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The existing doc on S3CheckpointIO is pretty sparse. Can there be a more complete example of using it within a script like https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_pretraining.py?
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Add more flexibility to the default data step inside megatron parallel.
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**Collection**: NLP
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Cheery-pick of https://github.com/NVIDIA/NeMo/pull/9632 to r2.0 branch
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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} | # What does this PR do ?
Parallel save is set to False in the GPT config, but not in the DistributedCheckpointIO constructor. This PR fixes that
**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
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} | 🚀 PR to release NeMo `2.0.0rc1`.
📝 Please remember the following to-do's before merge:
- [ ] Fill-in the comment `Highlights`
- [ ] Review the comment `Detailed Changelogs`
🚨 Please also keep in mind to _not_ delete the headings of the task commits. They are required by the post-merge automation.
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-07-07T15:45:18 | 2024-08-22T01:52:56 | 2024-08-22T01:52:55 | NONE | {
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I use the Diarization pipeline, which is vad -> spk_emb -> clustering(nmesc+kmeans). When I use myself speaker model, I found the diarization performance is even worse than the open-source titanet-large model on my own test dataset.
Note... | {
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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
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] | 2024-07-05T23:17:50 | 2024-08-15T16:39:24 | 2024-08-15T16:39:24 | CONTRIBUTOR | {
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} | These two sets of weight and bias have been renamed according to the `module_name_rewrite_list` given in https://github.com/NVIDIA/Megatron-LM/blob/e33c8f78a35765d5aa37475a144da60e8a2349d1/megatron/core/inference/gpt/state_dict_hooks.py#L116-L119
Also change the underscores in the two command-line options to hyphens... | {
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} | # What does this PR do ?
Fix NeMo 2 pretraining with different data split sizes and dataset weights
**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
# Usage
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"Hi, try to follow up here: I think we have few different version of NeMo checkpoints at the moment. The current script works for more recent nemo checkpoints but maybe not 2B.",
"The error is not caused by the input NeMo checkpoint. It is caused by this change in the output mcore checkpoint, i.e., these two sets... | 2024-07-05T22:46:36 | 2024-08-17T05:46:30 | 2024-08-15T16:39:53 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
RuntimeError "Unexpected key" when running checkpoint_converters script convert_got_nemo_to_mcore.py
**Steps/Code to reproduce bug**
Follow the instructions given in https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/dpo.html to convert a GPT-2B checkpoint to Megatron-... | {
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Updates Apex module replacements we do inside export() with Megatron Core modules replacement.
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NLP
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"An offline model is not supposed to be used with cache-aware scripts, specially with small chunk sizes. You need to use cache-aware models like:\r\nhttps://huggingface.co/nvidia/stt_en_fastconformer_hybrid_large_streaming_multi\r\n\r\nRiva does not support cache-aware models publicly yet as far as I know. You may ... | 2024-07-04T12:33:39 | 2024-09-09T01:57:04 | 2024-09-09T01:57:03 | NONE | {
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} | null | null | null | Hello. I am testing the [STT En Conformer-CTC Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large/files) model in streaming mode. To do this, I use the [speech_to_text_cache_aware_streaming_infer.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_cache_aware_streami... | {
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"Also, did you use nemo text normalization for text preprocessing? If yes, did you use only it?",
"> Hi. I'm interested what datasets did you use for this model? https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu\r\n\r\nWe used a combination of the... | 2024-07-04T11:26:58 | 2024-08-20T16:46:11 | 2024-08-20T16:46:11 | NONE | {
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https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu | {
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} | # What does this PR do ?
With distributed checkpoints, the mismatches between the runtime model and checkpoint model manifest during `dist_checkpoint.load` (not during `model.load_state_dict` as with regular checkpoints).
This PR adds a flag that allows to adjust load strictness (e.g. ignore unexpected keys).
Th... | {
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} | # What does this PR do ?
Adding Mamba but commented to resolve mcore hash issue
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.
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} | # What does this PR do ?
Support for preprocessing jsonl files in a folder.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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Fixed an issue for PP > 1 resume.
**Collection**: [Note which collection this PR will affect]
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1. Unpins transformers package version.
2. Guards deprecated imports in NeMo which were deleted in transformers >= 4.40
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Fix the script to convert Llama 2/3 from .nemo format to hf format. This will resolve #9496
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Support enabling inference server only in megatron_gpt_eval.py, thus overriding the first and second examples if `cfg.prompts` is empty.
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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Adding better support for triton deployment of in-framework models using `MegatronLLMDeployable`.
**Collection**: [Note which collection this PR will affect]
scripts/deploy
tests/deploy
# Changelog
- Add `NemoQueryLLMPyTorch` class for easy query of `MegatronLLMDeployable`
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"@KunalDhawan pls have a look at this issue. ",
"Hi @MarcisTU, Thank you for the detailed description! Could you please help me reproduce the issue? \r\n\r\nI tried to replicate the issue on my end but I am able to run the code snippet you shared above without any errors. Let me describe my replication setup in d... | 2024-07-03T03:43:25 | 2024-12-06T14:19:37 | 2024-09-09T01:57:07 | NONE | {
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After following installation instructions for Linux there is a weird bug about torch tensor not being on CPU when converting to numpy.
```
Transcribing: 0%| | 0/1 [00:02... | {
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"This PR was closed because it has been inactive for 7 days since being marked as stale.",
"@titu1994 why this was closed? ",
"@nithinraok why is this closed? sh... | 2024-07-02T22:38:18 | 2024-10-04T11:42:04 | 2024-10-04T11:42:04 | CONTRIBUTOR | {
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Fix semi_sorted paper link
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Adds support for LLAMA3 and fixes a few other issues with vLLM.
**Collection**: NLP
# Changelog
- Separated the vLLM export functionality from the common deployment script into deploy_vllm_triton.py.
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- Adds support for passing multiple paths to `PreTrainingDataModule`
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**Collection**: [Note which collection this PR will affect]
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This PR aims to add [LITA](https://github.com/NVlabs/LITA) from checkpoint conversion, dataset preprocessing for DVC datasets to finetuning.
It also adds vila to the lita model.
**Collection**: [Note which collection this PR will affect]
* multimodal
* nlp
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Adds the Fabric-API to nemo 2
# Changelog
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* You can potentially add a usage example below
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1. Adds "offline" data cache generation support. The training script will be stopped once data cache is generated.
2. Enable mcore's `num_dataset_builder_threads` param usage.
**Collection**: [Note which collection this PR will affect]
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Refinements in PTQ workflow (calibration + export) including:
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* `model_type` as optional parameter for "qnemo" export
* other minor updates
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lenth -> length
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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 have the same issue. Have you solved it? How can we avoid it？",
"First, to triage whether it's the model or data store as the problem, run with a subset of data, maybe 50 hours of so. What is the max duration of the data ? Reduce it to at most 40 seconds, preferably 30 sec. We have some tools to segment data a... | 2024-06-28T15:02:00 | 2024-09-18T01:55:49 | 2024-09-18T01:55:48 | NONE | {
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We're trying to finetune the stt_en fastconformer_ctc large model on around 20k hours of data on 2 h100s. We're using 128 batch size and 8 num_workers and we trained the tokenizer with 1024 vocab_size. The training is taking very long, more than 30 hours per epoch and after around 70-80% of the first epoch it's ... | {
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Fixes
- optimizer state restore
- model state dict loading
**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
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"Updating from Cuda 12.2 to Cuda 12.3 seems to have solved the issue.",
"Closing"
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I'm trying to fine-tune the Ambernet model on Cantonese and Chinese.
But I'm getting this Segmentation fault;
```
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]
Segmentation fault
```
[full_output.txt](https:/... | {
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"yes, it is due to additional `<blank>` token that gets added to the provided tokenizer. ",
"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.",
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Using the CTC Beam Search Decoder of DeepSpeech I get the following error:
[ctc_beam_search_decoder.cpp:279] FATAL: "(alphabet.GetSize()+1) == (class_dim)" check failed. Number of output classes in acoustic model does not ma... | {
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Adds a `MegatronNevaDeployable` class implementing `ITritonDeployable` enabling deployment of multimodal models like LLaVA/Neva to Triton inference server directly from .nemo checkpoints.
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"answered here: https://github.com/NVIDIA/NeMo/issues/9695 "
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} | null | null | null | **Describe the bug**
When I tried to generate timestamp with model: nvidia/parakeet-tdt-1.1b, I got following error,
```
ValueError: `char_offsets`: [{'char': [tensor(524)], 'start_offset': 2, 'end_offset': 3}, {'char': [tensor(106)], 'start_offset': 5, 'end_offset': 6}, {'char': [tensor(40)], 'start_offset': 6,... | {
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Fixes nemo export test. | {
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These tests were failing on main when torchaudio was present in the container. The changes are caused by the dynamic bucketing sampler optimization in the new lhotse version - the sampler simply draws different buckets in the test setup. I removed the conditional trigger on torchaudio since ... | {
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- Fix OOM issues that may happen for large models when both Python and C++ runtime are tested at once. The latter test is only launched when explicitly required by specifying `--test_cpp_runtime True`.
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