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renwei2024

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nemo-github-issues

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

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[ "Can we enable the gradient/activation checkpointing from Nemo trainer? This will make the GPU memory requirement for QAT similar to that of original finetuning.", "@realAsma should we disable FP8 KV cache for INT4 weight only?\r\n\r\n| FP8 KV Cache | PTQ | QAT |\r\n| --------|-------|-------- |\r\n| Enabled | 1.345 | 1.216 |\r\n| Disabled | 1.305 | 1.172 |\r\n\r\nBF16 baseline is 1.15", "LGTM;", "> @realAsma should we disable FP8 KV cache for INT4 weight only?\r\n> \r\n> FP8 KV Cache\tPTQ\tQAT\r\n> Enabled\t1.345\t1.216\r\n> Disabled\t1.305\t1.172\r\n> BF16 baseline is 1.15\r\n\r\nThat depends on how easy it is to enable KV cache quantization from the config file. If it is easy to do, sure we can enable. However this might not have huge value if there are already good PTQ examples demonstrating KV cache quantization.", "> That depends on how easy it is to enable KV cache quantization from the config file. If it is easy to do, sure we can enable. However this might not have huge value if there are already good PTQ examples demonstrating KV cache quantization.\r\n\r\nWe currently don't have KV cache quantization parameter in config and its enabled by default for all except int8_sq.\r\n\r\nFor INT4 having it enabled (default) makes QAT look less appealing as we are still a bit off from bf16 so I though it would be better to disable it.\r\n\r\nLet me make if a config parameter and disable it for now -- maybe speedup may be lower", "LGTM!" ]

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[ "pipeline passed: https://github.com/NVIDIA/NeMo/actions/runs/9382022367" ]

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[ "thanks, will merge once ci is ready." ]

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[ "Hi, can you please sign off all your commits? Please see here for instructions https://github.com/NVIDIA/NeMo/pull/9322/checks?check_run_id=25479558364" ]

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[ "is your issue solved ? if solved tell me how i am facing similar issue", "I came across this as well. @yonas-g if you found a solution would you mind sharing?\r\n\r\nCould be missing an import `pip install megatron_core`" ]

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[ "Please check https://github.com/NVIDIA/NeMo/issues/9272", "I tried using : _python3 -m pip install git+https://github.com/NVIDIA/NeMo.git#egg=nemo_toolkit[all]_ \r\n\r\nAfter this, I am getting different errors:\r\n\r\nFused optimized group norm has not been installed.\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py\", line 16, in <module>\r\n from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add\r\n**ModuleNotFoundError: No module named 'megatron'**\r\n\r\nAfter this, I did **pip install megatron,** which gave another error:\r\n**ModuleNotFoundError: No module named 'transformer_engine'**\r\n\r\nThen I tried installing transformer-engine using \r\n**pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable**\r\n, but engine crashed. Please suggest. Thanks", "Hi @yonas-g @titu1994 \r\nI am still facing this issue. Any suggestion. Thanks", "Transformer engine is not that easy to install, please look at installation instructions in NeMo readme or use container. ", "> Please check #9272\r\n\r\nI checked and it using https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements_lightning.txt#L7 it downgraded my transformers to 4.40.2 which fixed this problem as it pulled in https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/__init__.py#L1456", "> > Please check #9272\r\n> \r\n> I checked and it using https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements_lightning.txt#L7 it downgraded my transformers to 4.40.2 which fixed this problem as it pulled in https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/__init__.py#L1456\r\n\r\nHi @raybellwaves @uni-manjunath-ke,\r\n\r\nYes, some of the modules were removed from transformers >= 4.41. If you want to use it, please, install transformers version <= 4.40.2." ]

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[ "I have the same issue, it happens more when I use `use_semi_sorted_batching`\r\nany help will be appreciated. Thanks\r\n\r\nOn Mon, May 27, 2024, 1:51 PM hayrapetyan8768 ***@***.***>\r\nwrote:\r\n\r\n> I encountered an issue while trying to resume training from a checkpoint\r\n> in NeMo. When the validation dataset is less than twice the checkpoint step\r\n> size, I get the following error:\r\n>\r\n> File \"/train/train_nemo_model.py\", line 168, in train_nemo_model\r\n> trainer.fit(model, ckpt_path=os.path.join(config[\"model\"][\"custom_model_save_path\"], config[\"mlflow_cloud\"][\"run_id\"]),\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py\", line 532, in fit\r\n> call._call_and_handle_interrupt(\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py\", line 42, in _call_and_handle_interrupt\r\n> return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py\", line 93, in launch\r\n> return function(*args, **kwargs)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py\", line 571, in _fit_impl\r\n> self._run(model, ckpt_path=ckpt_path)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py\", line 980, in _run\r\n> results = self._run_stage()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py\", line 1023, in _run_stage\r\n> self.fit_loop.run()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py\", line 202, in run\r\n> self.advance()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py\", line 355, in advance\r\n> self.epoch_loop.run(self._data_fetcher)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py\", line 134, in run\r\n> self.on_advance_end()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py\", line 249, in on_advance_end\r\n> self.val_loop.run()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py\", line 181, in _decorator\r\n> return loop_run(self, *args, **kwargs)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py\", line 122, in run\r\n> return self.on_run_end()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py\", line 244, in on_run_end\r\n> self._on_evaluation_epoch_end()\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/loops/evaluation_loop.py\", line 326, in _on_evaluation_epoch_end\r\n> call._call_lightning_module_hook(trainer, hook_name)\r\n> File \"/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py\", line 145, in _call_lightning_module_hook\r\n> output = fn(*args, **kwargs)\r\n> File \"/nemo/core/classes/modelPT.py\", line 917, in on_validation_epoch_end\r\n> dataloader_logs = self.multi_validation_epoch_end(val_outputs, dataloader_idx=dataloader_idx)\r\n> File \"/nemo/collections/asr/models/ctc_models.py\", line 619, in multi_validation_epoch_end\r\n> metrics = super().multi_validation_epoch_end(outputs, dataloader_idx)\r\n> File \"/nemo/collections/asr/models/asr_model.py\", line 36, in multi_validation_epoch_end\r\n> if 'val_loss' in outputs[0]:\r\n> IndexError: list index out of range\r\n>\r\n> *For example:*\r\n> The checkpoint step size is 28. I encounter the error when the dataloader\r\n> processes 55 examples, but it works correctly with 57 examples. It seems\r\n> that the issue arises when the validation dataset size is less than twice\r\n> the checkpoint step size. Any advice on how to resolve this would be\r\n> appreciated.\r\n>\r\n> - Environment location: local\r\n> - Method of NeMo install: from source\r\n>\r\n> *Environment details*\r\n>\r\n> Otherwise, please provide:\r\n>\r\n> - OS version - Ubuntu 22.04.4 LTS\r\n> - PyTorch version - pytorch-lightning==2.0.7\r\n> - Python version - python3.10\r\n>\r\n> *Additional context*\r\n>\r\n> GPU model - NVIDIA GeForce RTX 3060\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/NVIDIA/NeMo/issues/9317>, or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ACERJIJ5EYJJFJR427LGDITZEMFZTAVCNFSM6AAAAABIK6UGM6VHI2DSMVQWIX3LMV43ASLTON2WKOZSGMYTQOBWGEZTQMA>\r\n> .\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: ***@***.***>\r\n>\r\n", "@KunalDhawan @titu1994 @fedorovgv \r\nMight be relevant to your work, thanks", "@hayrapetyan8768 @orena1 Can you please attach minimal reproducible code to reproduce the bug? ", "It will be hard, it only happens sometimes, only on multi-GPUs examples, and only with big-datasets. \r\nAlso, important to note, it only happens when we use mulitple validation datasets. \r\nhttps://github.com/NVIDIA/NeMo/pull/6306\r\nMaybe also relevant for @bmwshop ", "It is very strange that it is not deterministic and, if I understand correctly, it happens with and without ssb. Intuition suggests that this may not be a nemo, but pytorch lightning's >= 2.0 problem. Try to look in side like this https://github.com/Lightning-AI/pytorch-lightning/discussions/18110 ", "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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[ "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, could you also file a MR to enable specifying \"a code dataset\" that is required to calibrate \"code\" LLMs like StarCoder2? This should be another option in `get_calib_dataloader` here https://github.com/NVIDIA/NeMo/blob/b0f3138a6be7fab3175deb8935f8492aeb1445bd/examples/nlp/language_modeling/megatron_quantization.py#L72", "I think we should import this spec from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/inference/gpt/model_specs.py, it comes with `core_attention` update pursued here" ]

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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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[ "@yaoyu-33\r\nGot a question is it normal that the `NeMo/examples/multimodal/multimodal_llm/neva/conf/neva_inference.yaml` seems to do streaming inference. And I see continuous calls/streaming calls to `replace_media_embeddings` with the below command:\r\n```\r\nneva_model_file=/ws/converted_nemo_model/lita-vicuna-v1-3-13b-finetune.nemo\r\nprompt_file=/ws/test/prompt_file.json\r\noutput_file=/ws/test/output.json\r\nvideo_base_path=/ws/test\r\ntorchrun --nproc_per_node=1 /ws/NeMo/examples/multimodal/multimodal_llm/neva/neva_evaluation.py \\\r\n --config-path=/opt/NeMo/examples/multimodal/multimodal_llm/neva/conf/ \\\r\n --config-name=neva_inference.yaml \\\r\n tensor_model_parallel_size=1 \\\r\n pipeline_model_parallel_size=1 \\\r\n neva_model_file=$neva_model_file \\\r\n trainer.devices=1 \\\r\n trainer.precision=16 \\\r\n prompt_file=$prompt_file \\\r\n inference.media_base_path=$video_base_path \\\r\n inference.media_type=video \\\r\n output_file=$output_file \\\r\n inference.temperature=0.2 \\\r\n inference.top_k=0 \\\r\n inference.top_p=0.9 \\\r\n inference.greedy=False \\\r\n inference.add_BOS=False \\\r\n inference.all_probs=False \\\r\n inference.repetition_penalty=1.2 \\\r\n inference.insert_media_token=right \\\r\n inference.tokens_to_generate=256 \\\r\n quantization.algorithm=awq \\\r\n quantization.enable=False\r\n\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.", "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.", "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.", "Closing in favor of https://github.com/NVIDIA/NeMo/pull/9682" ]

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[ "@yaoyu-33 which enroot version did you use? somewhat like\r\n\r\n`nvcr.io+nvidia+nemo+24.03.framework.sqsh`\r\nor `nvcr.io+nvidia+nemo+24.03.01.framework.sqsh`\r\n\r\nplease specify your environment. Thanks!!" ]

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[ "CI Pipeline passed: https://github.com/NVIDIA/NeMo/actions/runs/9263320632" ]

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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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[ "@ericharper @titu1994 @blisc @okuchaiev Would you please review my PR? ", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Just in case, I've been using the latest version of NeMo with:\r\n```\r\napt-get update && apt-get install -y libsndfile1 ffmpeg\r\ngit clone https://github.com/NVIDIA/NeMo\r\ncd NeMo\r\n./reinstall.sh\r\n```", "@tango4j, could you check the above issue with `num_speakers` and `sentence_length_params`?", "> 1. How can I ensure that every session has exactly as many speakers as num_speakers?\r\n\r\nWe need a little more time to figure out why `enforce_num_speakers: true` is not working as expected for 10mins sessions and more than 4 speakers. @tango4j we have a primitive fix in mind but need to further test it\r\n\r\n> 2. Why the default value of sentence_length_params is not an integer?\r\n\r\nYou're right that the `k` in `sentence_length_params` should usually be an integer. We use a default 0.4 in order to match the segment length distribution in AMI dataset, but you can set it to other integer values, and that would generally increase the lengths of segments\r\n", "> We need a little more time to figure out why `enforce_num_speakers: true` is not working as expected for 10mins sessions and more than 4 speakers. \r\n\r\n@stevehuang52 Thank you for figuring out this issue. You can check these dataset I used and the simulated meetings I've generated in case it helps:\r\n[[dataset(4.0GB)](https://mini.sapphosound.com/pds/commonvoice_libri_17_0.zip)] [[alignments in simple,condensed format(2MB)](https://mini.sapphosound.com/pds/commonvoice_libri_17_0_alignments_nemo.tar.gz)] [[generated sim_meet over 2~6 speakers(4.3GB)](https://mini.sapphosound.com/pds/commonv_sim_meet.tar.gz)]\r\n\r\n> You're right that the `k` in `sentence_length_params` should usually be an integer. We use a default 0.4 in order to match the segment length distribution in AMI dataset, but you can set it to other integer values, and that would generally increase the lengths of segments\r\n\r\nThanks a lot. Then I'll set it to an integer in my case.", "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.", "(Just a bump)", "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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[ "Closing since we have this PR https://github.com/NVIDIA/NeMo/pull/9295" ]

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[ "Did you find a solution for this error", "> Did you find a solution for this error\r\n\r\nJust pin the setuptools version, e.g. `python3 -m pip install setuptools==69.5.1`", "why not switching to [`packaging`](https://pypi.org/project/packaging/)?", "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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[ "Docstring needs to be updated but overall ok ", "added the test from repro: https://github.com/NVIDIA/NeMo/pull/9281/files#diff-b1e596fcfe12238f95a6145a309dfbcdcee6bd67f68505aa42f8865d759f377bR461\r\nalso added removing existing .nemo checkpoints in safe way (rename existing, save new, remove old)", "@titu1994 CI on `main` branch is failing so I added a fix which is unrelated to this PR so that CI passes:\r\nhttps://github.com/NVIDIA/NeMo/pull/9281/files#diff-efe139cc50f2253e4a5f2b5a268dff86f1094531de824da4067590243be7f928R25", "There are some issues with the CI, @pablo-garay can you take a look?\r\n\r\n@titu1994 I assume it needs to go to 2.0.0.rc as well?", "Yup needs to be cherry picked into 2.0 as well " ]

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[ "jenkins", "jenkins", "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." ]

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[ "Could you demostrate/link how do you want to use that refactored `Quantizer`?\r\n", "Note that changes will need to be unstreamed to another project as well here https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/main/launcher_scripts/conf/ptq/model/quantization.yaml. I think we'll need to chop this slightly", "> Could you demostrate/link how do you want to use that refactored `Quantizer`?\r\n\r\nIts used in https://github.com/NVIDIA/NeMo/pull/9276", "We'll need to do follow up MRs to:\r\n* https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/main/launcher_scripts/conf/ptq/model/quantization.yaml \r\n* https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/llm_ptq/nemo_ptq.py\r\n(I know it's duplication but it is what it is)", "@janekl should I rename `alpha` to `sq_alpha` so it is clear alpha is used only for SQ?", "> @janekl should I rename `alpha` to `sq_alpha` so it is clear alpha is used only for SQ?\r\n\r\nOK, good idea (same applies to Launcher ofc.)" ]

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