renwei2024/nemo-github-issues huggingface dataset:
Delete commented tests from Jenkinsfile
State: closed | Pull request: yes

# What does this PR do ?

We've reached the limit for how large our Jenkins pipeline can be. We need to clean up the file.

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
* Related to # (issue)
[bugfix][TTS] pitch, voiced_mask, prob_voiced have the same values.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

relate to https://github.com/NVIDIA/NeMo/pull/4392

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] 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**:
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- [ ] Bugfix
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# Additional Information
* Related to # (issue)
[bugfix][TTS] pitch, voiced_mask, prob_voiced have the same values.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

related to https://github.com/NVIDIA/NeMo/pull/4392

**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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**PR Type**:
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# Additional Information
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[Bugfix][TTS] wrong order of returned tuple for general_collate_fn.
State: closed | Pull request: yes

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

related to https://github.com/NVIDIA/NeMo/pull/4388

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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# Additional Information
* Related to # (issue)
`StopIteration` Exception in Conformer Encoder because of `next(self.parameters())`
State: closed | Pull request: no

Hi, I get `StopIteration` exception here:

https://github.com/NVIDIA/NeMo/blob/41f27a5e68934761b05dd373e7b288aa798858e5/nemo/collections/asr/modules/conformer_encoder.py#L231

I call this method from `forward_for_export` here:

https://github.com/NVIDIA/NeMo/blob/41f27a5e68934761b05dd373e7b288aa798858e5/nemo/collections/asr/modules/conformer_encoder.py#L249

I found that `self.parameters()` is zero length. Found this happens when I use multiple GPUs in DP mode. When I run the code on one GPU I get `self.parameters() > 0`.

Issue about it: https://github.com/pytorch/pytorch/issues/40457

Maybe it would be better if the device was passed into the method, like so:
```
def set_max_audio_length(self, max_audio_length, device='cpu'):
    ....
```

Because it's strange to get such problems when getting a device, when it can be easily passed as a parameter.
Request the feature to send metadata in BLS.
State: closed | Pull request: no
Labels: name: feature

**Is your feature request related to a problem? Please describe.**
I want to change the Model according to the BLS input. But BLS requires the same input bach size. So I have to make input like ["model1", "model1"]. If my batch size is 100, should I send 100 same data like ["model1", "model1", "model1"].

**Describe the solution you'd like**
It would be nice to have the feature to send metadata other than batch data.
Re-apply fixes from r1.9.0
State: closed | Pull request: yes

Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com>

# What does this PR do ?

Re-apply the fixes to the Tacotron 2 tutorial from #4209. Need to open this PR since those fixes never made it to main and the 1.10 branch.

**Collection**: TTS

**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

# Additional Information
* See #4209 for original bugfix info.
bugfix for import error in Offline_ASR_with_VAD_for_CTC_models
State: closed | Pull request: yes

Signed-off-by: fayejf <fayejf07@gmail.com>

# What does this PR do ?

Fix import error in Offline_ASR_with_VAD_for_CTC_models in r1.10

**Collection**: ASR

# Changelog 
- change `main` to `$BRANCH `

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
* Related to # (issue)
Add T5 in prompt learning documentations
State: closed | Pull request: yes
Labels: name: stale

Signed-off-by: Jiaqi Zeng <jiaqizen@andrew.cmu.edu>

# What does this PR do ?

Update the prompt learning documentations.

**Collection**: [NLP]

# Changelog 
- Add T5-specific details
- Fix some typos 


# 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)
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**PR Type**:
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- [ ] Bugfix
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# Additional Information
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Add Squeezeformer to ASR
State: closed | Pull request: yes

# What does this PR do ?

Adds SqueezeFormer model to ASR collections

**Collection**: [ASR]

# Changelog 
- Adds prototype configs, code and model level support for Squeezeformer models
- Checkpoints may be provided at a later date after debugging smaller sized models.

# 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?
  
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
Fixing bugs in calling method ctc_decoder_predictions_tensor.
State: closed | Pull request: yes

# What does this PR do ?

Fixed a bug in calling the ctc_decoder_predictions_tensor method from the WER objects instead of WER.decoding.

# Changelog 
- Changed all the wer.ctc_decoder_predictions_tensor to wer.decoding.ctc_decoder_predictions_tensor .

**PR Type**:
- [ ] New Feature
- [x ] Bugfix
- [ ] Documentation
Add ASR Scores to Docs
State: closed | Pull request: yes

# What does this PR do ?

Adds current ASR model scores to "scores" page.

**Collection**: [ASR]

# Changelog 
- Adds the scores of all ASR models from NGC to docs.
# Usage
* You can potentially add a usage example below

# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Did you add or update any necessary documentation?
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
[NLP] P&C Fix multi node cache issue, add pynini guard
State: closed | Pull request: yes

Signed-off-by: ekmb <ebakhturina@nvidia.com>

# 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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [X ] Bugfix
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# Additional Information
* Related to # (issue)
why 44.1khz fastpitch align  config is removed in 1.9.0?
State: closed | Pull request: no
Labels: name: feature

I am working on training the TTS model using Fastpitch architecture using Librivox 44khz dataset.  I was getting issues in the trained model and looking for possible solutions online. but now I saw that in the 1.9.0 release of Nemo, you have removed the 44100 sample rate model configs. Can someone share the reason for that?
Fix hanging issue by multiprocessing in SD tutorial and add ETA for VAD processing
State: closed | Pull request: yes

# What does this PR do ?

Fix hanging/deadlock issue caused by multiprocessing in SD tutorial and add tdqm that informs ETA to reduce false alarm of hanging. 
reflected review in https://github.com/NVIDIA/NeMo/pull/4378

**Collection**: ASR

# Changelog 
Cherry pick updates to r1.10.0 https://github.com/NVIDIA/NeMo/pull/4317, (modify the code in place here to avoid sign-off issue) 
Add instruction and set num_workers=1 in SD tutorial as a workaround for python multiprocessing would hang with python in some cases.
Add tqdm that informs ETA for VAD processing

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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# Additional Information
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Need Help Understanding Preprocessing Step
State: closed | Pull request: no

Hi are we not using normalising using inferencing ? 

        cfg = copy.deepcopy(asr_model.confg)
        OmegaConf.set_struct(cfg.preprocessor, False)
        # some changes for streaming scenario
        cfg.preprocessor.dither = 0.0
        cfg.preprocessor.pad_to = 0
        cfg.preprocessor.normalize = "None"

Also while streaming thought mic  I was normalizing  the audio by dividing it by  audio / (1 << 15) (had seen this is some tutorial) 


Model Used.: ctcconformerbpe  small
Fixing import error in some cases
State: closed | Pull request: yes

Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>

# What does this PR do ?

Fixes import error in rare cases

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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# Additional Information
* Related to # (issue)
Fixing import error in some cases
State: closed | Pull request: yes

Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>

# What does this PR do ?

Fix an import error for some 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 
```

# 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?
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
* Related to # (issue)
Maximum sample-based training for Megatron NMT and Text Memmap based Seq2seq Pre-training
State: closed | Pull request: yes

# What does this PR do ?

1. Trains Megatron-based NMT models based on maximum number of samples.
2. Added support in `text_memmap` and `csv_memmap` in Megatron encoder-decoder models (T5, BART, UL2)

**Collection**: NLP

# Usage

Add to command line

```
  model.data.data_impl=text_mmap \
  +model.data.data_impl_kwargs.newline_int=10 \
  +model.data.data_impl_kwargs.header_lines=0 \
  +model.data.data_impl_kwargs.workers=null \
  +model.data.data_impl_kwargs.sort_dataset_paths=False
```

```python
# Add a code snippet demonstrating how to use this 
```

# 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?
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- [ ] 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
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# Additional Information
* Related to # (issue)
Wav2vec fix
State: closed | Pull request: yes

# What does this PR do ?

Fixes Wav2Vec configs so compatible with contrastive loss. (Adds MaskedAugmentation to pretrain models.) Removes Wav2Vec from experimental folder and moves wav2vec configs to SSL folder for consistency.

**Collection**: [Note which collection this PR will affect]

- ASR

# Changelog 
- Wav2Vec Pretrain (Base and Large): replaces SpecAug with MaskedPatchAugmentation. Adds appropriate contrastive loss flags.
- Deleted Wav2Vec folder from `examples/experimental`
- Moved Wav2Vec configs to `conf/SSL`

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ Y] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N] Did you write any new necessary tests?
- [ N] Did you add or update any necessary documentation?
- [ N] 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**:
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- [Y ] Bugfix
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# Additional Information
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Fix megatron change partitions
State: closed | Pull request: yes

# What does this PR do ?

Fix artifact restore

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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  - [ ] 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)
Add reconfigure on validation epoch start
State: closed | Pull request: yes

Signed-off-by: MaximumEntropy <sandeep.subramanian.1@umontreal.ca>

# What does this PR do ?

- Reconfigures micro-batch sizes for validation datasets on validation start in Megatron NMT

**Collection**: NLP

# Changelog 
- Add an `on_validation_epoch_start()` hook to reconfigure batch sizes.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# 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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## Who can review?

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# Additional Information
* Related to # (issue)
Add prompt learning for T5
State: closed | Pull request: yes

# What does this PR do ?

Implement prompt learning for T5 models.

**Collection**: [NLP]

# Changelog 
- A base prompt learning dataset class (will support both T5 and GPT in the future).
- A T5-specific prompt learning dataset class.
- A base prompt learning model class (will support both T5 and GPT in the future).
- A T5-specific prompt learning model class.
- Scripts for training and inference.
- Config files for training and inference. 
- Temporarily fix a bug in T5 decode function to support prompt learning inference.

# Usage

```
python megatron_t5_prompt_learning.py --config-name=megatron_t5_prompt_learning.yaml
python megatron_t5_prompt_learning_eval.py t5_model_file=models/megatron_t5_220m_f16.nemo virtual_prompt_model_file=models/p_tuning_sent_t5.nemo
```

# 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




## Who can review?

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# Additional Information
* Related to # (issue)
std::out_of_range error when training TransformerXL model
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

When training an TransformerXL model by following the script transformer_lm.py, I get the following error:

![image](https://user-images.githubusercontent.com/10922796/174274040-4e21f930-425f-4ad5-95eb-4f92aa8b913a.png)

For this, I use the same train, eval and test json files as I use for the CTC conformer models which train fine. I use this in combination with a tokenizer trained with the following command:

`python /workspace/NeMo/scripts/process_asr_text_tokenizer.py --manifest=/workspace/NeMo/data/DataFrames/Baseline/Train/train.json --data_root=/workspace/Tokenizer_AllData_1024 --vocab_size=1024 --tokenizer="spe" --spe_type="unigram" --spe_character_coverage=1.0 --no_lower_case --log`

Again, the conformer CTC trained fine with this tokenizer.

Since this doesn't seem to be a python error, I can't figure out where it goes wrong.

Cant these tokenizers be used in combination with transformerXL?

Tokenizer config:

![image](https://user-images.githubusercontent.com/10922796/174276395-d557f3f9-246b-4880-a63f-0dc13aaa95ca.png)


The goal is to do ASR -> Beam search N-Gram -> TransformerXL

**Steps/Code to reproduce bug**

Build tokenizer -> change paths in conf/transformer_lm_config.yaml -> python transformer_lm.py

**Expected behavior**

TransformerXL model trains without problems

**Environment overview (please complete the following information)**
```

docker run --gpus all -it --shm-size=8g -p 8888:8888 -p 0.0.0.0:6006:6006 --name TransformerXL --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:22.05-py3

Installed NeMo with git clone and ./reinstall.sh
```

Python 3.8.13
Torch: 1.12.0a0+8a1a93a
Docs: hide the right side nav bar
State: closed | Pull request: yes

# What does this PR do ?

Hide the right side bar

**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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# Additional Information
* Related to # (issue)
[Bugfix][TTS] wrong order of returned tuple for general_collate_fn.
State: closed | Pull request: yes
Labels: name: bug

Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com>

# What does this PR do ?

1. This bug blocks modeling using speaker IDs because `speaker_ids` (or saying `_` in this context) should be placed before `voiced_masks`.
2. `P_voiceds` was also not correct because it aggregates `voiced_mask` instead of `p_voiced`.

**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 
```

# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
* Related to # (issue)
Update on speaker_utils and NMESC speaker counting algorithm
State: closed | Pull request: yes

# What does this PR do ?

`nmesc_clustering.py`

- This PR adds an option named "maj_vote_spk_count" to NMESC clustering module.
Originally, speaker number was obtained from one p-value.
In the new implementation, maj_vote_spk_count = True, the system takes majority vote on 
all p-values that are included.
This feature almost halves the speaker counting error for 1.5sec window setting.
(CH109, 1.5/0.75 win/shift, speaker counting error becomes 12.8% -> 7.3%)

Default will be False for now. Yaml file will be later updated with other updates to provide the best performance.
`diarizer.clustering.parameters.maj_vote_spk_count=False`

-  This PR also adds error handling for extreamly short segments (1~10 segments)
when creating p_value_list, the minimum length is now forcibly assigned so that we can avoid having an empty list.

`speaker_utils.py`

- Removed getMinMaxOfRangeList() function 
- Also "RTTM label has been truncated since start is greater than duration of audio file" warning message is not appropriate, thus removed.

- Also, added error handling for empty audio clips. 
Empty audio clips happen when (1) VAD returns nothing (2) duration is 0 in manifest file.
For these cases, uniq ids are removed from self.AUDIO_RTTM_MAP dictionary to avoid the following errors.

- The modified change is tested with less than 1 second sample and silence sample, and then all together in one manifest file.

`clustering_diarizer.py`

- Error handling for empty audio file has been added. The empty uniq_id is removed from the AUDIO_RTTM_MAP dictionary.


**Collection**: [Note which collection this PR will affect]
ASR

# Changelog 
- Add specific line by line info of high level changes in this PR.

This PR adds mode function in NMEanalysis() function.
This implementation adds slightly more memory but no difference in time complexity except torch.mode operation.

# Usage
Please refer to [this document.](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_diarization/datasets.html#evaluation-on-benchmark-datasets)

# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Did you write any new necessary tests?
- [] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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## Who can review?

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# Additional Information
* Related to # (issue)
[TTS] Conditioning pitch, duration, decoder on speaker embedding in FastPitch
State: closed | Pull request: yes

Signed-off-by: Subhankar Ghosh <subhankar2321@gmail.com>

# What does this PR do ?

FastPitch multi-speaker code was setup such that speaker embeddings would only condition the encoder. This PR changes that and conditions encoder, pitch predictor, duration predictor and decoder on the speaker embeddings. Through experimentation this approach has been seen to produce better quality speech in a multi-speaker setting. This PR does not affect single speaker scenario.

**Collection**: [Note which collection this PR will affect]

tts/modules

# 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 
```

# 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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## Who can review?

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# Additional Information
* Related to # (issue)
fix a bug in T5 decode function
State: closed | Pull request: yes

# What does this PR do ?

Fix the bug of T5 decoding given input embeddings. 

**Collection**: [NLP]

# Changelog 
- Updating the way of passing arguments to avoid argument mismatching.
- Delete an argument which is not being utilized.



# 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. 
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# Additional Information
* Related to # (issue)
Quick wav2vec fix. In-place operation adding convolutional positions …
State: closed | Pull request: yes

…to encoder was overwriting leaf history. Wasn't caught on previous torch versions.

Signed-off-by: tbartley94 <tbartley@nvidia.com>

# What does this PR do ?

Fixes bug on backward pass with wav2vec module. See https://github.com/NVIDIA/NeMo/issues/4362

**Collection**: [Note which collection this PR will affect]
- ASR

# Changelog 
- Addition of convolutional position changed from inplace operation to standard variable assignment.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [Y ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N] Did you write any new necessary tests?
- [ N] Did you add or update any necessary documentation?
- [ N] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ Y] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [Y ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to # (issue)
remove redundant bias expand
State: closed | Pull request: yes

Signed-off-by: Xiaowei Ren <xren@nvidia.com>

# What does this PR do ?

Remove redundant bias expand. The bias should be broadcast automatically.

# Changelog 
- deleted redundant code in transformer.py

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# 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**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to # (issue)
Question answering bug fix
State: closed | Pull request: yes

# What does this PR do ?

Turns WandB logger off by default

**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 
```

# 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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## Who can review?

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# Additional Information
* Related to # (issue)
NeMo Megatron: Add sequence parallelism and selective activation checkpointing (rebased)
State: closed | Pull request: yes

# What does this PR do ?

Add sequence parallelism and selective activation checkpointing.

This PR currently depends on a fork of Apex: https://github.com/NVIDIA/apex/pull/1396

**Collection**: NLP

# Changelog 
- Update GPT config
- Update parallel_lm_logits
- Add CoreAttention
  - This is the block that gets recomputed when using selective activation checkpointing
- Update ParallelAttention to work with CoreAttention
- Update ParallelTransformerLayer
  - Layer norm calls to use sequence parallel
- Update ParallelTransformer
  - Update args
  - Update final layer norm
  - Add rng contex to forward when using sequence parallel
  - Remove transposes
- Update Pooler
  - Gather from sequence parallel regions
- Update embedding
  - Add sequence_parallel flag
  - add transpose in forward
  - scatter for dropout
- Update TransformerLanguageModel
- Update GPTModel
  - Add transposes
- All-reduce layer norm params when using sequence parallel
- Add sequence parallel arg to apex calls
- Update configs and arg checking for activation checkpointing

# Usage

```
# enable sequence parallelism
model.sequence_parallel=True

# enable selective activation checkpointing
model.activations_checkpoint_granularity=selective

# all transformer layers have CoreAttention checkpointed when using selective
model.activations_checkpoint_method=null
model.activations_checkpoint_num_layers=null
```

# 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?
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# Additional Information
* Related to # (issue)
Add option for specifying wandb save_dir from config
State: closed | Pull request: yes

Signed-off-by: Shantanu Acharya <shantanua@nvidia.com>

# What does this PR do ?

A user can now specify the name of the directory to save the wandb logs by setting the value `exp_manager.wandb_logger_kwargs.save_dir` in the config.

**Collection**: [utils]

# Changelog 

Files changed: `nemo/utils/exp_manager.py` (line: 670-671)

```
if wandb_kwargs.get('save_dir', None) is None:
    wandb_kwargs['save_dir'] = exp_dir
os.makedirs(wandb_kwargs['save_dir'], exist_ok=True)
wandb_logger = WandbLogger(version=version, **wandb_kwargs)
```

# 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**:
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## Who can review?

Anyone in NeMo
Fix hanging issue by multiprocessing in SD tutorial
State: closed | Pull request: yes

# What does this PR do ?
Fix hanging/deadlock issue caused by multiprocessing in SD tutorial

**Collection**: ASR

# Changelog 
- Cherry pick updates to r1.10.0 https://github.com/NVIDIA/NeMo/pull/4317- 
Add instruction and set num_workers=1 in SD tutorial as a workaround for python multiprocessing would hang with python in some cases

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# 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?

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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)