renwei2024/nemo-github-issues huggingface dataset:
[Nemo-UX] IO fixes
State: closed | Pull request: yes
Labels: name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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## Who can review?

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# Additional Information
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Add nemotron news
State: closed | Pull request: yes

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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# Additional Information
* Related to # (issue)
When Trying to import nlp collections in the Nemo Primer getting error "No Module named megatron"
State: closed | Pull request: no
Labels: name: bug

name: stale

This is being done in google colab environment.
with Nemo version 2.0.0rc0

![image](https://github.com/NVIDIA/NeMo/assets/168087860/9b4114f7-e12d-4cc7-8b33-dbf903257b34)


Hi @evodata123, the specific `'ApexGuardDefaults' is not defined` error you are seeing should be fixed by [this PR](https://github.com/NVIDIA/NeMo/pull/9732). But note that Megatron core is a required dependency of NeMo, so if Megatron core is not installed, you may see some orthogonal issues. 

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Expose num processes in PyT Dist
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

Expose multiprocessing setting for PyT Distributed checkpoint format.

**Collection**: NLP

# Changelog 
- Add `model.dist_ckpt_torch_dist_multiproc` flag controlling intra-rank parallelism for PyT Distributed checkpoint saving

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)


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This PR was closed because it has been inactive for 7 days since being marked as stale.
Expose num processes in PyT Dist for NeMo 24.05
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

Expose multiprocessing setting for PyT Distributed checkpoint format.

**Collection**: NLP

# Changelog 
- Add `model.dist_ckpt_torch_dist_multiproc` flag controlling intra-rank parallelism for PyT Distributed checkpoint saving

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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# Additional Information
* Related to # (issue)


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Enable MCore checkpointing optimizations
State: closed | Pull request: yes
Labels: name: core

name: NLP

name: Run CICD

# What does this PR do ?

Enables existing MCore optimizations, mainly for parallelizing the checkpoint save and load.
These are a lot of small changes, but it made sense to group them into a single MR because they are all somehow interconnected.

**Collection**: NLP

# Changelog 
- Enable MCoreDistOpt parallel R/W (controlled with `model.dist_ckpt_parallel_save` flag, loading is parallelized based on save)
- Enable model weight save parallelization (controlled with `model.dist_ckpt_parallel_save` flag)
- Set `model.dist_ckpt_parallel_save` flag to True by default (increases performance and allows DistOpt ckpt resharding; the only caveat is potentially larger number of files in the checkpoint: `O(ranks * model.dist_ckpt_torch_dist_multiproc`)
- Enable model weight load parallelization (controlled with `model.dist_ckpt_parallel_load` flag)
- Allow increasing intra-rank save parallelization with multiprocessing (controlled with `model.dist_ckpt_torch_dist_multiproc` flag)
- Skip state dict validation in subsequent saves (controlled with `model.dist_ckpt_assume_constant_structure` flag)
- Remove some async-related code that was moved to MCore (whole `nemo/utils/callbacks/torch_dist_async.py` file)

# Usage
Use by enabling the flags mentioned in the changelog.

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Add section on ".qnemo" checkpoints
State: closed | Pull request: yes

# What does this PR do ?

Add section on ".qnemo" checkpoints to https://github.com/NVIDIA/NeMo/pull/9329.

**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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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# Additional Information
* Related to # (issue)
How to use a pre-trained model for cache-aware FastConformer-Hybrid model?
State: closed | Pull request: no

Hi @titu1994,

Following our discussion in [this thread](https://github.com/NVIDIA/NeMo/discussions/7010#discussioncomment-9736124), I’m training a cache-aware FastConformer hybrid CTC-RNNT model for German using 1.2K hours of audio data. Despite training for 150 epochs, my validation WER is still around 0.28.

I suspect the dataset quality might be an issue. I reviewed the paper "Stateful Conformer with Cache-Based Inference for Streaming ASR" and noted the significant performance achieved even with training from scratch on LibriSpeech.

Since you recommended using a pre-trained model, I tried using [this model](https://huggingface.co/nvidia/stt_de_fastconformer_hybrid_large_pc/tree/main) from Hugging Face, but it's not a streaming model. Is it still viable as a pre-trained model for my use case, or are there other German models available that you would recommend?

Thank you for your guidance!


You can use this model, which is a chunk aware model - https://huggingface.co/nvidia/stt_en_fastconformer_hybrid_large_streaming_multi

Thank You @titu1994. I will try it.
But this has been trained on labeled English dataset. I want to understand the logic, how would it adapt to any other language?

It's a practical limitation. You can either get a ordinary fast conformer in German or a chunk aware Conformer in English. Depends on what your priority is - streaming or transcript accuracy. We have tutorial showing language transfer
to support deepseekv2
State: closed | Pull request: no
Labels: name: stale

hi, do you have plan to support deepseekv2 model?


Thanks for your inquiry. DeepSeek was in our roadmap but it has now been deprioritized. Here is a list of currently supported LLMs: https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/index.html

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LM on Parakeet models
State: closed | Pull request: no
Labels: name: stale

Hi team,

I am trying to add LM into parakeet models and i think this is the script related to it. 

[eval_beamsearch_ngram_transducer.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_transducer.py)

I have tried using it and encountered a couple of issues with EncDecRNNTBPEModel. For example, there are 3 outputs in [forward](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_transducer.py#L336) function now. 
Is there other scripts that i should be looking into? 

Thanks

For transducer, there were always 3 outputs in forward because it's just end encoder forward. During decoding we take those encoder outputs and pass them auto regressively to the decoder and joint. 

For beam search support, @karpnv 

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Export online model conversion and refit
State: closed | Pull request: yes

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
Can we add emotions to the produced audio?
State: closed | Pull request: no

Hello,

I was just trying some combinations of Mel-Spectogram-Generators and Vocoders to produce different audio samples. 

I was thinking if it was possible to make the produced audio sound more naturally by adding some kind of emotional embeddings in the Mel spectogram generator models. Or any other suitable approach to do this.

I am new to TTS so not sure if such thing is possible. 

I would be highly grateful if somebody could help me out.

Thanks. 


You will need to train your spectrogram generator / acoustic model with emotional data. Tag the emotional data as a new speaker and do inference with the emotional-speaker tag.
fix NameError: name 'ApexGuardDefaults' is not defined
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

I believe this should fix https://github.com/NVIDIA/NeMo/issues/9320 

**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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)


Please someone help me merge this PR pls, its only one line and blocking me from doing the work :)

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.

Thank you for reporting the issue. The suggested change is already in main https://github.com/NVIDIA/NeMo/blob/3e2bb2157fd99b963ae4c4da2464be754370c1c4/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py#L15 so I'll close the MR
Update NeMo to hf convertion for Llama 3
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Fix the script to convert Llama 3 from NeMo to hf format.

![image](https://github.com/NVIDIA/NeMo/assets/27715376/ab24e8b4-95cf-4e31-bb56-c60a6516cf6d)




# Changelog 
- Stop using LlamaTokenizer and start using AutoTokenizer from Transformers.

# Usage
```python
python3 NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py \
    --input_name_or_path llama3-8b.nemo \
    --output_path llama3-8b-hf/pytorch_model.bin \
    --hf_input_path Meta-Llama-3-8B \
    --hf_output_path llama3-8b-hf \
    --input_tokenizer Meta-Llama-3-8B \
    --hf_output_tokenizer llama3-8b-hf
```
you must except something like this when the script finishes:
![image](https://github.com/NVIDIA/NeMo/assets/27715376/1093b660-4b8a-460e-b6b2-9065c55ba2de)


# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* there is an issue in hf related but is not the same script https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/37


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Unable to reproduce cache aware streaming results for Conformer that were there for Fastconformer.
State: closed | Pull request: no
Labels: name: bug

I trained Conformer-CTC using cache aware method.  but unable to reproduce the results that were there with "Fastconformer cache aware streaming".  By using Conformer-CTC cache aware method i get partial sub-words, words combined together like ("hello world" ==> herld) ,  in streaming output. 

the same was the case when i deployed it in a RIVA pipeline. 

but when i use the "transcribe" function i get proper transcription for the audio file.
@titu1994 @VahidooX 
can you please enlighten on this. 
When to use and how to use streaming models in .nemo format and in RIVA pipeline. 
My question has some relevance with these issues 
[https://github.com/NVIDIA/NeMo/discussions/7010]
[https://github.com/NVIDIA/NeMo/discussions/5284]
Distributed checkpointing user guide
State: closed | Pull request: yes

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
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[NeMo-UX] Fix bug in import_ckpt
State: closed | Pull request: yes
Labels: name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
draft: bert cp support
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
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[NeMo-UX] Add PEFT
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Initial PR for PEFT in nemo 2.0

**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

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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## Who can review?

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# Additional Information
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TE guard to avoid MPI dependency
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Run CICD

# What does this PR do ?

TE version guard to support with and without MPI dependencies.

# Changelog 
- Added TE version guard for userbuffer communication initialization

# Usage
N/A

# GitHub Actions CI

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Make QLoRA more model-agnostic
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Run CICD

# What does this PR do ?

Support QLoRA for more models

**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

```python
# Add a code snippet demonstrating how to use this 
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Use Mcore's Modelopt Spec When Exporting
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Run CICD

# What does this PR do ?

Unified to use mcore's modelopt specs instead of the NeMo one


Thanks, approved. Just please make sure that CI pipeline passes

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 @suiyoubi, do you think we could get this in? It just needs conflict resolution

Hmm I think that @akoumpa extended https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py to cover MoE.

So we can't really replace the spec unless we also enable MoE layer spec in https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/inference/gpt/model_specs.py

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[NeMo-UX] Fixing defaults in llm.train & Mistral7BModel
State: closed | Pull request: yes
Labels: name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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**Pre checks**:
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[NeMo-UX] Integrate tokenizer import into model.import_ckpt
State: closed | Pull request: yes
Labels: name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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we will fix the mcore DDP prefix problem in a subsequent PR
Add return type hint to from_pretrained method
State: closed | Pull request: yes
Labels: name: core

name: stale

# What does this PR do ?

Add a type hint.

**Collection**: [core]

# Changelog 
Add return type hint to the `Model`'s `from_pretrained` method.

# GitHub Actions CI

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To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
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- [x] Bugfix
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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.
append to file
State: closed | Pull request: yes
Labels: name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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NeMo performance feature documentation
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# 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

```python
# Add a code snippet demonstrating how to use this 
```

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**Pre checks**:
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Thanks, @jgerh for the suggestions.
There seem to be too many rephrases. If the changes are mainly about rewording, can you push the changes to my branch?

I appreciate the feedback. I can update my edits and focus on copyediting only, rather than rephrasing.
Distributed optimizer aligns FP8 transposes over pipeline-parallel group
State: closed | Pull request: yes
Labels: name: core

name: stale

name: NLP

# What does this PR do ?

When training GPT in FP8 with interleaved pipeline parallelism, we have experienced some GPU idling due to load imbalances across the pipeline parallel group. The first microbatch in a training step requires computing an FP8 weight transpose, which is reused in later microbatches. The fix is to align the FP8 weight transposes across the pipeline parallel group, similar to how we align param all-gathers and grad reduce-scatters (see https://github.com/NVIDIA/apex/pull/1611 and https://github.com/NVIDIA/NeMo/pull/6183).

**Collection**: NLP

# Changelog 
- Distributed optimizer aligns FP8 transposes over pipeline-parallel group

# Usage

Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml.

The following settings are required for this optimization:
- `model.virtual_pipeline_model_parallel_size=<layers per virtual pipeline stage>`
- `model.transformer_engine=True`
- `model.fp8=True`
- `model.fp8_params=True`
- `model.optim.name=distributed_fused_adam`
- `model.optim.overlap_param_sync=True`

# GitHub Actions CI

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# Additional Information



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.