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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 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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[ "Following up regarding issue. Please let me know if any more information is needed.", "According to this [HF implementation](https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/opt/modeling_opt.py):\n* `project_out` should be the final output layer, so it should be mapped to `model.output_layer.weight`\n* `project_in` seems to be a linear layer projecting from embedding dim down to hidden size. Not sure if NeMo supports it.", "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.", "@qijiaxing \nisn't the 'lm_head.weight' key should be mapped to model.output_layer.weight?\nTo give more understanding, I mapped lm_head.weight to model.output_layer.weight, which seems to be working fine. \nnow I am left with mapping\nHF keys : ['model.decoder.project_out.weight', 'model.decoder.project_in.weight'] to\nNemo Keys.\n\nSurprising it complains ['model.decoder.final_layernorm.weight', 'model.decoder.final_layernorm.bias'] from Nemo side, even though I made sure it doesn't include final layer norm explicitly.\n\nPlease let me know if needed any more explaination.", "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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[ "> There are some unwanted changed in `cicd-main.yml`. Can you please revert these?\r\n\r\nDone.", "Does the commit support the encoder and decoder are put into different devices? Could the encoder and decoder be model parallelized with their own parallism config?" ]

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[ "How do we enable the collection callback?", "> How do we enable the collection callback?\r\n\r\nRefer to the usage in the PR description." ]

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[ "Looks good. \r\n\r\n> This can cause hands, but for now let's try it.\r\n\r\n@titu1994 can you elaborate? do you expect an uneven number of validation batches per node (why), or something else?", "Yes, the reason to avoid sync is two fold - I think I remember random hangs years ago due to a similar flag, dunno if it's still an issue now. Second one is it causes a global sync across all ranks every single step - which causes a large slowdown once you scale up nodes ", "This is on validation epoch end so only a single sync per validation loop. That should be OK both hang-wise and speed-wise. ", "Makes sense ", "Another note, from my experience, `sync_dist=True` is tricky with parallelisms, especially pipeline parallelisms. If some metrics are non-existent on some ranks (eg val_loss is only on last pp stage, so we have to sync it), this could cause a hang. If the metric is 0.0 on some ranks, the mean reduction over ranks could be wrong or not produce intended value. ", "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.", "@krishnacpuvvada @titu1994 what's the verdict on this one? Do we need a Canary-specific mechanism to synchronize the validation metrics if it can't be done at the modelPT level? ", "(1) Don't push this to modelPT. \r\n(2) If Canary needs it, it can override this function specifically and copy paste the code (for now). \r\n\r\nDo note - @nithinraok plans to remove modelpt level support for multi validation and test data loader, which basically would mean every model has to handle this stuff manually anyway. " ]

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[ "Hey, sorry for the slow response. If I understand your question, you can have 1 AR model predict all 8 codebooks at the same time (eg. input the final output of your network into 8 independent softmax functions). You do not need 8 separate AR models.", "> Hey, sorry for the slow response. If I understand your question, you can have 1 AR model predict all 8 codebooks at the same time (eg. input the final output of your network into 8 independent softmax functions). You do not need 8 separate AR models.\n\nYes, We can make a 8 prediction-layer AR model to predict the 8 codes at a single step, but it is not convenient sometimes. Nearly all AR LLM TTS are suffering from some Inherent defects like repetition, deletion, or some other problems on sampling. some work like [VALL-E2](https://arxiv.org/abs/2406.05370) take check on synthensize repetition. When there is only one prediction output sequence, that is feasible, but when there are 8 output sequence, things become trouble some. \nAnd, AR LLM use a special token to label the sequence ends of prediction in one sequence mode. What is the sequence end token when there are 8 output sequence? When all 8 sequence 'next token's are all the 'end token'? or just any of the 8 'next token's is a `sequence end token`?", "We have a few other works (and code/PRs in NeMo) that cover how exactly to do AR LLM TTS with different codecs:\n\n[https://arxiv.org/abs/2406.17957](https://arxiv.org/abs/2406.17957)\n[https://arxiv.org/abs/2409.12117](https://arxiv.org/abs/2409.12117)\n\nTo be clear, there are not 8 output sequences. There is 1 output sequence, where each element in the sequence has 8 values.\n\nSo far as I am aware, every audio codec today has more than 1 codebook/output sequence. What may be confusing is that most LLMs, given an audio codec with N codebooks, typically treat codebook 1 as special and predict it using one inference stream (where they might inject an end of speech token), while predicting codebooks 2 through N using different inference streams. Any algorithm you use with other codecs would work with the spectral codec (or with any of our other audio codecs in NeMo).\n\nThe only difference is that other codecs, which use RVQ codebooks, have the requirement that predicting codebook M at timestep T requires conditioning on the predictions for codebooks 1 through (M - 1). With our FSQ codebooks, you no longer need to condition on codebooks 1 through (M - 1), simplifying the inference.\n\nIn other words, RVQ based codecs typically require up to N inferences per timestep while FSQ codec only requires 1 inference per timestep. Though I guess this does mean where you choose to inject the end of speech token is arbitrary.\n\nDoes that make sense, or am I misunderstanding part of the question?", "Thank you for the kindly help!\nAs I know there are mainly two types AR LLMs for TTS, The first is VALL-E like, which takes many codebooks in codec, and predict only one of them in AR LLM. the VALL-E predict the first codebook sequence with an AR Transformer and the rest code sequences with another NAR transformer model; the second type is TorToise-TTS like, which takes a single-codebook VQVAE codec to code the audio, and a GPT like LLM to predict the single code sequence. What I asked above is based on the second type. The spect-codec equiped AR LLM can make Parallel prediction to all 8 codes in each step VS RVQ-codec codes may be time-dependent, it is really a progress. \nWe do like the single codebook codec in production, not only because of the `end of sequence token` prediction problem, one sequence in, one sequence out, that is the most natural NLP LLM mode, so some experience in NLP can be directly introduced into TTS AR LLM. say, if we want to evaluate the proformance of AR LLM, we can directly use the `top 5 hit-rate`; `top 10 hit-rate` like estimates used in NLP, in the single-codebook TTS AR LLM. but when the output is a sequence that each timestep contains 8 values, things may be different. To make sure equal of all the 8 pair of values from label and prediction is harder, while single-codebook only need to compare a single pair. Intuitively the two AR LLMs are not stands at a fair starting line. Here I only suggested a model estimation problem, I thinks there may be other kinds of things that single-value token sequence works better. \nThere is also some RVQ codec paper argue that we can prefer the codebook of the last layer to train the LLM, because the residual structure helps the last VQ layer learns more while keeps infomation from all former RVQ layers. I am not sure about it. \nsingle-codebook codecs may be too compressed to recover the speech (or spec) signal, Yes, it is really a hard problem. \nThank you for the good job and for your your kindly help." ]

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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.", "Thanks for the request. We don't offer exact functionality now. However you could use [this script ](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_context_biasing/eval_greedy_decoding_with_context_biasing.py)for CTC or hybrid RNNT-CTC models to boost such words while transcribing. " ]

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[ "@stevehuang52 pls have a look at this. ", "Thanks for pointing out the issue, it will be fixed in https://github.com/NVIDIA/NeMo/pull/10581", "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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[ "@meatybobby " ]

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[ "Closing this for now. Sounds like this conflicts with another plan. To summarize that discussion with @marcromeyn:\r\n\r\n1. if I can call a function to get access to old checkpoint settings `old_ckpt = io.load(self.ckpt_init_path).model.checkpoint` which does not impact global state for the current context/run (that's important)\r\n2. Additionally if I can access global state for the checkpoint associated with self to mutate it (along with in parallel mutating self). Maybe something like `setattr(io.ckpt_ctx.trainer.model.checkpoint, key, var)` along with `setattr(self, key, var)` which would happen in parallel in the same way on every execution process.\r\n3. Additionally if I don't touch any parallelism/cluster settings in the checkpoint which would mutate the global environment\r\n\r\nThese features are key in https://github.com/NVIDIA/bionemo-fw-ea/pull/97" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "jenkins", "jenkins", "jenkins", "We might also want to bump MCore commit [here](https://github.com/NVIDIA/NeMo/blob/5b88aaa82781d32b0e88b2a9b9976c6cf60d9bd2/Dockerfile.ci#L61C15-L61C55) to [45bf4c1821c2a87ca02e8a0377d61097bac92d07 ](https://github.com/NVIDIA/Megatron-LM/commit/45bf4c1821c2a87ca02e8a0377d61097bac92d07) so that it's clear how to use newly integrated code", "[🤖]: Hi @shanmugamr1992 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11075304842) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[🤖]: Hi @shanmugamr1992 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11076645801) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[🤖]: Hi @shanmugamr1992 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11112712532) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[🤖]: Hi @shanmugamr1992 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11112712532) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[🤖]: Hi @shanmugamr1992 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11131756579) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[oyilmaz-nvidia](https://github.com/oyilmaz-nvidia) previously approved these changes [4 days ago](https://github.com/NVIDIA/NeMo/pull/10238#pullrequestreview-2338400329)", "[🤖]: Hi @shanmugamr1992 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/$REPOSITORY/actions/runs/$RUN_ID) 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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[ "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.", "Add option to assert some value for attributes instead of just logging", "TODO: add some tests on a dummy model with asserts on precision", "Don't we need to enable the megatron_gpt_pretraining test in CI?", "@hemildesai I thought it is already being used in the L2 tests: https://github.com/NVIDIA/NeMo/blob/maanug/param-debugger/.github/workflows/cicd-main.yml#L5142", "> @hemildesai I thought it is already being used in the L2 tests: https://github.com/NVIDIA/NeMo/blob/maanug/param-debugger/.github/workflows/cicd-main.yml#L5142\r\n\r\nAh I see. Sorry my bad.", "[🤖]: Hi @maanug-nv 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11077400886) for this PR just finished successfully ✨\n\nSo it might be time to merge this PR or like to get some approvals 🚀\n\nBut I'm just a 🤖 so I'll leave it you what to do next.\n\nHave a great day! \n\n//cc @ko3n1g", "[🤖]: Hi @maanug-nv 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/$REPOSITORY/actions/runs/$RUN_ID) 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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[ "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.", "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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[ "Hi @yangzhipeng1108, looking at the error, \r\n\r\n> ```\r\n> 2: File \"/usr/local/lib/python3.10/dist-packages/lightning_fabric/plugins/environments/torchelastic.py\", line 63, in world_size\r\n> 2: return int(os.environ[\"WORLD_SIZE\"])\r\n> 2: File \"/usr/lib/python3.10/os.py\", line 680, in getitem\r\n> 2: raise KeyError(key) from None\r\n> 2: KeyError: 'WORLD_SIZE'\r\n> ```\r\n\r\nIt looks like the TorchElasticEnvironment is being used, which would be caused by setting \"cluster_type=BCP\" ([code](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/parts/megatron_trainer_builder.py#L143-L144)). But it looks like you are running via Slurm. So, I don't think you should be using \"++cluster_type=BCP\" in a Slurm environment? Did you add that yourself, or was this script generated via the NeMo Framework Launcher?", "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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[ "@jbieniusiewi could you also add the same check for 2.0. The unfinished checkpoint filtering is [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/resume.py#L112-L113) in 2.0.", "Thank you for the review @maanug-nv. \r\nI've added checks for NeMo 2.0. One more approval is needed to merge it, so I re-requested a review.", "~~-@ashors1 Do we have/plan to have tests for AutoResume?~~ Looks like they are in `tests/lightning/test_nemo_logger.py`. @jbieniusiewi final request, can we include the same test you added for exp manager to NeMoLogger/AutoResume as well?", "@maanug-nv Sure, I've added tests to ``tests/lightning/test_nemo_logger.py``.\r\n" ]

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[ "I feel like we should hold off on this until we merge the new entrypoint-API in NeMo-Run, see [docs](https://github.com/NVIDIA/NeMo-Run/blob/c284dcc7005ac5a2183dac661173eee46e05df87/examples/entrypoint/README.md). This will simplify this example considerably while adding more flexibility.", "> I feel like we should hold off on this until we merge the new entrypoint-API in NeMo-Run, see [docs](https://github.com/NVIDIA/NeMo-Run/blob/c284dcc7005ac5a2183dac661173eee46e05df87/examples/entrypoint/README.md). This will simplify this example considerably while adding more flexibility.\r\n\r\n@marcromeyn But what if we don't want the extra flexibility. The aim of this example is to be able to reproduce exactly what we have been doing. Users can then use CLI or the script itself for personalization. ", "Yeah I think we can create a separate CLI folder to host the CLI examples, but would be nice to have both so the users get a clear picture. It will also help show the benefits via a 1:1 comparison.", "@ShriyaPalsamudram if the script would only show how to do one thing, so let's say llama3-8b I would agree. But this script offers some form of parameterization using argparse + the `MODEL_SIZE_MAPPING` dict . I believe when we showcase scripts with a form of parameterization we should be showcasing the functionality inside NeMo-Run that is designed for that." ]

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[ "FYI @gdengk " ]

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[ "can you include some example of how this would be used (at least in PR description)" ]

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[ "Thank you for the bug report. We're still working on a patch.", "Hi,\r\n\r\nthis issue is fixed in our dev nightly container:\r\n[nvcr.io/nvidian/nemo:nightly](http://nvcr.io/nvidian/nemo:nightly)" ]

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[]

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[ "I know this is still in draft but I did a quick test and llama and nemotron seem working. Got some error with gemma. Looks like there is a API change for gemma. We also need update for the scripts/deploy/nlp/deploy_triton.py." ]

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