renwei2024/nemo-github-issues huggingface dataset:
Add `loop_labels` algorithm for TDT greedy decoding
State: closed | Pull request: yes
Labels: name: ASR

# What does this PR do ?

TDT greedy decoding using`loop_labels` algorithm introduced in #7926.

**Collection**: [ASR]

# 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 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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**:
- [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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# Additional Information
* Related to # (issue)


Should be reviewed/merged after #8286

@hainan-xv Do we have any small model for tests? I have no idea how to write tests for the decoding without the model

jenkins

jenkins

jenkins

jenkins
Unable to train ASR model on multiple GPUs
State: closed | Pull request: no
Labels: name: bug

name: stale

The following is my command to launch distributed training in NeMO

 python3 -m torch.distributed.launch --nproc_per_node=2 --nnode=1 examples/asr/asr_ctc/speech_to_text_ctc.py --config-path=examples/asr/conf/quartznet/  --config-name=quartznet_15x5
 
torch vision：2.0.1+cu118
GPU：A100 

then, the error would occur due to unrecognized argument：--local-rank
![image](https://github.com/NVIDIA/NeMo/assets/155972611/6e921d54-cafd-41b2-97b2-125a85b383c5)

How do I change the source code from "--local_rank" to "--local-rank"


This is not the correct way to use distributed training in NeMo. We utilize pytorch lightning which calls ddp and torch distributed for you. Simply call your script with python ABC.py --config-path xyz/ --config-name something.yaml. 

You can see some of the NeMo tutorials to see how we setup training with our scripts 

Thanks for your replying.
I have already trained on one node & multiple GPU cards.
@titu1994 
Here's the another question, how to set up multi nodes' information such as MASTER_ADDRESS or MASTER_PORT by PyTorch Lightning.
Is there any available tutorials?
Thanks a lot. 

It would be better to ask the folks at Pytorch Lightning since they might have the solution 

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can you provide msdd_5scl_15_05_50Povl_infer.yaml?Inference for training models
State: closed | Pull request: no
Labels: name: stale

**Is your feature request related to a problem? Please describe.**

A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

**Describe the solution you'd like**

A clear and concise description of what you want to happen.
Provide a code snippet on how new APIs/changes would be used by others.

**Describe alternatives you've considered**

A clear and concise description of any alternative solutions or features you've considered.

**Additional context**

Add any other context or screenshots about the feature request here.


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Add support in Neural Typecheck to disable semantic checks
State: closed | Pull request: yes
Labels: name: core

# What does this PR do ?

Adds support to temporarily / permanently (and globally) disable semantic type checks.

**Collection**: [Core]

# Changelog 
- Adds utility methods to disable global semantic type checking, or local context manager.
- Adds test to see if semantic types are properly ignored (but still input kwargs, shape and container checks)

# Usage

```python
import torch
from nemo.core import Typing, typecheck
from nemo.core.neural_types import *

class InputOutputTypes(Typing):
    @property
    def input_types(self):
        return {"x": NeuralType(('B',), LogprobsType())}

    @property
    def output_types(self):
        return {"y": NeuralType(('B',), LabelsType())}

    @typecheck()
    def __call__(self, x):
        x += 1
        return x

 # Provide input with wrong type after disabling semantic type checks
 with typecheck.disable_semantic_checks():
    input_data = torch.zeros(10)
    input_data.neural_type = NeuralType(('B',), LabelsType())  # Should be LogprobsType()
    result = obj(x=input_data)

    # assert that even if semantic types are disabled, output is attached with appropriate types
    assert result.sum() == torch.tensor(10.0)
    assert result.neural_type.compare(NeuralType(('B',), LabelsType())) == NeuralTypeComparisonResult.SAME

```

# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation


jenkins
Added support for neptune logger
State: closed | Pull request: yes
Labels: name: core

name: NLP

# What does this PR do ?

Adds Neptune logging support to NeMo which is already exposed by the PTL trainer.

**Collection**: Core, NLP

# Changelog 
- In experiment manager, consumed NeptuneLogger parameters from the OmegaConf
- Added tests for NeptuneLogger
- Added documentation for NeptuneLogger configuration


# Usage
* Can be directly used by configuring the mentioned keys in the docs under exp_manager similar to how WandB params are specified.

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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?
- [ ] 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?

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.

@titu1994 

# Additional Information
* Related to # (issue)


jenkins

Hi @titu1994 what would be the next process to get this merged to the main trunk ?

Could you avoid merging the main branch with this PR, I need to see the tests pass. It is fine if the branch is out of date with the main branch. 

jenkins

> jenkins

I guess the automation is completed and all checks have passed.
@titu1994
[Question] Issue of Invalid output device when using pyaudio in VAD inference
State: closed | Pull request: no
Labels: name: stale

@fayejf 
Hi,
I have implemented a code for using the Marble_Net VAD model according to the below tutorial:
[Online_Offline_Microphone_VAD_Demo](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/Online_Offline_Microphone_VAD_Demo.ipynb)  
But I got the following error (I have tested it on Colab and two other servers):
```
OSError: [Errno -9996] Invalid output device (no default output device)
```
How can I address it? (I have searched the error and examined some solutions, but none of them worked. The issue related to the `pyaudio` library)
Would you please guide me that how can I remove the `pyaudio library` and use the VAD model for inferencing just a mono-channel 16Khz audio file in HDD? 


@okuchaiev 

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.
Added Neptune logger support
State: closed | Pull request: yes
Labels: name: core

name: NLP

# What does this PR do ?

Adds Neptune logging support to NeMo which is already exposed by the PTL trainer.

**Collection**: [Note which collection this PR will affect]
Affects the core/experiment manager collection

# Changelog 
- In experiment manager, consumed NeptuneLogger parameters from the OmegaConf
- Added tests for NeptuneLogger
- Added documentation for NeptuneLogger configuration

# Usage
* Can be directly used by configuring the mentioned keys in the docs under exp_manager similar to how WandB params are specified.

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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?
- [ ] 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?

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.
@ericharper 

# Additional Information
* Related to # (issue)


Hi, thanks for the additional support for Neptune. Due to the ever growing dependency list for NeMo, we will make Neptune an optional dependency only. Please follow the suggestions above 

Oh also, could you follow DCOBot comments to sign your commits since that is a NeMo contribution requirement. 

> Oh also, could you follow DCOBot comments to sign your commits since that is a NeMo contribution requirement.

I have amended the commits to include Sign off.

> Hi, thanks for the additional support for Neptune. Due to the ever growing dependency list for NeMo, we will make Neptune an optional dependency only. Please follow the suggestions above

Got that, have made the changes to remove dependency from the requirements files.
Make TDT inference not require duration params
State: closed | Pull request: yes
Labels: name: ASR

# What does this PR do ?

Making it so that TDT inference doesn't require --duration params to be passed.

**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 
The model now infers the duration params from model's loss configs.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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**:
- [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)


@hainan-xv Thanks for the PR; I think it makes it more convenient to use decoding with TDT models. One minor comment about style.

jenkins
making TDT model inference easier without having to specify the duration parameters
State: closed | Pull request: yes
Labels: name: ASR

name: NLP

name: CI

name: common

# What does this PR do ?

This PR makes ASR TDT model inference easier by not having to specify the duration parameters.

**Collection**: [Note which collection this PR will affect]

# Changelog 
During inference, duration params are going to be inferred from the params from the 'loss' config of the model 

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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**:
- [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
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[docker] Install k2 before NeMo for faster image rebuilding
State: closed | Pull request: yes
Labels: name: CI

# What does this PR do ?

Speed up rebuilding NeMo images with NeMo code changes. Previously k2 would have to be rebuilt each time NeMo code changed because k2 building occurs in a later image layer. Proposed changes make it very fast to rebuild when changing NeMo python code, saving storage by not multiplying k2 build layers at the same time.

**Collection**: All

# Changelog 
- Build k2 before installing NeMo in docker for faster rebuilds.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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?
- [x] 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
* Related to # (issue)


jenkins

CI doesn't build container, no point waiting for it. Fast merged
RNN-T and TDT Greedy Decoding with TorchScript
State: closed | Pull request: yes
Labels: name: core

name: stale

name: ASR

name: common

# What does this PR do ?

Adds torch.jit.script support for "loop labels" decoding algorithm, introduced in #7926 

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 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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?
- [ ] 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)


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 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 the interface to use SHARP to FSDP strategy
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Add the SHARP argument interface to FSDP strategy.
SHARP here applies to the data-parallel process group.

# 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 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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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jenkins

jenkins
Rename Finetuning Scripts
State: closed | Pull request: yes
Labels: name: NLP

name: CI

# What does this PR do ?

Rename certain unified SFT/PEFT scripts to remove reference to 'PEFT' in the script name, since it was confusing for people to run SFT with a 'peft' script
Rename `*_peft_tuning*` to `*_finetuning*` to include both SFT and PEFT in naming.
Rename `*_peft_eval*` to `*_generate*` to achieve the above and also make clear that the script is simply used to generate text (run inference), and not calculate any metrics.
Old scripts are kept until the next release, with a deprecation message added.

Also making deprecating messages more prominent, with a banner and a wait timer so the message does not get drowned in other nemo logging messages.

**Collection**: NLP

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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
- [x] UX Improvement

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
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jenkins

jenkins

LGTM!
Concept draft of an extended input configuration format
State: closed | Pull request: yes
Labels: name: stale

name: common

# 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 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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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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.
Fix CPU Initialization and TP>1 for LoRA Merge Script
State: closed | Pull request: yes

# What does this PR do ?

Fix 1) merging on CPU, in cases where a GPU is not available, and 2) merging a LoRA model trained with TP>1 into a base model in the distributed checkpointing format.

Tested on Llama2-7b + TP {1, 2, 4} LoRA ckpt, with cpu and gpu options

# Usage

Merging on GPU/CPU can be controlled by
`trainer.accelerator='cpu' | 'gpu'`

Also, the script should always be run with 
`trainer.devices=1` now for distributed checkpoints. If the model cannot fit into one GPU, use the CPU option.


# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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
- [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
* Related to # (issue)


jenkins

jenkins

LGTM!

jenkins
Add include_text parameter to SFT dataloaders
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Adding a new parameter to include original text in the dictionary returned from a dataloader. This is needed for a cleaner support of metrics in NeMo-Aligner.

**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 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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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jenkins

jenkins
Add Lhotse support for `offset` key in NeMo manifests
State: closed | Pull request: yes
Labels: name: common

# What does this PR do ?

Add Lhotse support for `offset` key in NeMo manifests

**Collection**: All speech collections

# Changelog 
- Add Lhotse support for `offset` key in NeMo manifests

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.

# 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?
- [ ] 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?

Anyone in the community is free to review the PR once the checks have passed. 
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# Additional Information
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I think so, I’ll keep actively looking for anything I might have missed though. 

jenkins
[do-not-merge] Run ASR test with gdb
State: closed | Pull request: yes
Labels: name: ASR

name: CI

# 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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jenkins
ERRORs in repetition_penalty
State: closed | Pull request: no
Labels: name: bug

name: stale

**There's a computation error in repetition_penalty() function when there's negative numbers in logits**
- Version: nemo 1.22.0
- Path: NeMo/nemo/collections/nlp/modules/common/text_generation_utils.py
- Source code:
```
def repetition_penalty(logits, repetition_penalty, used_tokens):
    """ Implement the repetition penalty, check paper 
    https://arxiv.org/pdf/1909.05858.pdf
    """
    if used_tokens is not None and repetition_penalty != 1.0:
        logits_update = torch.gather(logits, 1, used_tokens)
        logits = torch.scatter(logits, 1, used_tokens, logits_update / repetition_penalty)
    return logits
```

**There's also a question about the repetition penalty. When setting compute_logprob=False, the all_generated_indices will not be updated and the repetition penalty seems not working, is this correct?**

Yes. You are right. 
```python
logits_update[logits_update >= 0] = logits_update / repetition_penalty
logits_update[logits_update < 0] = logits_update * repetition_penalty
```
we can fix it by treating negative and positive logits differently.

Regarding to `compute_logprob=False`, you are right. the all_generated_indices will not be updated and the repetition penalty is not working. This is a bug. `all_generated_indices` calculation should be pulled out of the `compute_logprob` condition. 

do you mind create a PR for it?

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[Draft] Refactor community model checkpoint converters
State: closed | Pull request: yes
Labels: name: CI

# What does this PR do ?

Move checkpoint converters to the same folder and update to use similar arguments

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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```python
# Add a code snippet demonstrating how to use this 
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jenkins
Gradient accumulation hack to support EP
State: closed | Pull request: yes
Labels: name: NLP

# 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.

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* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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Multimodal required NLP base model changes
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Some of the base codes need to be modified in order for multimodal optimization to work.

**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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```python
# Add a code snippet demonstrating how to use this 
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jenkins
add sbert
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Adding Megatron Sentence Bert

**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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add knobs for rope/swiglu fusion
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Add knob for rope fusion
Add knob for swiglu fusion
Change knob for bias_gelu fusion

The PR is related to the PR of MLM: https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/984

**Collection**: [NLP]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

- set `model.apply_rope_fusion: true` to enable rope fusion

- set `model.bias_activation_fusion: true` to enable bias+activation fusion, which works for both gelu and swiglu type.


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removed deprecated peft model
State: closed | Pull request: yes
Labels: name: NLP

# 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.

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* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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jenkins
Avoid the attention mask materialization
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

The attention mask requires 1 * seq_len * seq_len * 4 bytes memory per GPTDataset. It will overwhelm CPU RAM memory when we have extremely long sequence. The PR avoids mask materialization when it's not needed.

**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 
```

# Jenkins CI
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updated online sample mapping
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

second attempt at https://github.com/NVIDIA/NeMo/pull/8137

**Collection**: [Note which collection this PR will affect]

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# Add a code snippet demonstrating how to use this 
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jenkins
Remove left-over prints in NeMo+Lhotse code
State: closed | Pull request: yes
Labels: name: common

# What does this PR do ?

Remove left-over prints in NeMo+Lhotse code

**Collection**: All speech collections

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- Add specific line by line info of high level changes in this PR.

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```python
# Add a code snippet demonstrating how to use this 
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**PR Type**:
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- [x] Bugfix
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jenkins
Feature Request - MAML Support
State: closed | Pull request: no
Labels: name: stale

**Feature Request:**
I would like to request the addition of support for Model-Agnostic Meta-Learning (MAML) in the NVIDIA NeMo Toolkit. MAML is a powerful meta-learning algorithm that allows models to quickly adapt to new tasks.

**Why is this feature important?**
Its inclusion in the NeMo Toolkit would benefit for low resourced languages.

[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400)

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.
Support GPT MCore models in M-LM->NeMo Conversion Script
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Add support for MCore GPT models in the Megatron-LM to NeMo model conversion script. 
The script originally supported legacy M-LM -> legacy NeMo conversion. 
The script now supports two more paths: legacy M-LM -> MCore NeMo, and MCore M-LM -> MCore NeMo conversions.

MCore M-LM -> legacy NeMo is not a valid use case and hence not supported.

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
To convert to MCore NeMo model, add `mcore_gpt=True` in the hparams.yaml file that is the input to this script.
To convert from MCore M-LM model, pass in `--mcore_input` to the script.

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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.

jenkins

jenkins