49
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# What does this PR do ? Merge Toxicity classifier branch into main **Collection**: nlp # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fix nemo_llama_to_hf conversion
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2023-12-08T09:36:31
2024-03-21T18:26:59
2024-02-29T17:49:35
CONTRIBUTOR
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# What does this PR do ? Fix convert_nemo_llama_to_hf.py: - Correctly account for `megatron_amp_O2` flag - Save HF model with correct precision - Transfer over the NeMo tokenizer, instead of using the possibly-incompatible default tokenizer - Make sure to save fast version of tokenizer - Resize the model's embedding tensor to match the new tokenizer's vocab - Correct a typo in how-to-use example **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? @ericharper Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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.ctm in data simulator annotator compliant with RT-09 specification
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2023-12-08T00:19:15
2023-12-08T23:59:11
2023-12-08T23:59:11
CONTRIBUTOR
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An attempt to fix https://github.com/NVIDIA/NeMo/issues/7445 so that the data simulator .ctm are compliant with RT-09 specification (see https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf): ``` <SOURCE><SP><CHANNEL><SP> <BEG-TIME><SP><DURATION><SP><TOKEN><SP> <CONF><SP><TYPE><SP><SPEAKER><NEWLINE> ``` I have put <NA> for fields unknown e.g. <TOKEN>. This makes it also easy to use the generated sessions with https://github.com/lhotse-speech/lhotse
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7,998
Fix crash when converting to mcore a model using rotary embeddings
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2023-12-07T20:32:08
2023-12-12T02:39:19
2023-12-12T02:39:12
COLLABORATOR
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# What does this PR do ? Fix crash when converting to mcore a model using rotary embeddings. The problem is that mcore models don't register `rotary_pos_emb.inv_freq` as a buffer anymore. I made it a command-line argument so that if the same issue occurs with other parameters / buffers, users can still run the conversion script by providing them on the command-line (instead of having to wait for a new version of NeMo). I tested this PR on a model where the script failed previously with container `nemofw-training:23.11`. **Collection**: nlp # Changelog - Fix crash when converting to mcore a model using rotary embeddings # Before your PR is "Ready for review" **Pre checks**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [X] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Fixing conversion script to work for code llama
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2023-12-07T20:29:27
2023-12-08T19:37:13
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# What does this PR do ? Add rotary base paramter to conversion script for llama hf to nemo **Collection**: NLP ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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The process_asr_text_tokenizer file excludes an argument.
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2023-12-07T20:08:15
2023-12-23T18:09:07
2023-12-23T18:09:06
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**Describe the bug** There is no `spe_byte_fallback` argument in process_asr_text_tokenizer.py **Steps/Code to reproduce bug** Just run the process_asr_text_tokenizer with any args, e.g.: `python ./scripts/tokenizers/process_asr_text_tokenizer.py --manifest=/path/to/manifest --vocab_size=1024 --tokenizer=spe --data_root=/path/to/dataroot --spe_type=unigram **Expected behavior** The expected behavior is that this works, but the error thrown is `AttributeError: 'Namespace' object has no attribute 'spe_byte_fallback'`. This makes sense because this commit - https://github.com/NVIDIA/NeMo/commit/1d4d3975e339df819b97d29ceafdd98f37b34906 - added three of the four required args for the argparse, excluding the `spe_byte_fallback` one. @titu1994 ```[tasklist] ### Tasks ```
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# What does this PR do ? Notebook fixes **Collection**: [ASR] # Changelog - Update Librosa signature to use only kwargs # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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# What does this PR do ? Notebook fixes **Collection**: [ASR] # Changelog - Update Librosa signature to use only kwargs # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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fix librosa display issue
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# What does this PR do ? Fixes issue with librosa display issue with 0.10.1 https://github.com/librosa/librosa/issues/1763 # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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# What does this PR do ? Update branch name # 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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# What does this PR do ? Fixes issue with librosa display issue with 0.10.1 https://github.com/librosa/librosa/issues/1763 # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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# What does this PR do ? Update branch name # 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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Support for Mistral7b training?
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2023-12-07T08:48:41
2024-01-14T01:49:22
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Mistral has been shown to be a competitive open sourced model. Many recently competitive models on the open-llm-leaderboard are derived from mistral. However, I can't seem to find the supported training pipeline of mistral as that for llama. Is there a plan for incorporating mistral into the training pipeline? Or has it been incorporated and I am just dumb not being able to find it? Kind thanks
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Add LoRA support to all linear layers
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# What does this PR do ? Add LoRA support to all linear transformations in Attention and MLP modules in NeMo and MCore GPT models. **Collection**: [Note which collection this PR will affect] nlp # Usage To add LoRa to specific layer, just add the **target_modules** parameter to lora_tuning. The available configuration are below: ```yaml lora_tuning: target_modules: ['attention_qkv'] # default, adds LoRA to QKV projection layer target_modules: ['attention_qkv','attention_dense'] # adds LoRA to QKV projection and Dense layers attention target_modules: ['mlp_fc1','mlp_fc2']# adds LoRA to linear layers in the MLP module target_modules: ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] # adds LoRA to every linear layer in attention and MLP modules ``` There are also shortcut options defined as below: ```yaml lora_tuning: target_modules: ['attention'] # same as ['attention_qkv','attention_dense'] target_modules: ['mlp'] # same as ['mlp_fc1','mlp_fc2'] target_modules: ['attention','mlp'] # same as ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] target_modules: ['all'] # same as ['attention_qkv','attention_dense','mlp_fc1','mlp_fc2'] ``` If the target_modules parameter is not provided by the user, the default value is ```yaml target_modules: ['attention_qkv'] ``` **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? @arendu Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Rename a test that was shadowing another test. **Collection**: [Note which collection this PR will affect] # Changelog - Renamed test # Usage n/a # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [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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Remove host-to-device data copies in LLAMA SFT
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# What does this PR do ? Pre-create cumulative sequence argmins and the max sequence length per micro-batch on the CPU tensor. This avoids copying the GPU tensor to calculate this information on the CPU. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add news section to README. Edit: News section will temporarily be at the top of the page. We'll move the news lower in the README as the news ages. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Export ThutmoseTagger ITN Torchscript module
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[ "hi @MarceloSancinetti , this was my model, but I do not know about torchscript so can't help with that. Basically, ThutmoseTagger is just a token classification bert-based model. If you have questions about meaning of its elements, you can ask", "Hello @bene-ges, thank you for your response. I've been using your model for a while and it works very well, I'm just trying to figure out how to deploy it. I'll let you know if I have any questions about the model. ", "@MarceloSancinetti, in case if you don't find other ways, you can try the following\r\n\r\nyou can rewrite this class in pure pytorch, inheriting from nn.Module\r\n`class MyThutmoseModel(nn.Module):`\r\nthen include only `init` and `forward` methods.\r\n\r\nIf you unpack the nemo checkpoint\r\n`tar xvf itn_en_thutmose_bert.nemo`\r\nyou will see a number of files packed with the model,\r\namong them `model_weights.ckpt`\r\nthen you can initialize your class and load pretrained parameters into it:\r\n```\r\n model = MyThutmoseModel()\r\n state_dict = torch.load(\"model_weights.ckpt\", map_location=device)\r\n model.load_state_dict(state_dict, strict=False)\r\n```\r\nand so you will get a pure pytorch model that you can use for inference. \r\n`model_weights.ckpt` - is just a dictionary - look at its keys to better understand which class members it will affect. ", "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.", "@MarceloSancinetti Hello! I've faced similar need to deploy TT model\r\nI wonder if you have implemented this solution?\r\nCan I find your implementation somewhere?", "Hello @Dan1aR, I ended up exporting only the forward function and re implementing the pre and post processing part of the inference function in C++ myself.", "@MarceloSancinetti, Thank's a lot for quick reply!\r\nCan you please share your solution? That would save me a lot of trouble\r\nOr point me to some resources, where i could find one?" ]
2023-12-06T16:58:29
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Hello, I'm trying to export a ThutmoseTaggerModel using TorchScript. I was able to export the forward method, but not other methods. I was able to trace the forward function and run it in C++ by running this code: ``` from helpers import ITN_MODEL, instantiate_model_and_trainer from omegaconf import DictConfig, OmegaConf import torch import omegaconf from nemo.collections.nlp.data.text_normalization_as_tagging import bert_example cfg_path = "/home/msancinetti/NeMo/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml" cfg = omegaconf.OmegaConf.load(cfg_path) cfg["pretrained_model"] = "itn_en_thutmose_bert" _, model = instantiate_model_and_trainer(cfg, ITN_MODEL, False) model.eval() model.to('cpu') input_example = [torch.zeros([64,128], dtype=torch.int).to("cpu")] * 3 model.set_input_names(["input_1"]) model.export('itn_model.ts', verbose=True, input_example=input_example) ``` For this to work I had to: - Add `~/NeMo/examples/nlp/text_normalization_as_tagging/` to my PYTHONPATH for the first import - Add the following code to the ThutmoseTaggerModel class: ``` @property def input_names(self): return self._input_names def set_input_names(self, new_value): self._input_names = new_value ``` (because the input_names property is necessary to run the export method) However, the model.export method only traces the forward function. I would like to export other methods (that may not be traceable, so I think I have to use torch.jit.script). The problem is that if I try to script any method that uses "self", I get an error. As a minimal example, I added this method to the ThutmoseTaggerModel class: ``` def dummy(self): return self.num_labels ``` When I try to script that function, I get this error: ``` In [1]: torch.jit.script(model.dummy) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[1], line 1 ----> 1 torch.jit.script(model.dummy) File ~/miniconda3/envs/nemo_debug/lib/python3.8/site-packages/torch/jit/_script.py:1381, in script(obj, optimize, _frames_up, _rcb, example_inputs) 1379 if _rcb is None: 1380 _rcb = _jit_internal.createResolutionCallbackFromClosure(obj) -> 1381 fn = torch._C._jit_script_compile( 1382 qualified_name, ast, _rcb, get_default_args(obj) 1383 ) 1384 # Forward docstrings 1385 fn.__doc__ = obj.__doc__ RuntimeError: 'Tensor (inferred)' object has no attribute or method 'num_labels'.: File "/home/msancinetti/miniconda3/envs/nemo_debug/lib/python3.8/site-packages/nemo/collections/nlp/models/text_normalization_as_tagging/thutmose_tagger.py", line 503 def dummy(self): return self.num_labels ~~~~~~~~~~~~~~~ <--- HERE ``` I also get an error if I try to script the whole model with `torch.jit.script(model)` _TL;DR:_ How do I export methods from ThutmoseTagger ITN model other than forward to run them in C++? Any help will be appreciated!
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[NLP] Use register_artifact from superclass directly
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[ "I don't know why we need this. It breaks a bunch of things at this point. Is it worth it to change it?", "> I don't know why we need this. It breaks a bunch of things at this point. Is it worth it to change it?\r\n\r\nThat's quite surprising.. Let me retry the Jenkins pipeline.\r\n\r\nI'm not insisting on that though, it's a small cleanup which should be harmless.", "jenkins", "Eric pointed out that default for `verify_src_exists` is different across the two classes, which I missed. Hence pipeline fails.\r\n\r\nI'll figure that out as I think it's worth it as a simplification", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-06T10:26:39
2024-01-28T01:45:03
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# What does this PR do ? Removes explicit code to call superclass method (cleanup). **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Improvement ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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[NLP] Remove replace_sampler_ddp (deprecated in Trainer)
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# What does this PR do ? Removes deprecated trainer option in MPT conversion script. **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Bugfix If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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[NLP] ArtifactItem with init=True to make it debuggable
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[ "jenkins", "> Thanks LGTM\r\n\r\nThanks! I've just rebased it, I'll merge once pipeline completes." ]
2023-12-06T10:22:48
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# What does this PR do ? When debugging artifact items I struggled with `ArtifactItem` as it is defined with `init=False` which results in issues when printing an instance of this class. The following minimal example illustrates the problem - this code ``` from dataclasses import dataclass @dataclass(init=False) class Point: x: int y: int p = Point() print(p) ``` results in **AttributeError: 'Point' object has no attribute 'x'**. So it doesn't make it easier to check what's going on. **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Improvement ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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[NLP] Use helpers from utils_funcs.py in Llama conversion
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# What does this PR do ? An improvement to use `torch_dtype_from_precision` helper to resolve precision. **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Improvement ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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[NLP] Minor improvements in Llama conversion script
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# What does this PR do ? Minor extra fixes to Llama conversion script. **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Documentation - [x] Improvement If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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[NLP] Improve and unify loading state_dict for community models
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[ "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days." ]
2023-12-06T10:11:09
2024-01-19T16:18:18
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# What does this PR do ? - Provide a common way for loading state_dict for HF models among different conversion scripts - Deduplicate tokenizers in the Nemo file produced: the model now comes with a single copy instead of an extra redundant tokenizer file (which is an undesired side effect of [`load_model` function](https://github.com/NVIDIA/NeMo/blob/52d50e9e09a3e636d60535fd9882f3b3f32f92ad/scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py#L58)) **Collection**: [NLP] # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] Improvement ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/7935
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Draft: Cuda graphs greedy rnnt inference
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This is now at parity with the original blank_as_pad greedy RNN-T decoding implementation. Currently, I can decode librispeech test clean in 1 min, 39 seconds, while the original implementation runs in 2 min, 21 seconds, at batch size 16. This may seem disappointing, but it is clear that there is still CPU overhead being counted because loading data off disk is not fast enough. In addition, this result is using float32, not float16.\r\n\r\nThat is to say that the WER output here is 0:\r\n\r\nhttps://github.com/NVIDIA/NeMo/pull/7976/files#diff-e87be798c4c213fbdf438595c4c06898e1dabea0757aaa60334331cbf9f44cb1R83\r\n\r\nHowever, this still needs to be cleaned up. It has a lot of cruft, including an attempt to use TensorRT from before my realization that the problem was CPU overhead, not slowness in the GPU implementation.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-06T05:33:15
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# What does this PR do ? Greedy RNNT inference currently sees the GPU idle 90% of the time because of CPU overheads. This experiments with using cuda graphs to do greedy inference, so that we can decoder at much higher throughputs. Very WIP. I began to work on this because I discovered that using TensorRT was not helpful at all, because it speeds up such a small portion of the code right now. **Collection**: ASR # Changelog - Will have to do after cleaning up Not ready for serious review.
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MSDD Model `__call__` method throws error
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[ "Hi, @benhoff. \r\n`__call__()` function should be preceded by \r\n`diarize = NeuralDiarizer.from_pretrained(model_name='diar_msdd_telephonic')`\r\nTo run diarization as the simplest form, e.g., `diarize('audio.wav')`\r\n\r\nThis call function is designed for hugging face API, to minimize the preparation steps.\r\n[PR #5945 ](https://github.com/NVIDIA/NeMo/pull/5945) is explaining the use case of this.\r\nif you have to load the model regularly with `cfg` as in the tutorial, please use `.diarize()` function.\r\n\r\nWe will add the error handling for this case of illegal call.\r\n", "Hey @tango4j ! Been following your recently as I chunk through some of this stuff, looking forward to seeing the online diarizer PR land as it's closer to my use case.\r\n\r\nI'm looking to diarize short chunks of audio (<30 seconds) on a server. I've wrapped this code base with an API and I'm rewriting/updating the mainfest.json repeatably to use the `diarize` method. \r\n\r\nIs that the recommended way to do that? \r\n\r\nOr would it be better to use the `from_pretrained` and then the `__call__` method?", "@benhoff \r\nWe are preparing online diarization [PR #7896 ](https://github.com/NVIDIA/NeMo/pull/7896).\r\nI apologize that I did not have bandwidth to finalize this.\r\n\r\nOnline diarization system requires more sophisticated system where we need to implement history buffer mechanism to mermorize the past speaker profiles.\r\n\r\nWe won't be designing the online diarization to have such a long (30se) buffer, we will make it have 1~2 second of frame inputs. However, you will be able to tweak the system to serve as you intend.\r\n\r\nIn Part-2 PR, there will be tutorial, example and yaml file.\r\n\r\nFor the time being, I think doing offline diarization of all the cumulated audio and perform offline diarization is only way. you might want to match the speakers among multiple sequential outputs.\r\n ", "@tango4j , no worries on the PR, though good to know that the use case is targeting 1-2 seconds, I won't wait for it to land and take the offline approach you suggested instead. \r\n\r\nI was thinking about implementing the historical buffer myself, but I was surprised that most of the approaches don't prune some of the more spurious data or smaller data (for example, everything less than 0.5 seconds) automatically.\r\n\r\nMaybe the algorithmic clustering approaches do this via math, but in a meeting use case, my gut instinct is to throw away some of the noise and only keep longer, more robust phrases so that you can get a clean fingerprint of someone's voice.", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days." ]
2023-12-05T18:38:48
2024-01-08T23:36:10
2024-01-08T23:36:10
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**Describe the bug** If you use the `__call__` method from NeuralDiarizer instead of the `diarize` method, you get the below stack trace. ``` File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:1514, in NeuralDiarizer.__call__(self, audio_filepath, batch_size, num_workers, max_speakers, num_speakers, out_dir, verbose) 1503 self._initialize_configs( 1504 manifest_path=manifest_path, 1505 max_speakers=max_speakers, (...) 1510 verbose=verbose, 1511 ) 1513 self.msdd_model.cfg.test_ds.manifest_filepath = manifest_path -> 1514 self.diarize() 1516 pred_labels_clus = rttm_to_labels(f'{tmpdir}/pred_rttms/{Path(audio_filepath).stem}.rttm') 1517 return labels_to_pyannote_object(pred_labels_clus) File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs) 112 @functools.wraps(func) 113 def decorate_context(*args, **kwargs): 114 with ctx_factory(): --> 115 return func(*args, **kwargs) File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:1180, in NeuralDiarizer.diarize(self) 1173 @torch.no_grad() 1174 def diarize(self) -> Optional[List[Optional[List[Tuple[DiarizationErrorRate, Dict]]]]]: 1175 """ 1176 Launch diarization pipeline which starts from VAD (or a oracle VAD stamp generation), initialization clustering and multiscale diarization decoder (MSDD). 1177 Note that the result of MSDD can include multiple speakers at the same time. Therefore, RTTM output of MSDD needs to be based on `make_rttm_with_overlap()` 1178 function that can generate overlapping timestamps. `self.run_overlap_aware_eval()` function performs DER evaluation. 1179 """ -> 1180 self.clustering_embedding.prepare_cluster_embs_infer() 1181 self.msdd_model.pairwise_infer = True 1182 self.get_emb_clus_infer(self.clustering_embedding) File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:699, in ClusterEmbedding.prepare_cluster_embs_infer(self) 695 """ 696 Launch clustering diarizer to prepare embedding vectors and clustering results. 697 """ 698 self.max_num_speakers = self.cfg_diar_infer.diarizer.clustering.parameters.max_num_speakers --> 699 self.emb_sess_test_dict, self.emb_seq_test, self.clus_test_label_dict, _ = self.run_clustering_diarizer( 700 self._cfg_msdd.test_ds.manifest_filepath, self._cfg_msdd.test_ds.emb_dir 701 ) File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:879, in ClusterEmbedding.run_clustering_diarizer(self, manifest_filepath, emb_dir) 875 self._embs_and_timestamps = get_embs_and_timestamps( 876 self.clus_diar_model.multiscale_embeddings_and_timestamps, self.clus_diar_model.multiscale_args_dict 877 ) 878 session_scale_mapping_dict = self.get_scale_map(self._embs_and_timestamps) --> 879 emb_scale_seq_dict = self.load_emb_scale_seq_dict(emb_dir) 880 clus_labels = self.load_clustering_labels(emb_dir) 881 emb_sess_avg_dict, base_clus_label_dict = self.get_cluster_avg_embs( 882 emb_scale_seq_dict, clus_labels, speaker_mapping_dict, session_scale_mapping_dict 883 ) File ~/swdev/speech_processing/venvNemo/lib/python3.11/site-packages/nemo/collections/asr/models/msdd_models.py:961, in ClusterEmbedding.load_emb_scale_seq_dict(self, out_dir) 957 pickle_path = os.path.join( 958 out_dir, 'speaker_outputs', 'embeddings', f'subsegments_scale{scale_index}_embeddings.pkl' 959 ) 960 logging.info(f"Loading embedding pickle file of scale:{scale_index} at {pickle_path}") --> 961 with open(pickle_path, "rb") as input_file: 962 emb_dict = pkl.load(input_file) 963 for key, val in emb_dict.items(): FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpil5i2a_2/speaker_outputs/embeddings/subsegments_scale0_embeddings.pkl' ``` **Steps/Code to reproduce bug** If you go to the `Speaker_Diarization_Inference.ipynb` file, and run everything, you can change the code to `system_vad_msdd_model('data/an4_diarize_test.wav')`, and the error will get thrown.
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Token count and sequence length logging for MegatronGPTSFTModel
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[ "@vysarge \r\nAre you going to open this PR?", "We need this update, right? \r\nLet's merge this PR ASAP as the change looks to be straightforward. \r\n\r\n@vysarge \r\nPlease change the merge target to the main branch." ]
2023-12-05T18:08:11
2024-03-12T22:36:45
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# What does this PR do ? Adds progress bar logging for sequence length (including padding tokens) and real token count for each train step with MegatronGPTSFTModel to enable accurate calculation of tokens/sec and related metrics. Depends upon #7945. **Collection**: nlp/language_modeling # Changelog - Alters GPTSFTDataset and GPTSFTPackedDataset to pass out a list of token counts as part of the batch - Alters MegatronGPTSFTModel to expect, average, and log that list of token counts at the beginning of the step # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Depends upon #7945.
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Support O2 training of PEFT and SFT
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2023-12-05T01:04:36
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# What does this PR do ? Support O2 training (`model.megatron_amp_O2=True`) of PEFT and SFT using the refactored PEFT APIs. This was listed as a known issue since the PEFT Refactor (#7308). This PR fixes that issue. Code was tested on GPT and T5, PEFT and SFT. **Collection**: NLP # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] 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 #7308
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Add All Multimodal Source Code Part 2: Text to image, x to nerf
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2023-12-04T23:15:37
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# What does this PR do ? Add multimodal support, second PR, includes text to image, x to nerf source code **Collection**: Multimodal # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] 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 ? Add starcoder NeMo -> HF conversion script **Collection**: NLP # Usage See script header # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] 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 ? This change enabled dependabot to automatically generate PRs for updated images in Dockerfiles or updated github-action versions. This will help the maintainers keep dependencies up to date. **Collection**: N/A # Changelog - feat(dependabot): Add dependabot config for dockerfiles and github-actions # Usage * N/A # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x ] New Feature - [ ] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Virtual pipeline parallel support for MegatronGPTSFTModel
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2023-12-02T02:33:07
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# What does this PR do ? Enables running MegatronGPTSFTModel with virtual pipeline parallel when peft_scheme=null. **Collection**: NLP (language_modeling) # Changelog - Alters word embedding initialization and passing of several parameters in MegatronGPTSFTModel to accommodate virtual pipeline parallelism when self.use_peft is false - Adds a guard in `MegatronGPTSFTModel.__init__` to raise a ValueError if self.use_peft is true - Corrects the TP group initialization check in MegatronGPTSFTModel to account for both TE and MCore flags # Usage Run a GPT model with SFT and set `virtual_pipeline_model_parallel_size`, such as: ``` python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \ ++model.virtual_pipeline_model_parallel_size=10 \ ... ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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SFT patch: (1) enable sequence parallelism and (2) enable profile
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[ "jenkins", "@hsiehjackson \r\nI don't know the background of why this is set as False for inference.\r\nWhen we remove this condition, we use sequence parallelism for both training and inference but I didn't see any issue with this.", "@erhoo82 for inference do you mean generation mode instead of calculating validation loss? This assert needs config `sequence_parallel == False`. I think if you remove `_reset_sequence_parallelism_args` for models with sequence parallel, we may get assertion error during generation.", "jenkins" ]
2023-12-02T02:13:35
2023-12-18T20:18:27
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# What does this PR do ? SFT patch: (1) enable sequence parallelism and (2) enable profile # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Modify GPTSFTPackedDataset to respond to pad_to_max_length setting
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2023-12-02T02:10:53
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Several-line change to make GPTSFTPackedDataset pad sequences to max length if pad_to_max_length=true.
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fail to merge lora with base model: "TypeError: ModelPT.restore_from() got an unexpected keyword argument 'mcore'"
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**Describe the bug** Encounter this error when execute scripts/nlp_language_modeling/merge_lora_weights/merge.py with lora and base gpt 126m model. A clear and concise description of what the bug is. **Steps/Code to reproduce bug** execute this on A100 dgxa100.80g.2.norm ```bash python3 /opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py \ trainer.devices=1 \ trainer.num_nodes=1 \ trainer.precision=bf16 \ tensor_model_parallel_size=1 \ pipeline_model_parallel_size=1 \ gpt_model_file=/mount_workspace/results/gpt3_126m/convert_nemo/results/megatron_gpt.nemo \ lora_model_path=/mount_workspace/results/lora-tp1/checkpoints/megatron_gpt_peft_tuning-lora.nemo \ merged_model_path=/mount_workspace/results/lora-merge/checkpoints/lora-gpt126m-merged.nemo" ``` failed with error message ``` Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.precision=bf16', 'tensor_model_parallel_size=1', 'pipeline_model_parallel_size=1', 'gpt_model_file=/mount_workspace/results/gpt3_126m/convert_nemo/results/megatron_gpt.nemo', 'lora_model_path=/mount_workspace/results/lora-tp1/checkpoints/megatron_gpt_peft_tuning-lora.nemo', 'merged_model_path=/mount_workspace/results/lora-merge/checkpoints/lora-gpt126m-merged.nemo'] Traceback (most recent call last): File "/opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py", line 238, in <module> main() # noqa pylint: disable=no-value-for-parameter File "/opt/NeMo/nemo/core/config/hydra_runner.py", line 126, in wrapper _run_hydra( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra _run_app( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 452, in _run_app run_and_report( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report raise ex File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report return func() File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 453, in <lambda> lambda: hydra.run( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run _ = ret.return_value File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value raise self._return_value File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job ret.return_value = task_function(task_cfg) File "/opt/NeMo/scripts/nlp_language_modeling/merge_lora_weights/merge.py", line 198, in main lora_model_cfg = MegatronGPTLoRAModel.restore_from( TypeError: ModelPT.restore_from() got an unexpected keyword argument 'mcore' ``` Please list *minimal* steps or code snippet for us to be able to reproduce the bug. **Expected behavior** no error **Environment overview (please complete the following information)** - Environment location: Nvidia DGX cloud - Method of NeMo install: container "nemofw-training:23.08.03-ucx-downgrade-no-redirect" **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - OS version - PyTorch version - Python version **Additional context** failed at this [line](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/merge_lora_weights/merge.py#L199) I tried manually remove the `mcore` param in script it seems passed the point.
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conformer_ctc_bpe.yaml, I am getting high wer error rate above 90% , I am finetuning stt_en_conformer_small model using common voice hindi 20 recorded hours dataset...
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null
[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-12-01T04:36:26
2024-01-09T01:46:15
2024-01-09T01:46:15
NONE
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i follow the , https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/examples/kinyarwanda_asr.html this example..
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7,958
The `compute_logprob` inference flag is very confusing
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null
[ "The `compute_logprob` argument seems to have different meaning in different functions.\r\n\r\nWhen starting a model server, the `compute_logprob` argument will return the logprobs of the generated tokens as well. See the following example. I started a model server ([script](https://gitlab-master.nvidia.com/shengyangs/shengyangs-experiments/-/blob/main/preference_evaluation/serve_gpt_model.py?ref_type=heads)) and made two requests. The only difference is `tokens_to_generate`. The resulting `tokens` and `log_probs` lengths reflect the `tokens_to_generate`.\r\n\r\n```\r\n\r\n data = {\r\n \"sentences\": prompts,\r\n \"greedy\": True,\r\n \"tokens_to_generate\": 1,\r\n \"top_k\": 1,\r\n \"compute_logprob\": True\r\n }\r\n url = \"http://localhost:1424/generate\"\r\n response = requests.put(url, json=data)\r\n json_response = response.json()\r\n tokens = json_response['tokens'][0]\r\n log_probs = json_response['logprob'][0] # removing the last generated token\r\n \r\n data2 = {\r\n \"sentences\": prompts,\r\n \"greedy\": True,\r\n \"tokens_to_generate\": 10,\r\n \"top_k\": 1,\r\n \"compute_logprob\": True\r\n }\r\n url = \"http://localhost:1424/generate\"\r\n response2 = requests.put(url, json=data2)\r\n json_response2 = response2.json()\r\n tokens2 = json_response2['tokens'][0]\r\n log_probs2 = json_response2['logprob'][0] # removing the last generated token\r\n \r\n print(len(tokens), len(tokens2), len(log_probs), len(log_probs2))\r\n \r\n```\r\nOutput \r\n`173 182 172 181`\r\n\r\nLooking into the calling stack, when calling the model server, the `generate` function's calling stack does not involve the `get_computeprob_response ` that @odelalleau refers to. This causes mis-match.", "Thanks @shengyangs for mentioning this. There is indeed a discrepancy between calling `model.generate(...)` and `generate(model, ...)` w.r.t. how `compute_logprob` is handled. I guess this should be addressed as well.", "Adding a note here to keep track of it, but would be worth its own issue if confirmed:\r\n\r\nI also suspect that this method of computing log probs is inefficient in a batch setting, since it relies on `generate()` which will generate multiple tokens when input sequences have different lengths. If this is correct, then it could be fixed either by (1) implementing a separate function to compute logprobs, or (2) left-padding the input sequences so that they all start generation at the same time (this might require special care when handling the attention mask).", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-11-30T16:03:29
2024-01-31T01:44:12
2024-01-31T01:44:12
COLLABORATOR
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**Is your feature request related to a problem? Please describe.** It's very confusing to have a `compute_logprob` flag in `SamplingParam` that has nothing to do with sampling from the model, and actually completely changes the behavior of the `generate()` function so that instead of generating text, it only computes the logprobs of the input text. I've always thought this option could be used to obtain the logprobs of the *generated* data (and I was unable to find a function to compute the logprob of some text when I needed it). Having code like this: ```python compute_prob_response = get_computeprob_response(self.tokenizer, response, batch) return compute_prob_response ``` is also quite confusing since actually `get_computeprob_response()` doesn't compute the log prob of the response at all, only of the input. **Describe the solution you'd like** Something like this: * Rename `SamplingParam` into `InferenceParam` * Rename the `generate()` function(s) and variants (e.g. `megatron_gpt_generate()`) to `infer()` * Replace `compute_logprob` with a `mode` that can either be `"generate"` or `"compute_logprob"` * Move the generation-specific settings into a `generate` config node, so that the config looks like ``` inference_params: mode: generate generate: temperature: 1.0 top_k: 0 ... ``` (and could be extended with new modes and associated options if needed in the future) * Deprecate the old API while keeping it functional
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7,957
More Informative Performance Callback Data - NemoMegatron GPT
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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.", "Hi!\r\n\r\nAny update on this issue? I'm interested at least on logging `tokens_per_sec` to be able to compare performance between different configs.\r\n\r\nThanks,\r\n\r\nToni" ]
2023-11-30T15:31:05
2024-08-10T12:19:54
2024-01-07T01:48:53
NONE
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Made some updates to the Docker image to support Pytorch NGC 23.10 so that i can consume the performance updates in NCCL 2.19.3. I am running some benchmarks with a prepared NemoMegatron 7B w/ Wikipedia and was wondering how i get a more representative performance callback data. For example ``` Epoch 0: : 3%|▎ | 9000/301500 [1:42:42<55:37:46, v_num=0-56, reduced_train_loss=nan.0, global_step=9e+3, consumed_samples=6.3e+5, train_step_timing in s=0.615, val_loss=7.040] Epoch 0, global step 9000: 'val_loss' reached 7.04238 (best 7.04238), saving model to '/data/nemo_megatron/nemo_experiments/megatron_gpt/2023-11-29_23-30-56/checkpoints/megatron_gpt--val_loss=7.04-step=9000-consumed_samples=630000.0.ckpt' as top 3 ``` Wondering how i can get MFU or tokens/sec from this. In the legacy Megatron-LM implementation it had forward-compute/backward-compute and collective timings reported per step.
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multimodal/speech_cv collection update (Audio Visual Speech Recognition)
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2023-11-30T09:58:13
2024-01-09T01:46:17
2024-01-09T01:46:16
CONTRIBUTOR
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# What does this PR do ? Update the multimodal/speech_cv branch to support audio-visual speech recognition (CTC, RNN-T and Hybrid) **Collection**: [multimodal/speech_cv] # Changelog - Update speech_cv branch - bug fix - Add audio-visual datasets - Add audio-visual models: (AudioVisualEncDecCTCModelBPE, AudioVisualEncDecCTCModel, AudioVisualEncDecHybridRNNTCTCBPEModel, AudioVisualEncDecHybridRNNTCTCModel, AudioVisualEncDecRNNTBPEModel, AudioVisualEncDecRNNTModel) - Add audio-visual modules (FusionModule, InterCTCMixin) - Optional ConformerEncoder module 'pre_encode' by setting 'feat_in' to None # Usage * You can potentially add a usage example below ```python import nemo.collections.multimodal.speech_cv as nemo_cv from omegaconf import OmegaConf config_path = "model_config.yaml" params = OmegaConf.load(config_path) # hybrid CTC/RNN-T BPE audio-visual speech recognition model model = nemo_cv.models.AudioVisualEncDecHybridRNNTCTCBPEModel(cfg=params.model) ``` # Jenkins CI To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR. # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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7,955
How can I resume the training of a SpeakerNet?
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[ "looks like there are no existing checkpoints for it to restore from. Hence you also need to add `ignore_no_checkpoints` option as well to ignore when there are no checkpoints. Note that checkpoints need to be in `ckpt` format. ", "Hi, @nithinraok \r\n\r\nI trained for one epoch using [speaker_reco.py](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/speaker_reco.py) with [SpeakerNet_verification_3x2x256.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/SpeakerNet_verification_3x2x256.yaml). I thought `create_checkpoint_callback` in [SpeakerNet_verification_3x2x256.yaml](https://github.com/NVIDIA/NeMo/blob/274a21b492f337702f83f7b82abca338d6c9c09e/examples/speaker_tasks/recognition/conf/SpeakerNet_verification_3x2x256.yaml#L133C3-L133C29) would generate checkpoints every epoch, but if not, how do I generate checkpoints?", "Can you switch to https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/titanet-small.yaml \r\nYou need to enable checkpointing as mentioned here: https://github.com/NVIDIA/NeMo/blob/d81ea6a3f786b46cd204d41d5dc18268763f401c/examples/speaker_tasks/recognition/conf/titanet-small.yaml#L172 \r\n\r\ntry titanet configs. ", "Hi, @nithinraok \r\n\r\nI try to switch to [titanet-small.yaml](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/titanet-small.yaml), but got an error: `TypeError: 'NoneType' object is not iterable`\r\n\r\nThis is full error message:\r\n\r\n```\r\n`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..\r\n[NeMo I 2024-01-09 17:31:12 exp_manager:374] Experiments will be logged at exp/titanet_small/TitaNet-S/2024-01-09_17-31-12\r\n[NeMo I 2024-01-09 17:31:12 exp_manager:797] TensorboardLogger has been set up\r\n[NeMo I 2024-01-09 17:31:24 collections:301] Filtered duration for loading collection is 0.00 hours.\r\n[NeMo I 2024-01-09 17:31:24 collections:302] Dataset loaded with 1240651 items, total duration of 2700.55 hours.\r\n[NeMo I 2024-01-09 17:31:24 collections:304] # 1240651 files loaded accounting to # 7205 labels\r\n[NeMo W 2024-01-09 17:31:24 label_models:187] Total number of 7205 found in all the manifest files.\r\nError executing job with overrides: []\r\nTraceback (most recent call last):\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/examples/speaker_tasks/recognition/speaker_reco.py\", line 85, in <module>\r\n main()\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/core/config/hydra_runner.py\", line 105, in wrapper\r\n _run_hydra(\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/utils.py\", line 389, in _run_hydra\r\n _run_app(\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/utils.py\", line 452, in _run_app\r\n run_and_report(\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/utils.py\", line 216, in run_and_report\r\n raise ex\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/utils.py\", line 213, in run_and_report\r\n return func()\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/utils.py\", line 453, in <lambda>\r\n lambda: hydra.run(\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/_internal/hydra.py\", line 132, in run\r\n _ = ret.return_value\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/core/utils.py\", line 260, in return_value\r\n raise self._return_value\r\n File \"/home/DTDwind/.conda/envs/nemo/lib/python3.10/site-packages/hydra/core/utils.py\", line 186, in run_job\r\n ret.return_value = task_function(task_cfg)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/examples/speaker_tasks/recognition/speaker_reco.py\", line 61, in main\r\n speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/asr/models/label_models.py\", line 125, in __init__\r\n super().__init__(cfg=cfg, trainer=trainer)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/core/classes/modelPT.py\", line 143, in __init__\r\n self.setup_training_data(self._cfg.train_ds)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/asr/models/label_models.py\", line 274, in setup_training_data\r\n self._train_dl = self.__setup_dataloader_from_config(config=train_data_layer_config)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/asr/models/label_models.py\", line 192, in __setup_dataloader_from_config\r\n augmentor = process_augmentations(config['augmentor'])\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/asr/parts/preprocessing/perturb.py\", line 1230, in process_augmentations\r\n augmentation = augmentation_class(**augment_kwargs)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/asr/parts/preprocessing/perturb.py\", line 462, in __init__\r\n self._manifest = collections.ASRAudioText(manifest_path, parser=parsers.make_parser([]), index_by_file_id=True)\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/common/parts/preprocessing/collections.py\", line 223, in __init__\r\n for item in manifest.item_iter(manifests_files):\r\n File \"/mnt/HDD/HDD2/DTDwind/NeMo/nemo/collections/common/parts/preprocessing/manifest.py\", line 73, in item_iter\r\n for manifest_file in manifests_files:\r\nTypeError: 'NoneType' object is not iterable\r\n\r\n```\r\n\r\nI don't understand why I'm getting this error. The message indicates that it should have successfully retrieved my dataset, so manifest_files should not be NoneType.", "Looks like your noise augmentation manifest file is null, you may remove the noise augmentation from config or add the noise manifest ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-11-30T08:05:07
2024-03-04T01:46:52
2024-03-04T01:46:52
NONE
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Hi, How can I resume the training of a SpeakerNet? I already set `resume_if_exists: True `. But get error: `nemo.utils.exp_manager.NotFoundError: There was no checkpoint folder at checkpoint_dir :exp/SpeakerNet/SpeakerNet/checkpoints. Cannot resume.` I also set `create_checkpoint_callback: True`, I thought this would generate a checkpoint, but in reality, it did not. This is my exp folder architecture: - SpeakerNet - 2023-11-29_19-21-04 - cmd-args.log - events.out.tfevents.1701256897.biic188.12314.0 - git-info.log - hparams.yaml - labels.txt - lightning_logs.txt - nemo_error_log.txt - nemo_log_globalrank-0_localrank-0.txt - spkr.nemo Tool version python 3.10.13 NeMo 1.20.0 pytorch-lightning 1.9.4 torch 2.1.0
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2,017,021,360
I_kwDOC_bI7s54OU2w
7,954
[ASR] Beam Search Decoding How to Get Beam of Tokens as Output
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[ "I don't believe we've implemented beam search for transducer plus greedy word timestamp extraction.", "Would this be possible using CTC decoding, if I used a Conformer-CTC model instead?", "@titu1994 does this PR not give the results that I am looking for? Or does the 'preserve_alignments' part refer to something else? https://github.com/NVIDIA/NeMo/pull/5925 ", "Alignment extraction is one required step of greedy word timestamp collection, since that alignment information is used to build the consecutive chunks of sequences that correspond to tokens. \r\n\r\nHowever while alignment extraction may be implemented for all models, word timestamp calculation is implemented only for greedy decoding as far as I remember. You could implement it yourself with modifying the logic in AbstractRNNTDecoding or extracting the alignments and manually building the time stamp list", "So could I extract the alignments for beam search decoding and then building the timestamp list for each N full hypothesis (beam size = N)? Would this mean that with beam search decoding I could only extract N full hypotheses, or could I extract N words per word location and then build the time stamp list for each word location?", "Yes that can be done. Currently it's not implemented in NeMo but you can calculate greedy word timestamps for each of the N hypothesis generated per sample. \r\n\r\nWith the flags you have set, rnnt transcribe () will give you two items usually- first is the best hyp list and second is all hyp list (usually none unless you ser these flags). The second list is a list of NBest hypothesis which themselves contain a list of Hypothesize - each of which should have the alignment information. ", "So with the following flags set:\r\ndecoder_type='rnnt'\r\ncompute_timestamps=True\r\nrnnt_decoding.strategy='beam'\r\nrnnt_decoding.beam.beam_size=4\r\nrnnt_decoding.preserve_alignments=True\r\nrnnt_decoding.beam.return_best_hypothesis=False\r\n\r\n the transcriptions object returned contains:\r\n\r\n**1.** [0] = List of 2 elements, where: each element is a Hypothesis object, where: \r\n**1.1.** Element [0] has score=-4.7474, length=tensor(145) and has \"timestep\" and \"alignments\" fields; \r\n**1.2.** Element [1] has score=-10.1037, length=tensor(186) and also has \"timestep\" and \"alignments\" fields.\r\n**2.** [1] = List of 2 elements, where: each element is a Hypothesis object, where: \r\n**2.1.** Element [0] is a list of 4 Hypothesis objects, where: \r\n**2.1.1.** Element [0] has score=-4.7474, length=tensor(145) and has \"timestep\" and \"alignments\" fields; \r\n**2.1.2.** Element [1] has score=-4.9974, length=tensor(145) and also has \"timestep\" and \"alignments\" fields; \r\n**2.1.3.** Element [2] has score=-4.9976, length=tensor(145) and also has \"timestep\" and \"alignments\" fields; \r\n**2.1.4.** Element [3] has score=-5.088, length=tensor(145) and also has \"timestep\" and \"alignments\" fields.\r\n**2.2.** Element [1] is also a list of 4 Hypothesis objects, where:\r\n**2.2.1.** Element [0] has score=-10.1037, length=tensor(186) and has \"timestep\" and \"alignments\" fields; \r\n**2.2.2.** Element [1] has score=-10.4386, length=tensor(186) and has \"timestep\" and \"alignments\" fields; \r\n**2.2.3.** Element [2] has score=-10.6843, length=tensor(186) and has \"timestep\" and \"alignments\" fields; \r\n**2.2.4.** Element [3] has score=-10.7804, length=tensor(186) and has \"timestep\" and \"alignments\" fields.\r\n\r\nIf I have a beam size of 4, and I want to time align each beam, what 4 Hypotheses with \"timestep\" and \"alignments\" fields do I need to use from the returned transcriptions object? I am not sure how the lists transcriptions[0] and transcriptions[1] differ from each other. Also, could you point me to where I can find the implementation of time aligning a Hypothesis object based on \"timestep\" and \"alignments\" fields, as I am not sure how to go about extracting word-level time-alignment information, where all I need is the start time and end time for each word in each beam hypothesis.\r\nAlso am I providing some redundant flags?", "Ah sorry nevermind, I had a brief lapse of understanding. \r\n\r\nAs I understand, transcriptions[0] contains a list of just the **best** Hypothesis per audio file, and transcriptions[1] contains a list per each audio file, where the lists contain **all** the Hypothesis objects returned by beam search decoding for that audio file. Also creating the timestamp information can be done by following https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb#scrollTo=_5hfsiDGsM19\r\nAll that's needed is to write a custom function to write the results to separate files per hypothesis. \r\n\r\nOne question though: is the `rnnt_decoding.preserve_alignments=True` flag needed if I just want the timestamps for each word in each hypothesis? I don't see how it affects the generation of the `timestep` field of a Hypothesis object. Also, it seems like the `alignments` field of Hypothesis object is also not needed, what is its purpose? Or are the `timestep` fields created from the content of the `alignments` field?", "Yes the preserve alignments field is needed. The rest of the timestamps and alignments are values derived if you set that flag. It won't work otherwise ", "Thank you!" ]
2023-11-29T16:51:41
2023-12-04T20:59:45
2023-12-04T20:59:44
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I am using a Conformer-Transducer model and using the `transcribe_speech.py` script for transcript generation. I am looking to get a time-aligned word-level transcript output, but instead of just the best hypothesis, I want to get N best tokens for each token location, i.e. for each word location, I want the N best predicted words and the time for that location (beam size = N). For example, with a beam size of 2: Spoken text: "I can see clearly" What I have now: "timestamps_word": [{"word": "I", "start_offset": 16, "end_offset": 20}, {"word": "can", "start_offset": 20, "end_offset": 159}, {"word": "see", "start_offset": 159, "end_offset": 164}, {"word": "clearly", "start_offset": 164, "end_offset": 326}] Theoretically what I am looking for: "timestamps_word": [{"words": ["I", "eye"], "start_offset": 16, "end_offset": 20}, {"words": ["can", "cam"], "start_offset": 20, "end_offset": 159}, {"words": ["see", "sea"], "start_offset": 159, "end_offset": 164}, {"words": ["clearly", "freely"], "start_offset": 164, "end_offset": 326}] My current decoder-specific arguments to the script are as follows: ```decoder_type='rnnt' compute_timestamps=True rnnt_decoding.strategy='beam' rnnt_decoding.beam.beam_size=3 rnnt_decoding.preserve_alignments=True rnnt_decoding.beam.return_best_hypothesis=False``` Is what I am looking for possible?
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7,953
asr.transcribe() freezes inside multiprocessing class - rabbitmq queues
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[ "@titu1994 have you worked something similar before? ", "Is your model on Cuda ? \r\n\r\nCuda does not work with multi threading, only with multi processing. It simply does not support instantiating two separate cuda contexts (that's why all data loaders are multi process) on the same process. That can be one reason why it's hanging. \r\n\r\nBy default, ASR transcribe does not use worker processes. But it does use a PT data loader with 0 workers. \r\n\r\nI haven't implemented the queueing step but I have simply Fast API based inference pipeline that loads a single model and keeps it in memory calling transcribe one by one with async coroutine. But if you use multiple workers then your model gets replicated due to guvicorn. \r\n\r\nWhat about rearchitecing your solution a bit ? Have two async coroutines - one that queues your input audio and one that runs your model ? Don't put your model on a separate process, keep it on main process, but keep the queueing of input output only via rabbitmq under async coroutines \r\n\r\nPersonally I've never seen a single process class used to launch 3 processes. ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2023-11-29T16:39:55
2024-01-31T01:44:13
2024-01-31T01:44:13
CONTRIBUTOR
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Hi everybody, I have an application that communicates through rabbitmq queues. I am using a class that inherits multiprocessing.Process: `class ThreadedConsumer(multiprocessing.Process)` In this class, I would like to instantiate 3 processes, where each process transcribes an input file. Now, in order to use as little memory as possible, I would like to only create the 'asr_model' once, in the constructor of the ThreadedConsumer class, and each process to share the same model. I have the following problem: when I create the self.asr_model inside the __init__ class, when i do asr.transcribe() inside the callback (when I receive a message) everything freezes. (probably a thread gets latched or something) When I create the self.asr_model each time I receive a new message in the queue, everything works fine. However, in this way I would have 3 models loaded into the memory.. I do not understand why declaring the model inside the constructor results in the pipeline getting stuck. Could you please help me with an idea for model sharing, is it possible for ASR models? I know that it would be a solution to use a model with batch_size=3, however once new message come into the queue, I would like another queue that's free to transcribe them and not continously wait for 3 messages. If anybody has any insights or ideas on how to share the model/ use transcriber with multiprocessing.Process class, I would greatly appreciate any help! Thank you in advance, Gabi
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