renwei2024/nemo-github-issues huggingface dataset:
Canary-1b on long audio file.
State: closed | Pull request: no
Labels: name: stale

Hi,
I am trying to make working canary-1b on long audio file. Inspiring my self from [https://github.com/NVIDIA/NeMo/blob/b5798ded9f27168db9d7d77cbe4f9da80bf49268/examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py#L19](url) this notebook.
But the results are not convincing in regards of what whisper can do. Seems that script just chunk the audio file but do not share context to help th model to transcribe.. More over it does not include a VAD model, and it seems difficult to timestamp the segments.
Does anyone have succed to have something close to the whisper output, with better quality?
Thanks

any leads?


Hi, thanks for the question and interest. We are working on improving the performance of canary on long form audio. We do also plan to release the model with timestamp generation soon. 

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MoE Upcycling support for GPT based models
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

Adds support for upcycling pretrained weights to a MoE model. This follows the paper: SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS ([https://arxiv.org/pdf/2212.05055](https://arxiv.org/pdf/2212.05055))

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
```python
python NeMo/examples/nlp/language_modeling/megatron_gpt_upcycling.py \
--config-path=/path/to/MoE/config/ \
--config-name=MoE/config/name.yaml \
+restore_from_path=/path/to/base/model 
```
Where you MoE config should contain the target MoE model you want to train. The base model you want to upcycle can also be directly added to this config under the argument `restore_from_path`. 

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Cherry-pick 10383 into r2.0.0
State: closed | Pull request: yes
Labels: name: Run CICD

[🤖]: Hi @ashors1 👋,<br><br>we've cherry picked ` (10383)` into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience!

Duplicate of #10517
`always_save_nemo` not working properly
State: closed | Pull request: no
Labels: name: bug

name: stale

I'm using nemo 24.07 container to train LLMs.

I want to save checkpoints in `.nemo` format, so I set `exp_manager.checkpoint_callback_params.always_save_nemo: true`. And it works in the sense that it saves the checkpoint as a `.nemo`, but **the problem is that all the files get the same name, regardless of the step**. Therefore, it constantly replaces the same `.nemo` file, and, in the end of the training, I just get the last checkpoint. So it is useless.

I have tried setting `postfix: {step}.nemo` like it is done with the megatron checkpoint, but it does not work. It does not seem like any `.format()` is called on it, so I don't know how to change the name depending on the step number.

Please fix this issue somehow.


We likely won't be able to update this ourselves but if you'd like to submit a fix, we can help review it.

Thanks!

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Is there any plan to fix this? I echo that this needs to be fixed otherwise k>1 is kind of useless
ci: Fix Mcore ref
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

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Adds Llama 3.1 405b configurations
State: closed | Pull request: yes
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# What does this PR do ?

Adds a recipe for Llama 3.1 405B that uses the pre-existing Llama 3.1 405B configurations. 

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[NeMo-UX] Add llm.generate to nemo.collections.llm
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

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Adding @oyilmaz-nvidia for viz.

@hemildesai would you please add some code snippet in your MR description to demonstrate how to use generation implemented? I mean how to use it with a container like `nvcr.io/nvidia/nemo:dev` for some basic case like Llama with, say, TP=4 (PP > 1 example would also be great if that is already supported).

This is to have some basic example that users can immediately try out - generation is quite a common use-case.

> @hemildesai would you please add some code snippet in your MR description to demonstrate how to use generation implemented? I mean how to use it with a container like `nvcr.io/nvidia/nemo:dev` for some basic case like Llama with, say, TP=4 (PP > 1 example would also be great if that is already supported).
> 
> This is to have some basic example that users can immediately try out - generation is quite a common use-case.

Hi @janekl yes we will be adding an example for using llm.generate to this PR soon

@janekl I've added an example script in https://github.com/NVIDIA/NeMo/pull/10471/commits/6ad3d9425a1287cdc914e104f9bd93430e605227

[🤖]: Hi @hemildesai 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11333699450) for this PR just finished successfully

So it might be time to merge this PR or get some approvals

I'm just a bot so I'll leave it you what to do next.

//cc @pablo-garay @ko3n1g
Call reload_model_params only if there's no optimizer state
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Checkpoint restoration process (roughly):
1. generate a state dict that contains model's state dict and optimizer's state dict.
2. load the two state dicts from the file checkpoint
3. restore model's state from the state contained in the checkpoint
4. reload optimizer params from the model's state.
5. restore optimizer's state from the state contained in the checkpoint.


The bug was in (1) and (4), the optimizer state does not contain cloned tensors but rather references. As a result, when the parameters were reloaded in (4) they were actually overwriting what was restored in (2) (in particular the fp32_from_fp16_params).

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Fix asr warnings
State: closed | Pull request: yes
Labels: name: TTS

name: ASR

name: Run CICD

name: audio

name: r2.0.0

# What does this PR do ?

Replaces deprecated cuda.amp.autocast with amp.autocast(**Args)

**Collection**: All (majorly ASR, TTS, Audio) 

# Changelog 
- replace cuda.amp.autocast with amp.autocast(**Args) 

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[🤖]: Hi @nithinraok 👋,

I just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11056453334) for this PR just finished successfully ✨

So it might be time to merge this PR or like to get some approvals 🚀

But I'm just a 🤖 so I'll leave it you what to do next.

Have a great day! 

//cc @ko3n1g

In CI-CD stage for more than a week. Merging as it was failing for LLM issues unrelated to this PR.
Remove Apex dependency if not using MixedFusedLayerNorm
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Remove Apex dependency if not using MixedFusedLayerNorm

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ci: Fix issues with version bump
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Update adapter saving logic to be compatible with `save_weights_only`
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

#10241 introduced the `save_weights_only` arg, which had an unexpected consequence on PEFT checkpoint saving. Setting `save_weights_only=True` will bypass the `PEFT.on_save_checkpoint` function, since PEFT is a callback. (See [here](https://github.com/Lightning-AI/pytorch-lightning/blob/bc3c9c536dc88bfa9a46f63fbce22b382a86a9cb/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py#L487-L492))

This PR removes `PEFT.on_save_checkpoint` and moves the checkpoint saving logic in `WrappedAdapterIO.save_checkpoint`

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ci: Fix Mcore ref
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Fix nemo run entrypoints
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related https://github.com/NVIDIA/NeMo/pull/10350
Flexible model outputs and callback addition
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Allow model outputs to be dictionary instead of just tensor. This allows more flexible metric logging through megatron hooks.

**Collection**: all

# Changelog 
- update `setup_megatron_parallel` to fix callback addition
- update `training_step` and `validation_step` to allow dictionary as model outputs

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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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Add a checkpoint averaging script for the new .distcp checkpoint format
State: open | Pull request: no

**Is your feature request related to a problem? Please describe.**

We found checkpoint averaging to be very helpful, but the current scripts in https://github.com/NVIDIA/NeMo/tree/main/scripts/checkpoint_averaging don't work with the new format. 

**Describe the solution you'd like**

Please provide an example of the averaging script that works.


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would be great to address this
add parakeet-tdt_ctc-110m model
State: closed | Pull request: yes
Labels: name: ASR

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Need to cheery pick to rc2
remove exp dir
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

name: r2.0.0

# What does this PR do ?

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> Thanks a lot! Would be awesome if you could help inform other team colleagues too about this, it can prevent these infra-issues we've had before. Approving now

@ko3n1g of course, will let them know. Could you please approve this again? I needed to update some parameter naming.
handle logging case where grad_norm is None
State: closed | Pull request: yes
Labels: name: Run CICD

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Make nemo_run dependency optional (llm/__init__ )
State: closed | Pull request: yes
Labels: name: Run CICD

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added energon dataloader for neva training
State: closed | Pull request: yes
Labels: name: Multi Modal

name: Run CICD

# What does this PR do ?
added megatron-energon  based datamodule for neva training

**Collection**: [Note which collection this PR will affect]
Multimodal
# Changelog 
- Energon based 



```python
from nemo.collections.multimodal.data.energon import (
    ImageToken,
    MultiModalSampleConfig,
    SimpleMultiModalDataModule,
)
processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
tokenizer = processor.tokenizer
image_processor = processor.image_processor

config = MultiModalSampleConfig(
            image_token=ImageToken(token_str="<image>", token_id=-200), ignore_place_holder=-100
        )
       
data_module = SimpleMultiModalDataModule(
            path=dataset_path,
            tokenizer=tokenizer,
            image_processor=image_processor,
            num_workers=0,
            micro_batch_size=1,
            multimodal_sample_config=config,
        )
```

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Add support for lazy imports
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: common

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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# Add a code snippet demonstrating how to use this 
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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.
move mamba installation
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Should save 20-30mins in CI docker image builds.

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- Add specific line by line info of high level changes in this PR.

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Thanks @ko3n1g , I've reverted the git clone changes for megatron.
Packed Sequence [NeMo 2]
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Enable sequence packing in SFT/PEFT training in NeMo 2. 
Compared to NeMo 1, sequence packing now supports
- Online packing -- No need to run the data preparation script off line. Simply specify packed sequence size and the packed sequence will be prepared in the training run.
- mbs > 1 -- NeMo 2 will automatically convert data to mbs=1 by concatenating the input sequences. 

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# Changelog 
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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.

[🤖]: Hi @cuichenx 👋,

I just wanted to let you know that, you know, a [CICD pipeline](https://github.com/$REPOSITORY/actions/runs/$RUN_ID) for this PR just finished successfully ✨

So it might be time to merge this PR or like to get some approvals 🚀

But I'm just a 🤖 so I'll leave it you what to do next.

Have a great day! 

//cc @ko3n1g
Update doc for fp8 trt-llm export
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

name: r2.0.0

# What does this PR do ?

Update documentation for fp8 trt-llm export

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# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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# Add a code snippet demonstrating how to use this 
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Reflect CLI change nemorun -> nemo
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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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.

@marcromeyn said to merge this
Akoumparouli/nemo ux update param name
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

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# Changelog 
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# Usage
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