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renwei2024

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nemo-github-issues

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[ "Currently, this will dump the snapshot for all ranks. Should we add an option to only save a snapshot from rank 0? or a specifiable rank?", "> Currently, this will dump the snapshot for all ranks. Should we add an option to only save a snapshot from rank 0? or a specifiable rank?\r\n\r\nAddressed in latest commit\r\n", "also, can we allow disabling the profiling, so users can only run the cycle detector?", "Yeah I was just being cautious to avoid side effects of the import but I don't think this import should have any, so it's probably fine.\r\n\r\nGet Outlook for iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nFrom: Shriya Rishab ***@***.***>\r\nSent: Friday, August 23, 2024 10:07:29 AM\r\nTo: NVIDIA/NeMo ***@***.***>\r\nCc: Hemil Desai ***@***.***>; Comment ***@***.***>\r\nSubject: Re: [NVIDIA/NeMo] Add MemoryProfileCallback (PR #10166)\r\n\r\n\r\n@ShriyaPalsamudram commented on this pull request.\r\n\r\n________________________________\r\n\r\nIn nemo/lightning/pytorch/callbacks/memory_profiler.py<https://github.com/NVIDIA/NeMo/pull/10166#discussion_r1729126364>:\r\n\r\n> @@ -0,0 +1,78 @@\r\n+import os\r\n+\r\n+import torch\r\n+from pytorch_lightning.callbacks.callback import Callback\r\n+from torch.utils.viz._cycles import warn_tensor_cycles\r\n\r\n\r\nIsn't it better to fail early if there is an issue with this import than fail inside the callback?\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/NVIDIA/NeMo/pull/10166#discussion_r1729126364>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AB6Q25H6ZIEEJZOY5WCDAWDZS5F3DAVCNFSM6AAAAABMS6X6TCVHI2DSMVQWIX3LMV43YUDVNRWFEZLROVSXG5CSMV3GSZLXHMZDENJXGQ3TONZVGY>.\r\nYou are receiving this because you commented.Message ID: ***@***.***>\r\n" ]

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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.", "Eval numbers on CH109:\r\nRunning `NeMo/examples/speaker_tasks/diarization/clustering_diarizer/offline_diar_with_asr_infer.py` using `stt_en_fastconformer_ctc_large` as the ASR model gives the following performance on CallHome-109 dataset-\r\n```\r\n DER : 0.0474\r\n FA : 0.0111\r\n MISS : 0.0285\r\n CER : 0.0078\r\n Spk. counting acc. : 0.9174\r\n cpWER : 0.5659\r\n WER : 0.3142\r\n```" ]

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[ "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "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." ]

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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." ]

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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." ]

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[ "Closing this in favor of https://github.com/NVIDIA/NeMo/pull/10140" ]

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[ "8x3B Mixtral seems to be missing now, trying to add it in this PR", "@hemildesai For better integration" ]

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[ "@alpha0422 looks like these are deprecated as well: https://github.com/NVIDIA/NeMo/blob/11fece913d83b74bf419fca66b8f0951f281bfba/nemo/utils/callbacks/cuda_graph.py#L400", " File \"/__w/NeMo/NeMo/nemo/utils/callbacks/cuda_graph.py\", line 400, in on_train_end\r\n _ResultCollection.register_key = _ResultCollection.__orig_register_key__\r\nAttributeError: type object '_ResultCollection' has no attribute '__orig_register_key__'", "@thomasdhc Thanks for pointing it out, pushed changes." ]

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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." ]

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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.", "[🤖]: Hi @akoumpa 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11334331014) for this PR just finished successfully\n\nSo it might be time to merge this PR or get some approvals\n\nI'm just a bot so I'll leave it you what to do next.\n\n//cc @pablo-garay @ko3n1g" ]

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[ "stopping this for now until our CI is out of maintenance ", "Looks good, thanks! Can we clean up some of the old comments in the test script?" ]

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[ "CI passed (other than CPU unit tests which was made optional): https://github.com/NVIDIA/NeMo/actions/runs/10371225709" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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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." ]

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[ "@titu1994 Your help would be greatly appreciated. ", "@KunalDhawan can you add the flag to the tokenizer ?", "Hi @titu1994, after some investigation into the above issue, I don't think AggregateTokenizer is well suited for the current implementation of flashlight decoder/ word-boosting due to the following reasons:\n\n- With aggregate tokenizer, the tokenizer for each language might have it's own \\<unk\\> token, due to which we would have to maintain a list for unk_id and self.silence parameters (https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/flashlight_decoder.py#L135) and significantly modify the FlashLightKenLMBeamSearchDecoder algo. \n- In the AggregateTokenizer, the id -> token and id -> lang mapping is one-to-one, but to go from token to id (used [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/flashlight_decoder.py#L167)) we would also need to provide the language corresponding to each token, again because we might have multiple ids corresponding to the same token appearing in different constituent tokenizers of the AggregateTokenizer\n\nA possible way to address this could be - when using Flashlight decoder with Aggregate tokenizer, we can ask the users to input the target language to use. AggregateTokenizer with lang specified == standard tokenizer, which we can use as it with the current Flashlight implementation. We can also have the default lang as 'en' and error out if 1) target lang is not provided and 2) AggregateTokenizer doesn't have a tokenizer corresponding to 'en'. Would love to hear your thoughts on this.\n\n", "I created a quick patch to FlashLightKenLMBeamSearchDecoder with the above idea and verified that it works with an AggregateTokenizer within the assumptions highlighted in the previous comment: [https://github.com/KunalDhawan/NeMo/commit/0e8d6c53caa59cdd7d6ce3aeb61ecd8483c859e3](https://github.com/KunalDhawan/NeMo/commit/0e8d6c53caa59cdd7d6ce3aeb61ecd8483c859e3). I can create a PR to main with this if everything looks good! ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Hi, thank you for your question.\r\n\r\nYou can see the code used to clean the Spanish data here: https://github.com/NVIDIA/NeMo-speech-data-processor/tree/main/dataset_configs/spanish_pc.\r\n\r\nThe rules used to clean the German and French data were very similar.\r\n\r\nActually, IIRC, we did not use NeMo Text Processing at that time. You can see there some custom rules for handling Roman Numerals, handling common abbreviations like \"Dr\".\r\n\r\nFor Voxpopuli, there was normalized text without punctuation and capialization, we made a processor to add the normalization to the version of the text with punctuation and capitalization: https://github.com/NVIDIA/NeMo-speech-data-processor/blob/main/sdp/processors/datasets/voxpopuli/normalize_from_non_pc_text.py#L118.\r\n\r\nIn general, a lot of care was taken to clean each specific dataset. We browsed the data and looked for common issues using the NeMo [Speech Data Explorer](https://github.com/NVIDIA/NeMo/tree/main/tools/speech_data_explorer) tool." ]

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[ "CICD passed: https://github.com/NVIDIA/NeMo/actions/runs/10374317474" ]

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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." ]

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[ "Resolved: \r\nhttps://github.com/NVIDIA/NeMo/pull/10183\r\nhttps://github.com/NVIDIA/NeMo/pull/10137" ]

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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." ]

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[ "I managed to figure it out, and for those who are searching, they might find this as a helpful reference: \r\n\r\nTo address the issue, you can modify the code in `nemo/collections/asr/models/ctc_models.py`. Specifically, navigate to line 502, which is:\r\n\r\n```\r\nif self.spec_augmentation is not None and self.training:\r\n```\r\n\r\nYou should update this line to:\r\n\r\n```\r\nif self.spec_augmentation is not None:\r\n```\r\n\r\nThis change ensures that the `self.training` condition is no longer required for `spec_augmentation`. The rest of the configuration can remain unchanged.\r\n\r\nDisclaimer: For you it might be possible that you have used different config., you have to modify that script accordingly" ]

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[ "Could you also plot lr graph? Your validation loss kept on increasing. Could you start with 1024 tokens, howver you mentioned the same tokens work well for Conformer, does that mean you tried same set up with Conformer and it trained well and you are only seeing issues with FastConformer? \r\n\r\nIf possible share complete config. ", "Thank you for your support!\r\n\r\nI managed to resolve the issue, and I wanted to share the solution in case others encounter a similar problem. The root cause was related to the batch size and learning rate. Since my batch size was relatively small, I found it necessary to reduce the learning rate accordingly. The default configuration’s learning rate parameters are optimized for a global batch size of 2K, so using a smaller batch size requires a lower learning rate.\r\n\r\nInitially, the model converged well during the early stages when the learning rate was low. However, as the learning rate increased due to the warm-up settings, the training became unstable. By further reducing the learning rate, I was able to stabilize the training, and the model began converging as expected.\r\n\r\n\r\n\r\nI hope this information helps others who might be facing similar challenges with smaller batch sizes. Thanks again for your support!\r\n\r\n\r\n" ]